A single-cell transcriptome atlas of cell diversity in human prefrontal cortex across the postnatal lifespan

A single-cell transcriptome atlas of cell diversity in human prefrontal cortex across the postnatal lifespan | 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 A single-cell transcriptome atlas of cell diversity in human prefrontal cortex across the postnatal lifespan Rui-Ze Niu, Meng-Yuan Zhang, Zhi-Lan Yang, Cai-Hua Yang, Ying-Ying Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7406880/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Brain aging is a major risk factor for numerous diseases, including cerebrovascular diseases and neurodegenerative disorders, posing a significant threat to human health. Currently, the continuous changes of different cell types in human brain tissue throughout an individual's life course have not been fully elucidated. Here we describe the continuous changes in the transcriptomes of different cell types and their subpopulations in the prefrontal cortex (PFC) across the postnatal lifespan. We integrated single-nucleus RNA sequencing (snRNA-seq) data of the PFC from 158 healthy individuals aged 19–101 years across 15 datasets and constructed a PFC aging atlas of 587,878 nuclei. We found that the ages of 30s and 50s are the two most significant periods of brain transcriptome changes in adulthood. Synaptic development, integration, and transmission are generally downregulated during aging. Different subpopulations of various cell types undergo age-related transitions and participate in the brain aging process at different time points. The increase in apoptotic signals and the production of inflammatory factors in astrocytes of elderly individuals accelerate brain aging. Microglia are mainly in a homeostatic state in the early stage, which is beneficial to the normal function of the CNS, and mainly in an activated state in the later stage, showing an increase in the release of inflammatory factors and chemotaxis. The activation of microglia may be related to the abnormal development of synapses and dendritic spines, as well as the abnormal myelination. Abnormally activated microglia are involved in the occurrence and development of Alzheimer's disease (AD) and multiple sclerosis (MS) in elderly individuals. The function of trans-blood-brain-barrier transport in endothelial cells is significantly downregulated with age. Based on aging-related plasma proteomics data, FUT9 was identified as a plasma biomarker related to brain aging. Our study clarifies the temporal differences and potential connections in the aging of different cell types in the PFC, providing a reference for the selection of specific cell types and time windows for future anti-CNS aging interventions. Health sciences/Biomarkers Health sciences/Neurology Biological sciences/Neuroscience prefrontal cortex Aging Single-nucleus RNA sequencing Synapsis Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Aging is a natural biological process that causes many changes in the human body and affects every cell in the organism. It is regarded as the greatest risk factor for a series of diseases [ 1 – 3 ]. Brain aging refers to a series of changes in the brain tissue morphology, neurochemistry and other aspects as people age, leading to degenerative changes in structure and functional decline. The main manifestations are the decline of learning ability, memory ability, attention, decision-making speed, sensory perception and motor coordination. This not only has a huge impact on the daily life of the individual, but also adds a heavy burden to the individual's family and even the entire society [ 4 , 5 ]. In addition, brain aging is a major risk factor for numerous diseases, including cerebrovascular diseases such as ischemic stroke, neurodegenerative diseases like Alzheimer's disease (AD) and Parkinson's disease (PD), posing a significant threat to human health [ 4 , 6 ]. According to the WHO, the number and proportion of people aged 60 and above are increasing. This figure is expected to rise to 1.4 billion by 2030 and to 2.1 billion by 2050. Therefore, in-depth research on the cellular and molecular mechanisms of brain aging, exploration of effective strategies to delay brain aging, and improvement of the quality of life for the elderly population are currently urgent and important global scientific issues that need to be addressed. Neurological aging is a complex and persistent process throughout an individual's life, involving multi-regional and multi-gene alterations, which requires precise spatiotemporal transcriptional regulation. In recent years, the discovery of single-cell sequencing technology has made it possible to clarify the epigenomic, transcriptomic and proteomic changes of various cell types and their subtypes during the aging process. Currently, scRNA-seq is widely used in research on the nervous system, such as in the fields of development [ 7 ], AD [ 8 , 9 ], schizophrenia [ 10 ], depression [ 11 ], autism, [ 12 ] and glioma [ 13 ]. In the field of aging-related neuroscience, a single-cell transcriptome atlas study of fruit fly brain aging has revealed that not all brain cells age in the same way [ 7 ]. Researchers utilized scRNA-seq to assess the cell type-specific manifestations of age-related characteristics in mice, such as senescence, genomic instability, and changes in the immune system, providing a reference for understanding the cellular biological changes that occur in mammals throughout their life cycle [ 14 , 15 ]. snRNA-seq of human and macaque hippocampal tissues revealed unique molecularly defined subpopulation maps of astrocytes, microglia and oligodendrocytes throughout the postnatal life cycle of the human hippocampus, and uncovered their associations with specific physiological functions, age-dependent changes in abundance and disease relevance [ 16 ]. Currently, scRNA-seq is widely used to study aging in species such as fruit flies, mice, macaques, chimpanzees and humans [ 7 , 14 , 17 , 18 ]. More than a decade ago, a study of RNA-seq from the prefrontal cortex (PFC) of 268 subjects indicated that the transcriptome of brain tissue undergoes significant changes at the age of 50. The DEGs significantly associated with age are mainly related to synapses, axons, ATP synthesis and cell cycles [ 19 ]. However, current research has not clarified the changes in different cell types in the human brain's PFC throughout the life course after adulthood. In this study, we integrated snRNA-seq data from the PFC of 158 healthy individuals aged 19–101 years from 15 datasets. With a 10-year interval, we divided all samples into 8 groups and analyzed the transcriptomic alterations of eight major cell types and their subtypes in the PFC during the individual life course. Different subtypes of different cell types undergo transformation with age and participate in the brain aging process at different time points. Additionally, by integrating peripheral plasma proteomics data related to aging, we screened for potential markers of brain aging. This study provides specific time windows and targets for the diagnosis and treatment of CNS aging and aging-related diseases in the future. Materials and methods Data download and collection Through a literature review, we collected and organized snRNA-seq data from the human PFC including 15 datasets (Supplementary Table 1) . The database includes Gene Expression Omnibus (GEO), synapse, ArrayExpress, European Nucleotide Archive (ENA), European Genome-phenome Archive (EGA), and National Genomics Data Center (NGDC). Details of the samples, including the database, sample ID, age, gender, and diagnosis, can be found in Supplementary Table 1. Each dataset included normal individuals who underwent strict clinical assessments to ensure they had no active neuropsychiatric disorders. Pre-processing and quality control of snRNA-seq data All raw sequencing data were processed using Cell Ranger 7.0.1 (10x Genomics) to obtain expression matrices for downstream analysis. To ensure dataset quality, we retained only cells with more than 200 detected genes and less than 5% mitochondrial gene content. Doublets were detected using DoubletFinder (v.2.0.3). After sample integration and clustering, clusters lacking specific marker genes, with relatively low gene content, and high mitochondrial ratios were discarded. De-batch integration of multiple datasets We integrated the scRNA-seq data of all datasets individuals to remove batch effects. Briefly, unique molecular identifiers (UMIs) from each valid cell barcode were retained for all downstream analyses and processed using the Seurat R package (v.4.2.2) ( https://satijalab.org/seurat/ ) [ 20 ]. We first used reciprocal PCA (RPCA) instead of CCA to identify an effective space for finding anchors. When using RPCA to determine anchors between any two datasets, each dataset was projected into the PCA space of the other, and anchors were constrained by requiring mutual nearest neighbors. We randomly designated two samples each from adult males, adult females, elderly males, elderly females as the "reference" dataset for integration analysis, while the remaining datasets were designated as the "query" datasets. We also used the FindIntegrationAnchors and IntegrateData functions to remove batch effects, followed by clustering analysis using the FindNeighbors and FindClusters functions. Data visualization was performed using nonlinear dimensionality reduction methods such as UMAP and t-SNE. Cell type identification Cell type annotation was performed using the method provided by the SingleR package, which identifies cell types based on reference datasets. This method annotates the cells to be identified as the cell type that has the highest correlation with the single-cell reference expression profile dataset. The results of the dataset identification in this report are provided for reference, and further descriptions and validations of the cell populations will be made based on relevant genes from existing literature. The detailed markers for the main cell types are as follows: Neuron: RBFOX1 , SNAP25 , and SYT1 ; ExN: SLC17A and CAMK2A ; InN: GAD1 and GAD2 ; MOL: PLP1 and MBP; OPC: OLIG1 and OLIG2 ; Astro: GFAP and AQP4 ; Micro: CSF1R and CD74; Endo: FLT1 and CLDN5 ; Peri: DCN and COL1A2 . Cell extraction and subpopulation analysis The expression matrix of each cell type was extracted and integrated for subpopulation analysis. First, we used the SCTransform function to standardize the data, followed by PCA dimensionality reduction using the RunPCA function. We then performed batch integration using the RunHarmony function [ 21 ] with the specific parameters: group.by.vars = “SampleID”, assay.use = “SCT”, max.iter.harmony = 30. Next, we conducted clustering analysis using the FindNeighbors and FindClusters functions, and visualized the data using UMAP. Identification of differentially expressed genes (DEGs) across clusters FindMarkers function implemented in Seurat v4 [ 22 ] was used to identify DEGs across clusters with the options ‘‘logfc.thresh old = 0.25, min.pct = 0.1”. P -value was corrected using the Bonferroni method, and 0.05 was set as a threshold to define significance. Gene ontology (GO) term enrichment analysis The enrichGO function of clusterProfiler R package [ 23 ] was used for enrichment analysis, and the Benjamini-and-Hochberg (BH) method was employed for multiple test correction. A GO term with an adjusted P -value lower than 0.05 was considered as significantly enriched. Gene Set Enrichment Analysis (GSEA) GSEA was applied to identify a priori-defined gene sets that show statistically significant differences between two given clusters. We used the expression file as input, and implied gene sets of KEGG pathways and Gene Ontology, which were collected in Molecular Signatures Database (MSigDB) [ 24 , 25 ]. Construction of cellular communication network Intercellular communication between different immune cells was analyzed using CellChat (v.0.0.1) R package with default parameters [ 26 ]. Intercellular communications analysis was performed based on cell types. Cell–cell communication network was visualized using the netVisual_aggregate function, centrality score was computed and visualized using the netAnalysis_signalingRole_network function, relative contribution of each ligand-receptor pair was visualized using the netAnalysis_contribution function. Identification of differentially expressed genes (DEGs) To identify genes that are differentially expressed in aging or disease, P -values were calculated and FDR-corrected using the MAST method [ 27 ]. All nuclei from different sample groups corresponding to specific cell types were included. MAST was utilized to perform zero-inflated regression analysis by fitting a linear mixed model. To account for confounding factors such as age, sex, and the proportions of ribosomal and mitochondrial transcripts, the following model for aging and disease was fitted using MAST: zlm(~ condition + nCount_RNA + percent.mt + Sex, sca, method = glmer, ebayes = T) zlm(~ condition + nCount_RNA + percent.mt + Sex + Age, sca, method = glmer, ebayes = T) To identify DEGs due to age or disease effects, a likelihood ratio test was performed by comparing models with and without the diagnostic factor. Genes exhibiting at least a 25% increase or decrease in expression between groups, along with a false discovery rate (FDR)-corrected P-value of less than 0.05, were selected as differentially expressed. Virtual knockout of the gene of interest To analyze the function of module gene knockout in specific cell types of particular diseases, we extracted disease- and cell-specific snRNA-seq data and used the gene × cell expression matrix as input for scTenifoldKnk [ 28 ]. The genes perturbed by the virtual knockout with FDR-corrected P < 0.05 were selected as differentially expressed. Interaction enrichment analysis was conducted based on the STRING protein-protein interaction database ( https://version-12-0.string-db.org/ ). We selected biological processes (GO) and human phenotypes (Monarch) as our items of interest. Acquisition of immunohistochemical data To validate the results, we searched for the expression of proteins of interest in the Human Protein Atlas database ( https://www.proteinatlas.org/ ). All antibodies have undergone rigorous validation for specificity, reproducibility, and functionality, and have been tested in various experimental applications. Animal care and grouping APP/PS1 transgenic mice (AD mice) aged 8 months and wild-type mice (WT mice) of C57BL/6 strain aged 8 and 18 months were provided by the Center of Experimental Animals of Kunming Medical University. Animals were kept under standard conditions in the SPF