Novel Pathological Mechanisms Revealed by Spatial Transcriptomic Analysis of Hippocampus in Aged Control, Primary Age-Related Tauopathy, and Alzheimer’s Disease

Novel Pathological Mechanisms Revealed by Spatial Transcriptomic Analysis of Hippocampus in Aged Control, Primary Age-Related Tauopathy, and Alzheimer’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Novel Pathological Mechanisms Revealed by Spatial Transcriptomic Analysis of Hippocampus in Aged Control, Primary Age-Related Tauopathy, and Alzheimer’s Disease Hong-Wen Deng, Yun Gong, Qi-Lei Zhang, Di Wu, Anqi Liu, Tianying Li, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303622/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract While both Primary Age-Related Tauopathy (PART) and Alzheimer’s Disease (AD) involve the accumulation of hyperphosphorylated tau (pTau)-positive neurofibrillary tangles (NFTs) in the hippocampus, PART is distinguished by the absence of β-amyloid (Aβ) deposition and is generally associated with milder cognitive impairment than AD. To delineate cellular and molecular mechanisms that are common or uniquely linked to disease progression in PART and AD, we constructed a transcriptome-wide, high-resolution atlas of the human hippocampus using samples from six individuals spanning the aged control (AC), PART, and AD groups. Our results supported that PART represent a precursor stage of AD, as evidenced by the altered transcriptional profiles of excitatory neurons (Exc) in the PART group, which exhibited a markedly increased capacity to promote Aβ production compared to both AC and AD groups. While the microglia (Mic) were reactivated in the PART group, this response was reduced in AD samples despite the presence of Aβ deposition, and appeared to further induce NFTs formation as a loop consequently driving the progression from PART to AD. Furthermore, subregion interactions in the signalling pathways related to neuronal survival and the maintenance of blood-brain-barrier (BBB) integrity were decreasing in the PART and disrupted in the AD groups, compared to the AC group. Additionally, we found a P53 signalling-related gene, TP53INP2 , was uniquely upregulated in astrocytes near large vessels in AD. This suggests a potential mechanism of vessel-induced neuronal apoptosis in AD, a feature absent in AC and PART. In summary, our study offers new insights into the relationship between PART and AD, along with the molecular mechanisms driving the transition from PART to AD. Furthermore, we identified key molecular pathways associated with BBB disruption and vascular-associated neuronal degradation in AD which were absent in PART. These findings deepen our understanding of AD pathogenesis and may inform the development of targeted therapeutic strategies. Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease Biological sciences/Computational biology and bioinformatics/Data mining Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Alzheimer's disease (AD) is a neurodegenerative disorder primarily defined by its onset with memory deficits and cognitive difficulties, progressively extending to affect behavior, language, spatial perception, and motor functions ( 1 – 3 ). As of 2024, around 6.9 million Americans aged 65 and older are affected by AD, and this number is projected to nearly double to 13.8 million by 2060 ( 4 ). Given the devastating impact of this disease, numerous studies have investigated its underlying mechanisms. These studies have identified various risk factors, including aging ( 5 ), cardiovascular health ( 6 ), education ( 7 ), diet ( 8 ), social interactions ( 9 ), brain injuries ( 10 ), and more than 70 genetic markers ( 11 , 12 ). At the pathology level, AD brain is marked by the accumulation of extracellular amyloid-β (Aβ) plaques ( 13 ) and intracellular hyperphosphorylated tau aggregates as neurofibrillary tangles (NFTs) ( 14 ) in the gray matter. These pathological features can induce cytotoxicity, drive neuroinflammation, and impair mitochondrial function, which collectively contribute to neuronal stress, degeneration, and eventual brain atrophy ( 13 , 15 ). Despite these insights, the pathological mechanisms underlying AD remain unclear, limiting the development of effective medical interventions. Although several drugs targeting the management of this disease, particularly those aimed at clearing Aβ plaques, have been approved by the FDA, these therapeutic approaches have largely failed in clinical trials due to adverse side effects or insufficient efficacy ( 16 ). Thus, more comprehensive studies utilizing cutting-edge technologies are essential to deepen our understanding of this devastating disease and to develop more effective therapeutic strategies. Employing the cutting-edge technology of spatial transcriptome (ST) ( 17 ), several studies investigated different brain regions, including prefrontal cortex (PFC) and middle temporal gyrus (MTG), uncovering novel insights into the pathological mechanisms underlying AD ( 18 – 20 ). In addition to these regions, the hippocampus is also an important brain area of progressive pathology in AD and merits detailed spatial investigation. As a critical structure in the medial temporal lobe responsible for memory and cognition, the hippocampus is among the earliest brain regions affected by AD–related neurodegeneration ( 21 ), offering an important opportunity to investigate early molecular changes associated with this disease. Additionally, the hippocampus serves as a valuable model for studying Primary Age-Related Tauopathy (PART), a neurodegenerative condition characterized by tau protein accumulation in the medial temporal lobe in the absence of significant amyloid-beta (Aβ) deposition, and commonly observed in aging individuals ( 22 ). Compared to AD, PART is associated with less neuronal loss and typically results in milder cognitive impairment. Although the filament structure of the NFTs in PART and AD are similar ( 23 ), it remains unclear whether PART represents an early histopathological stage of AD or simply the products of normal brain aging ( 24 ). Thus, understanding the pathological divergence among aged control (AC), PART, and AD could yield critical insights into the shared and unique molecular mechanisms underlying PART and AD, as well as the molecular events that drive Aβ accumulation and neuronal degeneration in AD. These insights may provide a strong foundation for developing future therapeutic strategies against this devastating disease. While two studies ( 25 , 26 ) have investigated ST in the human hippocampus for AD, one did not include individuals with PART ( 26 ), and the other used the image-based GeoMx platform ( 25 ), which may not fully capture pathological molecular alterations between PART and AD due to its relatively low sensitivity compared to sequencing-based platforms ( 27 ). Therefore, studies using high-sensitive, sequencing-based ST platforms on hippocampal tissue from individuals with AC, PART, and AD are needed to comprehensively delineate the molecular differences among these conditions. In this study, we employed the 10x Genomics Visium ST platform to construct an unbiased transcriptional atlas of the human hippocampus across AC, PART, and AD groups. Our goal was to uncover pathological molecular alterations among these groups, identify potential links between PART and AD, and explore the mechanisms underlying neuronal degeneration in AD. This high-resolution approach revealed transcriptomic signatures indicating that PART may represent a transitional stage from AC toward AD. In PART, upregulated transcripts in Exc appeared to promote Aβ production. Mic reactivation was already enhanced in PART to clear excessive Aβ. However, this microglial response was diminished in AD, potentially contributing to Aβ accumulation, NFT formation, and the progression from PART to AD. Moreover, inter-subregion support for neuronal survival and blood-brain barrier (BBB) integrity was reduced in PART and nearly absent in AD. Furthermore, the BBB disruption in AD was associated with activation of apoptotic pathways in Ast located near large blood vessels, suggesting a critical mechanism worsening neuronal degeneration. RESULTS Human Formalin-Fixed Paraffin-Embedded (FFPE) hippocampal tissue samples were collected from six individuals for ST analysis using the 10x Visium platform ( Fig. 1A; Fig. S1 A-F ; Methods ). The cohort included two AC (Braak stages I and II, Thal phase 0; two males, aged 88 and 79), two individuals with PART (Braak stage III, Thal phase 0; one male and one female, aged 87 and 81), and two AD patients (Braak stages VI and IV, Thal phase 3; one female and one male, aged 92 and 82; Table S1 ). Following the 10X Visium profiling, a total of 26,038 spots were captured across all six samples, with each spot detecting an average of 4,096 genes and 4,273 molecular counts. Previous studies on the human brain ( 28 – 30 ) have shown that Exc exhibited more prominent features compared to inhibitory neurons (Inh) and glial cells due to their larger size and essential roles in synaptic transmission and signaling pathways ( 31 ). To further validate the quality of our dataset, we aligned the ST data with the eosin-stained image from the same slide. The results demonstrated that spots with a high number of detected genes corresponded to regions enriched in Exc, such as the DG region ( 32 , 33 ), supporting the high quality and reliability of our dataset for our comprehensive downstream data analysis ( Fig. S1 A1-F1; Fig. S1 A2-F2 ). ST revealed unique gene expression patterns in distinct regions of human hippocampus Conventionally, the human hippocampus is primarily composed of several soma-rich layer (SL) subregions, including stratum pyramidale (s.p.) of subiculum (Sub) and CA1–CA4, as well as the granule cell layer of dentate gyrus (DG). These areas are predominantly composed of Exc and play essential roles in information output, as well as in the integration and processing of neural signals ( 34 ). Surrounding the SL are distinct fiber layers (FL), which are organized above and below the cell body layers. These include the stratum oriens (s.o.), stratum radiatum (s.r.), and molecular layer (ML), which are primarily composed of dendrites, axons, and synaptic terminals. These fiber-rich regions are critical for synaptic input and output, serving as hubs for information integration and transmission ( 35 ). Furthermore, large vascular (VAS) structures are present across all subregions of the human hippocampus, supporting cellular oxidative metabolism and energy supply in these areas ( 36 ). To accurately delineate these spatial domains in our human hippocampal ST data, we applied a data-driven, unsupervised clustering algorithm, PRECAST ( 37 ), to group spatial spots into distinct domains based on their transcriptional profiles and spatial coordinates on the 10x Visium slides ( Methods ). We evaluated a range of spatial domain resolutions (k) and selected k = 15 based on the Bayesian Information Criterion (BIC) ( Fig. S2 A ). Unsupervised clustering at this resolution grouped the spatial spots into 15 distinct clusters ( Fig. S2 B ). To annotate these clusters, we mapped them onto the eosin-stained image and assigned anatomical labels based on their spatial positions ( Fig. S1 A1-F1 ). After grouping the clusters located within the same anatomical region, all clusters were annotated as 10 subregions, including SUB, CA1 to CA4, s.r., s.o., DG, ML, and VAS ( Fig. 1B and 1C ). Compared to previous ST studies using 10X Visium platform on human hippocampus ( 38 , 39 ), which were unable to clearly distinguish certain regions, particularly CA2, CA3, and CA4, our data demonstrated a clear delineation of these regions. This may be due to their use of the 10x Visium platform on frozen sections of the human hippocampus, which are more prone to RNA degradation and increased background noise, including expression from non–protein-coding genes ( 40 ). These factors may hinder accurate distinction between hippocampal subregions. This result underscored the significant advantages of using the 10x Visium platform on FFPE-preserved human hippocampal tissue to construct a transcriptome atlas, enabling comprehensive and unbiased profiling of protein-coding genes. We performed differential gene expression (DGE) analysis by comparing each subregion against all others, aiming to identify distinct transcriptional signatures for each hippocampal subregion ( Fig. 1D ; Table S2 ; Methods ). Briefly, subregions enriched with Ex, such as the SUB, CA1 to CA4, and DG, showed high expression of genes associated with Exc functions. These include genes involved in neuronal calcium signaling (e.g., CALM3 , CCK, SYN2, CALB1 ) and neuron synapse functions (e.g., STXBP6 , PPFIA2, SNCB ) ( Fig. 1E ). In contrast, subregions with a higher proportion of glial cells, including the s.r., s.o., and ML, displayed enrichment of genes related to myelin formation and maintenance (e.g., PLP1 , MBP ). Additionally, in the ML region, we observed upregulation of genes associated with neuronal dendrite function (e.g., NCS1 , SEPTIN5 ) ( 41 , 42 ), compared to other subregions. This is consistent with that the ML region is composed of neuronal dendrites originating from the DG ( 43 ). Furthermore, we performed Gene Ontology (GO) analysis on transcriptional markers to elucidate the distinct biological functions associated with each hippocampal subregion. GO terms associated with neuronal and synaptic functions, including “Chemical Synaptic Transmission”, “Neuron Development”, and “Axon Development”, were enriched in the SUB, CA1, CA2, CA3, and CA4 regions, which are predominantly composed of Exc. In contrast, GO terms related to myelin formation and cellular migration (e.g., “Myelination and Regulation of Cell Migration”) were highly enriched in SR and s.o., regions primarily consisting of glial cells, including Ast, Mic, and oligodendrocytes (Oli) ( Table S3 ). The subregion-specific transcriptional profiles in our data are consistent with previous ST data from the human hippocampus ( 26 ), further supporting the accuracy of our clustering and annotation. The intricate histological architecture of the hippocampus makes manual region annotation especially challenging, particularly for subfields such as CA1, CA2, CA3, and CA4, which lack clearly defined histological boundaries ( 44 ). This difficulty complicates the precise characterization of the unique biological functions of neurons and glial cells in these regions and hinders the study of their subregion-specific pathological changes during the early stages of AD. Therefore, identifying reliable biomarkers for each region is crucial for distinguishing these areas and capturing region-specific molecular dynamics of brain cells at the onset of AD. To address this, we identified subregion-specific marker genes based on the highest fold-change compared to their expression in other regions. These included FIBCD1 for CA1, DDN for SR, NRIP3 for CA2, CCK for CA3, UNC13C for CA4, STXBP6 for DG, PDZD4 for ML, CRYAB for SO, and NEFM for SUB ( Fig. 1F ). These hippocampal subregion-specific markers were validated through immunohistochemistry (IHC; Fig. S2 C-D ; Fig. S3 A-C ). Given the limited availability of markers to delineate the boundary between CA1 and CA2, and the absence of previous reports on NRIP3 expression in the human hippocampus, we examined the distribution of the NRIP3 positive cells. We observed that NRIP3 was highly expressed in the CA2 subregion but shows low expression in CA1, forming a clear boundary between the two ( Fig. 1G ). This distinct expression pattern suggests that NRIP3 may serve as a reliable marker for distinguishing CA2 from CA1. Furthermore, although certain region-specific markers showed significant differential expression between the AC, PART and AD groups within their respective regions ( Fig. S4 A ), these markers were still enriched in their corresponding subregions when DEG analyses were performed separately within the AC, PART, and AD groups ( Fig. S4 B ). This suggested that these markers are robust and can serve as reliable references for regional identification across AC, PART, and AD conditions. Deconvolution analysis to enhance the resolution from spot level to single-cell resolution Given that current 10X Visium platform cannot provide transcriptional data at the single-cell (sc) resolution, we applied the deconvolution methods to identify cell compositions and infer cell type-specific gene expression patterns within each 10X Visium spatial spot. We employed BayesPrism ( 45 ), a Bayesian algorithm that simultaneously infers cell-type proportions and their unique transcriptional profiles within each ST spot, without requiring snRNA-seq reference data from adjacent tissue. The snRNA-seq data from human hippocampal samples of AD cases and controls, published by Mathys et al. ( 46 ), was considered as the reference. Although the reference data from Mathys et al. were generated from Caucasian individuals and our ST data were derived from Asian donors, previous studies have reported that the core structural and functional features of the hippocampus are conserved between these populations ( 47 , 48 ). Furthermore, we performed snRNA-seq on a hippocampal sample from an age-matched Chinese individual with AD ( Fig. 1A ; Table S1 ; Methods ) and when merged with the data by Mathys et al. , the transcriptomic profiles from the Chinese individual aligned well with the major clusters observed in the Caucasian dataset ( Fig. S5 A ), suggesting minimal batch or population-related effects in hippocampal transcriptional profiles. Through the deconvolution analysis, we assessed the proportion of each cell type within individual spots across distinct hippocampal regions ( Fig. 2A-B ). Exc were predominantly enriched in SL, including the SUB, CA1, CA2, CA3, CA4, and DG subregions ( Fig. 2C; Fig. S5 B ). In contrast, Ast and Oli were largely localized to FL, such as the s.o., s.r., and ML. Notably, VC exhibited significantly higher proportions in the VAS region compared to both SL and FL These findings were consistent with previous studies ( 26 , 39 ). Given that BayesPrism was originally developed to infer cellular compositions from bulk RNA-seq data rather than ST, we evaluated its performance on ST data by comparing its inferred cell compositions to those generated by deconvolution methods specifically designed for ST, including CARD ( 49 ), SpaCet ( 50 ), and PANDA ( 51 ). Across all cell types, BayesPrism results showed significant positive correlations with those from other methods ( Fig. S5 C-E ), suggesting that BayesPrism, despite not incorporating spatial information, performs comparably to spatially aware deconvolution methods, supporting its reliability in this study. After estimating the cellular composition of each spatial spot, we inferred the corresponding cell type-specific transcriptional profiles ( Methods ). To evaluate the robustness of BayesPrism, we performed a permutation test by randomly splitting the snRNA-seq data into two groups, each containing half of the cells from every cell type. Each group was independently used as a reference in BayesPrism to infer cell type-specific gene expression profiles for each spot, and the inferred results were compared across runs to assess consistency. For comparison, we applied the same strategy using PANDA to benchmark BayesPrism’s performance. Since the mean correlation of gene expression levels across four permutation tests was higher using BayesPrism than PANDA ( Fig. S5 F ), this result suggested that BayesPrism offers greater robustness in inferring cell type–specific gene expression in each spatial spot. Therefore, BayesPrism is currently the most suitable method for our study. To enhance the ST data into sc resolution, we have constructed the spatial pseudo-sc matrix (PSM) data based on the inferred cell-type specific gene expression patterns within each spot ( Fig. 2D ). After removing the cells with fewer than 300 detected genes and genes that are expressed in fewer than three cells, we have captured 121,087 cells with median genes detected 621, which is similar to current cutting-edge ST platforms with sn resolution ( 26 , 52 – 54 ). Following cell type clustering and visualization, the pseudo-cells were annotated as distinct major brain cell types (Ast, Exc, Inh, Mic, Oli, OPC, VC) ( Fig. 2E ). Marker genes for each pseudo-cell cluster were compared and found to be consistent with those reported in previous snRNA-seq study on human hippocampus ( 46 ) ( Fig. 2F ), supporting that the PSM effectively preserved the key transcriptional profiles for major brain cell types. We further checked the major cell types contributing to the hippocampus subregion-specific markers identified at spatial spot level. In SL, the Exc was one of the major cell types expressing the subregion-specific markers due to its abundance and relatively large size in human hippocampus ( Fig. 2G ). The heterogeneity of the transcriptional profiles of these Exc across different subregions has contributed to the divergence of region-specific markers ( Fig. S5 G ). This heterogeneity in Exc were validated at the protein level on IHC sections ( Fig. S2 C-D ; Fig. S3 A-C ), further supporting the reliability of our approach to enhance ST data from the spatial spot level to pseudo–sc resolution. In addition, in the s.r. subregion, enriched by the synapse from Exc somas located in CA1, CA2, and CA3, DDN gene, a gene related to the neuron signaling transmission ( 55 ), was the marker and highly expressed by the Exc. Since Oli were highly enriched in the s.o. subregions, the Oli specific marker, CRYAB , was the major marker for the s.o. area. In summary, we successfully inferred the cellular composition and cell type-specific transcriptional profiles in each spot, as well as constructed a pseudo-sc matrix based on these profiles. Based on this matrix, we have revealed the heterogeneity of the Exc across subregions in SL. Subregion specific DGE analyses reveal the underlying mechanisms of selective vulnerability in individuals with PART and AD Selective vulnerability, a hallmark of AD, refers to the disproportionate impact of AD pathological hallmarks on neurons in specific brain regions ( 56 ). Interestingly, previous studies have also identified this hallmark in individuals with PART ( 57 ). In the hippocampus, Exc in the CA1 subregion are particularly vulnerable to developing NFTs compared to other regions ( 58 ), a pattern also observed in our samples ( Fig. S1 A4-F4; Fig. S1 A5-F5 ). To investigate the impact of this hallmark to each specific cell types in PART and AD, we compared the region-specific cellular compositions across the AC, PART, and AD groups in the SLs ( Fig. 3A ). Overall, the mean proportion of Exc was highest in AC and lowest in PART ( Fig. 3B ). Additionally, while the proportion of Ast increased in parallel with the rising abundance of AD pathological hallmarks, Mic increased primarily in PART but decreased in AD ( Fig. 3B ), suggesting distinct patterns of change between these two glial cell types. The reduced proportion of Mic in AD may contribute to the increased proportion of Exc observed in AD relative to PART. In contrast to the changes observed in Exc, Ast, and Mic, the proportion of Opc, Oli, and VC maintained relatively stable across AC, PART, and AD ( Fig. 3B ). To further explore regional differences in cellular composition of Exc, Ast, and Mic among AC, PART, and AD groups, we analyzed subregion-specific changes following the anatomical organization of the human hippocampus ( Fig. 3A ). Compared to AC group, the CA1 subregion in PART exhibited the most pronounced decrease in the proportion of Exc across all examined SLs (t-statistics=-21.04, adjusted p-value = 2.40X10 − 93 ), along with the largest increases in Ast (t-statistics = 22.20, adjusted p-value = 5.01X10 − 103 ) and Mic (t-statistics = 28.83, adjusted p-value = 1.18X10 − 166 ) ( Fig. 3C ). Given that previous studies have reported limited neuronal degeneration in PART ( 24 , 59 ), the observed decrease in Exc proportion in the CA1, along with increased proportions of Ast and Mic, likely reflects a decline in normal neuronal function and a pronounced glial cells reactivation against the stress. At the spatial spot resolution, compared to the AC group, the CA1 subregion exhibited the most significant downregulation of genes involved in neuronal structure, function, and signaling, including UBB , NSF , NEFM , RTN1 , and TUBA4A ( 60 ). In contrast, markers of Ast (e.g., S100B , CPE ) ( 61 , 62 ) and Mic (e.g., S100A9 , C1QB , P2RY12 ) ( 63 – 65 ) reactivation were highly enriched in the PART group ( Fig. 3D ). These molecular changes likely contributed to the decreased estimated proportion of Exc and increased proportions of Ast and Mic in the PART group ( Fig. 3B-C ). Notably, in the CA1 subregion, the divergence in the proportions of Exc, Ast, and Mic were relatively small when comparing the AC to AD groups ( Fig. 3C ). This pattern suggests that although glial cell activation occurs early in response to stress, it may transition into dysfunction or degeneration at the later stages of AD ( 66 – 68 ). The rapid decline in glial reactivation markers from the PART to the AD groups ( Fig. 3D ), particularly those associated with Mic (e.g., S100A9 , C1QB , P2RY12 ), likely contributes to the decreased estimated proportions of these glial cell types in the CA1 region in AD, which may in turn explain the relative increase in Exc proportions in the comparison between AD and PART. In addition to the CA1, we also found a significant reduction in the proportion of Exc in the SUB, CA2, CA3, and CA4 subregions in both the PART and AD groups compared to the AC group. Although the CA3 and CA4 exhibited less vulnerability compared to the SUB, CA1, and CA2 subregions ( 69 ), particularly in PART, the observed reduction in Exc proportions in the CA3 and CA4 suggests that these regions also experience significant stress, despite showing fewer hallmark AD pathologies. In contrast, the DG subregions showed relatively preserved Exc proportions ( Fig. 3C ), consistent with previous findings that granule cells, a subtype of Exc mainly located in the DG, were more resilience to the stress compared to the pyramidal Exc located in the SUB to CA4 areas ( 70 , 71 ). Notably, the proportion of Exc in the CA3 and CA4 progressively declined from AC to PART and further to AD, whereas Ast proportions generally increased across most SLs over the same progression, except in CA1, where Ast proportions significantly decreased from PART to AD. This may indicate that Ast dysfunction emerges earlier in the CA1 than in other regions, which could also be reflected by the large downregulation of Ast reactivation markers in AD compared to the PART group ( Fig. 3D ). For Mic, most SLs displayed increased proportions in PART relative to AC, followed by a decline in AD, suggesting that microglial activation peaks in PART and diminishes at later AD stages. Since Mic are the primary phagocytes responsible for Aβ clearance ( 72 ), their dysfunction may lead to Aβ plaque accumulation observed in AD, which was also observed in our previous ST studies on prefrontal cortex from AD samples ( 20 ). To better understand transcriptomic factors contributing to the selective vulnerability observed in PART and AD at pseudo-sc resolution, we first conducted cell type–specific DGE analyses in Exc across SLs among the AC, PART, and AD groups ( Fig. S6 A ; Table S4 ). Given that limited pathological changes were observed in all cell types in the DG subregion ( Fig. 3C ), we mainly focused on the molecular divergence of SUB to CA4 subregions. Compared to the AC group, genes involved in neuronal metabolism (e.g., SLC22A17 , SLC4A7 , ABHD12 ) and synaptic transmission (e.g., NSF , NEFM , TUBA4A ) were consistently downregulated in Exc across all SUB and CA subregions in both PART and AD groups ( Fig. 3E; Fig. S6 B ), suggesting that despite minimal neuronal loss in PART ( 24 , 59 ), functional impairments in Exc likely contribute to the observed mild cognitive decline. In PART, several genes associated with oxidative stress responses, such as CALM3, PRNP , and APP ( 73 – 76 ), were upregulated in Exc across the SUB to CA4 subregions compared to both AC and AD groups ( Fig. 3E ). Notably, Calmodulin 3 (encoded by CALM3 ) is a key subunit of phosphorylase kinase (PhK), which can phosphorylate tau and contribute to NFT formation ( 77 ). In addition, amyloid precursor protein (APP, encoded by APP ) is the source of Aβ peptides, and its elevated expression may promote increased Aβ production and deposition, potentially facilitating progression toward AD ( 78 ). In AD, a stress response factor, RAPGEF4 , was upregulated in Exc across the SUB to CA4 subregions compared to both AC and PART ( Fig. 3E ). RAPGEF4 encodes a cAMP-regulated guanine nucleotide exchange factor involved in cAMP-mediated signaling ( 79 ), which can influence kinases that induce tau protein phosphorylation ( 80 ). Thus, the upregulation of RAPGEF4 in AD may contribute to the exacerbation of tau pathology in the human hippocampus. Given that all Exc in the SUB and CA subregions were under high stress but NFT accumulation was observed only in Exc within the CA1 subregion in PART, the unique Exc-specific DEGs in PART compared to AC and AD may provide insight into this selective vulnerability. When compared to the AC and AD groups, the Exc in CA1 subregion in PART exhibited the highest number of uniquely upregulated genes, in contrast to the relatively minor region-specific transcriptional changes seen in Exc in SUB and CA2-CA4 ( Fig. S6 A ). Many of these CA1-specific, PART-upregulated genes in Exc were significantly enriched in GO terms related to neuroprotection and synaptic maintenance ( Fig. 3F ), including “Positive Regulation of Neurogenesis” (e.g., PTPRD , RGS14 ), “Positive Regulation of Synaptic Transmission” (e.g., PLK2 , SLC8A2 ), and “Regulation of Neurotransmitter Receptor Activity” (e.g., PRRT1 ). These findings suggested that Exc in the CA1 subregion may activate protective and compensatory pathways in the PART group to maintain synaptic integrity and counteract neuroinflammation-induced dysfunction. We also identified CX3CL1 as a uniquely upregulated gene in CA1 in PART ( Fig. 3E ). This gene encodes a neuron-derived chemokine that inhibits microglial hyperactivation, thereby reducing neuroinflammation and supporting neuronal function ( 81 ). Given the concurrent observation of heightened microglial activation in CA1 during PART, the elevated expression of CX3CL1 likely reflects a neuronal compensatory response to suppress excessive microglial activity and limit inflammatory damage. However, since previous studies ( 82 , 83 ) have shown that activated Mic are key contributors to Aβ clearance, sustained CX3CL1 upregulation may inadvertently impair microglial phagocytic function. As a result, this protective anti-inflammatory signaling could paradoxically facilitate Aβ accumulation, potentially promoting the transition from PART to AD through the formation of Aβ plaques. In contrast to genes identified in CA1, the uniquely upregulated genes in the PART group identified in the SUB and CA2–CA4 subregions did not include any genes known to directly promote NFT formation. Notably, in the PART group, PPP1R9B and PPP2CB were upregulated in relative to both AC and AD groups in SUB and CA4 regions, respectively ( Fig. 3E ; Fig. S6 B ). These two genes encode key regulatory components of protein phosphatase 1 and protein phosphatase 2A, respectively, both of which are primary enzymes responsible for tau dephosphorylation ( 84 ). Together, these findings highlighted transcriptomic signatures that might underlie the selective vulnerability of Exc in the CA1 subregion of the human hippocampus in PART. Similar to PART, Exc in the CA1 region in AD exhibited the highest number of uniquely upregulated genes across all SLs when compared to both AC and PART groups ( Fig. S6 A ). Notably, these uniquely upregulated genes in CA1-Exc in AD were strongly associated with NFT formation, rather than neuroprotective compensatory mechanisms observed in PART. Enriched GO terms included processes such as “ Phosphorylation ” (e.g., SMG1, GSK3A, DYRK2 ), “ Peptidyl-Serine Phosphorylation ” (e.g., TNKS, ROCK2 ), and “ Peptidyl-Threonine Phosphorylation ” (e.g., LMTK2, CDC42BPB ) ( Fig. 3G ), highlighting molecular mechanisms that may contribute to the higher NFT burden in AD compared to PART ( 85 ). Additionally, several GO terms related to transcriptional regulation, such as “ Positive Regulation of DNA-templated Transcription ” (e.g., CCNT1, ICE1 ) and “ Positive Regulation of Nucleic Acid-Templated Transcription ” (e.g., CDKN1C, TRRAP ), were also enriched in CA1-Exc in AD. These findings suggest that CA1-Exc in AD may be under considerable damage, potentially leading to transcriptional dysregulation and DNA damage. Importantly, such transcription-related responses were not observed in Exc populations from other SLs in AD. In the CA3 subregion, the enriched GO terms “ Synaptic Vesicle Exocytosis ” (e.g., SNAP25 , STX1B ) and “ Positive Regulation of Autophagy of Mitochondrion in Response to Mitochondrial Depolarization ” (e.g., TOMM7 ) suggest potential compensatory mechanisms for enhancing neuronal connectivity and promoting neuroprotection ( Fig. 3H ). Interestingly, we noticed that CDK5R1 and CDK5R2 , key promoters for NFT formation ( 86 , 87 ), showed the highest expression levels in the AD group across all SL subregions, except in CA1, where their expression was highest in the PART group ( Fig. 3E ). Since NFTs were also observed in the SUB and CA2–CA4 subregions during the late stages of AD, our findings suggests that the mechanisms underlying NFT formation may be shared between PART and AD. However, the elevated expression of CDK5R1 and CDK5R2 in these subregions appears only at the late stage of AD, which may explain why NFTs are present in these regions in AD but not in PART. Glial cells resilience in PART and AD Although predominant inflammation and Aβ production related genes were upregulated in Exc across the SUB to CA subregions in the PART group, neuronal degradation and Aβ plaque accumulation are rarely reported in PART. In addition to the intrinsic stress response mechanisms of Exc neurons, the resilience of glial cells, particularly their ability to clear excess Aβ and provide neuroprotection, also contributes to maintaining neuronal survival ( 46 ). To investigate the molecular mechanisms underlying this phenomenon, we analyzed subregion-specific transcriptional differences in glial cells across the SUB to CA4 subregions in the AC, PART, and AD groups. Since the proportions of Oli and OPC remained relatively stable across groups ( Fig. 3B ), our analysis primarily focused on Ast and Mic within the SLs. We observed a consistent upregulation of a stress-response gene, NTRK2 , in Ast across all SLs in the PART group, compared to AC and AD groups ( Fig. S6 C-D ). Additionally, pro-inflammatory genes such as SERPINA3, S100B , and SPP1 were also elevated, suggesting that Ast was the key contributor to the inflammatory environment observed in PART ( 88 ). Similar to Exc, Ast in the CA1 region in the PART group also showed the highest number of uniquely upregulated genes among SLs ( Fig. S6 A ). While no CA1-specific Ast genes were linked to NFT formation in PART, several genes associated with synaptic plasticity and neuronal survival, such as HPCA and SLC44A3 , were upregulated in the PART group compared to the AC and AD groups ( Fig. S6 C ), supporting the role of Ast in counteracting neurodegeneration. Furthermore, SNX3 was uniquely upregulated in CA1 Ast in PART compared to AC and AD. Sorting nexin 3, encoded by SNX3 , has been shown to inhibit Aβ generation by altering APP trafficking, and may underlie the absence of Aβ plaque deposition in the CA1 region in PART. Compared to CA1, the number of uniquely upregulated genes in Ast in PART versus AC and AD was lower within the SUB and CA2–CA4 subregions ( Fig. S6 A ). Furthermore, within these genes, only a few of them, such as S100A13 in SUB as well as GLUL and UQCRB in CA4 ( Fig. S6 D ), have been reported to be associated with neuroinflammation and AD pathological hallmark ( 89 , 90 ). This pattern indicates a lower level of Ast reactivation in these regions compared to CA1, suggesting less Exc damage. Notably, in the AD group, the gene, SPARC , which affects two central pathological features of AD: Aβ deposition ( 91 ) and blood-brain barrier (BBB) disruption ( 92 ), is among the common genes upregulated in AD compared to AC and PART across SLs ( Fig. S6 C-D ). In the CA1 subregion, unlike Ast in PART, which primarily upregulated pro-inflammatory genes, Ast in AD display a dual phenotype. On one hand, they express genes associated with axonal and synaptic degradation (e.g., DPYSL2 , RGMA , GABBR1 , TRIM2 ) ( Fig. S6 C ). On the other hand, they also upregulate genes related to compensatory neuroprotective mechanisms, such as antioxidative stress responses (e.g., GPX4 , SELENOW ), metabolic support (e.g., UGP2 , PFKP , PRDM16 ), and inflammation resolution (e.g., ZFP36L2 ). This dual profile aligns with previous studies showing that Ast exert both neuroprotective and neurotoxic effects in the later stages of AD ( 93 ). Similar patterns were also observed in Ast within the CA3 and CA4 subregions in AD, whereas such patterns were not evident in the SUB and CA2 subregions. Further, we have also identified several unique upregulated genes in Ast in the AD group that are related to the BBB disruption, including TGFB2 in CA3 ( 94 ) and PTK2B in CA4 ( 95 ) ( Fig. S6 D ). Together, the upregulated transcript molecules in Ast in AD, compared to AC and PART, exhibited both neuroprotective functions and, paradoxically, contributed to neurotoxin as well as blood-brain barrier disruption. Due to the limited numbers and relatively small size of Mic, fewer Mic-specific DEGs were detected across the SUB and CA subregions in the AC, PART, and AD groups ( Fig. S6 A, Table S4 ). Similar to Ast, Mic showed prominent reactivation across the subregions, evidenced by increased SPP1 expression in the PART group compared to both AC and AD groups. In the CA1 subregion, Mic from PART samples showed upregulated expression of autophagy-related genes (e.g., GABARAP, OTUB1, UCHL1 ) relative to those from AC and AD samples, suggesting enhanced Aβ clearance capacity ( Fig. S6 E ). In CA4, Mic in PART exhibited even stronger expression of genes directly linked to both Aβ clearance and autophagy (e.g., APOC1, HNRNPC ), indicating a robust neuroprotective response in this subregion ( Fig. S6 E ). This may help explain the relative resistance of CA4 to Aβ aggregation compared to CA1, consistent with prior findings ( 26 , 96 ). In contrast, Mic from SUB, CA1, and CA4 regions showed limited upregulation of Aβ clearance genes in AD samples compared to AC and PART. Instead, their transcriptomic profiles were enriched for genes associated with pro-inflammatory responses. Combined with the marked reduction in Mic proportions from PART to AD across SUB and CA subregions, these findings suggested a progressive decline in Aβ clearance capacity, contributing to plaque accumulation in AD. Moreover, dysfunctional Mic likely exacerbate neuroinflammation, further damaging Exc. Subregion communications within human hippocampus in AC, PART, and AD The interactions between subregions of the human hippocampus are essential for coordinating memory encoding, consolidation, and retrieval across various cognitive contexts ( 97 , 98 ). As a result, communication between hippocampal subregions may be alternated or disrupted, contributing to the cognitive decline seen in PART and, more prominently, in AD. To investigate how subregional communication patterns change with increasing abundance of AD pathological hallmarks, we first constructed interaction networks among all hippocampal subregions at spatial spot levels in individuals with AC, PART, and AD, respectively. Given that interaction strength between subregions may decrease with increasing Euclidean distance ( 99 ), we incorporated spatial distance as a covariate in the construction of the interaction networks. In general, the strength of the communication among all subregions was decreasing from AC to AD ( Fig. 4A ). While the VAS and s.r. subregions exhibit relatively high incoming and outgoing interactions across the AC, PART, and AD groups, the SUB, CA2, and CA3 subregions display comparatively lower interaction strengths among these groups ( Fig. 4B-D ). Specifically, SUB and CA1 exhibited a significant increase in both outgoing and incoming signaling strength in PART compared to AC, followed by a marked decrease in AD relative to PART. In contrast, CA3 and CA4 showed reduced signaling strength in PART compared to AC, but this increased in AD relative to PART ( Fig. S7 A ). Besides, the VAS showed the highest strength of the incoming signaling in PART compared to AC and AD, but exhibited a gradually decreased strength of the outgoing signaling with the increase of the AD pathological hallmarks. These findings suggested a dynamic, subregion-specific reorganization of hippocampal communication in response to PART and AD pathology. To pinpoint the divergence of subregion-specific communication patterns across the AC, PART, and AD groups, we first analyzed the interaction strength between all pairs of hippocampal subregions. The communication networks among hippocampal subregions varied across the AC, PART, and AD groups ( Fig. 4E ). The pairwise comparison of interactions strengths among subregions illustrated that the interaction strength among multiple SLs decreased in the PART group compared to the AC group, followed by an increase in the AD group relative to PART. However, overall strength of these interactions in AD remained lower than in AC ( Fig. 4F ). The divergence of these interaction strength between AC, PART, and AD was primarily driven by changes in signaling pathways involving neurotensin (NT), pleiotrophin (PTN), macrophage migration inhibitory factor (MIF), SPP1, semaphorin 3A (SEMA3A), and prosaposin (PSAP) ( Fig. 4G ). Among these interactions with divergence strength, the NT signaling exhibited the greatest progressive decline from AC through PART to AD. Previous studies suggested that the NT signaling pathway plays a critical role in chemical neurotransmission, involving a variety of neurotransmitters that act on diverse receptors, often through co-release and feedback mechanisms that fine-tune neuronal responses ( 100 ). Additionally, NT can both promote neuronal survival and induce neuronal apoptosis, depending on the cellular context and receptor signaling pathways involved ( 100 , 101 ). Thus, the pathological disruption of NT signaling observed in our data may contribute to the neuron damage in PART and AD. In the AC group, NT signaling primarily originated from the CA3 and CA4 regions, projecting to other SLs such as CA1 and CA2, respectively. Additionally, NT signaling from the DG was mainly directed toward CA3 and CA4, as well as glial cell-enriched subregions including the s.r., s.o., and ML ( Fig. 4H ). This pattern aligned with previous findings showing that granule cells in the DG form strong connections with Exc in CA3 and CA4, but have limited direct connectivity with CA1 and CA2 ( 102 ). However, in the PART and AD groups, the NT signaling from CA4 to CA1 and CA2 was dramatically reduced compared to the AC group, likely due to pathological changes in the gene expression patterns of the ligands and receptors in cells located in these regions. Interestingly, in the AD group, cells in the CA1 subregion exhibited upregulated intra-region ligand-receptor (LR) pairs involved in NT signaling ( Fig. 4H ), likely reflecting an effort to preserve signaling activity within CA1 under the severe stress induced by AD pathology. To illustrate the impact of AD-related pathological hallmarks on NT signaling, we analyzed the pathological divergence of specific LR pairs involved in this process ( Fig. 4I ). Notably, the neuroprotective LR pair BDNF–NTRK2, originating from CA3 and projecting to CA2 and CA4 subregions, showed a progressive decline in interaction from AC to PART, and further to AD. A similar decreasing trend was observed from DG to CA4 and ML subregions. Given that BDNF binding to NTRK2 activates key intracellular signaling pathways, including PI3K–AKT and MAPK–ERK, which are essential for neuronal survival and resilience to cellular stress ( 103 ), the reduced interaction of this LR pair likely reflects a loss of inter-subregion neurotrophic support, driven by increasing pathological burden in AD. In addition, we have noticed one cell apoptotic related LR pairs, BDNF-SORT1, was decreased among the CA2, CA3, CA4, and DG in the AD group compared to the AC and PART groups. SORT1 acts as a co-receptor for neurotrophins such as BDNF, and when bound to BDNF, it can trigger neuronal apoptosis ( 104 ). The reduced interactions of BDNF–SORT1 across several hippocampal regions may reflect a compensatory mechanism aimed at preserving neuronal integrity. In contrast, we found that BDNF–SORT1 interactions increased specifically in the CA1 subregion in the AD group compared to the AC and PART groups. Given that Exc in CA1 are among the most vulnerable to degeneration in AD, this heightened interaction may contribute to the severe neuron death in CA1. In addition to NT, we have observed significant variations in interaction strength within the PTN signaling pathway, which exhibited the strongest interactions strength among all signaling pathways across the hippocampal subregions ( Fig. 4G ). Previous studies have proposed that PTN promotes hippocampal neurogenesis by stimulating neural progenitor proliferation through the activation of AKT signaling ( 105 ) and stabilizes dendritic microtubules of the damaged neurons ( 106 ). Furthermore, PTN also acts as a protector of the BBB by supporting pericyte function, promoting angiogenesis, and facilitating vascular repair ( 107 ). In our data, we have observed that the PTN signaling pathway from multiple regions, including SUB to CA3, to VAS, was disrupted ( Fig. S7 B ). In addition, among the interactions involved in the PTN, the PTN-(ITGAV + ITGB3) LR pair, which directly supports the BBB integrity, was detected from almost all subregions in hippocampus to the VAS in the AC and PART group, but diminished in the AD group ( Fig. S7 C ). Given that PTN binding the ITGAV and ITGB3 can enhance the adhesion of endothelial cells located on the surface of the large blood vessels and thus maintain the BBB structural stability and prevent leakage ( 108 ), the disruption of this interaction may contribute to the BBB disruption observed in late stage of AD. In summary, our inter-subregion analysis identified specific molecular mechanisms supporting neuronal survival and BBB maintenance that are disrupted in AD but preserved in PART, offering insights into the neuronal degeneration and BBB breakdown seen in late-stage AD but not in PART. Cellular interactions between VAS and the nearby cells BBB disruption is a hallmark of late-stage AD ( 109 ), primarily driven by the activation of astrocytes and microglia in response to heightened cellular stress ( 68 ). This breakdown permits neurotoxic substances to infiltrate brain tissue ( 109 ), with their accumulation often occurring around large blood vessels. The resulting localized damage can propagate to nearby regions, contributing to further pathological changes in adjacent small vessels ( 110 ). Further, these pathological changes in adjacent small vessels can significantly influence the behavior of the glial cells, especially Ast and Mic, which are implicated in propagating neuroinflammation, disrupting synaptic support, and facilitating neuronal apoptosis ( 111 , 112 ). As a result, the cells in close proximity to the large vessels form a distinct microenvironment compared to those in distal regions, and uncovering the differences in cellular interactions between areas near and far from large vessels may be critical for understanding the mechanisms of vessel-induced neuronal degeneration. To address this, we first identified the location of each VAS spot and conducted a concentric analysis using a strategy similar to our previous study ( 52 ). Spatial spots were categorized into four levels (1 to 4) based on their distance from the VAS ( Fig. 5A-B ). Due to the proximity and minimal transcriptional divergence between levels 1 and 2, our DEG analysis focused on comparing level 1 (proximal) and level 3 (distal) spots. We found that while most DEGs identified in AC and PART groups were functionally similar, primarily related to neuronal maintenance, APP was significantly upregulated in Exc in level 1 compared to level 3 spots specifically in the PART group, but not in the AC or AD groups ( Fig. 5C ). This suggested that Exc near large vessels in PART may produce excessive APP, potentially enhancing Aβ plaque formation relative to distal neurons. In contrast, the absence of APP upregulation in the AD group may indicate that Exc near large vessels undergo apoptosis due to severe stress, leading to a reduction in APP expression in these neurons ( 113 ). Additionally, the inflammation-related gene S100B was upregulated in Ast in level 1 compared to level 3 in both PART and AD, but not in AC, indicating a stronger inflammatory response in vessel-proximal Ast within these conditions. In the AD group, downregulated genes in Exc in level 1 spots were associated with responses to mitochondrial dysfunction (e.g., UQCRQ, ATP6V1G2, POLR2F ), oxidative stress (e.g., PTGES3, MT3, HAGH ), and protein clearance (e.g., PSMD8, CHMP4B, VPS4A ), suggesting greater functional impairment in Exc near large vessels in AD ( Fig. 5C ). Conversely, the upregulation of the energy metabolism-related gene KIF5A ( 114 ) in level 1 spots in AD may reflect a compensatory response to mitochondrial stress in these neurons ( Fig. 5C ). Together, these findings highlight significant cell type-specific transcriptional differences in vessel-adjacent regions across AC, PART, and AD groups, potentially shedding light on the detrimental impact of BBB disruption in AD. We next constructed cell–cell communication networks within each level for the AC, PART, and AD groups to identify potential cellular interactions involved in neuroinflammation and degeneration ( Fig. S8 A ). Two key cellular survival-related signaling pathways, GAS and PDGF, originating from VC and targeting multiple cell types, particularly Ast, Mic, and Oli, were detected in levels 1 and 3 spots across the different groups ( Fig. S8 B-C ; Fig. 5D ). For the GAS signaling pathway, interactions were predominantly directed from VC to Mic in both the AC and PART groups, but were prominent from VC to Ast in the AD group ( Fig. S8 C ). Specifically, GAS6–MERTK interactions from VC to Mic were absent in the AC group and progressively increased in PART and AD, while GAS6–MERTK interactions between VC and Ast were only detected in the AD group This result is consistent with previous studies showing that the GAS6–MERTK pair plays a dual role in AD: while it promotes Aβ clearance via microglial activation, it can also drive neuroinflammation through Ast reactivation ( 115 , 116 ). Similar to GAS signaling, PDGF signaling from VC to Ast was observed only in the PART and AD groups, but not in AC ( Fig. 5D ). Specifically, PDGFB–PDGFRB interactions showed a progressive increase from AC to PART to AD ( Fig. 5E ). This LR pair was similarly strong in both levels 1 and 3 in the AD group, while it was stronger in level 1 than in level 3 in the PART group. PDGFB–PDGFRB signaling from VC to Ast activates Ast to recruit monocytes into the brain, thereby amplifying neuroinflammation and leading to neuron apoptosis ( 117 ). In summary, our results indicated that the pathological alterations in the transcriptional profiles of Ast located near large vessels was activated by VC in AD, which may play a critical role in accelerating disease progression. To further investigate transcriptional alterations in Ast located near large vessels during disease progression, we performed DGE analysis in Ast between the AC, PART, and AD groups across levels 1 to 4, respectively. Notably, the P53-related gene TP53INP2 was significantly upregulated in Ast in the AD group compared to both AC and PART groups at levels 1 to 3 ( Fig. 5F ). However, this upregulation was not observed at level 4. This spatial pattern suggests that TP53INP2 is highly enriched in Ast located near large vessels in AD. TP53INP2 (Tumor Protein P53 Inducible Nuclear Protein 2) encodes a stress-responsive nuclear protein known to promote cellular autophagy ( 118 ). Excessive expression of TP53INP2 may lead to hyperactivation of autophagy in Ast, contributing to remove misfolded proteins and toxic materials of nearby Exc to maintain the neuron health ( 119 ). Meanwhile, as a tumor suppressor, TP53INP2 also promote cellular apoptosis under pathological conditions ( 120 ), which may further exacerbate Exc degeneration. To validate the upregulation of TP53INP2 observed in our ST data, we performed IHC staining on six hippocampal samples used for ST and one hippocampal sample used for snRNA-seq, and included one additional independent hippocampal samples to the AC and PART groups, respectively. ( Table S1 ; Methods ). TP53INP2 protein was enriched around large vessels in all groups, with significantly elevated expression in astrocytes near vessels from AC to PART to AD, consistent with our ST data ( Fig. 5H-J ; Fig. S8 D-E ). Together, these results suggest that BBB disruption in large vessels leads to pathological alterations in the transcriptional profiles of nearby cells. In particular, the elevated expression of TP53INP2 in Ast adjacent to large vessels may play a central role in the neurodegenerative processes observed in AD. DISCUSSION In this study, we generated a comprehensive, data-driven, transcriptome-wide molecular atlas of the adult human hippocampus using the advanced 10x Visium ST platform to investigate molecular alterations across AC, PART, and AD. By integrating cutting-edge ST with innovative analytical strategies, we unveiled potential mechanisms driving the transition from PART to AD and offer novel insights into AD pathology. Furthermore, our analysis pipeline, enhancing spot-level data to pseudo-sc resolution, can be applied to existing non–sc ST datasets from other brain region ( 18 , 121 ), enabling deeper exploration of molecular mechanisms underlying AD. Here, we highlight several key findings. Although multiple earlier ST studies have mapped hippocampal subregions in humans ( 26 , 38 , 39 ), none have successfully identified and validated markers that distinguish the CA1 and CA2 regions. In our study, we identified NRIP3 as a canonical marker that reliably delineates the boundary between CA1 and CA2. This marker provides a robust criterion for distinguishing these two subregions and offers a valuable tool for investigating region-specific pathological changes in AD. To address the relatively low resolution of the 10X Visium platform, we applied BayesPrism ( 45 ), a robust deconvolution method, to enhance spatial resolution beyond the spot level and achieve pseudo-sc resolution. At this higher resolution, we found that the distinct subregional markers of Exc within SLs, initially identified at the spatial spot level, were largely driven by transcriptional heterogeneity within the Exc population. Additionally, pseudo-sc analysis revealed changes in both cellular composition and cell type–specific transcriptional profiles across hippocampal subregions between the AC, PART, and AD groups. The observed shifts in cell proportions and gene expression suggest that PART may represent a transitional state between AC and AD, consistent with findings by Duyckaerts et al. ( 122 ). In the PART group, Exc in the CA1 region with NFT burden showed expected signs of stress. Interestingly, Exc in other SL regions without NFTs also exhibited elevated stress markers, likely due to age-related oxidative stress and neuronal inflammation. Notably, while Stein-O’Brien et al. ( 25 ) reported upregulation of APP expression in NFT-bearing Exc, we observed increased APP expression even in Exc without NFTs, particularly in the SUB and CA2–CA4 regions of the PART group. This suggests that APP upregulation may result from stress independent of NFT pathology, potentially promoting Aβ production. Given that excess Aβ is cleared by Mic, the increased proportion of Mic in PART compared to the AC group may reflect a compensatory response aimed at Aβ clearance. However, prolonged Mic activation and exposure to Aβ can lead to Mic dysfunction and degeneration ( 123 ), leading to Aβ accumulation and plaque formation. The resulting Aβ deposition intensifies inflammation, which in turn exacerbates tau phosphorylation, increasing NFT burden and compromising BBB integrity. This feed-forward loop accelerates the transition from PART to AD. Ultimately, the combined effects of Aβ plaques, NFTs, and BBB disruption lead to Exc degeneration. Furthermore, our study identified molecular mechanisms underlying the selective vulnerability of Exc in the CA1 subregion in both PART and AD. In PART, although Exc across SUB and CA areas experienced oxidative stress and inflammation, Exc in CA1 uniquely upregulated NFT-associated genes CDK5R1 and CDK5R2 compared to both the AC and AD groups. In contrast, these genes were significantly enriched in the AD group, relative to AC and PART, in the SUB and CA2–CA4 subregions. Additionally, in PART, the region-specific upregulation of tau dephosphorylation-related genes, including PPP1R9B in SUB and PPP2CB in CA4, may contribute to the lower NFT burden observed in these regions. Beyond neuronal changes, we also observed enhanced glial resilience in PART, particularly in CA1, where Ast and Mic were robustly reactivated, potentially contributing to neuroprotection. However, in AD, this glial reactivation, especially in Ast, adopted a dual role: while still supporting neuronal survival, it also promoted BBB disruption, thereby exacerbating disease progression. Analysis of inter-subregion communication in the hippocampus revealed a progressive decline in both neuronal survival and BBB integrity from AC to PART and, ultimately, to AD. BBB disruption was associated with altered transcriptional profiles in VC, Ast, and Mic near large vessels, leading to reshaped cellular interactions. Specifically, VC engaged in pro-inflammatory signalling with glial cells, particularly Ast, thereby exacerbating neuroinflammation and promoting cell apoptosis. Notably, we observed a significant upregulation of TP53INP2 in Ast located near large vessels in the AD group, compared to the AC and PART groups. While this gene has not been widely reported in AD, a closely related gene, TP53INP1 , has been implicated in AD pathogenesis in several studies (138, 139). As a tumor suppressor, TP53INP2 may participate in a previously unrecognized Ast-related neural apoptosis pathway, potentially contributing to neurodegeneration. This finding warrants further validation through molecular and functional studies. Despite the significant advances and insights provided by this study, several limitations should be acknowledged. First, although all six samples were age-matched (ranging from 79 to 92 years), the sex distribution was unbalanced (four males and two females), potentially introducing sex-specific bias. Second, the sc reference dataset used for deconvolution was not derived from the same individuals as the ST samples, which limited our choice of deconvolution methods and may have introduced analytical bias. Additionally, while we enhanced spatial resolution from spot-level to pseudo–single-cell resolution using deconvolution algorithms, the resulting pseudo-sc matrix is still computationally inferred. As such, discrepancies may exist compared to true sc resolution platforms, such as Visium HD or Stereo-seq (53). Nevertheless, despite these limitations, our study provides valuable novel insights into the transcriptional landscape of PART and AD in the human hippocampus, offering a systematic and high-resolution view of disease progression and the molecular mechanisms driving neurodegeneration. METHODS Study subjects This study was approved by the Ethics Committee of Xiangya School of Medicine in Central South University (2020KT-37, 4/10/2020; #2023-KT084, 6/21/2023), and conducted in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Brains were banked through a willed body donation program, with donors’ clinical records collected when available ( 124 ). The brains were assessed for neuropathological changes following the Standard Brain Banking Protocol established by the China Brain Bank Consortium ( 125 ). Six postmortem human hippocampal samples from Chinese Han individuals aged 79–95 years were analyzed using ST and were classified into AC, PART, and AD based on 6E10 (BioLegend, #SIG-39320) IHC for Aβ plaques ( Fig. S1 A3-F3 ) as well as AT8 (Invitrogen, #MN1020) and Gallyas staining ( 126 ) for NFTs ( Fig. S1 A4-F4; Fig. S1 A5-F5 ). Specifically, the brains in the AC group were absent of Aβ deposition (Thal phase 0) and contained a