laboratory. All experimental procedures, including animal care and testing conformed to the Animal Care and Use Committee of Kunming Medical University (kmm’058). All studies were conducted in accordance with the United States Public Health Service's Policy on Humane Care and Use of Laboratory Animals. All mice were anesthetized and killed, and were immediately perfused with precooled 0.9% normal saline until the liver turned white. Their brains were removed, and the hippocampus tissues were collected for the immunostaining experiment. Immunofluorescence staining Immunofluorescence staining was performed as described previously. The pre-prepared hippocampus sections were washed with 0.01 mol/L PBS, and then were fixed with 4% paraformaldehyde for 20 min. Tissue sections were treated with 0.3% hydrogen peroxide in 20% methanol for 30 min to block endogenous peroxidase. Sections were incubated with 5% goat serum and 0.3% Triton X-100 in 0.01 mol/L PBS for 1 hour to block nonspecific immunostaining followed by immunostaining with primary antibodies ( Table 1 ) in 2% goat serum overnight at 4°C. After washing three times with 0.01 mol/L PBS, secondary antibodies (1:200, DyLight 488, Goat Anti-Rabbit, DyLight 594, Goat Anti-Rabbit, Abbkine) diluted with 2% goat serum were added, and the reaction was carried out at 37°C for 1 h. The samples were sealed following PBS washing and DAPI staining. Images were captured using an upright two-photon confocal microscope (Nikon, ARMP+, Japan), and each image was randomly acquired at 200× magnification. Image-Pro Plus software (version 6.0; Media Cybernetics, Silver Spring, MD, USA) was used to calculate the mean OD of each positive staining group. Table 1 Primary antibodies information used in this study Name Manufacturers Cat Species Dilution ratio Anti-SOX10 Abcam ab227680 Rabbit 1:400 Anti-IBA1 Wako 016-26721 Mouse 1:250 Anti-IL-1 beta Abcam ab254360 Rabbit 1:1000 Anti-GAPDH Abcam ab8245 Mouse 1:1000 ELISA Experiment To further verify the results of snRNA-seq, we collected peripheral blood from individuals of different ages and conducted an ELISA experiment. We selected 20 healthy adult volunteers aged between 20 and 27 years as the adult group, and 20 elderly volunteers aged between 64 and 84 years as the elderly group. All volunteers were informed of the purpose of the study and signed the informed consent form. The study was conducted in accordance with the Institutional Research Ethics guidelines and ethical principle involving human participation (Helsinki Declaration) and approved by the Medical Ethics Committee of Kunming Medical University (approval number: KMMU2022MEC092). Peripheral blood from all participants was collected and plasma was extracted. The protein level of FUT9 in plasma samples was detected using the FUT9 ELISA kit (Sigma, XG-E990756). The operation and detection were strictly carried out according to the kit instructions. The absorbance (OD) of each sample at a wavelength of 450 nm was measured using a spectrophotometer (Thermo Fisher, SPECTRONIC 200) for 15 minutes. Finally, the linear regression equation of the standard curve was calculated using the concentration and OD of the standard samples. Then, the concentration of FUT9 protein in plasma was calculated. Cell culture The human microglial cell lines HMC3 in our laboratory were purchased from Qingqi Biotechnology Development Co., Ltd. (Shanghai, China). The cell lines were cultured in high glucose medium containing 10% inactivated fetal bovine serum in an incubator with 5% CO 2 at 37 ℃. When the cells reached 80% − 90% confluence and grew well, they were digested with trypsin and sub-cultured. Cell senescence model construction The cells in logarithmic growth stage were inoculated in 96 well plate (200 µL/well) at a density of 1.6 × 10 5 /ml. There were normal control group and aging groups, with 6 replicated wells in each group. The cells in the normal control group were cultured in complete medium composed of high glucose medium, 10% fetal bovine serum, 1% penicillin-streptomycin double antibodies. The cells in the aging group were cultured in complete culture medium with D-galactose at concentrations of 5, 10, 20, 30, 40, 50, 80, and 100 mg/ml in an incubator with 5% CO 2 at 37 ℃ and for 24 hours, and then the complete culture medium was changed for another 24-hour incubation. Subsequently, 10 µL CCK-8 solution per 100 µL medium was added into each well, shaked and mixed up. After continuous culture for 0.5 hour, the absorbance value of each well at the wavelength of 450 nm was detected using a microplate reader. The test was repeated 3 times. The optimal drug concentrations were determined according to the CCK8 results, with HMC3 at 40 mg/ml. For western blotting experiment, the cells were placed in six-well plates with glucose and serum-free DMEM and subsequently placed in 37 ℃ hypoxia chambers. After the successful construction of the senescence model, protein was extracted for western blotting experiments. Aβ Aggregation Human synthetic β -amyloid 1–40 peptide was dissolved in dimethyl sulfoxide (DMSO) at a concentration of 10 mg/ml and immediately stored in aliquots at -20 ℃. Then, 25 µl of this peptide solution (10 mg/ml) was diluted to a final concentration of 80 µmol in 725 µl of PBS (Gibco, Grand Island, NY) and continuously stirred at 37 ℃ (200 rpm). The formation of Aβ aggregates was monitored using a conventional spectrophotometer (Shimadzu UV-150-02; γ405 nm; Sao Paulo, Brazil). The solution in PBS or 1, 5, 10 or 50 mmol ethanol was shaken at 600 rpm, and readings were taken every 5 min. Increase in turbidity was monitored and stopped after 200 min. Western blotting (WB) experiment Proteins were extracted using RIPA buffer in an ice bath at 4 ℃. Lysates were collected by centrifugation at 15,000 rpm for 15 min. The protein concentration was quantified using BCL protein assay reagent. An equal amount of the sample was separated in running buffer using a sodium dodecyl sulfate-polyacrylamide gradient gel to isolate the proteins. Subsequent transfers were performed, using 0.22 µm polyvinylidene difluoride microporous (PVDF) membranes. The membranes were blocked with 5% skim milk powder for 1 hour at room temperature and then incubated overnight at 4 ℃ with primary antibodies (Table 1 ). The blots were then washed four times with TBST for 10 min each and incubated with secondary antibodies (goat anti-rabbit, goat anti-mouse, rabbit anti-goat, 1:5000) for 1 hour. GAPDH was used as a loading control. Finally, densitometric analysis was performed using ImageJ software to calculate the relative protein content using the grayscale values of the target strips compared to the grayscale values of-actin. Statistical and reproducibility The statistical analyses were done in R (v.4.2.2) if not specified. Data visualization is implemented using R, Prism10, Cytoscape and Adobe Illustrator 2021. We state that no statistical method was used to predetermine sample size. All data are presented as the primary data or mean ± SEM. Statistical analysis for western blotting were performed using SPSS19.0 software. One-way analysis of variance (ANOVA) with Tukey's post hoc test was applied for comparison among multiple groups, and independent sample t test for comparison between two groups. GraphPad Prism software version 7.0 (GraphPad Software Inc.) was used for quantification histograms generation. P value < 0.05 was considered to be significant. Results Construction of the single-cell transcriptome atlas of PFC aging across the postnatal lifespan To elucidate the cellular composition and transcriptomic alterations of the PFC throughout the individual life course, we conducted an integrated analysis of snRNA-seq data from 158 healthy individuals across 15 datasets ( Fig. 1 A and Supplementary Table 1) . We divided all samples into 8 groups with a 10-year span. The ages between adjacent age groups were significantly different ( Fig. 1 A ) . After rigorous filtering and quality control, we obtained a total of 587,878 cell nuclei ( Fig. 1 B and Supplementary Table 2) . Through dimensionality reduction, UMAP visualization, and marker gene identification, we identified 9 cell populations with significant transcriptomic differences, including ExN ( SYT1, SNAP25 , and SLC17A7 ), InN ( SYT1, SNAP25 , and GAD1 ), Astro ( AQP4 ), Micro ( CSF1R ), OPC ( OLIG1 ), MOL ( MBP ), Endo ( FLT1 ), and Peri ( DCN ) ( Fig. 1 B and Supplementary Fig. 1A) . The snRNA-seq datasets analyzed here can be interrogated with an interactive web interface ( http://brainpfcatlas.cn/ ). Cellular composition and transcriptome differences during the aging process of PFC Firstly, we analyzed the changing trends of the quantities of different cell types with age. ExN and InN almost reached their peaks at 30s and then continuously declined, remaining almost unchanged at 60s. MOL was at its lowest at 30s, continuously rising until 70s when it remained stable. OPC did not show significant changes with age, demonstrating a weak and continuous downward trend. Astro rapidly declined at 20s and then decreased at a slow pace starting from 30s. Micro rapidly declined at 20s and then gradually increased starting from 30s. Endo rose until 40s when it reached its peak and then continuously declined until 70s when it remained stable. Peri showed no significant changes throughout the entire life course ( Fig. 2 A ) . Then, we calculated the DEGs with significant differences for each age group using 20s as the control ( Fig. 2 B and Supplementary Table 3) . Before 40s, ExN and InN had a large number of up-regulated DEGs ( Fig. 2 B ) , which might be related to individuals beginning to receive higher education and facing more social pressure. At 20s, ExN, InN, OPC and Astro had a large number of down-regulated DEGs, and the functions of these genes were related to dendritic spine development, regulation of neuronal projection development, trans-synaptic signaling regulation, and modulation of chemical synaptic transmission (see below). Around 50s, almost all cells began to undergo significant changes, and both up-regulated and down-regulated DEGs started to increase ( Fig. 2 B ) . These results are basically consistent with those previously reported by RNA-seq [ 19 ]. Next, we further analyzed the similarity of changes in different cells of the same age group. The heat map and cluster analysis reveal a significant overlap of DEGs across various age groups within distinct cell types ( Fig. 2 C ) . By analyzing the DEGs across various cell types, we can identify age-related DEGs that are conserved among these cells ( Fig. 2 D ) . We found that 9 DEGs changed in all cells of the corresponding age group (Fig. 2 E). Although ZBTB16 and MT-ND3 increase with age, their extremely high expression in the 90s suggests that they may be protective factors or longevity-related factors. The disease-free survival curves of these two genes also indicate that individuals with high levels have a longer survival period ( Fig. 2 E ). Studies have shown that aging is caused by an increase in transcriptional instability rather than a coordinated transcriptional program, and the increase in age-related transcriptional noise may lead to changes in cell fate and blurring of cell type identity [ 29 ]. To further understand transcriptional stability during aging, we calculated the transcriptional noise of different cell types. The transcriptional noise of all cell types significantly increased at 30s. The transcriptional noise of ExN continued to increase before 60s (Supplementary Fig. 1B) . Cell communication analysis indicated that the intensities of various cytokines, signaling molecules, and receptors differ across distinct age groups (Supplementary Fig. 2) . Notably, NRXN, NCAM, NRG, NEGR, and CNTN exhibited the most pronounced signals in PFC (Supplementary Fig. 2B) . Furthermore, we observed a continuous increase in CD45, CD22, COMPLEMENT, APP, and SPP1 levels with advancing age beyond the 20s (Supplementary Fig. 2B) . These molecules are associated with immune responses and inflammatory processes. Transcriptional differences of excitatory neurons in PFC during aging To investigate the changes in the transcriptome of excitatory neurons during aging, we analyzed the trends of the changes in the excitatory neuron-related DEGs throughout the life course ( Fig. 3 A ) as shown in Fig. 2 C. Based on the functional enrichment analysis, we found that a group of genes related to synaptic transmission and axon development (C3) in ExN were continuously downregulated with age ( Fig. 3 A ) . Additionally, we discovered a group of genes related to synaptic organization and dendritic spine development (C6) were highly expressed in the elderly ( Fig. 3 A ) ; since the upregulation of this group of genes occurred just before the high expression of microglia activation-related genes (see below), we hypothesized that ExN-C6 might be associated with the formation of immature or morphologically abnormal dendritic spines in the elderly. These abnormal dendritic spines could lead to excessive pruning by microglia and subsequently trigger inflammatory responses. Of course, there were also some gene clusters related to synaptic transmission that showed fluctuating expression with age, such as ExN-C5 and ExN-C2. Further subpopulation analysis of ExN identified 11 subgroups ( Fig. 3 B ) , which were classified into different subtypes based on the expression of depth-related markers in the cortex ( Fig. 3 C ) . The standardized cell proportions indicated that ExN_NRGN significantly decreased at 60s, L2_CUX2 significantly increased at 60s, and L3_5 neurons significantly increased at 70s ( Fig. 3 D and Supplementary Table 2) . Functional enrichment analysis revealed that all subgroups were associated with synaptic organization and synaptic transmission ( Fig. 3 E ) . Among them, ExN_NRGN exhibited specific functions related to energy conversion, axonal transport, and axo-dendritic transport ( Fig. 3 E ) . Monocle pseudo-temporal analysis found that deep-layer neurons and superficial-layer neurons were distributed at different time points ( Fig. 3 F-H ) . For instance, deep-layer neuron marker genes TLE4 and BCL11B were mainly expressed at the beginning of the differentiation trajectory, while the superficial-layer neuron marker gene CUX2 was mainly expressed at the other end of the differentiation trajectory ( Fig. 3 H ) . Further analysis of the time-dependent correlation genes in the branch where ExN_NRGN was located identified a cluster of genes (C1) related to axonal regeneration and synaptic integration ( Fig. 3 I ). Further analysis of 209 transcriptome data from the PFC revealed that the expression of NRGN was significantly downregulated in