few hyperphosphorylated tau (pTau) positive neurons (pre-tangle) but no Gallyas stained mature or ghost tangles in the hippocampal formation (Braak stage I-II). The two brains in the PART group showed only a few diffusion plaques in the prefrontal cortex (Thal phase 0), with AT8 and Gallyas stained NFT observed in the hippocampus and temporal lobe cortex (Braak stage III). The two brains in the AD had high Aβ plaque burden (Thal phase III) and heavy NFT pathology (Braak stages IV–V). In addition to the two AD cases included in the ST analysis, hippocampal tissue from the third AD individual was included for snRNA-seq. For the TP53INP2 IHC validation, we have added one additional sample to both the AC and PART groups. For details on human samples used in this study, please see Supplementary Table 1. Tissue preparation, FFPE section, 10X Visium library preparation, sequencing, data preprocessing, and IHC Human hippocampal samples were fixed in 4% paraformaldehyde solution (Cat # G1101-500ML, Servicebio) for 24–48 hours at 4℃ to preserve morphology and RNA integrity. Fixed tissues were processed using standard dehydration and paraffin-embedding protocols ( 127 ). FFPE blocks were sectioned at 5 µm thickness using a rotary microtome and mounted onto Visium Spatial Gene Expression Slides (10X Genomics), ensuring optimal placement over the capture area. Sections were baked at 60°C for 30 minutes, followed by deparaffinization, H&E staining, and imaging using a brightfield microscope to guide tissue annotation. Spatial gene expression profiling was performed using the Visium Spatial Gene Expression for FFPE protocol (10X Genomics), including probe hybridization, ligation, and amplification steps to construct spatially barcoded libraries. Sequencing was carried out on an Illumina NovaSeq 6000 platform with paired end reads, targeting a depth of at least 25,000 reads per capture spot. Raw sequencing data were processed using the 10X Genomics Space Ranger pipeline to align reads and generate spatial gene expression matrices for downstream analysis. Raw sequencing data (BCL files) were first demultiplexed using cellranger mkfastq (10X Genomics), generating FASTQ files for downstream analysis. Quality control on FASTQ files was performed using FastQC to assess read quality, adapter content, and duplication rates. The FASTQ files were then processed with SpaceRanger (v2.1, 10X Genomics), aligning reads to the GRCh38 human genome panel to generate the count matrix. Nuclei were isolated from frozen post-mortem brain tissue following a published protocol ( 128 ) for snRNA-seq (10X Genomics). In brief, approximately 40 mg of frozen, pulverized tissue was homogenized in chilled Nuclei EZ Lysis Buffer (MilliporeSigma #NUC101) using a glass dounce with about 15 strokes per pestle. The homogenate was passed through a 70 µm mesh strainer and centrifuged at 500 × g for 5 minutes at 4°C. The pellet was resuspended in EZ Lysis Buffer, centrifuged again, and then transferred into nuclei wash/resuspension buffer (1x PBS, 1% BSA, 0.2 U/µL RNase Inhibitor). Nuclei were washed and centrifuged three times in this buffer before being stained with DAPI (10 µg/mL). For snRNA-seq, libraries were prepared using the Chromium Single Cell 3’ Reagent Kits v3 according to the manufacturer’s protocol (10x Genomics). Sequencing was carried out on an Illumina NovaSeq 6000 platform with paired end reads, targeting a depth of at least 20,000 reads per capture nucleus. Raw scRNA-seq data were processed through cell ranger (10x Genomics) to be converted into the gene expression matrix. For the IHC validation, FFPE blocks were sectioned at 5 µm thickness and mounted onto slides. The sections were placed under a vented hood for air drying prior to FIBCD1, NRIP3, CCK, NEFM, STXBP6, and TP53INP2 staining. FIBCD1 (Cat # 25125-1AP, Proteintech), NRIP3 (Cat # 15664-1-AP, Proteintech), CCK (Cat # 13074-2-AP, Proteintech), NEFM (Cat # 25805-1-AP, Proteintech), STXBP6 (Cat # 10976-4-AP, Proteintech), TP53INP2 (Cat # PA5-72961, ThermoFisher) were used for IHC staining according to the vendor’s instructions. Bioinformatics analysis of ST data of human hippocampus ST data integration and clustering analysis for 10X Visium spatial spots At the spot level, we applied PRECAST ( 37 ), a data integration and unsupervised clustering method for ST data across multiple tissue slides, to identify subregions within the human hippocampus. PRECAST uses a two-layer hierarchical model comprising an integration layer and a clustering layer. In the integration layer, spatial spots from all tissue sections were projected into a shared low-dimensional embedding space. To preserve spatial continuity in gene expression patterns, an intrinsic Conditional Autoregressive (CAR) model was applied, encouraging nearby spots to have similar embeddings. Following integration and dimension reduction, spatial spots were clustered based on both their low-dimensional embeddings and physical coordinates. We selected K = 15 as the optimal number of clusters, guided by the elbow point of the Bayesian Information Criterion (BIC) curve. Each resulting cluster was annotated using specific gene markers. To validate our annotations, we visualized the spatial distribution of annotated spots for each sample based on their coordinates and aligned them with corresponding Eosin-stained sections. Sample orientation was verified by identifying the directions of the SUB, CA, and DG subregions on both ST data and Eosin-stained image. DGE analysis The “sc.tl.rank_genes_groups” function from the Scanpy package (v1.9.3) was utilized for DGE analysis. The Wilcoxon signed-rank test with FDR adjustment was applied to calculate the adjusted P-values, and the genes with adjusted P-value < 0.05 were considered as the DEGs. Deconvolution analysis In our study, we applied the BayesPrism algorithm ( 45 ) to enhance spatial resolution beyond the spatial spot level. Using snRNA-seq data on the human hippocampus, consisting of our in-house and public available data, we first inferred the proportion of each cell type within each spatial spot and subsequently estimated cell type-specific gene expression patterns for each spot. Briefly, BayesPrism employs a Bayesian framework that models prior distributions based on scRNA-seq data and infers the joint posterior distribution of cell type porportions and gene expression, conditioned on each spatial spot. To prepare the input data, we performed QC by filtering out outlier genes in the snRNA-seq dataset using the “ plot.bulk.outlier ” function with default parameters. We then combined the snRNA-seq and ST data into a BayesPrism object using the “new.prism” function, followed by running the “run.prism” function to estimate cell type proportions and infer gene expression profiles for each cell type in each spot. To assess the accuracy of BayesPrism’s deconvolution performance, we compared it with three additional spatial deconvolution algorithms: CARD ( 49 ), SpaCET ( 50 ), and PANDA ( 51 ). For CARD, we used the “CARDObject” function to construct the input object and applied “CARD_deconvolution” to estimate cell type proportions per spatial spot. For SpaCET, we used “create.SpaCET.object.10X” and “SpaCET.deconvolution”, both with default parameters. For PANDA, we employed the “sc_train” and “st_train” functions to estimate both cell type proportions and gene expression profiles per spot. To evaluate consistency between BayesPrism and the other methods, we calculated pairwise Spearman correlation coefficients between the cell type proportions inferred by BayesPrism and those estimated by CARD, SpaCET, and PANDA. Inter-subregion and cell-cell communication analysis Inter-subregion and cell-cell communication analyses were performed using CellChat (v2.1.0) ( 129 ) based on the expression of known LR pairs across different subregions and cell types. For inter-subregion and cell-cell communication, we computed communication strength by modeling ligand-receptor interactions between spatial spots labeled by subregion in AC, PART, and AD samples separately. This modeling was based on the Law of Mass Action and incorporated gene expression profiles projected onto a protein-protein interaction network. Additionally, subregion/cell proportion was incorporated to minimize bias arising from unequal comparisons of inter-subregion/cell-cell interactions. To include spatial context in inter-subregion communication, we used the spatial coordinates of each spot as a cofactor. In contrast, for constructing cell-cell communication networks, we did not include spatial location as a covariate due to the limited spatial distance variation among spots within each level. We followed the official workflow and applied the data processing functions “identify OverExpressedGenes” and “identifyOverExpressedInteractions”. The inter-subregion communication networks were inferred by the function “computeCommuProb”. Function “netVisual_bubble” was used to compare the communication probabilities mediated by L-R from certain subregion group to other groups. All the analysis were performed with the default parameter setting. Concentric circle analysis To understand how gene expression varies with proximity to the large vessels with BBB damaged, we mapped the VAS on a two-dimensional panel based on their coordinator for each sample, respectively, and drew three concentric circles around each high stress focal point to differentiate spatial spot distances. We first selected spatial spot located within 600 pixels (~ 600 um) from the VAS in each sample. Spots within a radius within a radius of 200 units of the pixel (~ 200 um) are categorized as level I, those between 200 and 400 units (approximately 200–400 um) as level II, and those in 400 to 600 units (approximately 400 to 600 um) as level III. Spots intersecting circles from multiple VAS areas were assigned to the closest level. Declarations CONFLICT OF INTEREST All authors have no conflicts of interest to declare. DATA AVAILIABILITY The ST data from two AC, two PART, and two AD samples, along with the snRNA-seq data generated in this study, will be deposited in the GEO database upon manuscript acceptance. The raw FASTQ data have been deposited in the SRA database and are accessible for download through the accession number (PRJNA1300973). The public snRNA-seq data on human hippocampus were from ROSMAP project (syn52293442). CODE AVAILIABILITY The codes are available and can be downloaded from Github ( https://github.com/Yungong1/Spatial-Transcriptome-Hippocampus ). AUTHOR CONTRIBUTIONS Y.G. conducted the major data analysis and wrote the main manuscript text; Q.Z., T.L., and Z.X. collected the human brain samples and performed experimental validation; Q.Z. and X.X.Y. performed histopathological characterization and immunohistochemical cross-validation experiments; Q.Z., D.W., A.L., X.X.Y., H.S., and H.W.D provided valuable suggestions throughout the study implementation; H.S., H.M.X., and H.W.D. were responsible for conceiving, designing, initiating, directing, supervising, language proofreading, and securing fundings for this study. All authors participated in the discussions of the project and reviewed and/or revised the manuscript. ACKNOWLEDGMENTS This investigators were benefited or partially funded by National Institutes of Health [U19AG055373, R01AG061917, R01AG068232, P30GM145498, P20GM109036], National Natural Science Foundation of China [#82071223), as well as Ministry of Science and Technology of China (Science Innovation 2030-Brain Science [#2021ZD0201803] and Brain-Inspired Intelligence Technology Major Projects [#2021ZD0201103]). References Knopman DS, Amieva H, Petersen RC, Chetelat G, Holtzman DM, Hyman BT et al (2021) Alzheimer disease. Nat Rev Dis Primers 7(1):33 Zhang W, Xiao D, Mao Q, Xia H (2023) Role of neuroinflammation in neurodegeneration development. Signal Transduct Target Ther 8(1):267 Goel P, Chakrabarti S, Goel K, Bhutani K, Chopra T, Bali S (2022) Neuronal cell death mechanisms in Alzheimer's disease: An insight. Front Mol Neurosci 15:937133 (2024) Alzheimer's disease facts and figures. Alzheimers Dement. 2024;20(5):3708 – 821 Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL et al (2019) Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol 15(10):565–581 Leszek J, Mikhaylenko EV, Belousov DM, Koutsouraki E, Szczechowiak K, Kobusiak-Prokopowicz M et al (2021) The Links between Cardiovascular Diseases and Alzheimer's Disease. Curr Neuropharmacol 19(2):152–169 Li X, Zhang Y, Zhang C, Zheng Y, Liu R, Xiao S (2023) Education counteracts the genetic risk of Alzheimer's disease without an interaction effect. Front Public Health 11:1178017 Dominguez LJ, Veronese N, Vernuccio L, Catanese G, Inzerillo F, Salemi G et al (2021) Nutrition, Physical Activity, and Other Lifestyle Factors in the Prevention of Cognitive Decline and Dementia. Nutrients. ;13(11) Hsiao YH, Chang CH, Gean PW (2018) Impact of social relationships on Alzheimer's memory impairment: mechanistic studies. J Biomed Sci 25(1):3 Mielke MM, Ransom JE, Mandrekar J, Turcano P, Savica R, Brown AW (2022) Traumatic Brain Injury and Risk of Alzheimer's Disease and Related Dementias in the Population. J Alzheimers Dis 88(3):1049–1059 Bellenguez C, Kucukali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N et al (2022) New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet 54(4):412–436 Zhang J, Wang Y, Zhang Y, Yao J (2023) Genome-wide association study in Alzheimer's disease: a bibliometric and visualization analysis. Front Aging Neurosci 15:1290657 Hampel H, Hardy J, Blennow K, Chen C, Perry G, Kim SH et al (2021) The Amyloid-beta Pathway in Alzheimer's Disease. Mol Psychiatry 26(10):5481–5503 Grundke-Iqbal I, Iqbal K, Quinlan M, Tung YC, Zaidi MS, Wisniewski HM (1986) Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J Biol Chem 261(13):6084–6089 Busche MA, Hyman BT (2020) Synergy between amyloid-beta and tau in Alzheimer's disease. Nat Neurosci 23(10):1183–1193 Zhang Y, Chen H, Li R, Sterling K, Song W (2023) Amyloid beta-based therapy for Alzheimer's disease: challenges, successes and future. Signal Transduct Target Ther 8(1):248 Marx V (2021) Method of the Year: spatially resolved transcriptomics. Nat Methods 18(1):9–14 Chen S, Chang Y, Li L, Acosta D, Li Y, Guo Q et al (2022) Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer's disease. Acta Neuropathol Commun 10(1):188 Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J et al (2023) Integrated multimodal cell atlas of Alzheimer's disease. Res Sq Gong Y, Haeri M, Zhang X, Li Y, Liu A, Wu D et al (2025) Stereo-seq of the prefrontal cortex in aging and Alzheimer's disease. Nat Commun 16(1):482 Epifanio I, Ventura-Campos N (2014) Hippocampal shape analysis in Alzheimer's disease using functional data analysis. Stat Med 33(5):867–880 Crary JF, Trojanowski JQ, Schneider JA, Abisambra JF, Abner EL, Alafuzoff I et al (2014) Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol 128(6):755–766 Goedert M, Crowther RA, Scheres SHW, Spillantini MG (2024) Tau and neurodegeneration. Cytoskeleton (Hoboken) 81(1):95–102 Hickman RA, Flowers XE, Wisniewski T (2020) Primary Age-Related Tauopathy (PART): Addressing the Spectrum of Neuronal Tauopathic Changes in the Aging Brain. Curr Neurol Neurosci Rep 20(9):39 Stein-O'Brien GL, Palaganas R, Meyer EM, Redding-Ochoa J, Pletnikova O, Guo H et al (2025) Transcriptional signatures of hippocampal tau pathology in primary age-related tauopathy and Alzheimer's disease. Cell Rep 44(3):115422 Wang P, Han L, Wang L, Tao Q, Guo Z, Luo T et al (2025) Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas. Neuron Dong Y, Saglietti C, Bayard Q, Espin Perez A, Carpentier S, Buszta D et al (2025) Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity. Nat Commun 16(1):4400 Rao YL, Ganaraja B, Murlimanju BV, Joy T, Krishnamurthy A, Agrawal A (2022) Hippocampus and its involvement in Alzheimer's disease: a review. 3 Biotech 12(2):55 Mrdjen D, Fox EJ, Bukhari SA, Montine KS, Bendall SC, Montine TJ (2019) The basis of cellular and regional vulnerability in Alzheimer's disease. Acta Neuropathol 138(5):729–749 Huuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B et al (2024) A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 384(6698):eadh1938 Hunt S, Leibner Y, Mertens EJ, Barros-Zulaica N, Kanari L, Heistek TS et al (2023) Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex. Cereb Cortex 33(6):2857–2878 Consalez GG, Goldowitz D, Casoni F, Hawkes R (2020) Origins, Development, and Compartmentation of the Granule Cells of the Cerebellum. Front Neural Circuits 14:611841 Amaral DG, Scharfman HE, Lavenex P (2007) The dentate gyrus: fundamental neuroanatomical organization (dentate gyrus for dummies). Prog Brain Res 163:3–22 Anand KS, Dhikav V (2012) Hippocampus in health and disease: An overview. Ann Indian Acad Neurol 15(4):239–246 Luszczewska-Sierakowska I, Wawrzyniak-Gacek A, Guz T, Tatara MR, Charuta A (2015) Morphometric Parameters of Pyramidal Cells in CA1-CA4 Fields in the Hippocampus of Arctic Fox (Vulpes lagopus). Folia Biol (Krakow) 63(4):263–267 Tatu L, Vuillier F (2014) Structure and vascularization of the human hippocampus. Front Neurol Neurosci 34:18–25 Liu W, Liao X, Luo Z, Yang Y, Lau MC, Jiao Y et al (2023) Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nat Commun 14(1):296 Ramnauth AD, Tippani M, Divecha HR, Papariello AR, Miller RA, Nelson ED et al (2025) Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation. Cell Rep 44(2):115300 Simard S, Rahimian R, Davoli MA, Theberge S, Matosin N, Turecki G et al (2024) Spatial transcriptomic analysis of adult hippocampal neurogenesis in the human brain. J Psychiatry Neurosci 49(5):E319–E33 Mirzazadeh R, Andrusivova Z, Larsson L, Newton PT, Galicia LA, Abalo XM et al (2023) Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples. Nat Commun 14(1):509 Fischer TT, Nguyen LD, Ehrlich BE (2021) Neuronal calcium sensor 1 (NCS1) dependent modulation of neuronal morphology and development. FASEB J 35(10):e21873 Radler MR, Liu X, Peng M, Doyle B, Toyo-Oka K, Spiliotis ET (2023) Pyramidal neuron morphogenesis requires a septin network that stabilizes filopodia and suppresses lamellipodia during neurite initiation. Curr Biol 33(3):434–448 e8 Danzer SC, Kotloski RJ, Walter C, Hughes M, McNamara JO (2008) Altered morphology of hippocampal dentate granule cell presynaptic and postsynaptic terminals following conditional deletion of TrkB. Hippocampus 18(7):668–678 Bienkowski MS (2023) Further refining the boundaries of the hippocampus CA2 with gene expression and connectivity: Potential subregions and heterogeneous cell types. Hippocampus 33(3):150–160 Chu T, Wang Z, Pe'er D, Danko CG (2022) Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer 3(4):505–517 Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP et al (2024) Single-cell multiregion dissection of Alzheimer's disease. Nature 632(8026):858–868 Scher AI, Xu Y, Korf ES, White LR, Scheltens P, Toga AW et al (2007) Hippocampal shape analysis in Alzheimer's disease: a population-based study. NeuroImage 36(1):8–18 Lou Y, Zhao L, Yu S, Sun B, Hou Z, Zhang Z et al (2020) Brain asymmetry differences between Chinese and Caucasian populations: a surface-based morphometric comparison study. Brain Imaging Behav 14(6):2323–2332 Ma Y, Zhou X (2022) Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol 40(9):1349–1359 Ru B, Huang J, Zhang Y, Aldape K, Jiang P (2023) Estimation of cell lineages in tumors from spatial transcriptomics data. Nat Commun 14(1):568 Wang MG, Chen L, Zhang XF (2024) Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA. Nucleic Acids Res 52(20):12173–12190 Gong Y, Haeri M, Zhang X, Li Y, Liu A, Wu D et al (2025) Stereo-seq of the prefrontal cortex in aging and Alzheimer's disease. Nat Commun 16(1):482 Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y et al (2022) Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185(10):1777–92e21 Chen A, Sun Y, Lei Y, Li C, Liao S, Meng J et al (2023) Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 186(17):3726–43e24 Kremerskothen J, Kindler S, Finger I, Veltel S, Barnekow A (2006) Postsynaptic recruitment of Dendrin depends on both dendritic mRNA transport and synaptic anchoring. J Neurochem 96(6):1659–1666 Fu H, Hardy J, Duff KE (2018) Selective vulnerability in neurodegenerative diseases. Nat Neurosci 21(10):1350–1358 Stein-O'Brien GL, Palaganas R, Meyer EM, Redding-Ochoa J, Pletnikova O, Guo H et al (2023) Transcriptional Signatures of Hippocampal Tau Pathology in Primary Age-Related Tauopathy and Alzheimer's Disease. medRxiv Zhang L, Jiang Y, Zhu J, Liang H, He X, Qian J et al (2020) Quantitative Assessment of Hippocampal Tau Pathology in AD and PART. J Mol Neurosci 70(11):1808–1811 Iida MA, Farrell K, Walker JM, Richardson TE, Marx GA, Bryce CH et al (2021) Predictors of cognitive impairment in primary age-related tauopathy: an autopsy study. Acta Neuropathol Commun 9(1):134 Yang J, Kong L, Zou L, Liu Y (2024) The role of synaptic protein NSF in the development and progression of neurological diseases. Front Neurosci 18:1395294 Brozzi F, Arcuri C, Giambanco I, Donato R (2009) S100B Protein Regulates Astrocyte Shape and Migration via Interaction with Src Kinase: IMPLICATIONS FOR ASTROCYTE DEVELOPMENT, ACTIVATION, AND TUMOR GROWTH. J Biol Chem 284(13):8797–8811 Klein RS, Das B, Fricker LD (1992) Secretion of carboxypeptidase E from cultured astrocytes and from AtT-20 cells, a neuroendocrine cell line: implications for neuropeptide biosynthesis. J Neurochem 58(6):2011–2018 Wu M, Xu L, Wang Y, Zhou N, Zhen F, Zhang Y et al (2018) S100A8/A9 induces microglia activation and promotes the apoptosis of oligodendrocyte precursor cells by activating the NF-kappaB signaling pathway. Brain Res Bull 143:234–245 Farber K, Cheung G, Mitchell D, Wallis R, Weihe E, Schwaeble W et al (2009) C1q, the recognition subcomponent of the classical pathway of complement, drives microglial activation. J Neurosci Res 87(3):644–652 Haynes SE, Hollopeter G, Yang G, Kurpius D, Dailey ME, Gan WB et al (2006) The P2Y12 receptor regulates microglial activation by extracellular nucleotides. Nat Neurosci 9(12):1512–1519 Mrak RE (2012) Microglia in Alzheimer brain: a neuropathological perspective. Int J Alzheimers Dis 2012:165021 Pumo A, Legeay S (2024) The dichotomous activities of microglia: A potential driver for phenotypic heterogeneity in Alzheimer's disease. Brain Res 1832:148817 Yue Q, Hoi MPM (2023) Emerging roles of astrocytes in blood-brain barrier disruption upon amyloid-beta insults in Alzheimer's disease. Neural Regen Res 18(9):1890–1902 Fukutani Y, Cairns NJ, Shiozawa M, Sasaki K, Sudo S, Isaki K et al (2000) Neuronal loss and neurofibrillary degeneration in the hippocampal cortex in late-onset sporadic Alzheimer's disease. Psychiatry Clin Neurosci 54(5):523–529 West MJ, Kawas CH, Stewart WF, Rudow GL, Troncoso JC (2004) Hippocampal neurons in pre-clinical Alzheimer's disease. Neurobiol Aging 25(9):1205–1212 Anacker C, Hen R (2017) Adult hippocampal neurogenesis and cognitive flexibility - linking memory and mood. Nat Rev Neurosci 18(6):335–346 Rogers J, Strohmeyer R, Kovelowski CJ, Li R (2002) Microglia and inflammatory mechanisms in the clearance of amyloid beta peptide. Glia 40(2):260–269 Zhao Y, Zhao B (2013) Oxidative stress and the pathogenesis of Alzheimer's disease. Oxid Med Cell Longev 2013:316523 Choi JE, Lee JJ, Kang W, Kim HJ, Cho JH, Han PL et al (2018) Proteomic Analysis of Hippocampus in a Mouse Model of Depression Reveals Neuroprotective Function of Ubiquitin C-terminal Hydrolase L1 (UCH-L1) via Stress-induced Cysteine Oxidative Modifications. Mol Cell Proteom 17(9):1803–1823 Kachiwala SJ, Harris SE, Wright AF, Hayward C, Starr JM, Whalley LJ et al (2005) Genetic influences on oxidative stress and their association with normal cognitive ageing. Neurosci Lett 386(2):116–120 Joels M, Velzing E, Nair S, Verkuyl JM, Karst H (2003) Acute stress increases calcium current amplitude in rat hippocampus: temporal changes in physiology and gene expression. Eur J Neurosci 18(5):1315–1324 Dhapola R, Beura SK, Sharma P, Singh SK, HariKrishnaReddy D (2024) Oxidative stress in Alzheimer's disease: current knowledge of signaling pathways and therapeutics. Mol Biol Rep 51(1):48 O'Brien RJ, Wong PC (2011) Amyloid precursor protein processing and Alzheimer's disease. Annu Rev Neurosci 34:185–204 Breckler M, Berthouze M, Laurent AC, Crozatier B, Morel E (2011) Lezoualc'h F. Rap-linked cAMP signaling Epac proteins: compartmentation, functioning and disease implications. Cell Signal 23(8):1257–1266 Jicha GA, Weaver C, Lane E, Vianna C, Kress Y, Rockwood J et al (1999) cAMP-dependent protein kinase phosphorylations on tau in Alzheimer's disease. J Neurosci 19(17):7486–7494 Lauro C, Cipriani R, Catalano M, Trettel F, Chece G, Brusadin V et al (2010) Adenosine A1 receptors and microglial cells mediate CX3CL1-induced protection of hippocampal neurons against Glu-induced death. Neuropsychopharmacology 35(7):1550–1559 Lee CY, Landreth GE (2010) The role of microglia in amyloid clearance from the AD brain. J Neural Transm (Vienna) 117(8):949–960 van Olst L, Simonton B, Edwards AJ, Forsyth AV, Boles J, Jamshidi P et al (2025) Microglial mechanisms drive amyloid-beta clearance in immunized patients with Alzheimer's disease. Nat Med Liu F, Grundke-Iqbal I, Iqbal K, Gong CX (2005) Contributions of protein phosphatases PP1, PP2A, PP2B and PP5 to the regulation of tau phosphorylation. Eur J Neurosci 22(8):1942–1950 Noda K, Sasaki K, Fujimi K, Wakisaka Y, Tanizaki Y, Wakugawa Y et al (2006) Quantitative analysis of neurofibrillary pathology in a general population to reappraise neuropathological criteria for senile dementia of the neurofibrillary tangle type (tangle-only dementia): the Hisayama Study. Neuropathology 26(6):508–518 Kimura T, Ishiguro K, Hisanaga S (2014) Physiological and pathological phosphorylation of tau by Cdk5. Front Mol Neurosci 7:65 Maccioni RB, Otth C, Concha II, Munoz JP (2001) The protein kinase Cdk5. Structural aspects, roles in neurogenesis and involvement in Alzheimer's pathology. Eur J Biochem 268(6):1518–1527 Giovannoni F, Quintana FJ (2020) The Role of Astrocytes in CNS Inflammation. Trends Immunol 41(9):805–819 Griffin JWD, Liu Y, Bradshaw PC, Wang K (2018) In Silico Preliminary Association of Ammonia Metabolism Genes GLS, CPS1, and GLUL with Risk of Alzheimer's Disease, Major Depressive Disorder, and Type 2 Diabetes. J Mol Neurosci 64(3):385–396 Ma SL, Tang NL, Lam LC (2016) Association of gene expression and methylation of UQCRC1 to the predisposition of Alzheimer's disease in a Chinese population. J Psychiatr Res 76:143–147 Strunz M, Jarrell JT, Cohen DS, Rosin ER, Vanderburg CR, Huang X (2019) Modulation of SPARC/Hevin Proteins in Alzheimer's Disease Brain Injury. J Alzheimers Dis 68(2):695–710 Okada T, Suzuki H, Travis ZD, Altay O, Tang J, Zhang JH (2021) SPARC Aggravates Blood-Brain Barrier Disruption via Integrin alphaVbeta3/MAPKs/MMP-9 Signaling Pathway after Subarachnoid Hemorrhage. Oxid Med Cell Longev 2021:9739977 Rodriguez-Giraldo M, Gonzalez-Reyes RE, Ramirez-Guerrero S, Bonilla-Trilleras CE, Guardo-Maya S, Nava-Mesa MO (2022) Astrocytes as a Therapeutic Target in Alzheimer's Disease-Comprehensive Review and Recent Developments. Int J Mol Sci. ;23(21) Schumacher L, Slimani R, Zizmare L, Ehlers J, Kleine Borgmann F, Fitzgerald JC et al (2023) TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes. Biomedicines. ;11(1) Bhattarai P, Yilmaz E, Cakir EO, Korkmaz HY, Lee AJ, Ma Y et al (2025) APOE- epsilon4-induced Fibronectin at the blood-brain barrier is a conserved pathological mediator of disrupted astrocyte-endothelia interaction in Alzheimer's disease. bioRxiv Padurariu M, Ciobica A, Mavroudis I, Fotiou D, Baloyannis S (2012) Hippocampal neuronal loss in the CA1 and CA3 areas of Alzheimer's disease patients. Psychiatr Danub 24(2):152–158 Hrybouski S, MacGillivray M, Huang Y, Madan CR, Carter R, Seres P et al (2019) Involvement of hippocampal subfields and anterior-posterior subregions in encoding and retrieval of item, spatial, and associative memories: Longitudinal versus transverse axis. NeuroImage 191:568–586 Seok JW, Cheong C (2020) Functional dissociation of hippocampal subregions corresponding to memory types and stages. J Physiol Anthropol 39(1):15 Keller CJ, Honey CJ, Entz L, Bickel S, Groppe DM, Toth E et al (2014) Corticocortical evoked potentials reveal projectors and integrators in human brain networks. J Neurosci 34(27):9152–9163 Tschumi CW, Beckstead MJ (2019) Diverse actions of the modulatory peptide neurotensin on central synaptic transmission. Eur J Neurosci 49(6):784–793 Liu Q, Hazan A, Grinman E, Angulo JA (2017) Pharmacological activation of the neurotensin receptor 1 abrogates the methamphetamine-induced striatal apoptosis in the mouse brain. Brain Res 1659:148–155 Kempermann G, Song H, Gage FH (2015) Neurogenesis in the Adult Hippocampus. Cold Spring Harb Perspect Biol 7(9):a018812 Thakker-Varia S (2012) Antidepressants activate survival-promoting pathways in hippocampal neurons despite nutrient deprivation stress (commentary on Yang. Eur J Neurosci 36(5):2571–2572 Teng HK, Teng KK, Lee R, Wright S, Tevar S, Almeida RD et al (2005) ProBDNF induces neuronal apoptosis via activation of a receptor complex of p75NTR and sortilin. J Neurosci 25(22):5455–5463 Li H, Xu L, Jiang W, Qiu X, Xu H, Zhu F et al (2023) Pleiotrophin ameliorates age-induced adult hippocampal neurogenesis decline and cognitive dysfunction. Cell Rep 42(9):113022 Asai H, Morita S, Miyata S (2011) Effect of pleiotrophin on glutamate-induced neurotoxicity in cultured hippocampal neurons. Cell Biochem Funct 29(8):660–665 Nikolakopoulou AM, Montagne A, Kisler K, Dai Z, Wang Y, Huuskonen MT et al (2019) Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss. Nat Neurosci 22(7):1089–1098 Pushpam M, Talukdar A, Anilkumar S, Maurya SK, Issac TG, Diwakar L (2024) Recurrent endothelin-1 mediated vascular insult leads to cognitive impairment protected by trophic factor pleiotrophin. Exp Neurol 381:114938 Sweeney MD, Sagare AP, Zlokovic BV (2018) Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol 14(3):133–150 Bell RD, Zlokovic BV (2009) Neurovascular mechanisms and blood-brain barrier disorder in Alzheimer's disease. Acta Neuropathol 118(1):103–113 Lee SY, Chung WS (2024) Astrocytic crosstalk with brain and immune cells in healthy and diseased conditions. Curr Opin Neurobiol 84:102840 Huang W, Xia Q, Zheng F, Zhao X, Ge F, Xiao J et al (2023) Microglia-Mediated Neurovascular Unit Dysfunction in Alzheimer's Disease. J Alzheimers Dis 94(s1):S335–S54 Nishimura I, Takazaki R, Kuwako K, Enokido Y, Yoshikawa K (2003) Upregulation and antiapoptotic role of endogenous Alzheimer amyloid precursor protein in dorsal root ganglion neurons. Exp Cell Res 286(2):241–251 Guerra San Juan I, Brunner JW, Eggan K, Toonen RF, Verhage M (2025) KIF5A regulates axonal repair and time-dependent axonal transport of SFPQ granules and mitochondria in human motor neurons. Neurobiol Dis 204:106759 Owlett LD, Karaahmet B, Le L, Belcher EK, Dionisio-Santos D, Olschowka JA et al (2022) Gas6 induces inflammation and reduces plaque burden but worsens behavior in a sex-dependent manner in the APP/PS1 model of Alzheimer's disease. J Neuroinflammation 19(1):38 Sanchez-Mico MV, Jimenez S, Gomez-Arboledas A, Munoz-Castro C, Romero-Molina C, Navarro V et al (2021) Amyloid-beta impairs the phagocytosis of dystrophic synapses by astrocytes in Alzheimer's disease. Glia 69(4):997–1011 Bethel-Brown C, Yao H, Hu G, Buch S (2012) Platelet-derived growth factor (PDGF)-BB-mediated induction of monocyte chemoattractant protein 1 in human astrocytes: implications for HIV-associated neuroinflammation. J Neuroinflammation 9:262 Nowak J, Archange C, Tardivel-Lacombe J, Pontarotti P, Pebusque MJ, Vaccaro MI et al (2009) The TP53INP2 protein is required for autophagy in mammalian cells. Mol Biol Cell 20(3):870–881 Rajpurohit CS, Kumar V, Cheffer A, Oliveira D, Ulrich H, Okamoto OK et al (2020) Mechanistic Insights of Astrocyte-Mediated Hyperactive Autophagy and Loss of Motor Neuron Function in SOD1(L39R) Linked Amyotrophic Lateral Sclerosis. Mol Neurobiol 57(10):4117–4133 Ivanova S, Polajnar M, Narbona-Perez AJ, Hernandez-Alvarez MI, Frager P, Slobodnyuk K et al (2019) Regulation of death receptor signaling by the autophagy protein TP53INP2. EMBO J. ;38(10) Miyoshi E, Morabito S, Henningfield CM, Das S, Rahimzadeh N, Shabestari SK et al (2024) Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's disease. Nat Genet 56(12):2704–2717 Duyckaerts C, Braak H, Brion JP, Buee L, Del Tredici K, Goedert M et al (2015) PART is part of Alzheimer disease. Acta Neuropathol 129(5):749–756 Fruhwurth S, Zetterberg H, Paludan SR (2024) Microglia and amyloid plaque formation in Alzheimer's disease - Evidence, possible mechanisms, and future challenges. J Neuroimmunol 390:578342 Yan XX, Ma C, Bao AM, Wang XM, Gai WP (2015) Brain banking as a cornerstone of neuroscience in China. Lancet Neurol 14(2):136 Qiu W, Zhang H, Bao A, Zhu K, Huang Y, Yan X et al (2019) Standardized Operational Protocol for Human Brain Banking in China. Neurosci Bull 35(2):270–276 Zhang Q-L, Wang Y, Coulibaly S, Sun Z-P, Cai X-L, Tu T et al (2024) Sortilin C-terminal fragment deposition depicts tangle-related nonamyloid neuritic plaque growth in Alzheimer’s disease. bioRxiv. :2024.11.11.622955 Tu T, Cai XL, Sun ZP, Yang C, Jiang J, Wan L et al (2025) Mossy fiber expression of alphaSMA in human hippocampus and its relevance to brain evolution and neuronal development. Sci Rep 15(1):15834 Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ et al (2019) Single-cell transcriptomic analysis of Alzheimer's disease. Nature 570(7761):332–337 Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH et al (2021) Inference and analysis of cell-cell communication using CellChat. Nat Commun 12(1):1088 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementalTable1final.xlsx Supplemental Table 1: Sample information. SupplementalTable2final.xlsx Supplemental Table 2: Distinct transcriptional markers for each hippocampal subregion. SupplementalTable3final.xlsx Supplemental Table 3: Enriched GO terms based on the highly expressed genes by each hippocampal subregion. SupplementalTable4final.xlsx Supplemental Table 4: Cell type–specific DEGs in all cell types across SLs among the AC, PART, and AD groups Supplementalfigure1final.pdf Fig. S1: Histopathological characterization of human hippocampal samples across AC, PART, and AD groups. (A-F) H&E staining (A, B, C, D, E, F), high-magnification eosin sections (A1–F1), number of genes detected per spatial transcriptomics spot (A2–F2), Aβ immunostaining using 6E10 (A3–F3), pTau immunostaining using AT8 (A4–F4), and Gallyas silver staining for neurofibrillary pathology (A5–F5) for M0309, M0310, M0618, M0623, M0802, and M0915 samples, respectively. Supplementalfigure2.pdf Fig. S2: Clustering of hippocampal subregions and validation of subregion-specific markers (A) BIC values across different cluster numbers (K). Each black dot represents the BIC value for a given K. The triangle indicates the selected K used for clustering in this study. (B) The 15 hippocampal subregions identified across six samples. (C) The validation of FIBCD1 on human hippocampus using IHC. The arrow indicates the cells with the expression of FIBCD1 in CA1 region (D) The validation of NRIP3 on human hippocampus using IHC. The arrow indicates the cells with the expression of NRIP3 in CA2 region. Supplementalfigure3.pdf Fig. S3: Validation of the subregion-specific markers (A) The validation of CCK on human hippocampus using IHC. The arrow indicates the cells with the expression of CCK in CA3 region. (B) The validation of NEFM on human hippocampus using IHC. The arrow indicates the cells with the expression of NEFM in SUB region. (C) The validation of STXBP6 on human hippocampus using IHC. The arrow indicates the cells with the expression of STXBP6 in DG region. Supplementalfigure4final.pdf Fig. S4: Expression patterns of the subregion-specific genes in each region across AC, PART, and AD groups (A) The comparison of expression levels of the subregion-specific genes in each subregion between AC, PART, and AD. N.S. indicates non-significant (adjusted P-value > 0.05) and *** represents adjusted P-value < 0.001. (B) The heatmap of the expression levels of transcriptional markers in each subregion across AC, PART, and AD. Supplementalfigure5final.pdf Fig. S5: Deconvolution results of the human spatial transcriptome data (A) UMAP visualization of the human hippocampal snRNA-seq reference dataset, colored by cell type and ethnicity, respectively. (B) Spatial distribution of cell type composition in the hippocampus across AC (M0310), PART (M0623), and AD (M0915) samples. Each spot represents a pie chart illustrating the proportional composition of cell types at that spatial location. (C) The comparison of relative proportions of Inh, Mic, and OPC in SL, FL, and VAS regions. The box plot represents the proportion of the specific cell type. N.S. indicates non-significant (adjusted P-value > 0.05) and *** represents adjusted P-value < 0.001. (D) Expression levels of subregion-specific markers across cell types within their corresponding hippocampal subregions. (E) The expression levels of the subregion-specific markers of Exc within their corresponding hippocampal subregions. Supplementalfigure6final.pdf Fig. S6: Cell type-specific transcriptional divergence in each human hippocampal subregions across AC, PART, and AD (A) Number of cell type–specific upregulated genes for Exc, Ast, and Mic in each SL, comparing each group (AC, PART, AD) to the other two. (B) Heatmap of the DEGs in Exc between AC, PART, and AD in SUB and CA2 subregions. The color of the square represents the expression levels of the genes. (C) Heatmap of the DEGs in Ast between AC, PART, and AD in SUB and CA1 subregions. The color of the square represents the expression levels of the genes. (D) Heatmap of the DEGs in Ast between AC, PART, and AD in CA2 to CA4 subregions. (E) Heatmap of the DEGs in Mic between AC, PART, and AD in CA1 to CA4 subregions. The color of the square represents the expression levels of the genes. Supplementalfigure7final.pdf Fig. S7: Subregion-subregion communication networks within AC, PART, and AD (A) Strength of the incoming and outgoing interactions in AC, PART, and AD. (B) The subregion-subregion interaction networks involved in the PTN signaling pathway. Each dot represents a hippocampal subregion, colored according to its identity. Edges represent inferred inter-subregion interactions, with line color indicating the source subregion and line width corresponding to the interaction strength. (C) The communication strength comparison of the specific LR pairs of PTN signaling pathways across AC, PART and AD groups. The x-axis represents the direction of the LR pairs, and the y-axis indicates the specific LR pairs. Permutation test and Bonferroni correction were used for adjusted P -value calculation. Dot size represents the adjusted p -value, and the color reflects the communication strength of the LR pairs. SupplementalFigure8final.pdf Fig. S8: cell-cell interactions networks among the cells nearby and faraway to large vessels (A) The cell-cell interactions of cells in level1 and level3 in AC, PART, and AD groups. (B) The relative strength of the signaling pathways among the cell-cell interaction networks in AC, PART, and AD groups. (C) Cell-cell communication networks involved in GAS of cells in level1 and level3 in AC, PART, and AD groups. (D) The communication strength comparison of the specific LR pairs of GAS signaling pathways across AC, PART and AD groups. The x-axis represents the direction of the LR pairs, and the y-axis indicates the specific LR pairs. Permutation test and Bonferroni correction were used for adjusted P -value calculation. Dot size represents the adjusted p-value, and the color reflects the communication strength of the LR pairs. (E-F) The IHC staining of TP53INP2 on hippocampus from AC and PART sample. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7303622","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503779495,"identity":"0418e55f-be53-4fec-8d69-841caa64eeea","order_by":0,"name":"Hong-Wen 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19:25:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7303622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7303622/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89646266,"identity":"bb099ff0-efaa-49ba-8bce-b1de86e36068","added_by":"auto","created_at":"2025-08-22 08:58:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2699772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatially resolved transcriptomic profiles of the multiple subregions in human hippocampus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The analysis pipeline of the study.\u003c/p\u003e\n\u003cp\u003e(B) Anatomical structure of the human hippocampus\u003c/p\u003e\n\u003cp\u003e(C) The UMAP visualization of six subregion clusters across six samples.\u003c/p\u003e\n\u003cp\u003e(D) The 10 hippocampal subregions identified across six samples in the AC, PART, and AD groups.\u003c/p\u003e\n\u003cp\u003e(E) Heatmap of the marker genes in each subregion. The x-axis represents the subregions. Colors represent the normalized expression level of the gene in each subregion.\u003c/p\u003e\n\u003cp\u003e(F) Scatter plots illustrating the expression levels of the subregion-specific gene markers across each hippocampal subregion.\u003c/p\u003e\n\u003cp\u003e(G) The validation of FIBCD1 on human hippocampus using IHC.\u003c/p\u003e","description":"","filename":"Figure181.png","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/335945ef1d37283e42196a30.png"},{"id":89646258,"identity":"ca3632aa-0692-41ef-8af4-a396e749c002","added_by":"auto","created_at":"2025-08-22 08:58:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":778097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial transcriptome deconvolution and cell type-specific transcriptional inference within each spatial spot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Spatial distribution of cell type composition in the hippocampus across AC, PART, and AD samples. Each spot represents a pie chart illustrating the proportional composition of cell types at that spatial location.\u003c/p\u003e\n\u003cp\u003e(B) Bar plot of the cellular composition of major cell types across hippocampal subregions. Each bar represents the relative abundance of cell types within a given subregion.\u003c/p\u003e\n\u003cp\u003e(C) The comparison of relative proportions of Exc, Ast, Oli, and VC in SL, FL, and VAS. The box plot represents the proportion of the specific cell type. N.S. indicates non-significant (adjusted P-value \u0026gt; 0.05) and *** represents adjusted P-value \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(D-F) The spearman correlation of the estimated cell type proportions from BayesPrism (x-axis) with those from CARD (D), SpaCet (E), and PANDA (F; y-axis) for Ast, Exc, Inh, Mic, Oli, OPC, and VC. Spearman correlation coefficients (ρ) and p-values are shown in each panel.\u003c/p\u003e\n\u003cp\u003e(G) Mean Spearman correlation coefficients of cell type–specific transcriptional profiles from four permutation tests for BayesPrism and PANDA, respectively. The y-axis indicates the mean correlation coefficients.\u003c/p\u003e\n\u003cp\u003e(H) Steps for pusedo-sc matrix construction.\u003c/p\u003e\n\u003cp\u003e(I) UMAP visualization of the Ast, Exc, Inh, Mic, Oli, OPC, and VC at psedu-sc resolution\u003c/p\u003e\n\u003cp\u003e(J) Expression levels of cell type–specific markers for Ast, Exc, Inh, Mic, Oli, OPC, and VC at pseudo–single-cell resolution. Dot size represents the proportion of cells expressing each marker, while color intensity indicates the expression level of the corresponding marker.\u003c/p\u003e","description":"","filename":"Figure182.png","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/0959dc619e007513d54a13d4.png"},{"id":89646262,"identity":"b6a9be91-a05d-43f5-849f-fb5716b943f9","added_by":"auto","created_at":"2025-08-22 08:58:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":252477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubregion-Specific Transcriptional Divergence in the Human Hippocampus Across AC, PART, and AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The anatomical structure of the SL in human hippocampus.\u003c/p\u003e\n\u003cp\u003e(B) The cellular proportion of Ast, Exc, Inh, Mic, Oli, OPC, and VC in SL across the AC, PART, and AD groups.\u003c/p\u003e\n\u003cp\u003e(C) Comparison of Exc, Ast, and Mic proportions across AC, PART, and AD groups within each subregion of the SL. N.S. indicates non-significant (adjusted P-value \u0026gt; 0.05) and * represents adjusted P-value \u0026lt; 0.05. ** and *** indicates adjusted P-value \u0026lt; 0.01 and \u0026lt; 0.001, respectively.\u003c/p\u003e\n\u003cp\u003e(D) Significant DEGs identified from subregion-specific comparisons between AC, PART, and AD at spatial spot resolution.\u003c/p\u003e\n\u003cp\u003e(E) Heatmap of the DEGs in Exc between AC, PART, and AD in CA1, CA3, and CA4 subregions. The color of the square represents the expression levels of the genes.\u003c/p\u003e\n\u003cp\u003e(F-H) GO enrichment analysis of DEGs highly expressed in Exc across hippocampal subregions and disease stages. GO terms enriched in DEGs from Exc located in CA1 in the PART group compared to the AC and AD groups (F); GO terms enriched in DEGs from Exc located in CA1 in the AD group compared to the AC and PART groups (G); GO terms enriched in DEGs from Exc located in CA3 in the AD group compared to the AC and PART groups (H). The x-axis represents the proportion of enriched DEGs relative to all DEGs (overlap ratio), and the y-axis lists the enriched GO terms. Dot size indicates the number of genes associated with each GO term, and dot color reflects the adjusted \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"Figure183.png","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/037172eab4953f9ca2d66b48.png"},{"id":89646260,"identity":"3c60800b-48ee-4165-9174-b77d7fb51a48","added_by":"auto","created_at":"2025-08-22 08:58:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInter-subregion interactions among human hippocampus in AC, PART, and AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Interaction strength of the subregion-subregion interactions in AC, PART, and AD, respectively.\u003c/p\u003e\n\u003cp\u003e(B-D) Outgoing and incoming interaction strength of hippocampal subregions in AC (B), PART (C), and AD (D) groups. The x-axis indicates the strength of outgoing interactions, while the y-axis indicates the strength of incoming interactions for each subregion. Dot color represents the subregion, and dot size corresponds to the total number of outgoing and incoming interactions combined.\u003c/p\u003e\n\u003cp\u003e(E) Subregion-subregion communication networks in the AC, PART, and AD groups. Each dot represents a hippocampal subregion, colored according to its identity. Edges represent inferred inter-subregion interactions, with line color indicating the source subregion and line width corresponding to the interaction strength.\u003c/p\u003e\n\u003cp\u003e(F) Pairwise comparison of subregion-subregion communication networks across AC, PART, and AD groups. The x-axis represents the strength of outgoing interactions, and the y-axis represents the strength of incoming interactions for each subregion. Bar plots show the total interaction strength, with bars oriented along the x-axis for outgoing interactions and along the y-axis for incoming interactions.\u003c/p\u003e\n\u003cp\u003e(G) Signaling pathway strength across subregion-to-subregion interactions in AC, PART, and AD groups. The x-axis is the signaling strength, and y-axis indicates the signaling pathways. The color represents AC, PART, and AD groups.\u003c/p\u003e\n\u003cp\u003e(H) The subregion-subregion interaction networks involved in the NT signaling pathway. Each dot represents a hippocampal subregion, colored according to its identity. Edges represent inferred inter-subregion interactions, with line color indicating the source subregion and line width corresponding to the interaction strength.\u003c/p\u003e\n\u003cp\u003e(I) The communication strength comparison of the specific LR pairs of NT signaling pathways across AC, PART and AD groups. The x-axis represents the direction of the LR pairs, and the y-axis indicates the specific LR pairs. Permutation test and Bonferroni correction were used for adjusted \u003cem\u003eP\u003c/em\u003e-value calculation. Dot size represents the adjusted p-value, and the color reflects the communication strength of the LR pairs.\u003c/p\u003e","description":"","filename":"Figure184.png","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/02882ea7fae07a2492ac0abd.png"},{"id":89646264,"identity":"9ec7512a-7152-4ae6-a9e7-335845392e20","added_by":"auto","created_at":"2025-08-22 08:58:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1525671,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptional divergence between cells located near and distant from large blood vessels\u003c/p\u003e\n\u003cp\u003e(A) Concentric analysis.\u003c/p\u003e\n\u003cp\u003e(B) The location of the VAS and the cells located in level1 to 4.\u003c/p\u003e\n\u003cp\u003e(C) The DEGs of the cells between level1 and 3 in the AC, PART, and AD groups, respectively. Dot size represents the proportion of cells expressing each marker, while color intensity indicates the expression level of the corresponding marker.\u003c/p\u003e\n\u003cp\u003e(D) The cell-cell interaction networks involved in PDGF signaling pathway. Each dot represents a hippocampal subregion, colored according to its identity. Edges represent inferred inter-subregion interactions, with line color indicating the source subregion and line width corresponding to the interaction strength.\u003c/p\u003e\n\u003cp\u003e(E) The communication strength comparison of the specific LR pairs of PDGF signaling pathways across AC, PART and AD groups. The x-axis represents the direction of the LR pairs, and the y-axis indicates the specific LR pairs. Permutation test and Bonferroni correction were used for adjusted \u003cem\u003eP\u003c/em\u003e-value calculation. Dot size represents the adjusted p-value, and the color reflects the communication strength of the LR pairs.\u003c/p\u003e\n\u003cp\u003e(F) Comparison of the expression levels of \u003cem\u003eTP53INP2 \u003c/em\u003ein Ast between AC, PART, and AD groups in level1 to 4, respectively. N.S. indicates non-significant (adjusted P-value \u0026gt; 0.05) and *** represents adjusted P-value \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(G) Comparison of the expression levels of \u003cem\u003eTP53INP2 \u003c/em\u003ein Ast between level1 to level4 in AC, PART, and AD groups, respectively. N.S. indicates non-significant (adjusted P-value \u0026gt; 0.05) and *** represents adjusted P-value \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(H) Experimental validation of the expression of TP53INP2 in Ast in AD samples through IHC.\u003c/p\u003e\n\u003cp\u003e(I) Selection of vessel-adjacent regions for spatial analysis. The upper panel shows a representative large blood vessel with a surrounding 200 μm radius (dashed circle) indicating the defined perivascular region. The lower panel highlights the area for downstream analysis.\u003c/p\u003e\n\u003cp\u003e(J) Comparison of the integrated optical density (IOD) of TP53INP2 in the selected area between AC, PART, and AD.\u003c/p\u003e","description":"","filename":"Figure185.png","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/89c751e6d529fd32fc4de949.png"},{"id":93767221,"identity":"140ab558-511b-427f-aafa-94fcbe5f7b2b","added_by":"auto","created_at":"2025-10-17 10:53:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7042677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/05fb1d83-006b-44dc-8510-902c6d1b3e98.pdf"},{"id":89646257,"identity":"71a719b5-fe7d-45fb-952d-f355b138790d","added_by":"auto","created_at":"2025-08-22 08:58:34","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 1: \u003c/strong\u003eSample information.\u003c/p\u003e","description":"","filename":"SupplementalTable1final.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/601565020f3ea530ae96a84a.xlsx"},{"id":89646783,"identity":"5c365be5-f30f-426c-85e6-40f98c9e04b5","added_by":"auto","created_at":"2025-08-22 09:06:34","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":606906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 2: \u003c/strong\u003eDistinct transcriptional markers for each hippocampal subregion.\u003c/p\u003e","description":"","filename":"SupplementalTable2final.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/3ae497dd18eb7b23d644c433.xlsx"},{"id":89647961,"identity":"b24d90fb-0e27-4777-8b3a-81621644b321","added_by":"auto","created_at":"2025-08-22 09:14:34","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":153267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 3: \u003c/strong\u003eEnriched\u003cstrong\u003e \u003c/strong\u003eGO terms based on the highly expressed genes by each hippocampal subregion.\u003c/p\u003e","description":"","filename":"SupplementalTable3final.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/2d5c825aacbc7f5746c2d555.xlsx"},{"id":89646269,"identity":"2ec66e24-2f2e-4e67-89c6-25a5a467f9d7","added_by":"auto","created_at":"2025-08-22 08:58:35","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5146501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Table 4\u003c/strong\u003e: Cell type–specific DEGs in all cell types across SLs among the AC, PART, and AD groups\u003c/p\u003e","description":"","filename":"SupplementalTable4final.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/c2ba2c473237b12d530bd0a1.xlsx"},{"id":89648447,"identity":"c79aed3f-bc11-4118-83be-c9a202e0e1e7","added_by":"auto","created_at":"2025-08-22 09:22:35","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9142024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1:\u003c/strong\u003e \u003cstrong\u003eHistopathological characterization of human hippocampal samples across AC, PART, and AD groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-F) H\u0026amp;E staining (A, B, C, D, E, F), high-magnification eosin sections (A1–F1), number of genes detected per spatial transcriptomics spot (A2–F2), Aβ immunostaining using 6E10 (A3–F3), pTau immunostaining using AT8 (A4–F4), and Gallyas silver staining for neurofibrillary pathology (A5–F5) for M0309, M0310, M0618, M0623, M0802, and M0915 samples, respectively.\u003c/p\u003e","description":"","filename":"Supplementalfigure1final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/b0793f37d87e8a81d171d86e.pdf"},{"id":89646791,"identity":"1cc3c929-3598-463f-8f6c-6fbd34fbba4e","added_by":"auto","created_at":"2025-08-22 09:06:35","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13173815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2\u003c/strong\u003e: \u003cstrong\u003eClustering of hippocampal subregions and validation of subregion-specific markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) BIC values across different cluster numbers (K). Each black dot represents the BIC value for a given K. The triangle indicates the selected K used for clustering in this study.\u003c/p\u003e\n\u003cp\u003e(B) The 15 hippocampal subregions identified across six samples.\u003c/p\u003e\n\u003cp\u003e(C) The validation of FIBCD1 on human hippocampus using IHC. The arrow indicates the cells with the expression of FIBCD1 in CA1 region\u003c/p\u003e\n\u003cp\u003e(D) The validation of NRIP3 on human hippocampus using IHC. The arrow indicates the cells with the expression of NRIP3 in CA2 region.\u003c/p\u003e","description":"","filename":"Supplementalfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/1a16ce53bb00e75659f4f2b0.pdf"},{"id":89646279,"identity":"8f1d65b5-aa7b-449a-97f3-4ff2e25cdaa4","added_by":"auto","created_at":"2025-08-22 08:58:35","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":14832801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e: \u003cstrong\u003eValidation of the subregion-specific markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The validation of CCK on human hippocampus using IHC. The arrow indicates the cells with the expression of CCK in CA3 region.\u003c/p\u003e\n\u003cp\u003e(B) The validation of NEFM on human hippocampus using IHC. The arrow indicates the cells with the expression of NEFM in SUB region.\u003c/p\u003e\n\u003cp\u003e(C) The validation of STXBP6 on human hippocampus using IHC. The arrow indicates the cells with the expression of STXBP6 in DG region.\u003c/p\u003e","description":"","filename":"Supplementalfigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/d8e5e76e96f3645fea0c38c4.pdf"},{"id":89646787,"identity":"e156d26a-efd5-4452-a32e-cf72b9041afa","added_by":"auto","created_at":"2025-08-22 09:06:35","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":765057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S4\u003c/strong\u003e: \u003cstrong\u003eExpression patterns of the subregion-specific genes in each region across AC, PART, and AD groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The comparison of expression levels of the subregion-specific genes in each subregion between AC, PART, and AD. N.S. indicates non-significant (adjusted P-value \u0026gt; 0.05) and *** represents adjusted P-value \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(B) The heatmap of the expression levels of transcriptional markers in each subregion across AC, PART, and AD.\u003c/p\u003e","description":"","filename":"Supplementalfigure4final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/d98236eb08724890f748aaec.pdf"},{"id":89646272,"identity":"faf0dda3-6439-40ea-947f-f50730d7bd13","added_by":"auto","created_at":"2025-08-22 08:58:35","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":5530279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S5\u003c/strong\u003e: \u003cstrong\u003eDeconvolution results of the human spatial transcriptome data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP visualization of the human hippocampal snRNA-seq reference dataset, colored by cell type and ethnicity, respectively.\u003c/p\u003e\n\u003cp\u003e(B) Spatial distribution of cell type composition in the hippocampus across AC (M0310), PART (M0623), and AD (M0915) samples. Each spot represents a pie chart illustrating the proportional composition of cell types at that spatial location.\u003c/p\u003e\n\u003cp\u003e(C) The comparison of relative proportions of Inh, Mic, and OPC in SL, FL, and VAS regions. The box plot represents the proportion of the specific cell type. N.S. indicates non-significant (adjusted P-value \u0026gt; 0.05) and *** represents adjusted P-value \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(D) Expression levels of subregion-specific markers across cell types within their corresponding hippocampal subregions.\u003c/p\u003e\n\u003cp\u003e(E) The expression levels of the subregion-specific markers of Exc within their corresponding hippocampal subregions.\u003c/p\u003e","description":"","filename":"Supplementalfigure5final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/a5945ea005553bf7b6ba3acd.pdf"},{"id":89647963,"identity":"aee022de-e16c-4b20-9f6b-bc3d34285208","added_by":"auto","created_at":"2025-08-22 09:14:35","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1094459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S6\u003c/strong\u003e: \u003cstrong\u003eCell type-specific transcriptional divergence in each human hippocampal subregions across AC, PART, and AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Number of cell type–specific upregulated genes for Exc, Ast, and Mic in each SL, comparing each group (AC, PART, AD) to the other two.\u003c/p\u003e\n\u003cp\u003e(B) Heatmap of the DEGs in Exc between AC, PART, and AD in SUB and CA2 subregions. The color of the square represents the expression levels of the genes.\u003c/p\u003e\n\u003cp\u003e(C) Heatmap of the DEGs in Ast between AC, PART, and AD in SUB and CA1 subregions. The color of the square represents the expression levels of the genes.\u003c/p\u003e\n\u003cp\u003e(D) Heatmap of the DEGs in Ast between AC, PART, and AD in CA2 to CA4 subregions.\u003c/p\u003e\n\u003cp\u003e(E) Heatmap of the DEGs in Mic between AC, PART, and AD in CA1 to CA4 subregions. The color of the square represents the expression levels of the genes.\u003c/p\u003e","description":"","filename":"Supplementalfigure6final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/a8bcbe7284238dc3bf1177a6.pdf"},{"id":89646275,"identity":"b72c2d8b-5ab1-4bb4-9b0c-4ddcce87cae3","added_by":"auto","created_at":"2025-08-22 08:58:35","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1382979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S7\u003c/strong\u003e: \u003cstrong\u003eSubregion-subregion communication networks within AC, PART, and AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Strength of the incoming and outgoing interactions in AC, PART, and AD.\u003c/p\u003e\n\u003cp\u003e(B) The subregion-subregion interaction networks involved in the PTN signaling pathway. Each dot represents a hippocampal subregion, colored according to its identity. Edges represent inferred inter-subregion interactions, with line color indicating the source subregion and line width corresponding to the interaction strength.