elderly individuals ( Fig. 3 J ) . Immunohistochemical staining (IHC) showed that compared with elderly individuals, NRGN -positive neurons in the cerebral cortex of middle-aged and young individuals were more deeply stained and had larger cell bodies ( Fig. 3 K ) . Transcriptional differences of inhibitory neurons in PFC during aging To investigate the transcriptional changes of inhibitory neurons during aging, we analyzed the trends of inhibitory neuron-related DEGs throughout the lifespan as shown in Fig. 2 C ( Fig. 4 A ) . Similar to ExN, based on functional enrichment analysis, we found that there was also a group of gene clusters (C2) related to synaptic transmission and axon development in InN that were continuously downregulated with age ( Fig. 4 A ) . There were 155 overlapping genes between ExN-C3 and InN-C2. Of course, there were also some gene clusters related to synaptic transmission that showed fluctuating trends with age, such as InN-C1 and InN-C6 ( Fig. 4 A ) . Further subpopulation analysis of InN identified nine subgroups ( Fig. 4 B ) , including the classic PV ( PVALB ), SST ( SST ) and VIP ( VIP ) interneurons ( Fig. 4 C ) . Consistent with previous studies, these inhibitory neurons originated from the medial ganglionic eminence ( MGE, LHX6 ) or the caudal ganglionic eminence ( CGE, NR2F2 ) [ 24 , 30 ]. The standardized cell proportions indicated that PVALB_SST_InN and SST_InN significantly decreased at 60s ( Fig. 4 D and Supplementary Table 2) . Functional enrichment analysis revealed that different subgroups were involved in distinct synaptic organization formation, postsynaptic organization and synaptic signal transmission processes ( Fig. 4 E ) . Among them, PVALB_SST_InN exhibited specific functions related to axonal transport, axo-dendritic transport and synaptic vesicle cycling ( Fig. 4 E ) . CytoTRACE analysis found that different subgroups showed different differentiation levels ( Fig. 4 F ) . Meanwhile, in the pseudo-time series of Monocle analysis, interneurons from MGE and CGE appeared on different branches ( Fig. 4 G ) . The time-dependent genes (C2) associated with PVALB_SST_InN were mainly related to axonal regeneration, dendritic development and synaptic signal transmission ( Fig. 4 H ) . Astrocyte transcriptomic diversity in postnatal human PFC Through the temporal analysis of DEGs in Astro, it was also found that a group of genes related to synaptic transmission and axon development (Astro-C2) were downregulated in the elderly group, but the occurrence time was slightly later than that in neurons and MOL (Supplementary Fig. 3A) . Additionally, we discovered a group of genes that were significantly upregulated at 70s (Astro-C3). Astro-C3 is associated with biological processes such as programmed cell death ( TNFRSF1A, ST6GAL1, IFI6, IL6ST ) and the production of inflammatory factors ( STAT3, IL6R, IL6ST, YAP1 ) (Supplementary Fig. 3B) . Further subpopulation analysis of Astro identified five subgroups (Fig. 7 A). Among them, Astro1 expresses neural stem cell-related genes such as SOX2, MGFE8 and WIF1 , showing partial progenitor cell potential ( Fig. 5 B ) , and is associated with learning and memory ( Fig. 5 C ) . Besides expressing SOX2, Astro2 also expresses genes related to promoting synaptic formation, such as DPP10 and GPC6 ( Fig. 5 B ) [ 31 , 32 ], and was associated with presynaptic organization and neural regeneration ( Fig. 5 C ). Astro1 and Astro2 were significantly decreased in the late stage of aging ( Fig. 5 D and Supplementary Table 2) . Astro3, which is associated with acute inflammatory responses, apoptosis and amoeboid cell migration, similar to reactive astrocytes [ 33 ], was significantly increased at 70s ( Fig. 5 D ). Milo analysis yielded the same result (Fig. 5 E). In the pseudo-time series analyzed by Monocle, three major subgroups (Astro1-3) appeared on different branches (Fig. 5 F-H). Functional enrichment of time-dependent differentially expressed genes revealed that a group of genes related to synaptic integration and synaptic signal transduction were expressed on the branch corresponding to Astro1, while genes associated with apoptotic signaling pathways were expressed on the branch corresponding to Astro3 ( Fig. 5 I ). Microglial transcriptomic diversity in postnatal human PFC Through the temporal analysis of DEGs in Micro, it was found that Micro-C6, which is related to axon formation and development, was significantly downregulated at 40s (Supplementary Fig. 4A) , indicating that early Micro has a protective or positive effect on CNS function. With the increase in age, genes related to leucocyte activation, antigen presentation, and inflammatory factor production (Micro-C4) significantly increased (Supplementary Fig. 4A) . Through large-scale gene knockout screening of the Micro-C4 gene set, it was found that FCGR3A KO could significantly perturb 143 genes (Supplementary Fig. 4B and Supplementary Table 4) . These genes are related to antigen presentation, ferroptosis, ion homeostasis, synaptic pruning, leukocyte migration, and inflammatory factor production (Supplementary Fig. 4B) . GSEA analysis revealed that FCGR3A could significantly upregulate signaling pathways such as antigen presentation, ferroptosis, oxidative phosphorylation, AD, chemokines, and HD (Supplementary Fig. 4C). Further subpopulation analysis of Micro identified six subsets, including three steady-state microglial clusters (HM1-3), two activated microglial clusters (ARM1 and ARM2), and one proliferative microglial cluster (CPM) ( Fig. 6 A and B) . Stable microglia highly express genes such as P2RY12, CX3CR1 and SALL1 , while activated microglia highly express disease-related microglia genes such as CTSB, HIF1A, SPP1, B2M, APOE and C1QA [ 34 ]. CPM highly expresses cell proliferation-related genes such as TOP2A, CDK1 and MKI67 ( Fig. 6 B ) . The standardized cell proportions show that HM1 and HM2 are significantly downregulated at 70s, while the corresponding ARM1 and ARM2 are significantly upregulated ( Fig. 6 C and Supplementary Table 2) . Milo analysis yields the same result ( Fig. 6 D ) . scDRS disease association analysis shows that HM1 and HM2 are associated with intelligence (INT), verbal-numerical reasoning (VNR), and various mental disorders ( Fig. 6 E ) ; while ARM1 and ARM2 are significantly associated with neurodegenerative diseases such as AD and MS. ARM2 is also associated with various mental disorders, suggesting the role of microglial activation in antipsychotic treatment. Further group analysis reveals that ARM1 and ARM2, which are significantly associated with AD and MS, are significantly correlated after 70s. Pseudo-temporal analysis based on CytoTRACE discovers the differentiation trajectory from stable microglia to activated microglia ( Fig. 6 F ) . Monocle analysis yields the same result ( Fig. 6 G-I ) . P2RY12 , which is related to stability, is expressed at one end of the pseudo-temporal trajectory, while C1AQ and C1QB , which are related to activation, and FTH1 and FTL , which are related to ferroptosis, are expressed at the other end ( Fig. 6 J ) . Meanwhile, we find that FCGR3A is highly expressed at the end where activated microglia are located ( Fig. 6 J ) . We found that SOX10 is a relatively specific expression of ARM transcription factor. Immunofluorescence staining showed that Iba-1 / SOX10 double positive cells were significantly increased in the PFC of wild-type old mice. In addition, we found that Iba-1 / SOX10 double positive cells were significantly elevated in the PFC of AD mice of the same age. These results suggest that ARM is not only a pathological feature of individual brain aging, but may also be involved in the pathogenesis of AD. Oligodendrocyte lineage transcriptomic alteration in postnatal human PFC The oligodendrocyte lineage mainly includes oligodendrocyte precursor cells (OPC), committed/new-formed oligodendrocytes (COP/NFOL) and mature oligodendrocytes (MOL), which are mainly responsible for the formation and maintenance of myelin [ 35 , 36 ]. In this study, we identified three distinct oligodendrocyte lineages, each of which performs specific biological functions (Supplementary Fig. 5A-C) . The corrected cell counts indicate that OPC and NFOL significantly decreased at 50s, while MOL exhibited a significant increase (Supplementary Fig. 5D and Supplementary Table 2) . Research shows that although the number of MOLs increases, the continuity of myelin structure deteriorates in the elderly group, presenting as interrupted or isolated myelin [ 29 ]. To investigate the changes in the transcriptome during the aging process of OPCs and oligodendrocytes, we analyzed the trends of DEGs in OPCs and oligodendrocytes throughout the lifespan as shown in Fig. 2 C (Supplementary Fig. 5E and F) . Based on functional enrichment analysis, we found that the OPC-C2 and MOL-C5 gene sets related to synaptic transmission and synaptic organization were continuously downregulated with age at 40s (Supplementary Fig. 5E and F) . In OPCs, there was a group of gene sets related to insulin response (OPC-C3) that gradually increased with age (Supplementary Fig. 5E) . In MOLs, there was a group of gene sets related to axon development and glial cell differentiation (MOL-C6) that gradually increased with age (Supplementary Fig. 5F) . Genes related to neuronal myelination, axonal myelination, and oligodendrocyte differentiation were significantly downregulated at 30s and then showed fluctuating changes with age (Supplementary Fig. 5F) . Transcriptomic differences of endothelial cells and pericytes in the PFC during the aging process The vascular system in the brain forms a special blood-brain barrier (BBB), which regulates the transport of nutrients, molecules and cells from the blood to the brain. The aging characteristics of the brain's vascular system are changes in vascular morphology and stiffness, as well as dysregulation of cerebral blood flow (CBF) and tissue oxygenation [ 5 ]. Traditionally, it was believed that the BBB begins to disintegrate with aging, allowing molecules that cause cognitive impairment to leak [ 5 ]. As one of the most important constituent cells of the BBB, endothelial cells and pericytes are crucial to understand their changes during aging. Through temporal analysis of the differentially expressed genes in Endo, we found that genes related to BBB transport (Endo-C2) were significantly downregulated with age, while genes related to viral invasion (Endo-C1) were significantly upregulated in the elderly ( Fig. 7 A and Supplementary Table 2) . Further subpopulation analysis of Endo identified seven transcriptionally distinct and functionally specific cell subpopulations ( Fig. 7 B-D ) . For instance, Endo1, which is associated with BBB transport and vascular transport, was significantly decreased at 60s ( Fig. 7 E ) , while Endo2, related to cytokine-mediated signaling pathways, and Endo4, related to T cell activation and lipid transport, were significantly increased at 60s ( Fig. 7 E ) . In the pseudo-temporal sequence of Monocle analysis, we found that Endo1 showed significant differences in differentiation trajectories compared to Endo2 and Endo4 ( Fig. 7 F-H ) , with Endo1 appearing in the early stage and Endo2 and Endo4 in the late stage. Functional enrichment of time-dependent differentially expressed genes revealed that genes related to the differentiation of Endo2 and Endo4 were associated with T cell immune responses ( Fig. 7 I ) . Next, we conducted a temporal analysis of the differentially expressed genes in Peri and found that genes related to functions such as amino acid transport, neurotransmitter reuptake, and synaptic transmission (Peri-C1) were significantly downregulated with age, while genes related to T cell differentiation (Peri-C2) showed fluctuating increases with age ( Fig. 7 J ) . Screening of plasma markers related to brain aging based on aging-related plasma proteomics data To further screen plasma markers that can diagnose PFC aging, we conducted an integrated analysis with aging-related plasma proteomics data [ 37 ]. We identified 10 genes/proteins that showed significant age-related changes ( Fig. 8 A ) . Among them, 4 genes/proteins ( RBM39, STAT3, DGKB , and FUT9 ) showed significant age-related changes in both men and women ( Fig. 8 B ) . Further analysis of other CNS regions and species revealed that FUT9 also showed significant changes in aging MOLs in other areas such as ACC, HIP, and SC. Additionally, FUT9 exhibited significant changes in aging MOLs in the HIP of macaques and tree shrews ( Fig. 8 C ) . FUT9 underwent significant changes during the aging process in both MOL and ExN. To further investigate the function of FUT9 , we performed FUT9 gene knockout in MOL and ExN, respectively. The results showed that the knockout of FUT9 affected the expression of 101 and 140 genes in MOL and ExN, respectively (Supplementary Fig. 6 and Supplementary Table 5) . The functions of these genes were also different. For example, in MOL, the genes affected by FUT9 were mainly related to neurodegenerative diseases, neurofiber development, axon development, and neurofiber bundle formation ( Fig. 8 D ) . In ExN, the genes affected by FUT9 were mainly related to GABA metabolism, glutamate metabolism, neuroprojection development, trans-synaptic signaling, neuronal migration, and memory ( Fig. 8 D ) . Then, to further verify whether FUT9 could serve as a CNS aging marker, we collected plasma from 20 adult individuals and 20 elderly individuals and conducted ELISA experiments. The results indicated that FUT9 showed age-related downregulation in peripheral plasma ( Fig. 8 E ) . Transcriptional dysregulation of cell subpopulation-specific in the prefrontal cortex of AD individuals To investigate the changes of the above-mentioned cell subpopulations under disease conditions, we conducted a comparative analysis of snRNA-seq data from 427 participants (including 189 normal controls and 238 AD cases) ( Fig. 9 A-E ) [ 38 ]. We found that, apart from astrocytes ( Fig. 9 B ) , the cell subpopulations that underwent significant changes during aging showed even more pronounced alterations in disease. For instance, microglia subpopulations ARM2, CPM, HM1 and HM2 ( Fig. 9 A ) , endothelial cell subpopulations Endo2 and Endo7 ( Fig. 9 C ) , excitatory neuron subpopulations L3_5_RORB_PCP4 and ExN_NRGN ( Fig. 9 D ) , and inhibitory neuron subpopulation PVALB_SST_InN ( Fig. 9 E ) . Further, by constructing in vitro aging and AD models of microglia, WB experiments showed that IL-1β , which is significantly associated with activated microglia, was significantly elevated in both the aging group and the AD group ( Fig. 9 F ) , which further verified the results of snRNA-seq. In