\u003c/p\u003e\n\u003cp\u003e(C) The communication strength comparison of the specific LR pairs of PTN signaling pathways across AC, PART and AD groups. The x-axis represents the direction of the LR pairs, and the y-axis indicates the specific LR pairs. Permutation test and Bonferroni correction were used for adjusted \u003cem\u003eP\u003c/em\u003e-value calculation. Dot size represents the adjusted p -value, and the color reflects the communication strength of the LR pairs.\u003c/p\u003e","description":"","filename":"Supplementalfigure7final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/41a40f880fa81c2c01aba29d.pdf"},{"id":89646283,"identity":"e3209425-423e-48d3-85df-b620730f3d66","added_by":"auto","created_at":"2025-08-22 08:58:35","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":18235056,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S8: cell-cell interactions networks among the cells nearby and faraway to large vessels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The cell-cell interactions of cells in level1 and level3 in AC, PART, and AD groups.\u003c/p\u003e\n\u003cp\u003e(B) The relative strength of the signaling pathways among the cell-cell interaction networks in AC, PART, and AD groups.\u003c/p\u003e\n\u003cp\u003e(C) Cell-cell communication networks involved in GAS of cells in level1 and level3 in AC, PART, and AD groups.\u003c/p\u003e\n\u003cp\u003e(D) The communication strength comparison of the specific LR pairs of GAS signaling pathways across AC, PART and AD groups. The x-axis represents the direction of the LR pairs, and the y-axis indicates the specific LR pairs. Permutation test and Bonferroni correction were used for adjusted \u003cem\u003eP\u003c/em\u003e-value calculation. Dot size represents the adjusted p-value, and the color reflects the communication strength of the LR pairs.\u003c/p\u003e\n\u003cp\u003e(E-F) The IHC staining of TP53INP2 on hippocampus from AC and PART sample.\u003c/p\u003e","description":"","filename":"SupplementalFigure8final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303622/v1/c50c61ede390d477a68b125a.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Novel Pathological Mechanisms Revealed by Spatial Transcriptomic Analysis of Hippocampus in Aged Control, Primary Age-Related Tauopathy, and Alzheimer’s Disease","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAlzheimer's disease (AD) is a neurodegenerative disorder primarily defined by its onset with memory deficits and cognitive difficulties, progressively extending to affect behavior, language, spatial perception, and motor functions (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As of 2024, around 6.9\u0026nbsp;million Americans aged 65 and older are affected by AD, and this number is projected to nearly double to 13.8\u0026nbsp;million by 2060 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Given the devastating impact of this disease, numerous studies have investigated its underlying mechanisms. These studies have identified various risk factors, including aging (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), cardiovascular health (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), education (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), diet (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), social interactions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), brain injuries (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and more than 70 genetic markers (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). At the pathology level, AD brain is marked by the accumulation of extracellular amyloid-β (Aβ) plaques (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and intracellular hyperphosphorylated tau aggregates as neurofibrillary tangles (NFTs) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) in the gray matter. These pathological features can induce cytotoxicity, drive neuroinflammation, and impair mitochondrial function, which collectively contribute to neuronal stress, degeneration, and eventual brain atrophy (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Despite these insights, the pathological mechanisms underlying AD remain unclear, limiting the development of effective medical interventions. Although several drugs targeting the management of this disease, particularly those aimed at clearing Aβ plaques, have been approved by the FDA, these therapeutic approaches have largely failed in clinical trials due to adverse side effects or insufficient efficacy (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Thus, more comprehensive studies utilizing cutting-edge technologies are essential to deepen our understanding of this devastating disease and to develop more effective therapeutic strategies.\u003c/p\u003e\u003cp\u003eEmploying the cutting-edge technology of spatial transcriptome (ST) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), several studies investigated different brain regions, including prefrontal cortex (PFC) and middle temporal gyrus (MTG), uncovering novel insights into the pathological mechanisms underlying AD (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In addition to these regions, the hippocampus is also an important brain area of progressive pathology in AD and merits detailed spatial investigation. As a critical structure in the medial temporal lobe responsible for memory and cognition, the hippocampus is among the earliest brain regions affected by AD\u0026ndash;related neurodegeneration (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), offering an important opportunity to investigate early molecular changes associated with this disease. Additionally, the hippocampus serves as a valuable model for studying Primary Age-Related Tauopathy (PART), a neurodegenerative condition characterized by tau protein accumulation in the medial temporal lobe in the absence of significant amyloid-beta (Aβ) deposition, and commonly observed in aging individuals (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Compared to AD, PART is associated with less neuronal loss and typically results in milder cognitive impairment. Although the filament structure of the NFTs in PART and AD are similar (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), it remains unclear whether PART represents an early histopathological stage of AD or simply the products of normal brain aging (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Thus, understanding the pathological divergence among aged control (AC), PART, and AD could yield critical insights into the shared and unique molecular mechanisms underlying PART and AD, as well as the molecular events that drive Aβ accumulation and neuronal degeneration in AD. These insights may provide a strong foundation for developing future therapeutic strategies against this devastating disease. While two studies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) have investigated ST in the human hippocampus for AD, one did not include individuals with PART (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), and the other used the image-based GeoMx platform (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), which may not fully capture pathological molecular alterations between PART and AD due to its relatively low sensitivity compared to sequencing-based platforms (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Therefore, studies using high-sensitive, sequencing-based ST platforms on hippocampal tissue from individuals with AC, PART, and AD are needed to comprehensively delineate the molecular differences among these conditions.\u003c/p\u003e\u003cp\u003eIn this study, we employed the 10x Genomics Visium ST platform to construct an unbiased transcriptional atlas of the human hippocampus across AC, PART, and AD groups. Our goal was to uncover pathological molecular alterations among these groups, identify potential links between PART and AD, and explore the mechanisms underlying neuronal degeneration in AD. This high-resolution approach revealed transcriptomic signatures indicating that PART may represent a transitional stage from AC toward AD. In PART, upregulated transcripts in Exc appeared to promote Aβ production. Mic reactivation was already enhanced in PART to clear excessive Aβ. However, this microglial response was diminished in AD, potentially contributing to Aβ accumulation, NFT formation, and the progression from PART to AD. Moreover, inter-subregion support for neuronal survival and blood-brain barrier (BBB) integrity was reduced in PART and nearly absent in AD. Furthermore, the BBB disruption in AD was associated with activation of apoptotic pathways in Ast located near large blood vessels, suggesting a critical mechanism worsening neuronal degeneration.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eHuman Formalin-Fixed Paraffin-Embedded (FFPE) hippocampal tissue samples were collected from six individuals for ST analysis using the 10x Visium platform (\u003cb\u003eFig.\u0026nbsp;1A; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-F\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e). The cohort included two AC (Braak stages I and II, Thal phase 0; two males, aged 88 and 79), two individuals with PART (Braak stage III, Thal phase 0; one male and one female, aged 87 and 81), and two AD patients (Braak stages VI and IV, Thal phase 3; one female and one male, aged 92 and 82; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Following the 10X Visium profiling, a total of 26,038 spots were captured across all six samples, with each spot detecting an average of 4,096 genes and 4,273 molecular counts. Previous studies on the human brain (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) have shown that Exc exhibited more prominent features compared to inhibitory neurons (Inh) and glial cells due to their larger size and essential roles in synaptic transmission and signaling pathways (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). To further validate the quality of our dataset, we aligned the ST data with the eosin-stained image from the same slide. The results demonstrated that spots with a high number of detected genes corresponded to regions enriched in Exc, such as the DG region (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), supporting the high quality and reliability of our dataset for our comprehensive downstream data analysis (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA1-F1; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA2-F2\u003c/b\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eST revealed unique gene expression patterns in distinct regions of human hippocampus\u003c/h2\u003e\u003cp\u003eConventionally, the human hippocampus is primarily composed of several soma-rich layer (SL) subregions, including stratum pyramidale (s.p.) of subiculum (Sub) and CA1\u0026ndash;CA4, as well as the granule cell layer of dentate gyrus (DG). These areas are predominantly composed of Exc and play essential roles in information output, as well as in the integration and processing of neural signals (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Surrounding the SL are distinct fiber layers (FL), which are organized above and below the cell body layers. These include the stratum oriens (s.o.), stratum radiatum (s.r.), and molecular layer (ML), which are primarily composed of dendrites, axons, and synaptic terminals. These fiber-rich regions are critical for synaptic input and output, serving as hubs for information integration and transmission (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Furthermore, large vascular (VAS) structures are present across all subregions of the human hippocampus, supporting cellular oxidative metabolism and energy supply in these areas (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). To accurately delineate these spatial domains in our human hippocampal ST data, we applied a data-driven, unsupervised clustering algorithm, PRECAST (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), to group spatial spots into distinct domains based on their transcriptional profiles and spatial coordinates on the 10x Visium slides (\u003cb\u003eMethods\u003c/b\u003e). We evaluated a range of spatial domain resolutions (k) and selected \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15 based on the Bayesian Information Criterion (BIC) (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). Unsupervised clustering at this resolution grouped the spatial spots into 15 distinct clusters (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e). To annotate these clusters, we mapped them onto the eosin-stained image and assigned anatomical labels based on their spatial positions (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA1-F1\u003c/b\u003e). After grouping the clusters located within the same anatomical region, all clusters were annotated as 10 subregions, including SUB, CA1 to CA4, s.r., s.o., DG, ML, and VAS (\u003cb\u003eFig.\u0026nbsp;1B and 1C\u003c/b\u003e). Compared to previous ST studies using 10X Visium platform on human hippocampus (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), which were unable to clearly distinguish certain regions, particularly CA2, CA3, and CA4, our data demonstrated a clear delineation of these regions. This may be due to their use of the 10x Visium platform on frozen sections of the human hippocampus, which are more prone to RNA degradation and increased background noise, including expression from non\u0026ndash;protein-coding genes (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These factors may hinder accurate distinction between hippocampal subregions. This result underscored the significant advantages of using the 10x Visium platform on FFPE-preserved human hippocampal tissue to construct a transcriptome atlas, enabling comprehensive and unbiased profiling of protein-coding genes.\u003c/p\u003e\u003cp\u003eWe performed differential gene expression (DGE) analysis by comparing each subregion against all others, aiming to identify distinct transcriptional signatures for each hippocampal subregion (\u003cb\u003eFig.\u0026nbsp;1D\u003c/b\u003e; \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Methods\u003c/b\u003e). Briefly, subregions enriched with Ex, such as the SUB, CA1 to CA4, and DG, showed high expression of genes associated with Exc functions. These include genes involved in neuronal calcium signaling (e.g., \u003cem\u003eCALM3\u003c/em\u003e, \u003cem\u003eCCK, SYN2, CALB1\u003c/em\u003e) and neuron synapse functions (e.g., \u003cem\u003eSTXBP6\u003c/em\u003e, \u003cem\u003ePPFIA2, SNCB\u003c/em\u003e) (\u003cb\u003eFig.\u0026nbsp;1E\u003c/b\u003e). In contrast, subregions with a higher proportion of glial cells, including the s.r., s.o., and ML, displayed enrichment of genes related to myelin formation and maintenance (e.g., \u003cem\u003ePLP1\u003c/em\u003e, \u003cem\u003eMBP\u003c/em\u003e). Additionally, in the ML region, we observed upregulation of genes associated with neuronal dendrite function (e.g., \u003cem\u003eNCS1\u003c/em\u003e, \u003cem\u003eSEPTIN5\u003c/em\u003e) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), compared to other subregions. This is consistent with that the ML region is composed of neuronal dendrites originating from the DG (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Furthermore, we performed Gene Ontology (GO) analysis on transcriptional markers to elucidate the distinct biological functions associated with each hippocampal subregion. GO terms associated with neuronal and synaptic functions, including \u0026ldquo;Chemical Synaptic Transmission\u0026rdquo;, \u0026ldquo;Neuron Development\u0026rdquo;, and \u0026ldquo;Axon Development\u0026rdquo;, were enriched in the SUB, CA1, CA2, CA3, and CA4 regions, which are predominantly composed of Exc. In contrast, GO terms related to myelin formation and cellular migration (e.g., \u0026ldquo;Myelination and Regulation of Cell Migration\u0026rdquo;) were highly enriched in SR and s.o., regions primarily consisting of glial cells, including Ast, Mic, and oligodendrocytes (Oli) (\u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). The subregion-specific transcriptional profiles in our data are consistent with previous ST data from the human hippocampus (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), further supporting the accuracy of our clustering and annotation.\u003c/p\u003e\u003cp\u003eThe intricate histological architecture of the hippocampus makes manual region annotation especially challenging, particularly for subfields such as CA1, CA2, CA3, and CA4, which lack clearly defined histological boundaries (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). This difficulty complicates the precise characterization of the unique biological functions of neurons and glial cells in these regions and hinders the study of their subregion-specific pathological changes during the early stages of AD. Therefore, identifying reliable biomarkers for each region is crucial for distinguishing these areas and capturing region-specific molecular dynamics of brain cells at the onset of AD. To address this, we identified subregion-specific marker genes based on the highest fold-change compared to their expression in other regions. These included \u003cem\u003eFIBCD1\u003c/em\u003e for CA1, \u003cem\u003eDDN\u003c/em\u003e for SR, \u003cem\u003eNRIP3\u003c/em\u003e for CA2, \u003cem\u003eCCK\u003c/em\u003e for CA3, \u003cem\u003eUNC13C\u003c/em\u003e for CA4, \u003cem\u003eSTXBP6\u003c/em\u003e for DG, \u003cem\u003ePDZD4\u003c/em\u003e for ML, \u003cem\u003eCRYAB\u003c/em\u003e for SO, and \u003cem\u003eNEFM\u003c/em\u003e for SUB (\u003cb\u003eFig.\u0026nbsp;1F\u003c/b\u003e). These hippocampal subregion-specific markers were validated through immunohistochemistry (IHC; \u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC-D\u003c/b\u003e; \u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA-C\u003c/b\u003e). Given the limited availability of markers to delineate the boundary between CA1 and CA2, and the absence of previous reports on \u003cem\u003eNRIP3\u003c/em\u003e expression in the human hippocampus, we examined the distribution of the NRIP3 positive cells. We observed that NRIP3 was highly expressed in the CA2 subregion but shows low expression in CA1, forming a clear boundary between the two (\u003cb\u003eFig.\u0026nbsp;1G\u003c/b\u003e). This distinct expression pattern suggests that NRIP3 may serve as a reliable marker for distinguishing CA2 from CA1. Furthermore, although certain region-specific markers showed significant differential expression between the AC, PART and AD groups within their respective regions (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA\u003c/b\u003e), these markers were still enriched in their corresponding subregions when DEG analyses were performed separately within the AC, PART, and AD groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB\u003c/b\u003e). This suggested that these markers are robust and can serve as reliable references for regional identification across AC, PART, and AD conditions.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDeconvolution analysis to enhance the resolution from spot level to single-cell resolution\u003c/h3\u003e\n\u003cp\u003eGiven that current 10X Visium platform cannot provide transcriptional data at the single-cell (sc) resolution, we applied the deconvolution methods to identify cell compositions and infer cell type-specific gene expression patterns within each 10X Visium spatial spot. We employed BayesPrism (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), a Bayesian algorithm that simultaneously infers cell-type proportions and their unique transcriptional profiles within each ST spot, without requiring snRNA-seq reference data from adjacent tissue. The snRNA-seq data from human hippocampal samples of AD cases and controls, published by Mathys \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), was considered as the reference. Although the reference data from Mathys \u003cem\u003eet al.\u003c/em\u003e were generated from Caucasian individuals and our ST data were derived from Asian donors, previous studies have reported that the core structural and functional features of the hippocampus are conserved between these populations (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Furthermore, we performed snRNA-seq on a hippocampal sample from an age-matched Chinese individual with AD (\u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e) and when merged with the data by Mathys \u003cem\u003eet al.\u003c/em\u003e, the transcriptomic profiles from the Chinese individual aligned well with the major clusters observed in the Caucasian dataset (\u003cb\u003eFig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA\u003c/b\u003e), suggesting minimal batch or population-related effects in hippocampal transcriptional profiles.\u003c/p\u003e\u003cp\u003eThrough the deconvolution analysis, we assessed the proportion of each cell type within individual spots across distinct hippocampal regions (\u003cb\u003eFig.\u0026nbsp;2A-B\u003c/b\u003e). Exc were predominantly enriched in SL, including the SUB, CA1, CA2, CA3, CA4, and DG subregions (\u003cb\u003eFig.\u0026nbsp;2C; Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eB\u003c/b\u003e). In contrast, Ast and Oli were largely localized to FL, such as the s.o., s.r., and ML. Notably, VC exhibited significantly higher proportions in the VAS region compared to both SL and FL These findings were consistent with previous studies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Given that BayesPrism was originally developed to infer cellular compositions from bulk RNA-seq data rather than ST, we evaluated its performance on ST data by comparing its inferred cell compositions to those generated by deconvolution methods specifically designed for ST, including CARD (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), SpaCet (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), and PANDA (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Across all cell types, BayesPrism results showed significant positive correlations with those from other methods (\u003cb\u003eFig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eC-E\u003c/b\u003e), suggesting that BayesPrism, despite not incorporating spatial information, performs comparably to spatially aware deconvolution methods, supporting its reliability in this study. After estimating the cellular composition of each spatial spot, we inferred the corresponding cell type-specific transcriptional profiles (\u003cb\u003eMethods\u003c/b\u003e). To evaluate the robustness of BayesPrism, we performed a permutation test by randomly splitting the snRNA-seq data into two groups, each containing half of the cells from every cell type. Each group was independently used as a reference in BayesPrism to infer cell type-specific gene expression profiles for each spot, and the inferred results were compared across runs to assess consistency. For comparison, we applied the same strategy using PANDA to benchmark BayesPrism\u0026rsquo;s performance. Since the mean correlation of gene expression levels across four permutation tests was higher using BayesPrism than PANDA (\u003cb\u003eFig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eF\u003c/b\u003e), this result suggested that BayesPrism offers greater robustness in inferring cell type\u0026ndash;specific gene expression in each spatial spot. Therefore, BayesPrism is currently the most suitable method for our study.\u003c/p\u003e\u003cp\u003eTo enhance the ST data into sc resolution, we have constructed the spatial pseudo-sc matrix (PSM) data based on the inferred cell-type specific gene expression patterns within each spot (\u003cb\u003eFig.\u0026nbsp;2D\u003c/b\u003e). After removing the cells with fewer than 300 detected genes and genes that are expressed in fewer than three cells, we have captured 121,087 cells with median genes detected 621, which is similar to current cutting-edge ST platforms with sn resolution (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Following cell type clustering and visualization, the pseudo-cells were annotated as distinct major brain cell types (Ast, Exc, Inh, Mic, Oli, OPC, VC) (\u003cb\u003eFig.\u0026nbsp;2E\u003c/b\u003e). Marker genes for each pseudo-cell cluster were compared and found to be consistent with those reported in previous snRNA-seq study on human hippocampus (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) (\u003cb\u003eFig.\u0026nbsp;2F\u003c/b\u003e), supporting that the PSM effectively preserved the key transcriptional profiles for major brain cell types.\u003c/p\u003e\u003cp\u003eWe further checked the major cell types contributing to the hippocampus subregion-specific markers identified at spatial spot level. In SL, the Exc was one of the major cell types expressing the subregion-specific markers due to its abundance and relatively large size in human hippocampus (\u003cb\u003eFig.\u0026nbsp;2G\u003c/b\u003e). The heterogeneity of the transcriptional profiles of these Exc across different subregions has contributed to the divergence of region-specific markers (\u003cb\u003eFig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eG\u003c/b\u003e). This heterogeneity in Exc were validated at the protein level on IHC sections (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC-D\u003c/b\u003e; \u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA-C\u003c/b\u003e), further supporting the reliability of our approach to enhance ST data from the spatial spot level to pseudo\u0026ndash;sc resolution. In addition, in the s.r. subregion, enriched by the synapse from Exc somas located in CA1, CA2, and CA3, \u003cem\u003eDDN\u003c/em\u003e gene, a gene related to the neuron signaling transmission (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), was the marker and highly expressed by the Exc. Since Oli were highly enriched in the s.o. subregions, the Oli specific marker, \u003cem\u003eCRYAB\u003c/em\u003e, was the major marker for the s.o. area. In summary, we successfully inferred the cellular composition and cell type-specific transcriptional profiles in each spot, as well as constructed a pseudo-sc matrix based on these profiles. Based on this matrix, we have revealed the heterogeneity of the Exc across subregions in SL.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubregion specific DGE analyses reveal the underlying mechanisms of selective vulnerability in individuals with PART and AD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSelective vulnerability, a hallmark of AD, refers to the disproportionate impact of AD pathological hallmarks on neurons in specific brain regions (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Interestingly, previous studies have also identified this hallmark in individuals with PART (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). In the hippocampus, Exc in the CA1 subregion are particularly vulnerable to developing NFTs compared to other regions (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), a pattern also observed in our samples (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA4-F4; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA5-F5\u003c/b\u003e). To investigate the impact of this hallmark to each specific cell types in PART and AD, we compared the region-specific cellular compositions across the AC, PART, and AD groups in the SLs (\u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e). Overall, the mean proportion of Exc was highest in AC and lowest in PART (\u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e). Additionally, while the proportion of Ast increased in parallel with the rising abundance of AD pathological hallmarks, Mic increased primarily in PART but decreased in AD (\u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e), suggesting distinct patterns of change between these two glial cell types. The reduced proportion of Mic in AD may contribute to the increased proportion of Exc observed in AD relative to PART. In contrast to the changes observed in Exc, Ast, and Mic, the proportion of Opc, Oli, and VC maintained relatively stable across AC, PART, and AD (\u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo further explore regional differences in cellular composition of Exc, Ast, and Mic among AC, PART, and AD groups, we analyzed subregion-specific changes following the anatomical organization of the human hippocampus (\u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e). Compared to AC group, the CA1 subregion in PART exhibited the most pronounced decrease in the proportion of Exc across all examined SLs (t-statistics=-21.04, adjusted p-value\u0026thinsp;=\u0026thinsp;2.40X10\u003csup\u003e\u0026minus;\u0026thinsp;93\u003c/sup\u003e), along with the largest increases in Ast (t-statistics\u0026thinsp;=\u0026thinsp;22.20, adjusted p-value\u0026thinsp;=\u0026thinsp;5.01X10\u003csup\u003e\u0026minus;\u0026thinsp;103\u003c/sup\u003e) and Mic (t-statistics\u0026thinsp;=\u0026thinsp;28.83, adjusted p-value\u0026thinsp;=\u0026thinsp;1.18X10\u003csup\u003e\u0026minus;\u0026thinsp;166\u003c/sup\u003e) (\u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e). Given that previous studies have reported limited neuronal degeneration in PART (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), the observed decrease in Exc proportion in the CA1, along with increased proportions of Ast and Mic, likely reflects a decline in normal neuronal function and a pronounced glial cells reactivation against the stress. At the spatial spot resolution, compared to the AC group, the CA1 subregion exhibited the most significant downregulation of genes involved in neuronal structure, function, and signaling, including \u003cem\u003eUBB\u003c/em\u003e, \u003cem\u003eNSF\u003c/em\u003e, \u003cem\u003eNEFM\u003c/em\u003e, \u003cem\u003eRTN1\u003c/em\u003e, and \u003cem\u003eTUBA4A\u003c/em\u003e (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). In contrast, markers of Ast (e.g., \u003cem\u003eS100B\u003c/em\u003e, \u003cem\u003eCPE\u003c/em\u003e) (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) and Mic (e.g., \u003cem\u003eS100A9\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eP2RY12\u003c/em\u003e) (\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) reactivation were highly enriched in the PART group (\u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e). These molecular changes likely contributed to the decreased estimated proportion of Exc and increased proportions of Ast and Mic in the PART group (\u003cb\u003eFig.