summary, our data contribute to understanding the cellular diversity in the brain tissue of AD patients and identifying specific cell subpopulations that may have a higher susceptibility to the disease. Discussion In this study, to clarify the cellular composition and transcriptome changes of the PFC throughout an individual's life course, we conducted an integrated analysis of snRNA-seq data from 158 healthy individuals in 15 datasets. Based on this, we constructed the largest single-cell transcriptome atlas of PFC aging to date and elucidated the specific and consistent changes of different cell types and their subtypes with age through differential gene analysis and subpopulation analysis. Additionally, by integrating peripheral plasma proteomics data related to aging, we screened for potential markers of brain aging. This study provides specific time windows and targets for the diagnosis and treatment of CNS aging and aging-related diseases in the future. Research on PFC aging across the life course clarifies the conservation and differences among cell types. The aging of the human brain is a cause of cognitive decline in the elderly and a major risk factor for various neurodegenerative diseases. However, when the brain begins to age has always been a research hotspot. In 2004, Lu et al. defined a group of genes that decreased in expression after the age of 40 by analyzing the transcriptional profiles of the prefrontal cortex of 30 individuals aged 26 to 106. These genes play a core role in synaptic plasticity, vesicle transport and mitochondrial function. At the same time, the induction of stress response, antioxidant and DNA repair genes occurs in the aging cortex [ 39 ]. In 2011, Colantuoni et al.'s RNA-seq study on the PFC of 268 subjects indicated that the transcriptome of brain tissue undergoes significant changes at the age of 50. The DEGs significantly associated with age are mainly related to synapses, axons, ATP synthesis, and cell cycles [ 19 ]. In 2023, Hahn et al. conducted spatiotemporal RNA sequencing of the mouse brain and discovered a gliocyte senescence atlas across the entire brain [ 40 ]. However, current research has not clarified the changes in different cell types in human brain tissue with age. Therefore, based on single-nucleus transcriptome sequencing, we analyzed the changes in different cell types in the human brain's prefrontal cortex throughout the life course. Initially, our analysis of DEGs across various cell types revealed that neurons experienced a significant transcriptomic alteration as early as the age of 30, followed by a second notable change at the age of 50. These findings are largely consistent with the transcriptome sequencing results reported by Colantuoni et al [ 19 ]. Cross-brain region studies in mice also indicate that the time when the transcriptome undergoes significant changes occurs around 15 months (equivalent to 50 years old in humans) [ 40 ]. Further analysis was conducted on the specificity and consistency of DEGs between cells. Although different cell types have a large number of DEGs with specific changes, there are also consistent DEGs. Targeting the treatment of consistent DEGs can improve the state and function of these cells as a whole during aging. Targeting those cell-specific DEGs can more precisely improve the function of cells. Synapse-related functional genes undergo aging-associated alterations in different cell types. Through the analysis of age-related differential gene expression across various cell types, we identified conserved functional gene alterations among these cell types, with particular emphasis on the significant changes observed in synaptic-related functional genes. For instance, genes related to synaptic integration and transmission in ExN and InN were significantly downregulated at 30 seconds, those in OPC, MOL and Micro were significantly downregulated at 40 seconds, and those in Astro were significantly downregulated at 60 seconds. One of the hallmarks of the brain as the most complex organ is its large-scale synapses. In the brain tissue of normal healthy aging, there is no extensive loss of nerve cells. The most common age-related structural changes that nerve cells undergo are a reduction in the number and length of dendrites, loss of dendritic spines, a decrease in the number of axons, a significant loss of synapses, and a decline in synaptic connections and synaptic function. These changes may have a significant relationship with behavioral disorders and cognitive decline that accompany normal aging [ 41 – 43 ]. Furthermore, we also found that a group of genes related to dendritic spine development and synaptic organization were elevated in elderly individuals. Similarly, the study of life space synaptome architecture (LSA) indicated that different subtypes of synapses changed differently at different stages. For instance, in the brain tissue of the elderly, subtypes 2, 27, and 34 were increased in most brain regions; however, subtypes 12, 14, 15, and 16 were decreased in the olfactory area and thalamus [ 44 ]. We speculate that this significantly increased group of genes may lead to abnormal development of dendritic spines and synapses, and these abnormally developed dendritic spines may cause abnormal activation of microglia [ 45 ]. Analysis of microglial cell differential genes has demonstrated that the time of gene changes related to synaptic pruning and leukocyte activation overlaps and is slightly later than the time of significant increase in synaptic-related genes. Activated microglial cells release more inflammatory factors, and over-activated microglial cells may phagocytize normal synapses and dendritic spines, thus forming a vicious cycle. Additionally, at 70s, the significant upregulation of genes related to programmed cell death and inflammatory factor production in astrocytes may be associated with the significant increase in synaptic-related genes. This is because studies have shown that in the developing brain, astrocytes can eliminate excess synapses through phagocytic receptors Mertk and Megf10 , as well as indirectly by inducing the expression of complement cascade components in neurons [ 18 , 46 – 48 ]. Microglia undergo a transformation from a homeostatic state to an activated state during the aging process. Microglia, as the resident immune cells of the CNS, provide immune surveillance for the body and are widely recognized as playing a crucial role in neural development, homeostasis and neuroinflammation [ 49 ]. Although microglia are generally regarded as playing a protective role in the nervous system, in the microenvironment of aging-induced glial activation, increased complement factors and inflammatory mediators, microglia may reveal their evil side. Aging, as a key risk factor for many neurological diseases, an increasing amount of evidence indicates that microglia are associated with age-related neurofunctional disorders. [ 50 – 52 ]. Through the analysis of microglial cell subgroups, we discovered the activation of microglia associated with aging and disease. Research shows that the activation of microglia has neurotoxic effects on neurodegenerative diseases, while in some aspects, it is also an important defender against many neurodegenerative diseases [ 53 – 56 ]. In this study, we found that the steady-state related microglia were significantly downregulated at 60s, while ARM1 was significantly upregulated at 70s and remained at a high level thereafter. ARM2 may be a protective activated state of microglia because it also maintained a relatively high level before the age of 40. Disease association analysis demonstrated a significant association between ARM1 and AD and MS. Additionally, we discovered that the expression of microglial FCGR3A was dysregulated during aging. Studies have shown that FCGR3A is involved in antibody-dependent cell-mediated cytotoxicity and antibody-dependent viral infection enhancement and other reactions, and is related to NK cell activation and the production of pro-inflammatory cytokines [ 57 , 58 ]. In this study, we found that FCGR3A was significantly elevated in aging and activated microglia. Gene knockout demonstrated that FCGR3A was significantly associated with functions such as antigen presentation, ferroptosis, ion homeostasis, synaptic pruning, leukocyte migration, and the production of inflammatory factors. Previous studies have shown that dysregulation of FCGR3A expression in peripheral immune cells is related to diseases such as AD and SCZ [ 59 – 61 ]. These results suggest that FCGR3A may influence brain aging and aging-related diseases from different perspectives, such as peripheral immune cells and central innate immune cells. Endothelial cells mediate the activation and infiltration of T cells during the aging process. By conducting subpopulation analysis on PFC endothelial cells, we discovered that two endothelial cell subpopulations were significantly elevated in elderly individuals. The functions of these two subpopulations are mainly related to cytokine-mediated signaling pathways and the activation and migration of T cells. Studies have shown that in the tau pathological regions of mice and the brains of AD patients, the number of T cells (especially cytotoxic T cells) has significantly increased, and the number of T cells is correlated with the degree of neuronal loss [ 62 – 64 ]. The alterations of the relevant microenvironment in the brain parenchyma may have guiding significance for the recruitment and guidance of T cell transformation. Although studies have shown that there is communication between T cells and activated microglia in brain tissue, the mechanism of T cell infiltration into brain tissue remains unclear. In this study, we found that senescent endothelial cells may provide a microenvironment for T cell activation and infiltration. Further, reactive astrocytes and microglia promote the chemotaxis and migration of T cells. Anti-aging measures targeting brain endothelial cells may be one of the important directions for future anti-brain aging and related diseases. FUT9 can serve as a potential plasma biomarker for CNS aging. In recent years, scientists have delved increasingly deeper into the research of biomarkers for quantifying biological aging, especially those based on "omics" [ 65 ]. Biomarkers of aging are crucial tools for the identification and evaluation of human longevity intervention measures within a realistic time frame [ 66 ]. These biomarkers have the potential to predict outcomes related to aging and can function as surrogate endpoints for assessing interventions aimed at promoting healthy aging and longevity [ 65 ]. Moreover, recent aging biomarkers have focused more on predicting biological age and age-related health outcomes rather than chronological age. An ideal aging biomarker should have a moderate to strong correlation with age and be able to predict multiple aging-related outcomes other than death, such as functional decline, frailty, chronic diseases and disabilities, as well as (multiple) morbidities [ 65 ]. In this study, through integrated analysis with the plasma proteomics data related to aging, we identified 10 genes/proteins that showed significant changes with age. Among them, 4 genes/proteins ( RBM39, STAT3, DGKB , and FUT9 ) demonstrated significant age-related changes in both men and women. RBM39 has been proven to be associated with lifespan and age in multiple species [ 67 , 68 ]. Studies have shown that STAT3 in the spinal cord of aging mice shows an age-related increase, accompanied by an increase in the expression of P16 [ 69 ]. The STAT3 -ubiquitin aggregates formed by lysine-48 and lysine-63 bonds significantly increase in the spinal cord of aging mice [ 69 ]. Research indicates that DGKB is associated with cognitive complexity, anxiety, depression and eating disorders [ 70 , 71 ]. The expression of Lex carbohydrate structure in the brain is developmentally regulated and is believed to play a role in intercellular interactions during neuronal development. FUT9 is the most important enzyme for the synthesis of Lex in the brain [ 72 ]. We found that FUT9 showed significant age-related down-regulation and underwent significant changes in multiple CNS regions (PFC, ACC, HIP and SC) and in multiple species (human, macaque and tree shrew) MOL. Gene knockout demonstrated that the function of FUT9 is related to neurodegenerative diseases, neurofilament development, axon development and nerve fiber bundle formation. ELISA experiments demonstrated significant changes in FUT9 in peripheral plasma. These findings indicate that FUT9 may serve as a potential plasma biomarker for the aging of the central nervous system. Conclusions In this study, we focused on PFC as our research subject and integrated snRNA-seq data from 158 healthy individuals across 15 datasets. With a decade-long interval, we categorized all samples into eight groups to analyze the transcriptomic changes of various cell types in the PFC throughout an individual's life course. Through temporal analysis, we detailed the transcriptomic alterations of eight major cell types during different stages of life. Our findings indicate that synaptic development, integration, and transmission are generally downregulated with aging; furthermore, distinct subpopulations within these cell types exhibit age-related changes and contribute to brain aging at different time points. The increase in apoptotic signals and inflammatory factor production in astrocytes among older adults accelerates brain aging processes. Microglia predominantly maintain a steady state during early life stages but transition to an activated state later on, characterized by increased release of inflammatory factors and chemotaxis. This activation may be associated with abnormal synapse development and dendritic spine formation as well as irregular myelination patterns. Abnormally activated microglia are implicated in the onset and progression of AD and MS in elderly populations. Additionally, transport functions across the BB in endothelial cells significantly decline with age while pericyte regulatory functions concerning neurotransmitters show a decreasing trend over time. Abbreviations PFC prefrontal cortex ExN excitatory neurons InN inhibitory neurons MOL mature oligodencyte OPC oligodendrocyte precursor cells Astro astrocytes Micro microglia Endo endothelial cells Peri pericytes UMIs unique molecular identifiers. Declarations Animal ethics approval All experimental procedures, involving animal care and testing conformed to the Animal Care and Use Committee of Kunming Medical University (approval number: KMMU2019058). Human ethics and consent to participate : The study was conducted in accordance with the Institutional Research Ethics guidelines and ethical principle involving human participation (Helsinki Declaration) and approved by the Medical Ethics Committee of Kunming Medical University (approval number: KMMU2022MEC092). All volunteers for peripheral blood collection were informed of the purpose of the study and signed the informed consent form. Clinical trial number: not applicable. Consent to publish Not applicable. Data and code availability The datasets (GSE140231, GSE141552, GSE144136, GSE157827, GSE168408, GSE174367, GSE213982, PMID34582785, PRJNA434002, PRJNA544731, syn18485175, syn21125841, syn38120890, EGAD00001008287) analysed during the current study are available in the Gene Expression Omnibus (GEO), Synapse, ArrayExpress, European Nucleotide Archive (ENA), European Genome-phenome Archive (EGA), and National Genomics Data Center (NGDC). The processed expression matrices and corresponding code used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests All authors declare no competing interests. Funding This study was supported by Kunming Health Science and Technology Talent Training Project (thousand project, 2024-SW (Reserve)-72; 2024-SW (Reserve)-73), Yunnan Education Department Fund (2025J0246), National Natural Science Foundation of China (Grant number 82160269; 82360275; 82160272), Yunnan Provincial Department of Science and Technology Science and Technology Plan Project (202405AC350104), and Scientific research project of the Provincial Clinical Medical Center of Yunnan Province (2024YNLCYXZX0280). Author contributions RZN, THB, and JL conceptualized, acquired funding, and supervised this study. Data were processed, analyzed and visualized by RZN, YYZ and ZLY. The manuscript was drafted by NRZ, MYZ and CHY, and was reviewed and edited by THB, and XFZ. All authors discussed results and commented on the manuscript. Acknowledgments We are grateful to xiyoucloud for providing computational infrastructure. 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Supplementary Files SupplementalTables.xlsx Supplementarymaterials.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 14 Sep, 2025 Editor assigned by journal 04 Sep, 2025 Submission checks completed at journal 22 Aug, 2025 First submitted to journal 19 Aug, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":531061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the single-cell transcriptomic atlas of PFC aging.\u003c/strong\u003eA. Data set sources of PFC samples (left first), gender composition (left third), and age groups (left fourth). B. UMAP visualization shows the cell composition and cell type-specific marker genes of different age groups.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/1d60e5a0e926168013e09d10.jpeg"},{"id":91921887,"identity":"0587ecf5-e11b-443b-804e-fb4ff163c3cc","added_by":"auto","created_at":"2025-09-23 01:28:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":816455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellular composition and transcriptomic differences during PFC aging. \u003c/strong\u003eA. The line chart shows the percentage of different cell types changing with age. B. The number of DEGs (|logFC|≧0.25, FDR\u0026lt;0.05) that significantly changed in different cell types at different age groups, with 20s as the control. C. The overlap of DEGs (|logFC|≧0.25, FDR\u0026lt;0.05) among different cell types in the same age group. **-log10(\u003cem\u003eP\u003c/em\u003e value) \u0026gt; 100, *-log10(\u003cem\u003eP\u003c/em\u003evalue) \u0026gt; 30 (one-sided Fisher's exact test). D. The scatter plot shows the specificity of upregulated and downregulated genes among different cell types in the same age group. E. The left dot plot shows the expression of 9 genes that changed in all cell types in different groups. The right survival curve shows the impact of ZBTB16 and MT-ND3 on disease-free survival.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/9b49c2b57c86c805c46ee467.jpeg"},{"id":91922031,"identity":"94cf8258-a865-46b7-a7a4-ce6ffaa4da56","added_by":"auto","created_at":"2025-09-23 01:36:37","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":955857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal and subpopulation analysis of DEGs in excitatory neurons. \u003c/strong\u003eA. Temporal analysis of DEGs in ExN throughout the life course. The left line graph represents the average expression levels and trends of DEGs based on clustering in each age group, the middle heatmap represents the expression levels of DEGs in each cell, and the right side shows the functional enrichment annotations of the corresponding gene clusters (top 5 are displayed). B. UMAP visualization of ExN subpopulations. C. Dot plot shows the expression of marker genes in ExN cell subpopulations. D. Proportions of ExN cell subpopulations in different groups. E. Heatmap shows the GO terms enriched in the specific marker genes of different cell subpopulations. F-I. Pseudotemporal analysis of microglial subpopulations based on monocle. F. Shows the distribution of different subpopulations on the pseudotemporal trajectory. G. Shows the differentiation direction of the pseudotemporal trajectory. H. Shows the expression distribution of specific marker genes on the differentiation trajectory. I. Differential genes and functional enrichment analysis related to branch point 3. J. Scatter plot shows the expression of \u003cem\u003eNRGN\u003c/em\u003e in the prefrontal cortex of the young and old groups. K. IHC staining shows the distribution of \u003cem\u003eNRGN\u003c/em\u003e positive cells in the frontal cortex of the young and old groups. Scale bar: 100 μm.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/fe32022720b189f3cbc11a25.jpeg"},{"id":91921891,"identity":"3b1f6438-5db3-4743-8f40-41b92209a5b0","added_by":"auto","created_at":"2025-09-23 01:28:37","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":960563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal and subpopulation analysis of DEGs in inhibitory neurons.\u003c/strong\u003e A. Temporal analysis of DEGs in InN throughout the life course. The left line graph represents the average expression levels and trends of DEGs based on clustering in each age group, the middle heatmap represents the expression levels of DEGs in each cell, and the right side shows the functional enrichment annotations of the corresponding gene clusters (top 5 are displayed). B. UMAP visualization of InN subpopulations. C. Dot plot showing the expression of marker genes in InN cell subpopulations. D. Proportions of InN cell subpopulations in different groups. E. Heatmap showing the GO terms enriched in the specific marker genes of different cell subpopulations. F. Pseudotemporal analysis of InN cell subpopulations based on CytoTRACE. UMAP (left) and box plot (right) show the predicted differentiation scores of different cell subpopulations. G. Pseudotemporal analysis of InN cell subpopulations based on monocle. The left figure shows the distribution of different subpopulations on the pseudotemporal trajectory; the right figure shows the differentiation direction of the pseudotemporal trajectory. H. Differential genes and functional enrichment analysis related to branch point 3.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/3857bac03d27eeee05cba09f.jpeg"},{"id":91921889,"identity":"eae9bda8-bce4-40cc-8518-2f86b33c5ebb","added_by":"auto","created_at":"2025-09-23 01:28:37","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":840462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of astrocyte subpopulations. \u003c/strong\u003eA. UMAP visualization of Astro subpopulations. B. Dot plot showing the expression of marker genes in Astro cell subpopulations. C. Heatmap showing the GO terms enriched for subpopulation-specific marker genes. D. Proportion of Astro cell subpopulations in different groups. F-I. Pseudotemporal analysis of Astro cell subpopulations based on monocle. F shows the distribution of different subpopulations on the pseudotemporal trajectory (top) and the differentiation direction of the pseudotemporal trajectory (bottom); G shows the density of cells in different groups along the differentiation direction; H shows the expression distribution of specific marker genes along the differentiation trajectory. I. Differential gene expression and functional enrichment analysis related to branch point 1.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/dcd94fca1d38c0e309c1b144.jpeg"},{"id":91922033,"identity":"4b4c1413-06a1-4d47-9cad-486e4d4a470b","added_by":"auto","created_at":"2025-09-23 01:36:37","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":865967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of microglial cell subpopulations. \u003c/strong\u003eA. UMAP visualization of microglial cell subpopulations. B. Dot plot showing the expression of marker genes in microglial cell subpopulations. C. Proportion of microglial cell subpopulations in different groups. D. Milo analysis showing the changes in the number of microglial cell subpopulations. E. scDRS disease association analysis based on microglial cell subpopulations. F. Pseudotime analysis of microglial cell subpopulations based on CytoTRACE. G-J. Pseudotime analysis of microglial cell subpopulations based on monocle. G shows the positions of different subpopulations on the pseudotime trajectory. H shows the differentiation direction of the pseudotime trajectory. I shows the enrichment of different subpopulations on the trajectory. J shows the expression distribution of specific marker genes on the differentiation trajectory. K. Representative microscopic fields of \u003cem\u003eIba-1 \u003c/em\u003e(green) /\u003cem\u003eSOX10\u003c/em\u003e (red) positive cells in the PFC of mouse from 8m WT, 18m WT and 8m AD. Blue, DAPI. Scale bar, 50 μm. (L) Quantification of \u003cem\u003eIba-1 \u003c/em\u003e(green) /\u003cem\u003eSOX10\u003c/em\u003e (red) positive cells in PFC of mouse. \u003cem\u003eN\u003c/em\u003e = 5 per group.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/e420042a8eb3ac8daaa979d2.jpeg"},{"id":91923892,"identity":"3c2ec292-3b3e-4bb7-a2b8-f5f53e6801b5","added_by":"auto","created_at":"2025-09-23 01:44:37","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":873082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal analysis of DEGs in Endo and Peri. \u003c/strong\u003eA. The temporal analysis of DEGs in Endo throughout the life course. The line graph on the left represents the average expression levels and trends of DEGs based on clustering in each age group, the heatmap in the middle represents the expression levels of DEGs in each cell, and the right side shows the functional enrichment annotations of the corresponding gene clusters (top 5 are displayed). B. UMAP visualization of Endo subgroups. C. Dot plot shows the expression of marker genes in Endo cell subgroups. D. Heatmap shows the GO terms enriched in the specific marker genes of different cell subgroups. E. Proportion of Endo cell subgroups in different groups. F-I. Pseudotime analysis of Endo cell subgroups based on monocle. F shows the distribution of different subgroups on the pseudotime trajectory; G shows the differentiation direction of the pseudotime trajectory; H shows the density of cells in different groups in the differentiation direction; I. The time-dependent differential genes and functional enrichment analysis. J. The temporal analysis of DEGs in Peri throughout the life course.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/0b44192caf4bc0c147447869.jpeg"},{"id":91922036,"identity":"a4ba4dc8-6ac3-4066-977c-b83191859e86","added_by":"auto","created_at":"2025-09-23 01:36:37","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":638758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of plasma biomarkers related to brain aging. \u003c/strong\u003eA. Overlapping molecules of proteins in plasma that are consistent with age-related upregulation or downregulation of genes in the PFC. B. Four genes/proteins significantly associated with age in both males and females. C. Expression of \u003cem\u003eFUT9\u003c/em\u003e in the ACC, HIP, and SC of humans, as well as in the HIP and MOL of macaques and tree shrews. D. Functional enrichment of genes related to MOL and ExN significantly affected by \u003cem\u003eFUT9\u003c/em\u003e gene knockout (FDR \u0026lt; 0.05). E. Expression levels of \u003cem\u003eFUT9\u003c/em\u003e protein in the plasma of adult and elderly individuals.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/6b64e0e80d3b49b07d495b04.jpeg"},{"id":91921892,"identity":"32dfcd68-c81c-493f-83ae-4c3476f27ee2","added_by":"auto","created_at":"2025-09-23 01:28:37","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":463086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell subpopulation-specific dysregulation in the PFC of AD individuals. \u003c/strong\u003eA. Left: UMAP visualization shows the state of microglial subpopulations in the AD query dataset evaluated based on the aging reference dataset; right: bar chart shows the proportion of each group's cell subpopulations mapped to our aging microglial reference dataset in the query dataset. Cells with a prediction score for any subpopulation below 0.5 were classified as \"unclassified\". B-E are the cell subpopulation mapping and proportion analysis of astrocytes, endothelial cells, excitatory neurons, and inhibitory neurons, respectively. F. WB experiments detected the expression of \u003cem\u003eIL-1β\u003c/em\u003ein in vitro aging microglia and AD models, respectively.