\u0026nbsp;3B-C\u003c/b\u003e). Notably, in the CA1 subregion, the divergence in the proportions of Exc, Ast, and Mic were relatively small when comparing the AC to AD groups (\u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e). This pattern suggests that although glial cell activation occurs early in response to stress, it may transition into dysfunction or degeneration at the later stages of AD (\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). The rapid decline in glial reactivation markers from the PART to the AD groups (\u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e), particularly those associated with Mic (e.g., \u003cem\u003eS100A9\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eP2RY12\u003c/em\u003e), likely contributes to the decreased estimated proportions of these glial cell types in the CA1 region in AD, which may in turn explain the relative increase in Exc proportions in the comparison between AD and PART.\u003c/p\u003e\u003cp\u003eIn addition to the CA1, we also found a significant reduction in the proportion of Exc in the SUB, CA2, CA3, and CA4 subregions in both the PART and AD groups compared to the AC group. Although the CA3 and CA4 exhibited less vulnerability compared to the SUB, CA1, and CA2 subregions (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), particularly in PART, the observed reduction in Exc proportions in the CA3 and CA4 suggests that these regions also experience significant stress, despite showing fewer hallmark AD pathologies. In contrast, the DG subregions showed relatively preserved Exc proportions (\u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e), consistent with previous findings that granule cells, a subtype of Exc mainly located in the DG, were more resilience to the stress compared to the pyramidal Exc located in the SUB to CA4 areas (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Notably, the proportion of Exc in the CA3 and CA4 progressively declined from AC to PART and further to AD, whereas Ast proportions generally increased across most SLs over the same progression, except in CA1, where Ast proportions significantly decreased from PART to AD. This may indicate that Ast dysfunction emerges earlier in the CA1 than in other regions, which could also be reflected by the large downregulation of Ast reactivation markers in AD compared to the PART group (\u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e). For Mic, most SLs displayed increased proportions in PART relative to AC, followed by a decline in AD, suggesting that microglial activation peaks in PART and diminishes at later AD stages. Since Mic are the primary phagocytes responsible for Aβ clearance (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e), their dysfunction may lead to Aβ plaque accumulation observed in AD, which was also observed in our previous ST studies on prefrontal cortex from AD samples (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo better understand transcriptomic factors contributing to the selective vulnerability observed in PART and AD at pseudo-sc resolution, we first conducted cell type\u0026ndash;specific DGE analyses in Exc across SLs among the AC, PART, and AD groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u003c/b\u003e; \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). Given that limited pathological changes were observed in all cell types in the DG subregion (\u003cb\u003eFig.\u0026nbsp;3C\u003c/b\u003e), we mainly focused on the molecular divergence of SUB to CA4 subregions. Compared to the AC group, genes involved in neuronal metabolism (e.g., \u003cem\u003eSLC22A17\u003c/em\u003e, \u003cem\u003eSLC4A7\u003c/em\u003e, \u003cem\u003eABHD12\u003c/em\u003e) and synaptic transmission (e.g., \u003cem\u003eNSF\u003c/em\u003e, \u003cem\u003eNEFM\u003c/em\u003e, \u003cem\u003eTUBA4A\u003c/em\u003e) were consistently downregulated in Exc across all SUB and CA subregions in both PART and AD groups (\u003cb\u003eFig.\u0026nbsp;3E; Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eB\u003c/b\u003e), suggesting that despite minimal neuronal loss in PART (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), functional impairments in Exc likely contribute to the observed mild cognitive decline. In PART, several genes associated with oxidative stress responses, such as \u003cem\u003eCALM3, PRNP\u003c/em\u003e, and \u003cem\u003eAPP\u003c/em\u003e (\u003cspan additionalcitationids=\"CR74 CR75\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), were upregulated in Exc across the SUB to CA4 subregions compared to both AC and AD groups (\u003cb\u003eFig.\u0026nbsp;3E\u003c/b\u003e). Notably, Calmodulin 3 (encoded by \u003cem\u003eCALM3\u003c/em\u003e) is a key subunit of phosphorylase kinase (PhK), which can phosphorylate tau and contribute to NFT formation (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). In addition, amyloid precursor protein (APP, encoded by \u003cem\u003eAPP\u003c/em\u003e) is the source of Aβ peptides, and its elevated expression may promote increased Aβ production and deposition, potentially facilitating progression toward AD (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). In AD, a stress response factor, \u003cem\u003eRAPGEF4\u003c/em\u003e, was upregulated in Exc across the SUB to CA4 subregions compared to both AC and PART (\u003cb\u003eFig.\u0026nbsp;3E\u003c/b\u003e). \u003cem\u003eRAPGEF4\u003c/em\u003e encodes a cAMP-regulated guanine nucleotide exchange factor involved in cAMP-mediated signaling (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), which can influence kinases that induce tau protein phosphorylation (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Thus, the upregulation of \u003cem\u003eRAPGEF4\u003c/em\u003e in AD may contribute to the exacerbation of tau pathology in the human hippocampus.\u003c/p\u003e\u003cp\u003eGiven that all Exc in the SUB and CA subregions were under high stress but NFT accumulation was observed only in Exc within the CA1 subregion in PART, the unique Exc-specific DEGs in PART compared to AC and AD may provide insight into this selective vulnerability. When compared to the AC and AD groups, the Exc in CA1 subregion in PART exhibited the highest number of uniquely upregulated genes, in contrast to the relatively minor region-specific transcriptional changes seen in Exc in SUB and CA2-CA4 (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u003c/b\u003e). Many of these CA1-specific, PART-upregulated genes in Exc were significantly enriched in GO terms related to neuroprotection and synaptic maintenance (\u003cb\u003eFig.\u0026nbsp;3F\u003c/b\u003e), including \u0026ldquo;Positive Regulation of Neurogenesis\u0026rdquo; (e.g., \u003cem\u003ePTPRD\u003c/em\u003e, \u003cem\u003eRGS14\u003c/em\u003e), \u0026ldquo;Positive Regulation of Synaptic Transmission\u0026rdquo; (e.g., \u003cem\u003ePLK2\u003c/em\u003e, \u003cem\u003eSLC8A2\u003c/em\u003e), and \u0026ldquo;Regulation of Neurotransmitter Receptor Activity\u0026rdquo; (e.g., \u003cem\u003ePRRT1\u003c/em\u003e). These findings suggested that Exc in the CA1 subregion may activate protective and compensatory pathways in the PART group to maintain synaptic integrity and counteract neuroinflammation-induced dysfunction. We also identified \u003cem\u003eCX3CL1\u003c/em\u003e as a uniquely upregulated gene in CA1 in PART (\u003cb\u003eFig.\u0026nbsp;3E\u003c/b\u003e). This gene encodes a neuron-derived chemokine that inhibits microglial hyperactivation, thereby reducing neuroinflammation and supporting neuronal function (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Given the concurrent observation of heightened microglial activation in CA1 during PART, the elevated expression of \u003cem\u003eCX3CL1\u003c/em\u003e likely reflects a neuronal compensatory response to suppress excessive microglial activity and limit inflammatory damage. However, since previous studies (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e) have shown that activated Mic are key contributors to Aβ clearance, sustained CX3CL1 upregulation may inadvertently impair microglial phagocytic function. As a result, this protective anti-inflammatory signaling could paradoxically facilitate Aβ accumulation, potentially promoting the transition from PART to AD through the formation of Aβ plaques. In contrast to genes identified in CA1, the uniquely upregulated genes in the PART group identified in the SUB and CA2\u0026ndash;CA4 subregions did not include any genes known to directly promote NFT formation. Notably, in the PART group, \u003cem\u003ePPP1R9B\u003c/em\u003e and \u003cem\u003ePPP2CB\u003c/em\u003e were upregulated in relative to both AC and AD groups in SUB and CA4 regions, respectively (\u003cb\u003eFig.\u0026nbsp;3E\u003c/b\u003e; \u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eB\u003c/b\u003e). These two genes encode key regulatory components of protein phosphatase 1 and protein phosphatase 2A, respectively, both of which are primary enzymes responsible for tau dephosphorylation (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). Together, these findings highlighted transcriptomic signatures that might underlie the selective vulnerability of Exc in the CA1 subregion of the human hippocampus in PART.\u003c/p\u003e\u003cp\u003eSimilar to PART, Exc in the CA1 region in AD exhibited the highest number of uniquely upregulated genes across all SLs when compared to both AC and PART groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u003c/b\u003e). Notably, these uniquely upregulated genes in CA1-Exc in AD were strongly associated with NFT formation, rather than neuroprotective compensatory mechanisms observed in PART. Enriched GO terms included processes such as \u003cb\u003e\u0026ldquo;\u003c/b\u003ePhosphorylation\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eSMG1, GSK3A, DYRK2\u003c/em\u003e), \u003cb\u003e\u0026ldquo;\u003c/b\u003ePeptidyl-Serine Phosphorylation\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eTNKS, ROCK2\u003c/em\u003e), and \u003cb\u003e\u0026ldquo;\u003c/b\u003ePeptidyl-Threonine Phosphorylation\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eLMTK2, CDC42BPB\u003c/em\u003e) (\u003cb\u003eFig.\u0026nbsp;3G\u003c/b\u003e), highlighting molecular mechanisms that may contribute to the higher NFT burden in AD compared to PART (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Additionally, several GO terms related to transcriptional regulation, such as \u003cb\u003e\u0026ldquo;\u003c/b\u003ePositive Regulation of DNA-templated Transcription\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eCCNT1, ICE1\u003c/em\u003e) and \u003cb\u003e\u0026ldquo;\u003c/b\u003ePositive Regulation of Nucleic Acid-Templated Transcription\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eCDKN1C, TRRAP\u003c/em\u003e), were also enriched in CA1-Exc in AD. These findings suggest that CA1-Exc in AD may be under considerable damage, potentially leading to transcriptional dysregulation and DNA damage. Importantly, such transcription-related responses were not observed in Exc populations from other SLs in AD. In the CA3 subregion, the enriched GO terms \u003cb\u003e\u0026ldquo;\u003c/b\u003eSynaptic Vesicle Exocytosis\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eSNAP25\u003c/em\u003e, \u003cem\u003eSTX1B\u003c/em\u003e) and \u003cb\u003e\u0026ldquo;\u003c/b\u003ePositive Regulation of Autophagy of Mitochondrion in Response to Mitochondrial Depolarization\u003cb\u003e\u0026rdquo;\u003c/b\u003e (e.g., \u003cem\u003eTOMM7\u003c/em\u003e) suggest potential compensatory mechanisms for enhancing neuronal connectivity and promoting neuroprotection (\u003cb\u003eFig.\u0026nbsp;3H\u003c/b\u003e). Interestingly, we noticed that \u003cem\u003eCDK5R1\u003c/em\u003e and \u003cem\u003eCDK5R2\u003c/em\u003e, key promoters for NFT formation (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e), showed the highest expression levels in the AD group across all SL subregions, except in CA1, where their expression was highest in the PART group (\u003cb\u003eFig.\u0026nbsp;3E\u003c/b\u003e). Since NFTs were also observed in the SUB and CA2\u0026ndash;CA4 subregions during the late stages of AD, our findings suggests that the mechanisms underlying NFT formation may be shared between PART and AD. However, the elevated expression of \u003cem\u003eCDK5R1\u003c/em\u003e and \u003cem\u003eCDK5R2\u003c/em\u003e in these subregions appears only at the late stage of AD, which may explain why NFTs are present in these regions in AD but not in PART.\u003c/p\u003e\n\u003ch3\u003eGlial cells resilience in PART and AD\u003c/h3\u003e\n\u003cp\u003eAlthough predominant inflammation and Aβ production related genes were upregulated in Exc across the SUB to CA subregions in the PART group, neuronal degradation and Aβ plaque accumulation are rarely reported in PART. In addition to the intrinsic stress response mechanisms of Exc neurons, the resilience of glial cells, particularly their ability to clear excess Aβ and provide neuroprotection, also contributes to maintaining neuronal survival (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). To investigate the molecular mechanisms underlying this phenomenon, we analyzed subregion-specific transcriptional differences in glial cells across the SUB to CA4 subregions in the AC, PART, and AD groups. Since the proportions of Oli and OPC remained relatively stable across groups (\u003cb\u003eFig.\u0026nbsp;3B\u003c/b\u003e), our analysis primarily focused on Ast and Mic within the SLs. We observed a consistent upregulation of a stress-response gene, \u003cem\u003eNTRK2\u003c/em\u003e, in Ast across all SLs in the PART group, compared to AC and AD groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC-D\u003c/b\u003e). Additionally, pro-inflammatory genes such as \u003cem\u003eSERPINA3, S100B\u003c/em\u003e, and \u003cem\u003eSPP1\u003c/em\u003e were also elevated, suggesting that Ast was the key contributor to the inflammatory environment observed in PART (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). Similar to Exc, Ast in the CA1 region in the PART group also showed the highest number of uniquely upregulated genes among SLs (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u003c/b\u003e). While no CA1-specific Ast genes were linked to NFT formation in PART, several genes associated with synaptic plasticity and neuronal survival, such as \u003cem\u003eHPCA\u003c/em\u003e and \u003cem\u003eSLC44A3\u003c/em\u003e, were upregulated in the PART group compared to the AC and AD groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC\u003c/b\u003e), supporting the role of Ast in counteracting neurodegeneration. Furthermore, \u003cem\u003eSNX3\u003c/em\u003e was uniquely upregulated in CA1 Ast in PART compared to AC and AD. Sorting nexin 3, encoded by \u003cem\u003eSNX3\u003c/em\u003e, has been shown to inhibit Aβ generation by altering APP trafficking, and may underlie the absence of Aβ plaque deposition in the CA1 region in PART. Compared to CA1, the number of uniquely upregulated genes in Ast in PART versus AC and AD was lower within the SUB and CA2\u0026ndash;CA4 subregions (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u003c/b\u003e). Furthermore, within these genes, only a few of them, such as \u003cem\u003eS100A13\u003c/em\u003e in SUB as well as \u003cem\u003eGLUL\u003c/em\u003e and \u003cem\u003eUQCRB\u003c/em\u003e in CA4 (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eD\u003c/b\u003e), have been reported to be associated with neuroinflammation and AD pathological hallmark (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). This pattern indicates a lower level of Ast reactivation in these regions compared to CA1, suggesting less Exc damage.\u003c/p\u003e\u003cp\u003eNotably, in the AD group, the gene, \u003cem\u003eSPARC\u003c/em\u003e, which affects two central pathological features of AD: Aβ deposition (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e) and blood-brain barrier (BBB) disruption (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), is among the common genes upregulated in AD compared to AC and PART across SLs (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC-D\u003c/b\u003e). In the CA1 subregion, unlike Ast in PART, which primarily upregulated pro-inflammatory genes, Ast in AD display a dual phenotype. On one hand, they express genes associated with axonal and synaptic degradation (e.g., \u003cem\u003eDPYSL2\u003c/em\u003e, \u003cem\u003eRGMA\u003c/em\u003e, \u003cem\u003eGABBR1\u003c/em\u003e, \u003cem\u003eTRIM2\u003c/em\u003e) (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC\u003c/b\u003e). On the other hand, they also upregulate genes related to compensatory neuroprotective mechanisms, such as antioxidative stress responses (e.g., \u003cem\u003eGPX4\u003c/em\u003e, \u003cem\u003eSELENOW\u003c/em\u003e), metabolic support (e.g., \u003cem\u003eUGP2\u003c/em\u003e, \u003cem\u003ePFKP\u003c/em\u003e, \u003cem\u003ePRDM16\u003c/em\u003e), and inflammation resolution (e.g., \u003cem\u003eZFP36L2\u003c/em\u003e). This dual profile aligns with previous studies showing that Ast exert both neuroprotective and neurotoxic effects in the later stages of AD (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Similar patterns were also observed in Ast within the CA3 and CA4 subregions in AD, whereas such patterns were not evident in the SUB and CA2 subregions. Further, we have also identified several unique upregulated genes in Ast in the AD group that are related to the BBB disruption, including \u003cem\u003eTGFB2\u003c/em\u003e in CA3 (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e) and \u003cem\u003ePTK2B\u003c/em\u003e in CA4 (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e) (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eD\u003c/b\u003e). Together, the upregulated transcript molecules in Ast in AD, compared to AC and PART, exhibited both neuroprotective functions and, paradoxically, contributed to neurotoxin as well as blood-brain barrier disruption.\u003c/p\u003e\u003cp\u003eDue to the limited numbers and relatively small size of Mic, fewer Mic-specific DEGs were detected across the SUB and CA subregions in the AC, PART, and AD groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA, Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). Similar to Ast, Mic showed prominent reactivation across the subregions, evidenced by increased \u003cem\u003eSPP1\u003c/em\u003e expression in the PART group compared to both AC and AD groups. In the CA1 subregion, Mic from PART samples showed upregulated expression of autophagy-related genes (e.g., \u003cem\u003eGABARAP, OTUB1, UCHL1\u003c/em\u003e) relative to those from AC and AD samples, suggesting enhanced Aβ clearance capacity (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eE\u003c/b\u003e). In CA4, Mic in PART exhibited even stronger expression of genes directly linked to both Aβ clearance and autophagy (e.g., \u003cem\u003eAPOC1, HNRNPC\u003c/em\u003e), indicating a robust neuroprotective response in this subregion (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eE\u003c/b\u003e). This may help explain the relative resistance of CA4 to Aβ aggregation compared to CA1, consistent with prior findings (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). In contrast, Mic from SUB, CA1, and CA4 regions showed limited upregulation of Aβ clearance genes in AD samples compared to AC and PART. Instead, their transcriptomic profiles were enriched for genes associated with pro-inflammatory responses. Combined with the marked reduction in Mic proportions from PART to AD across SUB and CA subregions, these findings suggested a progressive decline in Aβ clearance capacity, contributing to plaque accumulation in AD. Moreover, dysfunctional Mic likely exacerbate neuroinflammation, further damaging Exc.\u003c/p\u003e\n\u003ch3\u003eSubregion communications within human hippocampus in AC, PART, and AD\u003c/h3\u003e\n\u003cp\u003eThe interactions between subregions of the human hippocampus are essential for coordinating memory encoding, consolidation, and retrieval across various cognitive contexts (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e). As a result, communication between hippocampal subregions may be alternated or disrupted, contributing to the cognitive decline seen in PART and, more prominently, in AD. To investigate how subregional communication patterns change with increasing abundance of AD pathological hallmarks, we first constructed interaction networks among all hippocampal subregions at spatial spot levels in individuals with AC, PART, and AD, respectively. Given that interaction strength between subregions may decrease with increasing Euclidean distance (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e), we incorporated spatial distance as a covariate in the construction of the interaction networks. In general, the strength of the communication among all subregions was decreasing from AC to AD (\u003cb\u003eFig.\u0026nbsp;4A\u003c/b\u003e). While the VAS and s.r. subregions exhibit relatively high incoming and outgoing interactions across the AC, PART, and AD groups, the SUB, CA2, and CA3 subregions display comparatively lower interaction strengths among these groups (\u003cb\u003eFig.\u0026nbsp;4B-D\u003c/b\u003e). Specifically, SUB and CA1 exhibited a significant increase in both outgoing and incoming signaling strength in PART compared to AC, followed by a marked decrease in AD relative to PART. In contrast, CA3 and CA4 showed reduced signaling strength in PART compared to AC, but this increased in AD relative to PART (\u003cb\u003eFig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA\u003c/b\u003e). Besides, the VAS showed the highest strength of the incoming signaling in PART compared to AC and AD, but exhibited a gradually decreased strength of the outgoing signaling with the increase of the AD pathological hallmarks. These findings suggested a dynamic, subregion-specific reorganization of hippocampal communication in response to PART and AD pathology.\u003c/p\u003e\u003cp\u003eTo pinpoint the divergence of subregion-specific communication patterns across the AC, PART, and AD groups, we first analyzed the interaction strength between all pairs of hippocampal subregions. The communication networks among hippocampal subregions varied across the AC, PART, and AD groups (\u003cb\u003eFig.\u0026nbsp;4E\u003c/b\u003e). The pairwise comparison of interactions strengths among subregions illustrated that the interaction strength among multiple SLs decreased in the PART group compared to the AC group, followed by an increase in the AD group relative to PART. However, overall strength of these interactions in AD remained lower than in AC (\u003cb\u003eFig.\u0026nbsp;4F\u003c/b\u003e). The divergence of these interaction strength between AC, PART, and AD was primarily driven by changes in signaling pathways involving neurotensin (NT), pleiotrophin (PTN), macrophage migration inhibitory factor (MIF), SPP1, semaphorin 3A (SEMA3A), and prosaposin (PSAP) (\u003cb\u003eFig.\u0026nbsp;4G\u003c/b\u003e). Among these interactions with divergence strength, the NT signaling exhibited the greatest progressive decline from AC through PART to AD. Previous studies suggested that the NT signaling pathway plays a critical role in chemical neurotransmission, involving a variety of neurotransmitters that act on diverse receptors, often through co-release and feedback mechanisms that fine-tune neuronal responses (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). Additionally, NT can both promote neuronal survival and induce neuronal apoptosis, depending on the cellular context and receptor signaling pathways involved (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e). Thus, the pathological disruption of NT signaling observed in our data may contribute to the neuron damage in PART and AD. In the AC group, NT signaling primarily originated from the CA3 and CA4 regions, projecting to other SLs such as CA1 and CA2, respectively. Additionally, NT signaling from the DG was mainly directed toward CA3 and CA4, as well as glial cell-enriched subregions including the s.r., s.o., and ML (\u003cb\u003eFig.\u0026nbsp;4H\u003c/b\u003e). This pattern aligned with previous findings showing that granule cells in the DG form strong connections with Exc in CA3 and CA4, but have limited direct connectivity with CA1 and CA2 (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e). However, in the PART and AD groups, the NT signaling from CA4 to CA1 and CA2 was dramatically reduced compared to the AC group, likely due to pathological changes in the gene expression patterns of the ligands and receptors in cells located in these regions. Interestingly, in the AD group, cells in the CA1 subregion exhibited upregulated intra-region ligand-receptor (LR) pairs involved in NT signaling (\u003cb\u003eFig.\u0026nbsp;4H\u003c/b\u003e), likely reflecting an effort to preserve signaling activity within CA1 under the severe stress induced by AD pathology.\u003c/p\u003e\u003cp\u003eTo illustrate the impact of AD-related pathological hallmarks on NT signaling, we analyzed the pathological divergence of specific LR pairs involved in this process (\u003cb\u003eFig.\u0026nbsp;4I\u003c/b\u003e). Notably, the neuroprotective LR pair BDNF\u0026ndash;NTRK2, originating from CA3 and projecting to CA2 and CA4 subregions, showed a progressive decline in interaction from AC to PART, and further to AD. A similar decreasing trend was observed from DG to CA4 and ML subregions. Given that BDNF binding to NTRK2 activates key intracellular signaling pathways, including PI3K\u0026ndash;AKT and MAPK\u0026ndash;ERK, which are essential for neuronal survival and resilience to cellular stress (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e), the reduced interaction of this LR pair likely reflects a loss of inter-subregion neurotrophic support, driven by increasing pathological burden in AD. In addition, we have noticed one cell apoptotic related LR pairs, BDNF-SORT1, was decreased among the CA2, CA3, CA4, and DG in the AD group compared to the AC and PART groups. SORT1 acts as a co-receptor for neurotrophins such as BDNF, and when bound to BDNF, it can trigger neuronal apoptosis (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e). The reduced interactions of BDNF\u0026ndash;SORT1 across several hippocampal regions may reflect a compensatory mechanism aimed at preserving neuronal integrity. In contrast, we found that BDNF\u0026ndash;SORT1 interactions increased specifically in the CA1 subregion in the AD group compared to the AC and PART groups. Given that Exc in CA1 are among the most vulnerable to degeneration in AD, this heightened interaction may contribute to the severe neuron death in CA1.\u003c/p\u003e\u003cp\u003eIn addition to NT, we have observed significant variations in interaction strength within the PTN signaling pathway, which exhibited the strongest interactions strength among all signaling pathways across the hippocampal subregions (\u003cb\u003eFig.\u0026nbsp;4G\u003c/b\u003e). Previous studies have proposed that PTN promotes hippocampal neurogenesis by stimulating neural progenitor proliferation through the activation of AKT signaling (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e) and stabilizes dendritic microtubules of the damaged neurons (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e). Furthermore, PTN also acts as a protector of the BBB by supporting pericyte function, promoting angiogenesis, and facilitating vascular repair (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e). In our data, we have observed that the PTN signaling pathway from multiple regions, including SUB to CA3, to VAS, was disrupted (\u003cb\u003eFig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eB\u003c/b\u003e). In addition, among the interactions involved in the PTN, the PTN-(ITGAV\u0026thinsp;+\u0026thinsp;ITGB3) LR pair, which directly supports the BBB integrity, was detected from almost all subregions in hippocampus to the VAS in the AC and PART group, but diminished in the AD group (\u003cb\u003eFig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eC\u003c/b\u003e). Given that PTN binding the ITGAV and ITGB3 can enhance the adhesion of endothelial cells located on the surface of the large blood vessels and thus maintain the BBB structural stability and prevent leakage (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e), the disruption of this interaction may contribute to the BBB disruption observed in late stage of AD.\u003c/p\u003e\u003cp\u003eIn summary, our inter-subregion analysis identified specific molecular mechanisms supporting neuronal survival and BBB maintenance that are disrupted in AD but preserved in PART, offering insights into the neuronal degeneration and BBB breakdown seen in late-stage AD but not in PART.\u003c/p\u003e\n\u003ch3\u003eCellular interactions between VAS and the nearby cells\u003c/h3\u003e\n\u003cp\u003eBBB disruption is a hallmark of late-stage AD (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e), primarily driven by the activation of astrocytes and microglia in response to heightened cellular stress (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). This breakdown permits neurotoxic substances to infiltrate brain tissue (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e), with their accumulation often occurring around large blood vessels. The resulting localized damage can propagate to nearby regions, contributing to further pathological changes in adjacent small vessels (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e). Further, these pathological changes in adjacent small vessels can significantly influence the behavior of the glial cells, especially Ast and Mic, which are implicated in propagating neuroinflammation, disrupting synaptic support, and facilitating neuronal apoptosis (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e). As a result, the cells in close proximity to the large vessels form a distinct microenvironment compared to those in distal regions, and uncovering the differences in cellular interactions between areas near and far from large vessels may be critical for understanding the mechanisms of vessel-induced neuronal degeneration.\u003c/p\u003e\u003cp\u003eTo address this, we first identified the location of each VAS spot and conducted a concentric analysis using a strategy similar to our previous study (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Spatial spots were categorized into four levels (1 to 4) based on their distance from the VAS (\u003cb\u003eFig.