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/af20f9043fc0f5f8f025cd7b.jpeg"},{"id":91924084,"identity":"ea00e3e0-7c12-4051-b6ff-4c50d27112c9","added_by":"auto","created_at":"2025-09-23 01:52:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9337927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/d1d48184-2695-4d06-8c94-667a7b0f0762.pdf"},{"id":91922030,"identity":"de199e43-0d37-4716-a4c9-dd49abba17fc","added_by":"auto","created_at":"2025-09-23 01:36:37","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":70685,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/ee28836353b90d7c57624c9f.xlsx"},{"id":91921890,"identity":"d2f1cd1c-74a4-4075-8bd1-043f59e8c0e6","added_by":"auto","created_at":"2025-09-23 01:28:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4193492,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7406880/v1/06491de0b6744ce3e1c1d6ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A single-cell transcriptome atlas of cell diversity in human prefrontal cortex across the postnatal lifespan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAging is a natural biological process that causes many changes in the human body and affects every cell in the organism. It is regarded as the greatest risk factor for a series of diseases [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Brain aging refers to a series of changes in the brain tissue morphology, neurochemistry and other aspects as people age, leading to degenerative changes in structure and functional decline. The main manifestations are the decline of learning ability, memory ability, attention, decision-making speed, sensory perception and motor coordination. This not only has a huge impact on the daily life of the individual, but also adds a heavy burden to the individual's family and even the entire society [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, brain aging is a major risk factor for numerous diseases, including cerebrovascular diseases such as ischemic stroke, neurodegenerative diseases like Alzheimer's disease (AD) and Parkinson's disease (PD), posing a significant threat to human health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to the WHO, the number and proportion of people aged 60 and above are increasing. This figure is expected to rise to 1.4\u0026nbsp;billion by 2030 and to 2.1\u0026nbsp;billion by 2050. Therefore, in-depth research on the cellular and molecular mechanisms of brain aging, exploration of effective strategies to delay brain aging, and improvement of the quality of life for the elderly population are currently urgent and important global scientific issues that need to be addressed.\u003c/p\u003e\u003cp\u003eNeurological aging is a complex and persistent process throughout an individual's life, involving multi-regional and multi-gene alterations, which requires precise spatiotemporal transcriptional regulation. In recent years, the discovery of single-cell sequencing technology has made it possible to clarify the epigenomic, transcriptomic and proteomic changes of various cell types and their subtypes during the aging process. Currently, scRNA-seq is widely used in research on the nervous system, such as in the fields of development [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], AD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], schizophrenia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], depression [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], autism, [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and glioma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the field of aging-related neuroscience, a single-cell transcriptome atlas study of fruit fly brain aging has revealed that not all brain cells age in the same way [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Researchers utilized scRNA-seq to assess the cell type-specific manifestations of age-related characteristics in mice, such as senescence, genomic instability, and changes in the immune system, providing a reference for understanding the cellular biological changes that occur in mammals throughout their life cycle [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. snRNA-seq of human and macaque hippocampal tissues revealed unique molecularly defined subpopulation maps of astrocytes, microglia and oligodendrocytes throughout the postnatal life cycle of the human hippocampus, and uncovered their associations with specific physiological functions, age-dependent changes in abundance and disease relevance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrently, scRNA-seq is widely used to study aging in species such as fruit flies, mice, macaques, chimpanzees and humans [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. More than a decade ago, a study of RNA-seq from the prefrontal cortex (PFC) of 268 subjects indicated that the transcriptome of brain tissue undergoes significant changes at the age of 50. The DEGs significantly associated with age are mainly related to synapses, axons, ATP synthesis and cell cycles [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, current research has not clarified the changes in different cell types in the human brain's PFC throughout the life course after adulthood. In this study, we integrated snRNA-seq data from the PFC of 158 healthy individuals aged 19\u0026ndash;101 years from 15 datasets. With a 10-year interval, we divided all samples into 8 groups and analyzed the transcriptomic alterations of eight major cell types and their subtypes in the PFC during the individual life course. Different subtypes of different cell types undergo transformation with age and participate in the brain aging process at different time points. Additionally, by integrating peripheral plasma proteomics data related to aging, we screened for potential markers of brain aging. This study provides specific time windows and targets for the diagnosis and treatment of CNS aging and aging-related diseases in the future.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData download and collection\u003c/h2\u003e\u003cp\u003eThrough a literature review, we collected and organized snRNA-seq data from the human PFC including 15 datasets \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e. The database includes Gene Expression Omnibus (GEO), synapse, ArrayExpress, European Nucleotide Archive (ENA), European Genome-phenome Archive (EGA), and National Genomics Data Center (NGDC). Details of the samples, including the database, sample ID, age, gender, and diagnosis, can be found in Supplementary Table\u0026nbsp;1. Each dataset included normal individuals who underwent strict clinical assessments to ensure they had no active neuropsychiatric disorders.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePre-processing and quality control of snRNA-seq data\u003c/h3\u003e\n\u003cp\u003eAll raw sequencing data were processed using Cell Ranger 7.0.1 (10x Genomics) to obtain expression matrices for downstream analysis. To ensure dataset quality, we retained only cells with more than 200 detected genes and less than 5% mitochondrial gene content. Doublets were detected using DoubletFinder (v.2.0.3). After sample integration and clustering, clusters lacking specific marker genes, with relatively low gene content, and high mitochondrial ratios were discarded.\u003c/p\u003e\n\u003ch3\u003eDe-batch integration of multiple datasets\u003c/h3\u003e\n\u003cp\u003eWe integrated the scRNA-seq data of all datasets individuals to remove batch effects. Briefly, unique molecular identifiers (UMIs) from each valid cell barcode were retained for all downstream analyses and processed using the Seurat R package (v.4.2.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satijalab.org/seurat/\u003c/span\u003e\u003cspan address=\"https://satijalab.org/seurat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We first used reciprocal PCA (RPCA) instead of CCA to identify an effective space for finding anchors. When using RPCA to determine anchors between any two datasets, each dataset was projected into the PCA space of the other, and anchors were constrained by requiring mutual nearest neighbors. We randomly designated two samples each from adult males, adult females, elderly males, elderly females as the \"reference\" dataset for integration analysis, while the remaining datasets were designated as the \"query\" datasets. We also used the \u003cem\u003eFindIntegrationAnchors\u003c/em\u003e and \u003cem\u003eIntegrateData\u003c/em\u003e functions to remove batch effects, followed by clustering analysis using the \u003cem\u003eFindNeighbors\u003c/em\u003e and \u003cem\u003eFindClusters\u003c/em\u003e functions. Data visualization was performed using nonlinear dimensionality reduction methods such as UMAP and t-SNE.\u003c/p\u003e\n\u003ch3\u003eCell type identification\u003c/h3\u003e\n\u003cp\u003eCell type annotation was performed using the method provided by the SingleR package, which identifies cell types based on reference datasets. This method annotates the cells to be identified as the cell type that has the highest correlation with the single-cell reference expression profile dataset. The results of the dataset identification in this report are provided for reference, and further descriptions and validations of the cell populations will be made based on relevant genes from existing literature. The detailed markers for the main cell types are as follows: Neuron: \u003cem\u003eRBFOX1\u003c/em\u003e, \u003cem\u003eSNAP25\u003c/em\u003e, and \u003cem\u003eSYT1\u003c/em\u003e; ExN: \u003cem\u003eSLC17A\u003c/em\u003e and \u003cem\u003eCAMK2A\u003c/em\u003e; InN: \u003cem\u003eGAD1\u003c/em\u003e and \u003cem\u003eGAD2\u003c/em\u003e; MOL: \u003cem\u003ePLP1\u003c/em\u003e and MBP; OPC: \u003cem\u003eOLIG1\u003c/em\u003e and \u003cem\u003eOLIG2\u003c/em\u003e; Astro: \u003cem\u003eGFAP\u003c/em\u003e and \u003cem\u003eAQP4\u003c/em\u003e; Micro: \u003cem\u003eCSF1R\u003c/em\u003e and \u003cem\u003eCD74;\u003c/em\u003e Endo: \u003cem\u003eFLT1\u003c/em\u003e and \u003cem\u003eCLDN5\u003c/em\u003e; Peri: \u003cem\u003eDCN\u003c/em\u003e and \u003cem\u003eCOL1A2\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003eCell extraction and subpopulation analysis\u003c/h3\u003e\n\u003cp\u003eThe expression matrix of each cell type was extracted and integrated for subpopulation analysis. First, we used the \u003cem\u003eSCTransform\u003c/em\u003e function to standardize the data, followed by PCA dimensionality reduction using the \u003cem\u003eRunPCA\u003c/em\u003e function. We then performed batch integration using the \u003cem\u003eRunHarmony\u003c/em\u003e function [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] with the specific parameters: group.by.vars = \u0026ldquo;SampleID\u0026rdquo;, assay.use = \u0026ldquo;SCT\u0026rdquo;, max.iter.harmony\u0026thinsp;=\u0026thinsp;30. Next, we conducted clustering analysis using the \u003cem\u003eFindNeighbors\u003c/em\u003e and \u003cem\u003eFindClusters\u003c/em\u003e functions, and visualized the data using UMAP.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of differentially expressed genes (DEGs) across clusters\u003c/h2\u003e\u003cp\u003e\u003cem\u003eFindMarkers\u003c/em\u003e function implemented in Seurat v4 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used to identify DEGs across clusters with the options \u0026lsquo;\u0026lsquo;logfc.thresh old\u0026thinsp;=\u0026thinsp;0.25, min.pct\u0026thinsp;=\u0026thinsp;0.1\u0026rdquo;. \u003cem\u003eP\u003c/em\u003e-value was corrected using the Bonferroni method, and 0.05 was set as a threshold to define significance.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGene ontology (GO) term enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe \u003cem\u003eenrichGO\u003c/em\u003e function of clusterProfiler R package [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was used for enrichment analysis, and the Benjamini-and-Hochberg (BH) method was employed for multiple test correction. A GO term with an adjusted \u003cem\u003eP\u003c/em\u003e-value lower than 0.05 was considered as significantly enriched.\u003c/p\u003e\n\u003ch3\u003eGene Set Enrichment Analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eGSEA was applied to identify a priori-defined gene sets that show statistically significant differences between two given clusters. We used the expression file as input, and implied gene sets of KEGG pathways and Gene Ontology, which were collected in Molecular Signatures Database (MSigDB) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of cellular communication network\u003c/h2\u003e\u003cp\u003eIntercellular communication between different immune cells was analyzed using CellChat (v.0.0.1) R package with default parameters [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Intercellular communications analysis was performed based on cell types. Cell\u0026ndash;cell communication network was visualized using the \u003cem\u003enetVisual_aggregate\u003c/em\u003e function, centrality score was computed and visualized using the \u003cem\u003enetAnalysis_signalingRole_network\u003c/em\u003e function, relative contribution of each ligand-receptor pair was visualized using the \u003cem\u003enetAnalysis_contribution\u003c/em\u003e function.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of differentially expressed genes (DEGs)\u003c/h2\u003e\u003cp\u003eTo identify genes that are differentially expressed in aging or disease, \u003cem\u003eP\u003c/em\u003e-values were calculated and FDR-corrected using the MAST method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. All nuclei from different sample groups corresponding to specific cell types were included. MAST was utilized to perform zero-inflated regression analysis by fitting a linear mixed model. To account for confounding factors such as age, sex, and the proportions of ribosomal and mitochondrial transcripts, the following model for aging and disease was fitted using MAST:\u003c/p\u003e\u003cp\u003ezlm(~\u0026thinsp;condition\u0026thinsp;+\u0026thinsp;nCount_RNA\u0026thinsp;+\u0026thinsp;percent.mt\u0026thinsp;+\u0026thinsp;Sex, sca, method\u0026thinsp;=\u0026thinsp;glmer, ebayes\u0026thinsp;=\u0026thinsp;T)\u003c/p\u003e\u003cp\u003ezlm(~\u0026thinsp;condition\u0026thinsp;+\u0026thinsp;nCount_RNA\u0026thinsp;+\u0026thinsp;percent.mt\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age, sca, method\u0026thinsp;=\u0026thinsp;glmer, ebayes\u0026thinsp;=\u0026thinsp;T)\u003c/p\u003e\u003cp\u003eTo identify DEGs due to age or disease effects, a likelihood ratio test was performed by comparing models with and without the diagnostic factor. Genes exhibiting at least a 25% increase or decrease in expression between groups, along with a false discovery rate (FDR)-corrected P-value of less than 0.05, were selected as differentially expressed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eVirtual knockout of the gene of interest\u003c/h2\u003e\u003cp\u003eTo analyze the function of module gene knockout in specific cell types of particular diseases, we extracted disease- and cell-specific snRNA-seq data and used the gene \u0026times; cell expression matrix as input for scTenifoldKnk [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The genes perturbed by the virtual knockout with FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected as differentially expressed. Interaction enrichment analysis was conducted based on the STRING protein-protein interaction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://version-12-0.string-db.org/\u003c/span\u003e\u003cspan address=\"https://version-12-0.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We selected biological processes (GO) and human phenotypes (Monarch) as our items of interest.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAcquisition of immunohistochemical data\u003c/h2\u003e\u003cp\u003eTo validate the results, we searched for the expression of proteins of interest in the Human Protein Atlas database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All antibodies have undergone rigorous validation for specificity, reproducibility, and functionality, and have been tested in various experimental applications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAnimal care and grouping\u003c/h2\u003e\u003cp\u003eAPP/PS1 transgenic mice (AD mice) aged 8 months and wild-type mice (WT mice) of C57BL/6 strain aged 8 and 18 months were provided by the Center of Experimental Animals of Kunming Medical University. Animals were kept under standard conditions in the SPF laboratory. All experimental procedures, including animal care and testing conformed to the Animal Care and Use Committee of Kunming Medical University (kmm’058). All studies were conducted in accordance with the United States Public Health Service's Policy on Humane Care and Use of Laboratory Animals. All mice were anesthetized and killed, and were immediately perfused with precooled 0.9% normal saline until the liver turned white. Their brains were removed, and the hippocampus tissues were collected for the immunostaining experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImmunofluorescence staining\u003c/h2\u003e\u003cp\u003eImmunofluorescence staining was performed as described previously. The pre-prepared hippocampus sections were washed with 0.01 mol/L PBS, and then were fixed with 4% paraformaldehyde for 20 min. Tissue sections were treated with 0.3% hydrogen peroxide in 20% methanol for 30 min to block endogenous peroxidase. Sections were incubated with 5% goat serum and 0.3% Triton X-100 in 0.01 mol/L PBS for 1 hour to block nonspecific immunostaining followed by immunostaining with primary antibodies \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e in 2% goat serum overnight at 4\u0026deg;C. After washing three times with 0.01 mol/L PBS, secondary antibodies (1:200, DyLight 488, Goat Anti-Rabbit, DyLight 594, Goat Anti-Rabbit, Abbkine) diluted with 2% goat serum were added, and the reaction was carried out at 37\u0026deg;C for 1 h. The samples were sealed following PBS washing and DAPI staining. Images were captured using an upright two-photon confocal microscope (Nikon, ARMP+, Japan), and each image was randomly acquired at 200\u0026times; magnification. Image-Pro Plus software (version 6.0; Media Cybernetics, Silver Spring, MD, USA) was used to calculate the mean OD of each positive staining group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimary antibodies information used in this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManufacturers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDilution ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-SOX10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbcam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eab227680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-IBA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWako\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e016-26721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-IL-1 beta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbcam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eab254360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRabbit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:1000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-GAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbcam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eab8245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1:1000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eELISA Experiment\u003c/h2\u003e\u003cp\u003eTo further verify the results of snRNA-seq, we collected peripheral blood from individuals of different ages and conducted an ELISA experiment. We selected 20 healthy adult volunteers aged between 20 and 27 years as the adult group, and 20 elderly volunteers aged between 64 and 84 years as the elderly group. All volunteers were informed of the purpose of the study and signed the informed consent form. The study was conducted in accordance with the Institutional Research Ethics guidelines and ethical principle involving human participation (Helsinki Declaration) and approved by the Medical Ethics Committee of Kunming Medical University (approval number: KMMU2022MEC092). Peripheral blood from all participants was collected and plasma was extracted. The protein level of \u003cem\u003eFUT9\u003c/em\u003e in plasma samples was detected using the \u003cem\u003eFUT9\u003c/em\u003e ELISA kit (Sigma, XG-E990756). The operation and detection were strictly carried out according to the kit instructions. The absorbance (OD) of each sample at a wavelength of 450 nm was measured using a spectrophotometer (Thermo Fisher, SPECTRONIC 200) for 15 minutes. Finally, the linear regression equation of the standard curve was calculated using the concentration and OD of the standard samples. Then, the concentration of \u003cem\u003eFUT9\u003c/em\u003e protein in plasma was calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eCell culture\u003c/h2\u003e\u003cp\u003eThe human microglial cell lines HMC3 in our laboratory were purchased from Qingqi Biotechnology Development Co., Ltd. (Shanghai, China). The cell lines were cultured in high glucose medium containing 10% inactivated fetal bovine serum in an incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37 ℃. When the cells reached 80% \u0026minus;\u0026thinsp;90% confluence and grew well, they were digested with trypsin and sub-cultured.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eCell senescence model construction\u003c/h2\u003e\u003cp\u003eThe cells in logarithmic growth stage were inoculated in 96 well plate (200 \u0026micro;L/well) at a density of 1.6 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e/ml. There were normal control group and aging groups, with 6 replicated wells in each group. The cells in the normal control group were cultured in complete medium composed of high glucose medium, 10% fetal bovine serum, 1% penicillin-streptomycin double antibodies. The cells in the aging group were cultured in complete culture medium with D-galactose at concentrations of 5, 10, 20, 30, 40, 50, 80, and 100 mg/ml in an incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37 ℃ and for 24 hours, and then the complete culture medium was changed for another 24-hour incubation. Subsequently, 10 \u0026micro;L CCK-8 solution per 100 \u0026micro;L medium was added into each well, shaked and mixed up. After continuous culture for 0.5 hour, the absorbance value of each well at the wavelength of 450 nm was detected using a microplate reader. The test was repeated 3 times. The optimal drug concentrations were determined according to the CCK8 results, with HMC3 at 40 mg/ml. For western blotting experiment, the cells were placed in six-well plates with glucose and serum-free DMEM and subsequently placed in 37 ℃ hypoxia chambers. After the successful construction of the senescence model, protein was extracted for western blotting experiments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAβ Aggregation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman synthetic \u003cem\u003eβ\u003c/em\u003e-amyloid 1\u0026ndash;40 peptide was dissolved in dimethyl sulfoxide (DMSO) at a concentration of 10 mg/ml and immediately stored in aliquots at -20 ℃. Then, 25 \u0026micro;l of this peptide solution (10 mg/ml) was diluted to a final concentration of 80 \u0026micro;mol in 725 \u0026micro;l of PBS (Gibco, Grand Island, NY) and continuously stirred at 37 ℃ (200 rpm). The formation of Aβ aggregates was monitored using a conventional spectrophotometer (Shimadzu UV-150-02; γ405 nm; Sao Paulo, Brazil). The solution in PBS or 1, 5, 10 or 50 mmol ethanol was shaken at 600 rpm, and readings were taken every 5 min. Increase in turbidity was monitored and stopped after 200 min.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eWestern blotting (WB) experiment\u003c/h2\u003e\u003cp\u003eProteins were extracted using RIPA buffer in an ice bath at 4 ℃. Lysates were collected by centrifugation at 15,000 rpm for 15 min. The protein concentration was quantified using BCL protein assay reagent. An equal amount of the sample was separated in running buffer using a sodium dodecyl sulfate-polyacrylamide gradient gel to isolate the proteins. Subsequent transfers were performed, using 0.22 \u0026micro;m polyvinylidene difluoride microporous (PVDF) membranes. The membranes were blocked with 5% skim milk powder for 1 hour at room temperature and then incubated overnight at 4 ℃ with primary antibodies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The blots were then washed four times with TBST for 10 min each and incubated with secondary antibodies (goat anti-rabbit, goat anti-mouse, rabbit anti-goat, 1:5000) for 1 hour. GAPDH was used as a loading control. Finally, densitometric analysis was performed using ImageJ software to calculate the relative protein content using the grayscale values of the target strips compared to the grayscale values of-actin.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eStatistical and reproducibility\u003c/h2\u003e\u003cp\u003eThe statistical analyses were done in R (v.4.2.2) if not specified. Data visualization is implemented using R, Prism10, Cytoscape and Adobe Illustrator 2021. We state that no statistical method was used to predetermine sample size. All data are presented as the primary data or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Statistical analysis for western blotting were performed using SPSS19.0 software. One-way analysis of variance (ANOVA) with Tukey's post hoc test was applied for comparison among multiple groups, and independent sample t test for comparison between two groups. GraphPad Prism software version 7.0 (GraphPad Software Inc.) was used for quantification histograms generation. \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the single-cell transcriptome atlas of PFC aging across the postnatal lifespan\u003c/h2\u003e\u003cp\u003eTo elucidate the cellular composition and transcriptomic alterations of the PFC throughout the individual life course, we conducted an integrated analysis of snRNA-seq data from 158 healthy individuals across 15 datasets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u003cb\u003eand Supplementary Table\u0026nbsp;1)\u003c/b\u003e. We divided all samples into 8 groups with a 10-year span. The ages between adjacent age groups were significantly different \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. After rigorous filtering and quality control, we obtained a total of 587,878 cell nuclei \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u003cb\u003eand Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Through dimensionality reduction, UMAP visualization, and marker gene identification, we identified 9 cell populations with significant transcriptomic differences, including ExN (\u003cem\u003eSYT1, SNAP25\u003c/em\u003e, and \u003cem\u003eSLC17A7\u003c/em\u003e), InN (\u003cem\u003eSYT1, SNAP25\u003c/em\u003e, and \u003cem\u003eGAD1\u003c/em\u003e), Astro (\u003cem\u003eAQP4\u003c/em\u003e), Micro (\u003cem\u003eCSF1R\u003c/em\u003e), OPC (\u003cem\u003eOLIG1\u003c/em\u003e), MOL (\u003cem\u003eMBP\u003c/em\u003e), Endo (\u003cem\u003eFLT1\u003c/em\u003e), and Peri (\u003cem\u003eDCN\u003c/em\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u003cb\u003eand Supplementary Fig.\u0026nbsp;1A)\u003c/b\u003e. The snRNA-seq datasets analyzed here can be interrogated with an interactive web interface (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://brainpfcatlas.cn/\u003c/span\u003e\u003cspan address=\"http://brainpfcatlas.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eCellular composition and transcriptome differences during the aging process of PFC\u003c/h2\u003e\u003cp\u003eFirstly, we analyzed the changing trends of the quantities of different cell types with age. ExN and InN almost reached their peaks at 30s and then continuously declined, remaining almost unchanged at 60s. MOL was at its lowest at 30s, continuously rising until 70s when it remained stable. OPC did not show significant changes with age, demonstrating a weak and continuous downward trend. Astro rapidly declined at 20s and then decreased at a slow pace starting from 30s. Micro rapidly declined at 20s and then gradually increased starting from 30s. Endo rose until 40s when it reached its peak and then continuously declined until 70s when it remained stable. Peri showed no significant changes throughout the entire life course \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Then, we calculated the DEGs with significant differences for each age group using 20s as the control \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u003cb\u003eand Supplementary Table\u0026nbsp;3)\u003c/b\u003e. Before 40s, ExN and InN had a large number of up-regulated DEGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, which might be related to individuals beginning to receive higher education and facing more social pressure. At 20s, ExN, InN, OPC and Astro had a large number of down-regulated DEGs, and the functions of these genes were related to dendritic spine development, regulation of neuronal projection development, trans-synaptic signaling regulation, and modulation of chemical synaptic transmission (see below). Around 50s, almost all cells began to undergo significant changes, and both up-regulated and down-regulated DEGs started to increase \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. These results are basically consistent with those previously reported by RNA-seq [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNext, we further analyzed the similarity of changes in different cells of the same age group. The heat map and cluster analysis reveal a significant overlap of DEGs across various age groups within distinct cell types \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. By analyzing the DEGs across various cell types, we can identify age-related DEGs that are conserved among these cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. We found that 9 DEGs changed in all cells of the corresponding age group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Although \u003cem\u003eZBTB16\u003c/em\u003e and \u003cem\u003eMT-ND3\u003c/em\u003e increase with age, their extremely high expression in the 90s suggests that they may be protective factors or longevity-related factors. The disease-free survival curves of these two genes also indicate that individuals with high levels have a longer survival period \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStudies have shown that aging is caused by an increase in transcriptional instability rather than a coordinated transcriptional program, and the increase in age-related transcriptional noise may lead to changes in cell fate and blurring of cell type identity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To further understand transcriptional stability during aging, we calculated the transcriptional noise of different cell types. The transcriptional noise of all cell types significantly increased at 30s. The transcriptional noise of ExN continued to increase before 60s \u003cb\u003e(Supplementary Fig.