\u0026nbsp;5A-B\u003c/b\u003e). Due to the proximity and minimal transcriptional divergence between levels 1 and 2, our DEG analysis focused on comparing level 1 (proximal) and level 3 (distal) spots. We found that while most DEGs identified in AC and PART groups were functionally similar, primarily related to neuronal maintenance, \u003cem\u003eAPP\u003c/em\u003e was significantly upregulated in Exc in level 1 compared to level 3 spots specifically in the PART group, but not in the AC or AD groups (\u003cb\u003eFig.\u0026nbsp;5C\u003c/b\u003e). This suggested that Exc near large vessels in PART may produce excessive APP, potentially enhancing Aβ plaque formation relative to distal neurons. In contrast, the absence of \u003cem\u003eAPP\u003c/em\u003e upregulation in the AD group may indicate that Exc near large vessels undergo apoptosis due to severe stress, leading to a reduction in \u003cem\u003eAPP\u003c/em\u003e expression in these neurons (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e). Additionally, the inflammation-related gene \u003cem\u003eS100B\u003c/em\u003e was upregulated in Ast in level 1 compared to level 3 in both PART and AD, but not in AC, indicating a stronger inflammatory response in vessel-proximal Ast within these conditions. In the AD group, downregulated genes in Exc in level 1 spots were associated with responses to mitochondrial dysfunction (e.g., \u003cem\u003eUQCRQ, ATP6V1G2, POLR2F\u003c/em\u003e), oxidative stress (e.g., \u003cem\u003ePTGES3, MT3, HAGH\u003c/em\u003e), and protein clearance (e.g., \u003cem\u003ePSMD8, CHMP4B, VPS4A\u003c/em\u003e), suggesting greater functional impairment in Exc near large vessels in AD (\u003cb\u003eFig.\u0026nbsp;5C\u003c/b\u003e). Conversely, the upregulation of the energy metabolism-related gene \u003cem\u003eKIF5A\u003c/em\u003e (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e) in level 1 spots in AD may reflect a compensatory response to mitochondrial stress in these neurons (\u003cb\u003eFig.\u0026nbsp;5C\u003c/b\u003e). Together, these findings highlight significant cell type-specific transcriptional differences in vessel-adjacent regions across AC, PART, and AD groups, potentially shedding light on the detrimental impact of BBB disruption in AD.\u003c/p\u003e\u003cp\u003eWe next constructed cell\u0026ndash;cell communication networks within each level for the AC, PART, and AD groups to identify potential cellular interactions involved in neuroinflammation and degeneration (\u003cb\u003eFig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eA\u003c/b\u003e). Two key cellular survival-related signaling pathways, GAS and PDGF, originating from VC and targeting multiple cell types, particularly Ast, Mic, and Oli, were detected in levels 1 and 3 spots across the different groups (\u003cb\u003eFig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eB-C\u003c/b\u003e; \u003cb\u003eFig.\u0026nbsp;5D\u003c/b\u003e). For the GAS signaling pathway, interactions were predominantly directed from VC to Mic in both the AC and PART groups, but were prominent from VC to Ast in the AD group (\u003cb\u003eFig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eC\u003c/b\u003e). Specifically, GAS6\u0026ndash;MERTK interactions from VC to Mic were absent in the AC group and progressively increased in PART and AD, while GAS6\u0026ndash;MERTK interactions between VC and Ast were only detected in the AD group This result is consistent with previous studies showing that the GAS6\u0026ndash;MERTK pair plays a dual role in AD: while it promotes Aβ clearance via microglial activation, it can also drive neuroinflammation through Ast reactivation (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e). Similar to GAS signaling, PDGF signaling from VC to Ast was observed only in the PART and AD groups, but not in AC (\u003cb\u003eFig.\u0026nbsp;5D\u003c/b\u003e). Specifically, PDGFB\u0026ndash;PDGFRB interactions showed a progressive increase from AC to PART to AD (\u003cb\u003eFig.\u0026nbsp;5E\u003c/b\u003e). This LR pair was similarly strong in both levels 1 and 3 in the AD group, while it was stronger in level 1 than in level 3 in the PART group. PDGFB\u0026ndash;PDGFRB signaling from VC to Ast activates Ast to recruit monocytes into the brain, thereby amplifying neuroinflammation and leading to neuron apoptosis (\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e). In summary, our results indicated that the pathological alterations in the transcriptional profiles of Ast located near large vessels was activated by VC in AD, which may play a critical role in accelerating disease progression.\u003c/p\u003e\u003cp\u003eTo further investigate transcriptional alterations in Ast located near large vessels during disease progression, we performed DGE analysis in Ast between the AC, PART, and AD groups across levels 1 to 4, respectively. Notably, the P53-related gene \u003cem\u003eTP53INP2\u003c/em\u003e was significantly upregulated in Ast in the AD group compared to both AC and PART groups at levels 1 to 3 (\u003cb\u003eFig.\u0026nbsp;5F\u003c/b\u003e). However, this upregulation was not observed at level 4. This spatial pattern suggests that TP53INP2 is highly enriched in Ast located near large vessels in AD. \u003cem\u003eTP53INP2\u003c/em\u003e (Tumor Protein P53 Inducible Nuclear Protein 2) encodes a stress-responsive nuclear protein known to promote cellular autophagy (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e). Excessive expression of TP53INP2 may lead to hyperactivation of autophagy in Ast, contributing to remove misfolded proteins and toxic materials of nearby Exc to maintain the neuron health (\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e). Meanwhile, as a tumor suppressor, TP53INP2 also promote cellular apoptosis under pathological conditions (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e), which may further exacerbate Exc degeneration. To validate the upregulation of TP53INP2 observed in our ST data, we performed IHC staining on six hippocampal samples used for ST and one hippocampal sample used for snRNA-seq, and included one additional independent hippocampal samples to the AC and PART groups, respectively. (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e; \u003cb\u003eMethods\u003c/b\u003e). TP53INP2 protein was enriched around large vessels in all groups, with significantly elevated expression in astrocytes near vessels from AC to PART to AD, consistent with our ST data (\u003cb\u003eFig.\u0026nbsp;5H-J\u003c/b\u003e; \u003cb\u003eFig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eD-E\u003c/b\u003e). Together, these results suggest that BBB disruption in large vessels leads to pathological alterations in the transcriptional profiles of nearby cells. In particular, the elevated expression of \u003cem\u003eTP53INP2\u003c/em\u003e in Ast adjacent to large vessels may play a central role in the neurodegenerative processes observed in AD.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we generated a comprehensive, data-driven, transcriptome-wide molecular atlas of the adult human hippocampus using the advanced 10x Visium ST platform to investigate molecular alterations across AC, PART, and AD. By integrating cutting-edge ST with innovative analytical strategies, we unveiled potential mechanisms driving the transition from PART to AD and offer novel insights into AD pathology. Furthermore, our analysis pipeline, enhancing spot-level data to pseudo-sc resolution, can be applied to existing non\u0026ndash;sc ST datasets from other brain region (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e), enabling deeper exploration of molecular mechanisms underlying AD.\u003c/p\u003e\u003cp\u003eHere, we highlight several key findings. Although multiple earlier ST studies have mapped hippocampal subregions in humans (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), none have successfully identified and validated markers that distinguish the CA1 and CA2 regions. In our study, we identified NRIP3 as a canonical marker that reliably delineates the boundary between CA1 and CA2. This marker provides a robust criterion for distinguishing these two subregions and offers a valuable tool for investigating region-specific pathological changes in AD. To address the relatively low resolution of the 10X Visium platform, we applied BayesPrism (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), a robust deconvolution method, to enhance spatial resolution beyond the spot level and achieve pseudo-sc resolution. At this higher resolution, we found that the distinct subregional markers of Exc within SLs, initially identified at the spatial spot level, were largely driven by transcriptional heterogeneity within the Exc population. Additionally, pseudo-sc analysis revealed changes in both cellular composition and cell type\u0026ndash;specific transcriptional profiles across hippocampal subregions between the AC, PART, and AD groups. The observed shifts in cell proportions and gene expression suggest that PART may represent a transitional state between AC and AD, consistent with findings by Duyckaerts \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e). In the PART group, Exc in the CA1 region with NFT burden showed expected signs of stress. Interestingly, Exc in other SL regions without NFTs also exhibited elevated stress markers, likely due to age-related oxidative stress and neuronal inflammation. Notably, while Stein-O\u0026rsquo;Brien et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) reported upregulation of \u003cem\u003eAPP\u003c/em\u003e expression in NFT-bearing Exc, we observed increased APP expression even in Exc without NFTs, particularly in the SUB and CA2\u0026ndash;CA4 regions of the PART group. This suggests that APP upregulation may result from stress independent of NFT pathology, potentially promoting Aβ production. Given that excess Aβ is cleared by Mic, the increased proportion of Mic in PART compared to the AC group may reflect a compensatory response aimed at Aβ clearance. However, prolonged Mic activation and exposure to Aβ can lead to Mic dysfunction and degeneration (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e), leading to Aβ accumulation and plaque formation. The resulting Aβ deposition intensifies inflammation, which in turn exacerbates tau phosphorylation, increasing NFT burden and compromising BBB integrity. This feed-forward loop accelerates the transition from PART to AD. Ultimately, the combined effects of Aβ plaques, NFTs, and BBB disruption lead to Exc degeneration.\u003c/p\u003e\u003cp\u003eFurthermore, our study identified molecular mechanisms underlying the selective vulnerability of Exc in the CA1 subregion in both PART and AD. In PART, although Exc across SUB and CA areas experienced oxidative stress and inflammation, Exc in CA1 uniquely upregulated NFT-associated genes \u003cem\u003eCDK5R1\u003c/em\u003e and \u003cem\u003eCDK5R2\u003c/em\u003e compared to both the AC and AD groups. In contrast, these genes were significantly enriched in the AD group, relative to AC and PART, in the SUB and CA2\u0026ndash;CA4 subregions. Additionally, in PART, the region-specific upregulation of tau dephosphorylation-related genes, including \u003cem\u003ePPP1R9B\u003c/em\u003e in SUB and \u003cem\u003ePPP2CB\u003c/em\u003e in CA4, may contribute to the lower NFT burden observed in these regions. Beyond neuronal changes, we also observed enhanced glial resilience in PART, particularly in CA1, where Ast and Mic were robustly reactivated, potentially contributing to neuroprotection. However, in AD, this glial reactivation, especially in Ast, adopted a dual role: while still supporting neuronal survival, it also promoted BBB disruption, thereby exacerbating disease progression.\u003c/p\u003e\u003cp\u003eAnalysis of inter-subregion communication in the hippocampus revealed a progressive decline in both neuronal survival and BBB integrity from AC to PART and, ultimately, to AD. BBB disruption was associated with altered transcriptional profiles in VC, Ast, and Mic near large vessels, leading to reshaped cellular interactions. Specifically, VC engaged in pro-inflammatory signalling with glial cells, particularly Ast, thereby exacerbating neuroinflammation and promoting cell apoptosis. Notably, we observed a significant upregulation of \u003cem\u003eTP53INP2\u003c/em\u003e in Ast located near large vessels in the AD group, compared to the AC and PART groups. While this gene has not been widely reported in AD, a closely related gene, \u003cem\u003eTP53INP1\u003c/em\u003e, has been implicated in AD pathogenesis in several studies (138, 139). As a tumor suppressor, \u003cem\u003eTP53INP2\u003c/em\u003e may participate in a previously unrecognized Ast-related neural apoptosis pathway, potentially contributing to neurodegeneration. This finding warrants further validation through molecular and functional studies.\u003c/p\u003e\u003cp\u003eDespite the significant advances and insights provided by this study, several limitations should be acknowledged. First, although all six samples were age-matched (ranging from 79 to 92 years), the sex distribution was unbalanced (four males and two females), potentially introducing sex-specific bias. Second, the sc reference dataset used for deconvolution was not derived from the same individuals as the ST samples, which limited our choice of deconvolution methods and may have introduced analytical bias. Additionally, while we enhanced spatial resolution from spot-level to pseudo\u0026ndash;single-cell resolution using deconvolution algorithms, the resulting pseudo-sc matrix is still computationally inferred. As such, discrepancies may exist compared to true sc resolution platforms, such as Visium HD or Stereo-seq (53). Nevertheless, despite these limitations, our study provides valuable novel insights into the transcriptional landscape of PART and AD in the human hippocampus, offering a systematic and high-resolution view of disease progression and the molecular mechanisms driving neurodegeneration.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStudy subjects\u003c/h2\u003e\u003cp\u003e This study was approved by the Ethics Committee of Xiangya School of Medicine in Central South University (2020KT-37, 4/10/2020; #2023-KT084, 6/21/2023), and conducted in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Brains were banked through a willed body donation program, with donors\u0026rsquo; clinical records collected when available (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e). The brains were assessed for neuropathological changes following the Standard Brain Banking Protocol established by the China Brain Bank Consortium (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e). Six postmortem human hippocampal samples from Chinese Han individuals aged 79\u0026ndash;95 years were analyzed using ST and were classified into AC, PART, and AD based on 6E10 (BioLegend, #SIG-39320) IHC for Aβ plaques (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA3-F3\u003c/b\u003e) as well as AT8 (Invitrogen, #MN1020) and Gallyas staining (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e) for NFTs (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA4-F4; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA5-F5\u003c/b\u003e). Specifically, the brains in the AC group were absent of Aβ deposition (Thal phase 0) and contained a few hyperphosphorylated tau (pTau) positive neurons (pre-tangle) but no Gallyas stained mature or ghost tangles in the hippocampal formation (Braak stage I-II). The two brains in the PART group showed only a few diffusion plaques in the prefrontal cortex (Thal phase 0), with AT8 and Gallyas stained NFT observed in the hippocampus and temporal lobe cortex (Braak stage III). The two brains in the AD had high Aβ plaque burden (Thal phase III) and heavy NFT pathology (Braak stages IV\u0026ndash;V). In addition to the two AD cases included in the ST analysis, hippocampal tissue from the third AD individual was included for snRNA-seq.\u0026nbsp;For the TP53INP2 IHC validation, we have added one additional sample to both the AC and PART groups. For details on human samples used in this study, please see Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTissue preparation, FFPE section, 10X Visium library preparation, sequencing, data preprocessing, and IHC\u003c/h2\u003e\u003cp\u003eHuman hippocampal samples were fixed in 4% paraformaldehyde solution (Cat # G1101-500ML, Servicebio) for 24\u0026ndash;48 hours at 4℃ to preserve morphology and RNA integrity. Fixed tissues were processed using standard dehydration and paraffin-embedding protocols (\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e). FFPE blocks were sectioned at 5 \u0026micro;m thickness using a rotary microtome and mounted onto Visium Spatial Gene Expression Slides (10X Genomics), ensuring optimal placement over the capture area. Sections were baked at 60\u0026deg;C for 30 minutes, followed by deparaffinization, H\u0026amp;E staining, and imaging using a brightfield microscope to guide tissue annotation. Spatial gene expression profiling was performed using the Visium Spatial Gene Expression for FFPE protocol (10X Genomics), including probe hybridization, ligation, and amplification steps to construct spatially barcoded libraries. Sequencing was carried out on an Illumina NovaSeq 6000 platform with paired end reads, targeting a depth of at least 25,000 reads per capture spot. Raw sequencing data were processed using the 10X Genomics Space Ranger pipeline to align reads and generate spatial gene expression matrices for downstream analysis.\u003c/p\u003e\u003cp\u003eRaw sequencing data (BCL files) were first demultiplexed using \u003cem\u003ecellranger mkfastq\u003c/em\u003e (10X Genomics), generating FASTQ files for downstream analysis. Quality control on FASTQ files was performed using \u003cem\u003eFastQC\u003c/em\u003e to assess read quality, adapter content, and duplication rates. The FASTQ files were then processed with SpaceRanger (v2.1, 10X Genomics), aligning reads to the GRCh38 human genome panel to generate the count matrix.\u003c/p\u003e\u003cp\u003eNuclei were isolated from frozen post-mortem brain tissue following a published protocol (\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e) for snRNA-seq (10X Genomics). In brief, approximately 40 mg of frozen, pulverized tissue was homogenized in chilled Nuclei EZ Lysis Buffer (MilliporeSigma #NUC101) using a glass dounce with about 15 strokes per pestle. The homogenate was passed through a 70 \u0026micro;m mesh strainer and centrifuged at 500 \u0026times; g for 5 minutes at 4\u0026deg;C. The pellet was resuspended in EZ Lysis Buffer, centrifuged again, and then transferred into nuclei wash/resuspension buffer (1x PBS, 1% BSA, 0.2 U/\u0026micro;L RNase Inhibitor). Nuclei were washed and centrifuged three times in this buffer before being stained with DAPI (10 \u0026micro;g/mL). For snRNA-seq, libraries were prepared using the Chromium Single Cell 3\u0026rsquo; Reagent Kits v3 according to the manufacturer\u0026rsquo;s protocol (10x Genomics). Sequencing was carried out on an Illumina NovaSeq 6000 platform with paired end reads, targeting a depth of at least 20,000 reads per capture nucleus. Raw scRNA-seq data were processed through cell ranger (10x Genomics) to be converted into the gene expression matrix.\u003c/p\u003e\u003cp\u003eFor the IHC validation, FFPE blocks were sectioned at 5 \u0026micro;m thickness and mounted onto slides. The sections were placed under a vented hood for air drying prior to FIBCD1, NRIP3, CCK, NEFM, STXBP6, and TP53INP2 staining. FIBCD1 (Cat # 25125-1AP, Proteintech), NRIP3 (Cat # 15664-1-AP, Proteintech), CCK (Cat # 13074-2-AP, Proteintech), NEFM (Cat # 25805-1-AP, Proteintech), STXBP6 (Cat # 10976-4-AP, Proteintech), TP53INP2 (Cat # PA5-72961, ThermoFisher) were used for IHC staining according to the vendor\u0026rsquo;s instructions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBioinformatics analysis of ST data of human hippocampus\u003c/h2\u003e\u003cp\u003eST data integration and clustering analysis for 10X Visium spatial spots\u003c/p\u003e\u003cp\u003eAt the spot level, we applied PRECAST (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), a data integration and unsupervised clustering method for ST data across multiple tissue slides, to identify subregions within the human hippocampus. PRECAST uses a two-layer hierarchical model comprising an integration layer and a clustering layer. In the integration layer, spatial spots from all tissue sections were projected into a shared low-dimensional embedding space. To preserve spatial continuity in gene expression patterns, an intrinsic Conditional Autoregressive (CAR) model was applied, encouraging nearby spots to have similar embeddings. Following integration and dimension reduction, spatial spots were clustered based on both their low-dimensional embeddings and physical coordinates. We selected K\u0026thinsp;=\u0026thinsp;15 as the optimal number of clusters, guided by the elbow point of the Bayesian Information Criterion (BIC) curve. Each resulting cluster was annotated using specific gene markers. To validate our annotations, we visualized the spatial distribution of annotated spots for each sample based on their coordinates and aligned them with corresponding Eosin-stained sections. Sample orientation was verified by identifying the directions of the SUB, CA, and DG subregions on both ST data and Eosin-stained image.\u003c/p\u003e\u003cp\u003eDGE analysis\u003c/p\u003e\u003cp\u003eThe \u0026ldquo;sc.tl.rank_genes_groups\u0026rdquo; function from the Scanpy package (v1.9.3) was utilized for DGE analysis. The Wilcoxon signed-rank test with FDR adjustment was applied to calculate the adjusted P-values, and the genes with adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as the DEGs.\u003c/p\u003e\u003cp\u003eDeconvolution analysis\u003c/p\u003e\u003cp\u003eIn our study, we applied the BayesPrism algorithm (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) to enhance spatial resolution beyond the spatial spot level. Using snRNA-seq data on the human hippocampus, consisting of our in-house and public available data, we first inferred the proportion of each cell type within each spatial spot and subsequently estimated cell type-specific gene expression patterns for each spot. Briefly, BayesPrism employs a Bayesian framework that models prior distributions based on scRNA-seq data and infers the joint posterior distribution of cell type porportions and gene expression, conditioned on each spatial spot. To prepare the input data, we performed QC by filtering out outlier genes in the snRNA-seq dataset using the \u0026ldquo;\u003cem\u003eplot.bulk.outlier\u003c/em\u003e\u0026rdquo; function with default parameters. We then combined the snRNA-seq and ST data into a BayesPrism object using the \u0026ldquo;new.prism\u0026rdquo; function, followed by running the \u0026ldquo;run.prism\u0026rdquo; function to estimate cell type proportions and infer gene expression profiles for each cell type in each spot.\u003c/p\u003e\u003cp\u003eTo assess the accuracy of BayesPrism\u0026rsquo;s deconvolution performance, we compared it with three additional spatial deconvolution algorithms: CARD (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), SpaCET (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), and PANDA (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). For CARD, we used the \u0026ldquo;CARDObject\u0026rdquo; function to construct the input object and applied \u0026ldquo;CARD_deconvolution\u0026rdquo; to estimate cell type proportions per spatial spot. For SpaCET, we used \u0026ldquo;create.SpaCET.object.10X\u0026rdquo; and \u0026ldquo;SpaCET.deconvolution\u0026rdquo;, both with default parameters. For PANDA, we employed the \u0026ldquo;sc_train\u0026rdquo; and \u0026ldquo;st_train\u0026rdquo; functions to estimate both cell type proportions and gene expression profiles per spot. To evaluate consistency between BayesPrism and the other methods, we calculated pairwise Spearman correlation coefficients between the cell type proportions inferred by BayesPrism and those estimated by CARD, SpaCET, and PANDA.\u003c/p\u003e\u003cp\u003eInter-subregion and cell-cell communication analysis\u003c/p\u003e\u003cp\u003eInter-subregion and cell-cell communication analyses were performed using CellChat (v2.1.0) (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e) based on the expression of known LR pairs across different subregions and cell types. For inter-subregion and cell-cell communication, we computed communication strength by modeling ligand-receptor interactions between spatial spots labeled by subregion in AC, PART, and AD samples separately. This modeling was based on the Law of Mass Action and incorporated gene expression profiles projected onto a protein-protein interaction network. Additionally, subregion/cell proportion was incorporated to minimize bias arising from unequal comparisons of inter-subregion/cell-cell interactions. To include spatial context in inter-subregion communication, we used the spatial coordinates of each spot as a cofactor. In contrast, for constructing cell-cell communication networks, we did not include spatial location as a covariate due to the limited spatial distance variation among spots within each level. We followed the official workflow and applied the data processing functions \u0026ldquo;identify OverExpressedGenes\u0026rdquo; and \u0026ldquo;identifyOverExpressedInteractions\u0026rdquo;. The inter-subregion communication networks were inferred by the function \u0026ldquo;computeCommuProb\u0026rdquo;. Function \u0026ldquo;netVisual_bubble\u0026rdquo; was used to compare the communication probabilities mediated by L-R from certain subregion group to other groups. All the analysis were performed with the default parameter setting.\u003c/p\u003e\u003cp\u003eConcentric circle analysis\u003c/p\u003e\u003cp\u003eTo understand how gene expression varies with proximity to the large vessels with BBB damaged, we mapped the VAS on a two-dimensional panel based on their coordinator for each sample, respectively, and drew three concentric circles around each high stress focal point to differentiate spatial spot distances. We first selected spatial spot located within 600 pixels (~\u0026thinsp;600 um) from the VAS in each sample. Spots within a radius within a radius of 200 units of the pixel (~\u0026thinsp;200 um) are categorized as level I, those between 200 and 400 units (approximately 200\u0026ndash;400 um) as level II, and those in 400 to 600 units (approximately 400 to 600 um) as level III. Spots intersecting circles from multiple VAS areas were assigned to the closest level.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e\u003cp\u003eAll authors have no conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDATA AVAILIABILITY\u003c/h2\u003e\u003cp\u003eThe ST data from two AC, two PART, and two AD samples, along with the snRNA-seq data generated in this study, will be deposited in the GEO database upon manuscript acceptance. The raw FASTQ data have been deposited in the SRA database and are accessible for download through the accession number (PRJNA1300973). The public snRNA-seq data on human hippocampus were from ROSMAP project (syn52293442).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCODE AVAILIABILITY\u003c/strong\u003e\u003cp\u003eThe codes are available and can be downloaded from Github (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Yungong1/Spatial-Transcriptome-Hippocampus\u003c/span\u003e\u003cspan address=\"https://github.com/Yungong1/Spatial-Transcriptome-Hippocampus\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e\u003cp\u003eY.G. conducted the major data analysis and wrote the main manuscript text; Q.Z., T.L., and Z.X. collected the human brain samples and performed experimental validation; Q.Z. and X.X.Y. performed histopathological characterization and immunohistochemical cross-validation experiments; Q.Z., D.W., A.L., X.X.Y., H.S., and H.W.D provided valuable suggestions throughout the study implementation; H.S., H.M.X., and H.W.D. were responsible for conceiving, designing, initiating, directing, supervising, language proofreading, and securing fundings for this study. All authors participated in the discussions of the project and reviewed and/or revised the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e\u003cp\u003eThis investigators were benefited or partially funded by National Institutes of Health [U19AG055373, R01AG061917, R01AG068232, P30GM145498, P20GM109036], National Natural Science Foundation of China [#82071223), as well as Ministry of Science and Technology of China (Science Innovation 2030-Brain Science [#2021ZD0201803] and Brain-Inspired Intelligence Technology Major Projects [#2021ZD0201103]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKnopman DS, Amieva H, Petersen RC, Chetelat G, Holtzman DM, Hyman BT et al (2021) Alzheimer disease. Nat Rev Dis Primers 7(1):33\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang W, Xiao D, Mao Q, Xia H (2023) Role of neuroinflammation in neurodegeneration development. Signal Transduct Target Ther 8(1):267\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoel P, Chakrabarti S, Goel K, Bhutani K, Chopra T, Bali S (2022) Neuronal cell death mechanisms in Alzheimer's disease: An insight. Front Mol Neurosci 15:937133\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e(2024) Alzheimer's disease facts and figures. Alzheimers Dement. 2024;20(5):3708\u0026thinsp;\u0026ndash;\u0026thinsp;821\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL et al (2019) Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol 15(10):565\u0026ndash;581\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeszek J, Mikhaylenko EV, Belousov DM, Koutsouraki E, Szczechowiak K, Kobusiak-Prokopowicz M et al (2021) The Links between Cardiovascular Diseases and Alzheimer's Disease. Curr Neuropharmacol 19(2):152\u0026ndash;169\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X, Zhang Y, Zhang C, Zheng Y, Liu R, Xiao S (2023) Education counteracts the genetic risk of Alzheimer's disease without an interaction effect. Front Public Health 11:1178017\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDominguez LJ, Veronese N, Vernuccio L, Catanese G, Inzerillo F, Salemi G et al (2021) Nutrition, Physical Activity, and Other Lifestyle Factors in the Prevention of Cognitive Decline and Dementia. Nutrients. ;13(11)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsiao YH, Chang CH, Gean PW (2018) Impact of social relationships on Alzheimer's memory impairment: mechanistic studies. J Biomed Sci 25(1):3\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMielke MM, Ransom JE, Mandrekar J, Turcano P, Savica R, Brown AW (2022) Traumatic Brain Injury and Risk of Alzheimer's Disease and Related Dementias in the Population. J Alzheimers Dis 88(3):1049\u0026ndash;1059\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellenguez C, Kucukali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N et al (2022) New insights into the genetic etiology of Alzheimer's disease and related dementias. Nat Genet 54(4):412\u0026ndash;436\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Wang Y, Zhang Y, Yao J (2023) Genome-wide association study in Alzheimer's disease: a bibliometric and visualization analysis. Front Aging Neurosci 15:1290657\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHampel H, Hardy J, Blennow K, Chen C, Perry G, Kim SH et al (2021) The Amyloid-beta Pathway in Alzheimer's Disease. Mol Psychiatry 26(10):5481\u0026ndash;5503\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrundke-Iqbal I, Iqbal K, Quinlan M, Tung YC, Zaidi MS, Wisniewski HM (1986) Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J Biol Chem 261(13):6084\u0026ndash;6089\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBusche MA, Hyman BT (2020) Synergy between amyloid-beta and tau in Alzheimer's disease. Nat Neurosci 23(10):1183\u0026ndash;1193\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Chen H, Li R, Sterling K, Song W (2023) Amyloid beta-based therapy for Alzheimer's disease: challenges, successes and future. Signal Transduct Target Ther 8(1):248\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarx V (2021) Method of the Year: spatially resolved transcriptomics. Nat Methods 18(1):9\u0026ndash;14\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen S, Chang Y, Li L, Acosta D, Li Y, Guo Q et al (2022) Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer's disease. Acta Neuropathol Commun 10(1):188\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J et al (2023) Integrated multimodal cell atlas of Alzheimer's disease. Res Sq\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong Y, Haeri M, Zhang X, Li Y, Liu A, Wu D et al (2025) Stereo-seq of the prefrontal cortex in aging and Alzheimer's disease. Nat Commun 16(1):482\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpifanio I, Ventura-Campos N (2014) Hippocampal shape analysis in Alzheimer's disease using functional data analysis. Stat Med 33(5):867\u0026ndash;880\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrary JF, Trojanowski JQ, Schneider JA, Abisambra JF, Abner EL, Alafuzoff I et al (2014) Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol 128(6):755\u0026ndash;766\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoedert M, Crowther RA, Scheres SHW, Spillantini MG (2024) Tau and neurodegeneration. Cytoskeleton (Hoboken) 81(1):95\u0026ndash;102\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHickman RA, Flowers XE, Wisniewski T (2020) Primary Age-Related Tauopathy (PART): Addressing the Spectrum of Neuronal Tauopathic Changes in the Aging Brain. Curr Neurol Neurosci Rep 20(9):39\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStein-O'Brien GL, Palaganas R, Meyer EM, Redding-Ochoa J, Pletnikova O, Guo H et al (2025) Transcriptional signatures of hippocampal tau pathology in primary age-related tauopathy and Alzheimer's disease. Cell Rep 44(3):115422\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang P, Han L, Wang L, Tao Q, Guo Z, Luo T et al (2025) Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas. Neuron\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong Y, Saglietti C, Bayard Q, Espin Perez A, Carpentier S, Buszta D et al (2025) Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity. Nat Commun 16(1):4400\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRao YL, Ganaraja B, Murlimanju BV, Joy T, Krishnamurthy A, Agrawal A (2022) Hippocampus and its involvement in Alzheimer's disease: a review. 3 Biotech 12(2):55\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMrdjen D, Fox EJ, Bukhari SA, Montine KS, Bendall SC, Montine TJ (2019) The basis of cellular and regional vulnerability in Alzheimer's disease. Acta Neuropathol 138(5):729\u0026ndash;749\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B et al (2024) A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 384(6698):eadh1938\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHunt S, Leibner Y, Mertens EJ, Barros-Zulaica N, Kanari L, Heistek TS et al (2023) Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex. Cereb Cortex 33(6):2857\u0026ndash;2878\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConsalez GG, Goldowitz D, Casoni F, Hawkes R (2020) Origins, Development, and Compartmentation of the Granule Cells of the Cerebellum. Front Neural Circuits 14:611841\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmaral DG, Scharfman HE, Lavenex P (2007) The dentate gyrus: fundamental neuroanatomical organization (dentate gyrus for dummies). Prog Brain Res 163:3\u0026ndash;22\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnand KS, Dhikav V (2012) Hippocampus in health and disease: An overview. Ann Indian Acad Neurol 15(4):239\u0026ndash;246\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuszczewska-Sierakowska I, Wawrzyniak-Gacek A, Guz T, Tatara MR, Charuta A (2015) Morphometric Parameters of Pyramidal Cells in CA1-CA4 Fields in the Hippocampus of Arctic Fox (Vulpes lagopus). Folia Biol (Krakow) 63(4):263\u0026ndash;267\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTatu L, Vuillier F (2014) Structure and vascularization of the human hippocampus. Front Neurol Neurosci 34:18\u0026ndash;25\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu W, Liao X, Luo Z, Yang Y, Lau MC, Jiao Y et al (2023) Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nat Commun 14(1):296\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamnauth AD, Tippani M, Divecha HR, Papariello AR, Miller RA, Nelson ED et al (2025) Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation. Cell Rep 44(2):115300\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimard S, Rahimian R, Davoli MA, Theberge S, Matosin N, Turecki G et al (2024) Spatial transcriptomic analysis of adult hippocampal neurogenesis in the human brain. J Psychiatry Neurosci 49(5):E319\u0026ndash;E33\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMirzazadeh R, Andrusivova Z, Larsson L, Newton PT, Galicia LA, Abalo XM et al (2023) Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples. Nat Commun 14(1):509\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischer TT, Nguyen LD, Ehrlich BE (2021) Neuronal calcium sensor 1 (NCS1) dependent modulation of neuronal morphology and development. FASEB J 35(10):e21873\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRadler MR, Liu X, Peng M, Doyle B, Toyo-Oka K, Spiliotis ET (2023) Pyramidal neuron morphogenesis requires a septin network that stabilizes filopodia and suppresses lamellipodia during neurite initiation. Curr Biol 33(3):434\u0026ndash;448 e8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDanzer SC, Kotloski RJ, Walter C, Hughes M, McNamara JO (2008) Altered morphology of hippocampal dentate granule cell presynaptic and postsynaptic terminals following conditional deletion of TrkB. Hippocampus 18(7):668\u0026ndash;678\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBienkowski MS (2023) Further refining the boundaries of the hippocampus CA2 with gene expression and connectivity: Potential subregions and heterogeneous cell types. Hippocampus 33(3):150\u0026ndash;160\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChu T, Wang Z, Pe'er D, Danko CG (2022) Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer 3(4):505\u0026ndash;517\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP et al (2024) Single-cell multiregion dissection of Alzheimer's disease. Nature 632(8026):858\u0026ndash;868\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScher AI, Xu Y, Korf ES, White LR, Scheltens P, Toga AW et al (2007) Hippocampal shape analysis in Alzheimer's disease: a population-based study. NeuroImage 36(1):8\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLou Y, Zhao L, Yu S, Sun B, Hou Z, Zhang Z et al (2020) Brain asymmetry differences between Chinese and Caucasian populations: a surface-based morphometric comparison study. Brain Imaging Behav 14(6):2323\u0026ndash;2332\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa Y, Zhou X (2022) Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol 40(9):1349\u0026ndash;1359\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRu B, Huang J, Zhang Y, Aldape K, Jiang P (2023) Estimation of cell lineages in tumors from spatial transcriptomics data. Nat Commun 14(1):568\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang MG, Chen L, Zhang XF (2024) Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA. Nucleic Acids Res 52(20):12173\u0026ndash;12190\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong Y, Haeri M, Zhang X, Li Y, Liu A, Wu D et al (2025) Stereo-seq of the prefrontal cortex in aging and Alzheimer's disease. Nat Commun 16(1):482\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen A, Liao S, Cheng M, Ma K, Wu L, Lai Y et al (2022) Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185(10):1777\u0026ndash;92e21\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen A, Sun Y, Lei Y, Li C, Liao S, Meng J et al (2023) Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 186(17):3726\u0026ndash;43e24\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKremerskothen J, Kindler S, Finger I, Veltel S, Barnekow A (2006) Postsynaptic recruitment of Dendrin depends on both dendritic mRNA transport and synaptic anchoring. J Neurochem 96(6):1659\u0026ndash;1666\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu H, Hardy J, Duff KE (2018) Selective vulnerability in neurodegenerative diseases. Nat Neurosci 21(10):1350\u0026ndash;1358\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStein-O'Brien GL, Palaganas R, Meyer EM, Redding-Ochoa J, Pletnikova O, Guo H et al (2023) Transcriptional Signatures of Hippocampal Tau Pathology in Primary Age-Related Tauopathy and Alzheimer's Disease. medRxiv\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang L, Jiang Y, Zhu J, Liang H, He X, Qian J et al (2020) Quantitative Assessment of Hippocampal Tau Pathology in AD and PART. J Mol Neurosci 70(11):1808\u0026ndash;1811\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIida MA, Farrell K, Walker JM, Richardson TE, Marx GA, Bryce CH et al (2021) Predictors of cognitive impairment in primary age-related tauopathy: an autopsy study. Acta Neuropathol Commun 9(1):134\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang J, Kong L, Zou L, Liu Y (2024) The role of synaptic protein NSF in the development and progression of neurological diseases. Front Neurosci 18:1395294\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrozzi F, Arcuri C, Giambanco I, Donato R (2009) S100B Protein Regulates Astrocyte Shape and Migration via Interaction with Src Kinase: IMPLICATIONS FOR ASTROCYTE DEVELOPMENT, ACTIVATION, AND TUMOR GROWTH. J Biol Chem 284(13):8797\u0026ndash;8811\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlein RS, Das B, Fricker LD (1992) Secretion of carboxypeptidase E from cultured astrocytes and from AtT-20 cells, a neuroendocrine cell line: implications for neuropeptide biosynthesis. J Neurochem 58(6):2011\u0026ndash;2018\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu M, Xu L, Wang Y, Zhou N, Zhen F, Zhang Y et al (2018) S100A8/A9 induces microglia activation and promotes the apoptosis of oligodendrocyte precursor cells by activating the NF-kappaB signaling pathway. Brain Res Bull 143:234\u0026ndash;245\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarber K, Cheung G, Mitchell D, Wallis R, Weihe E, Schwaeble W et al (2009) C1q, the recognition subcomponent of the classical pathway of complement, drives microglial activation. J Neurosci Res 87(3):644\u0026ndash;652\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaynes SE, Hollopeter G, Yang G, Kurpius D, Dailey ME, Gan WB et al (2006) The P2Y12 receptor regulates microglial activation by extracellular nucleotides. Nat Neurosci 9(12):1512\u0026ndash;1519\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMrak RE (2012) Microglia in Alzheimer brain: a neuropathological perspective. Int J Alzheimers Dis 2012:165021\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePumo A, Legeay S (2024) The dichotomous activities of microglia: A potential driver for phenotypic heterogeneity in Alzheimer's disease. Brain Res 1832:148817\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYue Q, Hoi MPM (2023) Emerging roles of astrocytes in blood-brain barrier disruption upon amyloid-beta insults in Alzheimer's disease. Neural Regen Res 18(9):1890\u0026ndash;1902\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFukutani Y, Cairns NJ, Shiozawa M, Sasaki K, Sudo S, Isaki K et al (2000) Neuronal loss and neurofibrillary degeneration in the hippocampal cortex in late-onset sporadic Alzheimer's disease. Psychiatry Clin Neurosci 54(5):523\u0026ndash;529\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWest MJ, Kawas CH, Stewart WF, Rudow GL, Troncoso JC (2004) Hippocampal neurons in pre-clinical Alzheimer's disease. Neurobiol Aging 25(9):1205\u0026ndash;1212\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnacker C, Hen R (2017) Adult hippocampal neurogenesis and cognitive flexibility - linking memory and mood. Nat Rev Neurosci 18(6):335\u0026ndash;346\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRogers J, Strohmeyer R, Kovelowski CJ, Li R (2002) Microglia and inflammatory mechanisms in the clearance of amyloid beta peptide. Glia 40(2):260\u0026ndash;269\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, Zhao B (2013) Oxidative stress and the pathogenesis of Alzheimer's disease. Oxid Med Cell Longev 2013:316523\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi JE, Lee JJ, Kang W, Kim HJ, Cho JH, Han PL et al (2018) Proteomic Analysis of Hippocampus in a Mouse Model of Depression Reveals Neuroprotective Function of Ubiquitin C-terminal Hydrolase L1 (UCH-L1) via Stress-induced Cysteine Oxidative Modifications. Mol Cell Proteom 17(9):1803\u0026ndash;1823\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKachiwala SJ, Harris SE, Wright AF, Hayward C, Starr JM, Whalley LJ et al (2005) Genetic influences on oxidative stress and their association with normal cognitive ageing. Neurosci Lett 386(2):116\u0026ndash;120\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoels M, Velzing E, Nair S, Verkuyl JM, Karst H (2003) Acute stress increases calcium current amplitude in rat hippocampus: temporal changes in physiology and gene expression. Eur J Neurosci 18(5):1315\u0026ndash;1324\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhapola R, Beura SK, Sharma P, Singh SK, HariKrishnaReddy D (2024) Oxidative stress in Alzheimer's disease: current knowledge of signaling pathways and therapeutics. Mol Biol Rep 51(1):48\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Brien RJ, Wong PC (2011) Amyloid precursor protein processing and Alzheimer's disease. Annu Rev Neurosci 34:185\u0026ndash;204\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBreckler M, Berthouze M, Laurent AC, Crozatier B, Morel E (2011) Lezoualc'h F. Rap-linked cAMP signaling Epac proteins: compartmentation, functioning and disease implications. Cell Signal 23(8):1257\u0026ndash;1266\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJicha GA, Weaver C, Lane E, Vianna C, Kress Y, Rockwood J et al (1999) cAMP-dependent protein kinase phosphorylations on tau in Alzheimer's disease. J Neurosci 19(17):7486\u0026ndash;7494\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLauro C, Cipriani R, Catalano M, Trettel F, Chece G, Brusadin V et al (2010) Adenosine A1 receptors and microglial cells mediate CX3CL1-induced protection of hippocampal neurons against Glu-induced death. Neuropsychopharmacology 35(7):1550\u0026ndash;1559\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee CY, Landreth GE (2010) The role of microglia in amyloid clearance from the AD brain. J Neural Transm (Vienna) 117(8):949\u0026ndash;960\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Olst L, Simonton B, Edwards AJ, Forsyth AV, Boles J, Jamshidi P et al (2025) Microglial mechanisms drive amyloid-beta clearance in immunized patients with Alzheimer's disease. Nat Med\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu F, Grundke-Iqbal I, Iqbal K, Gong CX (2005) Contributions of protein phosphatases PP1, PP2A, PP2B and PP5 to the regulation of tau phosphorylation. Eur J Neurosci 22(8):1942\u0026ndash;1950\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoda K, Sasaki K, Fujimi K, Wakisaka Y, Tanizaki Y, Wakugawa Y et al (2006) Quantitative analysis of neurofibrillary pathology in a general population to reappraise neuropathological criteria for senile dementia of the neurofibrillary tangle type (tangle-only dementia): the Hisayama Study. Neuropathology 26(6):508\u0026ndash;518\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKimura T, Ishiguro K, Hisanaga S (2014) Physiological and pathological phosphorylation of tau by Cdk5. Front Mol Neurosci 7:65\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaccioni RB, Otth C, Concha II, Munoz JP (2001) The protein kinase Cdk5. Structural aspects, roles in neurogenesis and involvement in Alzheimer's pathology. Eur J Biochem 268(6):1518\u0026ndash;1527\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiovannoni F, Quintana FJ (2020) The Role of Astrocytes in CNS Inflammation. Trends Immunol 41(9):805\u0026ndash;819\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGriffin JWD, Liu Y, Bradshaw PC, Wang K (2018) In Silico Preliminary Association of Ammonia Metabolism Genes GLS, CPS1, and GLUL with Risk of Alzheimer's Disease, Major Depressive Disorder, and Type 2 Diabetes. J Mol Neurosci 64(3):385\u0026ndash;396\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa SL, Tang NL, Lam LC (2016) Association of gene expression and methylation of UQCRC1 to the predisposition of Alzheimer's disease in a Chinese population. J Psychiatr Res 76:143\u0026ndash;147\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStrunz M, Jarrell JT, Cohen DS, Rosin ER, Vanderburg CR, Huang X (2019) Modulation of SPARC/Hevin Proteins in Alzheimer's Disease Brain Injury. J Alzheimers Dis 68(2):695\u0026ndash;710\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkada T, Suzuki H, Travis ZD, Altay O, Tang J, Zhang JH (2021) SPARC Aggravates Blood-Brain Barrier Disruption via Integrin alphaVbeta3/MAPKs/MMP-9 Signaling Pathway after Subarachnoid Hemorrhage. Oxid Med Cell Longev 2021:9739977\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodriguez-Giraldo M, Gonzalez-Reyes RE, Ramirez-Guerrero S, Bonilla-Trilleras CE, Guardo-Maya S, Nava-Mesa MO (2022) Astrocytes as a Therapeutic Target in Alzheimer's Disease-Comprehensive Review and Recent Developments. Int J Mol Sci. ;23(21)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchumacher L, Slimani R, Zizmare L, Ehlers J, Kleine Borgmann F, Fitzgerald JC et al (2023) TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes. Biomedicines. ;11(1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhattarai P, Yilmaz E, Cakir EO, Korkmaz HY, Lee AJ, Ma Y et al (2025) APOE- epsilon4-induced Fibronectin at the blood-brain barrier is a conserved pathological mediator of disrupted astrocyte-endothelia interaction in Alzheimer's disease. bioRxiv\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadurariu M, Ciobica A, Mavroudis I, Fotiou D, Baloyannis S (2012) Hippocampal neuronal loss in the CA1 and CA3 areas of Alzheimer's disease patients. Psychiatr Danub 24(2):152\u0026ndash;158\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHrybouski S, MacGillivray M, Huang Y, Madan CR, Carter R, Seres P et al (2019) Involvement of hippocampal subfields and anterior-posterior subregions in encoding and retrieval of item, spatial, and associative memories: Longitudinal versus transverse axis. NeuroImage 191:568\u0026ndash;586\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeok JW, Cheong C (2020) Functional dissociation of hippocampal subregions corresponding to memory types and stages. J Physiol Anthropol 39(1):15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeller CJ, Honey CJ, Entz L, Bickel S, Groppe DM, Toth E et al (2014) Corticocortical evoked potentials reveal projectors and integrators in human brain networks. J Neurosci 34(27):9152\u0026ndash;9163\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTschumi CW, Beckstead MJ (2019) Diverse actions of the modulatory peptide neurotensin on central synaptic transmission. Eur J Neurosci 49(6):784\u0026ndash;793\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Q, Hazan A, Grinman E, Angulo JA (2017) Pharmacological activation of the neurotensin receptor 1 abrogates the methamphetamine-induced striatal apoptosis in the mouse brain. Brain Res 1659:148\u0026ndash;155\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKempermann G, Song H, Gage FH (2015) Neurogenesis in the Adult Hippocampus. Cold Spring Harb Perspect Biol 7(9):a018812\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThakker-Varia S (2012) Antidepressants activate survival-promoting pathways in hippocampal neurons despite nutrient deprivation stress (commentary on Yang. Eur J Neurosci 36(5):2571\u0026ndash;2572\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeng HK, Teng KK, Lee R, Wright S, Tevar S, Almeida RD et al (2005) ProBDNF induces neuronal apoptosis via activation of a receptor complex of p75NTR and sortilin. J Neurosci 25(22):5455\u0026ndash;5463\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Xu L, Jiang W, Qiu X, Xu H, Zhu F et al (2023) Pleiotrophin ameliorates age-induced adult hippocampal neurogenesis decline and cognitive dysfunction. Cell Rep 42(9):113022\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsai H, Morita S, Miyata S (2011) Effect of pleiotrophin on glutamate-induced neurotoxicity in cultured hippocampal neurons. Cell Biochem Funct 29(8):660\u0026ndash;665\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNikolakopoulou AM, Montagne A, Kisler K, Dai Z, Wang Y, Huuskonen MT et al (2019) Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss. Nat Neurosci 22(7):1089\u0026ndash;1098\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushpam M, Talukdar A, Anilkumar S, Maurya SK, Issac TG, Diwakar L (2024) Recurrent endothelin-1 mediated vascular insult leads to cognitive impairment protected by trophic factor pleiotrophin. Exp Neurol 381:114938\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSweeney MD, Sagare AP, Zlokovic BV (2018) Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol 14(3):133\u0026ndash;150\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBell RD, Zlokovic BV (2009) Neurovascular mechanisms and blood-brain barrier disorder in Alzheimer's disease. Acta Neuropathol 118(1):103\u0026ndash;113\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee SY, Chung WS (2024) Astrocytic crosstalk with brain and immune cells in healthy and diseased conditions. Curr Opin Neurobiol 84:102840\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang W, Xia Q, Zheng F, Zhao X, Ge F, Xiao J et al (2023) Microglia-Mediated Neurovascular Unit Dysfunction in Alzheimer's Disease. J Alzheimers Dis 94(s1):S335\u0026ndash;S54\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNishimura I, Takazaki R, Kuwako K, Enokido Y, Yoshikawa K (2003) Upregulation and antiapoptotic role of endogenous Alzheimer amyloid precursor protein in dorsal root ganglion neurons. Exp Cell Res 286(2):241\u0026ndash;251\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuerra San Juan I, Brunner JW, Eggan K, Toonen RF, Verhage M (2025) KIF5A regulates axonal repair and time-dependent axonal transport of SFPQ granules and mitochondria in human motor neurons. Neurobiol Dis 204:106759\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOwlett LD, Karaahmet B, Le L, Belcher EK, Dionisio-Santos D, Olschowka JA et al (2022) Gas6 induces inflammation and reduces plaque burden but worsens behavior in a sex-dependent manner in the APP/PS1 model of Alzheimer's disease. J Neuroinflammation 19(1):38\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanchez-Mico MV, Jimenez S, Gomez-Arboledas A, Munoz-Castro C, Romero-Molina C, Navarro V et al (2021) Amyloid-beta impairs the phagocytosis of dystrophic synapses by astrocytes in Alzheimer's disease. Glia 69(4):997\u0026ndash;1011\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBethel-Brown C, Yao H, Hu G, Buch S (2012) Platelet-derived growth factor (PDGF)-BB-mediated induction of monocyte chemoattractant protein 1 in human astrocytes: implications for HIV-associated neuroinflammation. J Neuroinflammation 9:262\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNowak J, Archange C, Tardivel-Lacombe J, Pontarotti P, Pebusque MJ, Vaccaro MI et al (2009) The TP53INP2 protein is required for autophagy in mammalian cells. Mol Biol Cell 20(3):870\u0026ndash;881\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRajpurohit CS, Kumar V, Cheffer A, Oliveira D, Ulrich H, Okamoto OK et al (2020) Mechanistic Insights of Astrocyte-Mediated Hyperactive Autophagy and Loss of Motor Neuron Function in SOD1(L39R) Linked Amyotrophic Lateral Sclerosis. Mol Neurobiol 57(10):4117\u0026ndash;4133\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIvanova S, Polajnar M, Narbona-Perez AJ, Hernandez-Alvarez MI, Frager P, Slobodnyuk K et al (2019) Regulation of death receptor signaling by the autophagy protein TP53INP2. EMBO J. ;38(10)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiyoshi E, Morabito S, Henningfield CM, Das S, Rahimzadeh N, Shabestari SK et al (2024) Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's disease. Nat Genet 56(12):2704\u0026ndash;2717\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuyckaerts C, Braak H, Brion JP, Buee L, Del Tredici K, Goedert M et al (2015) PART is part of Alzheimer disease. Acta Neuropathol 129(5):749\u0026ndash;756\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFruhwurth S, Zetterberg H, Paludan SR (2024) Microglia and amyloid plaque formation in Alzheimer's disease - Evidence, possible mechanisms, and future challenges. J Neuroimmunol 390:578342\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan XX, Ma C, Bao AM, Wang XM, Gai WP (2015) Brain banking as a cornerstone of neuroscience in China. Lancet Neurol 14(2):136\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu W, Zhang H, Bao A, Zhu K, Huang Y, Yan X et al (2019) Standardized Operational Protocol for Human Brain Banking in China. Neurosci Bull 35(2):270\u0026ndash;276\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Q-L, Wang Y, Coulibaly S, Sun Z-P, Cai X-L, Tu T et al (2024) Sortilin C-terminal fragment deposition depicts tangle-related nonamyloid neuritic plaque growth in Alzheimer\u0026rsquo;s disease. bioRxiv. :2024.11.11.622955\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTu T, Cai XL, Sun ZP, Yang C, Jiang J, Wan L et al (2025) Mossy fiber expression of alphaSMA in human hippocampus and its relevance to brain evolution and neuronal development. Sci Rep 15(1):15834\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ et al (2019) Single-cell transcriptomic analysis of Alzheimer's disease. Nature 570(7761):332\u0026ndash;337\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH et al (2021) Inference and analysis of cell-cell communication using CellChat. Nat Commun 12(1):1088\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7303622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7303622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile both Primary Age-Related Tauopathy (PART) and Alzheimer\u0026rsquo;s Disease (AD) involve the accumulation of hyperphosphorylated tau (pTau)-positive neurofibrillary tangles (NFTs) in the hippocampus, PART is distinguished by the absence of β-amyloid (Aβ) deposition and is generally associated with milder cognitive impairment than AD. To delineate cellular and molecular mechanisms that are common or uniquely linked to disease progression in PART and AD, we constructed a transcriptome-wide, high-resolution atlas of the human hippocampus using samples from six individuals spanning the aged control (AC), PART, and AD groups. Our results supported that PART represent a precursor stage of AD, as evidenced by the altered transcriptional profiles of excitatory neurons (Exc) in the PART group, which exhibited a markedly increased capacity to promote Aβ production compared to both AC and AD groups. While the microglia (Mic) were reactivated in the PART group, this response was reduced in AD samples despite the presence of Aβ deposition, and appeared to further induce NFTs formation as a loop consequently driving the progression from PART to AD. Furthermore, subregion interactions in the signalling pathways related to neuronal survival and the maintenance of blood-brain-barrier (BBB) integrity were decreasing in the PART and disrupted in the AD groups, compared to the AC group. Additionally, we found a P53 signalling-related gene, \u003cem\u003eTP53INP2\u003c/em\u003e, was uniquely upregulated in astrocytes near large vessels in AD. This suggests a potential mechanism of vessel-induced neuronal apoptosis in AD, a feature absent in AC and PART. In summary, our study offers new insights into the relationship between PART and AD, along with the molecular mechanisms driving the transition from PART to AD. Furthermore, we identified key molecular pathways associated with BBB disruption and vascular-associated neuronal degradation in AD which were absent in PART. These findings deepen our understanding of AD pathogenesis and may inform the development of targeted therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Novel Pathological Mechanisms Revealed by Spatial Transcriptomic Analysis of Hippocampus in Aged Control, Primary Age-Related Tauopathy, and Alzheimer’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 08:58:29","doi":"10.21203/rs.3.rs-7303622/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fe4a477-b166-4d76-bd43-68fac28b921e","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53522823,"name":"Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease"},{"id":53522824,"name":"Biological sciences/Computational biology and bioinformatics/Data mining"}],"tags":[],"updatedAt":"2025-10-17T10:45:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 08:58:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7303622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7303622","identity":"rs-7303622","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}