\u0026nbsp;1B)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eCell communication analysis indicated that the intensities of various cytokines, signaling molecules, and receptors differ across distinct age groups \u003cb\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e. Notably, NRXN, NCAM, NRG, NEGR, and CNTN exhibited the most pronounced signals in PFC \u003cb\u003e(Supplementary Fig.\u0026nbsp;2B)\u003c/b\u003e. Furthermore, we observed a continuous increase in CD45, CD22, COMPLEMENT, APP, and SPP1 levels with advancing age beyond the 20s \u003cb\u003e(Supplementary Fig.\u0026nbsp;2B)\u003c/b\u003e. These molecules are associated with immune responses and inflammatory processes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eTranscriptional differences of excitatory neurons in PFC during aging\u003c/h2\u003e\u003cp\u003eTo investigate the changes in the transcriptome of excitatory neurons during aging, we analyzed the trends of the changes in the excitatory neuron-related DEGs throughout the life course \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. Based on the functional enrichment analysis, we found that a group of genes related to synaptic transmission and axon development (C3) in ExN were continuously downregulated with age \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Additionally, we discovered a group of genes related to synaptic organization and dendritic spine development (C6) were highly expressed in the elderly \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e; since the upregulation of this group of genes occurred just before the high expression of microglia activation-related genes (see below), we hypothesized that ExN-C6 might be associated with the formation of immature or morphologically abnormal dendritic spines in the elderly. These abnormal dendritic spines could lead to excessive pruning by microglia and subsequently trigger inflammatory responses. Of course, there were also some gene clusters related to synaptic transmission that showed fluctuating expression with age, such as ExN-C5 and ExN-C2.\u003c/p\u003e\u003cp\u003eFurther subpopulation analysis of ExN identified 11 subgroups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, which were classified into different subtypes based on the expression of depth-related markers in the cortex \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The standardized cell proportions indicated that ExN_NRGN significantly decreased at 60s, L2_CUX2 significantly increased at 60s, and L3_5 neurons significantly increased at 70s \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD \u003cb\u003eand Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Functional enrichment analysis revealed that all subgroups were associated with synaptic organization and synaptic transmission \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Among them, ExN_NRGN exhibited specific functions related to energy conversion, axonal transport, and axo-dendritic transport \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Monocle pseudo-temporal analysis found that deep-layer neurons and superficial-layer neurons were distributed at different time points \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-H\u003cb\u003e)\u003c/b\u003e. For instance, deep-layer neuron marker genes \u003cem\u003eTLE4\u003c/em\u003e and \u003cem\u003eBCL11B\u003c/em\u003e were mainly expressed at the beginning of the differentiation trajectory, while the superficial-layer neuron marker gene \u003cem\u003eCUX2\u003c/em\u003e was mainly expressed at the other end of the differentiation trajectory \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. Further analysis of the time-dependent correlation genes in the branch where ExN_NRGN was located identified a cluster of genes (C1) related to axonal regeneration and synaptic integration \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI\u003cb\u003e).\u003c/b\u003e Further analysis of 209 transcriptome data from the PFC revealed that the expression of \u003cem\u003eNRGN\u003c/em\u003e was significantly downregulated in elderly individuals \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e. Immunohistochemical staining (IHC) showed that compared with elderly individuals, \u003cem\u003eNRGN\u003c/em\u003e-positive neurons in the cerebral cortex of middle-aged and young individuals were more deeply stained and had larger cell bodies \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eTranscriptional differences of inhibitory neurons in PFC during aging\u003c/h2\u003e\u003cp\u003eTo investigate the transcriptional changes of inhibitory neurons during aging, we analyzed the trends of inhibitory neuron-related DEGs throughout the lifespan as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Similar to ExN, based on functional enrichment analysis, we found that there was also a group of gene clusters (C2) related to synaptic transmission and axon development in InN that were continuously downregulated with age \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. There were 155 overlapping genes between ExN-C3 and InN-C2. Of course, there were also some gene clusters related to synaptic transmission that showed fluctuating trends with age, such as InN-C1 and InN-C6 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFurther subpopulation analysis of InN identified nine subgroups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, including the classic PV (\u003cem\u003ePVALB\u003c/em\u003e), SST (\u003cem\u003eSST\u003c/em\u003e) and VIP (\u003cem\u003eVIP\u003c/em\u003e) interneurons \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Consistent with previous studies, these inhibitory neurons originated from the medial ganglionic eminence (\u003cem\u003eMGE, LHX6\u003c/em\u003e) or the caudal ganglionic eminence (\u003cem\u003eCGE, NR2F2\u003c/em\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The standardized cell proportions indicated that PVALB_SST_InN and SST_InN significantly decreased at 60s \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD \u003cb\u003eand Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Functional enrichment analysis revealed that different subgroups were involved in distinct synaptic organization formation, postsynaptic organization and synaptic signal transmission processes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Among them, PVALB_SST_InN exhibited specific functions related to axonal transport, axo-dendritic transport and synaptic vesicle cycling \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. CytoTRACE analysis found that different subgroups showed different differentiation levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Meanwhile, in the pseudo-time series of Monocle analysis, interneurons from MGE and CGE appeared on different branches \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. The time-dependent genes (C2) associated with PVALB_SST_InN were mainly related to axonal regeneration, dendritic development and synaptic signal transmission \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eAstrocyte transcriptomic diversity in postnatal human PFC\u003c/h2\u003e\u003cp\u003eThrough the temporal analysis of DEGs in Astro, it was also found that a group of genes related to synaptic transmission and axon development (Astro-C2) were downregulated in the elderly group, but the occurrence time was slightly later than that in neurons and MOL \u003cb\u003e(Supplementary Fig.\u0026nbsp;3A)\u003c/b\u003e. Additionally, we discovered a group of genes that were significantly upregulated at 70s (Astro-C3). Astro-C3 is associated with biological processes such as programmed cell death (\u003cem\u003eTNFRSF1A, ST6GAL1, IFI6, IL6ST\u003c/em\u003e) and the production of inflammatory factors (\u003cem\u003eSTAT3, IL6R, IL6ST, YAP1\u003c/em\u003e) \u003cb\u003e(Supplementary Fig.\u0026nbsp;3B)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFurther subpopulation analysis of Astro identified five subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Among them, Astro1 expresses neural stem cell-related genes such as \u003cem\u003eSOX2, MGFE8\u003c/em\u003e and \u003cem\u003eWIF1\u003c/em\u003e, showing partial progenitor cell potential \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, and is associated with learning and memory \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Besides expressing SOX2, Astro2 also expresses genes related to promoting synaptic formation, such as \u003cem\u003eDPP10\u003c/em\u003e and \u003cem\u003eGPC6\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and was associated with presynaptic organization and neural regeneration \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e).\u003c/b\u003e Astro1 and Astro2 were significantly decreased in the late stage of aging \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD \u003cb\u003eand Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Astro3, which is associated with acute inflammatory responses, apoptosis and amoeboid cell migration, similar to reactive astrocytes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], was significantly increased at 70s \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e Milo analysis yielded the same result (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In the pseudo-time series analyzed by Monocle, three major subgroups (Astro1-3) appeared on different branches (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-H). Functional enrichment of time-dependent differentially expressed genes revealed that a group of genes related to synaptic integration and synaptic signal transduction were expressed on the branch corresponding to Astro1, while genes associated with apoptotic signaling pathways were expressed on the branch corresponding to Astro3 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eMicroglial transcriptomic diversity in postnatal human PFC\u003c/h2\u003e\u003cp\u003eThrough the temporal analysis of DEGs in Micro, it was found that Micro-C6, which is related to axon formation and development, was significantly downregulated at 40s \u003cb\u003e(Supplementary Fig.\u0026nbsp;4A)\u003c/b\u003e, indicating that early Micro has a protective or positive effect on CNS function. With the increase in age, genes related to leucocyte activation, antigen presentation, and inflammatory factor production (Micro-C4) significantly increased \u003cb\u003e(Supplementary Fig.\u0026nbsp;4A)\u003c/b\u003e. Through large-scale gene knockout screening of the Micro-C4 gene set, it was found that \u003cem\u003eFCGR3A\u003c/em\u003e KO could significantly perturb 143 genes \u003cb\u003e(Supplementary Fig.\u0026nbsp;4B and Supplementary Table\u0026nbsp;4)\u003c/b\u003e. These genes are related to antigen presentation, ferroptosis, ion homeostasis, synaptic pruning, leukocyte migration, and inflammatory factor production \u003cb\u003e(Supplementary Fig.\u0026nbsp;4B)\u003c/b\u003e. GSEA analysis revealed that \u003cem\u003eFCGR3A\u003c/em\u003e could significantly upregulate signaling pathways such as antigen presentation, ferroptosis, oxidative phosphorylation, AD, chemokines, and HD \u003cb\u003e(Supplementary Fig.\u0026nbsp;4C).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFurther subpopulation analysis of Micro identified six subsets, including three steady-state microglial clusters (HM1-3), two activated microglial clusters (ARM1 and ARM2), and one proliferative microglial cluster (CPM) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u003cb\u003eand B)\u003c/b\u003e. Stable microglia highly express genes such as \u003cem\u003eP2RY12, CX3CR1\u003c/em\u003e and \u003cem\u003eSALL1\u003c/em\u003e, while activated microglia highly express disease-related microglia genes such as \u003cem\u003eCTSB, HIF1A, SPP1, B2M, APOE\u003c/em\u003e and \u003cem\u003eC1QA\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. CPM highly expresses cell proliferation-related genes such as \u003cem\u003eTOP2A, CDK1\u003c/em\u003e and \u003cem\u003eMKI67\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The standardized cell proportions show that HM1 and HM2 are significantly downregulated at 70s, while the corresponding ARM1 and ARM2 are significantly upregulated \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC \u003cb\u003eand Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Milo analysis yields the same result \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. scDRS disease association analysis shows that HM1 and HM2 are associated with intelligence (INT), verbal-numerical reasoning (VNR), and various mental disorders \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e; while ARM1 and ARM2 are significantly associated with neurodegenerative diseases such as AD and MS. ARM2 is also associated with various mental disorders, suggesting the role of microglial activation in antipsychotic treatment. Further group analysis reveals that ARM1 and ARM2, which are significantly associated with AD and MS, are significantly correlated after 70s. Pseudo-temporal analysis based on CytoTRACE discovers the differentiation trajectory from stable microglia to activated microglia \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Monocle analysis yields the same result \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-I\u003cb\u003e)\u003c/b\u003e. \u003cem\u003eP2RY12\u003c/em\u003e, which is related to stability, is expressed at one end of the pseudo-temporal trajectory, while \u003cem\u003eC1AQ\u003c/em\u003e and \u003cem\u003eC1QB\u003c/em\u003e, which are related to activation, and \u003cem\u003eFTH1\u003c/em\u003e and \u003cem\u003eFTL\u003c/em\u003e, which are related to ferroptosis, are expressed at the other end \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e. Meanwhile, we find that \u003cem\u003eFCGR3A\u003c/em\u003e is highly expressed at the end where activated microglia are located \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e. We found that \u003cem\u003eSOX10\u003c/em\u003e is a relatively specific expression of ARM transcription factor. Immunofluorescence staining showed that \u003cem\u003eIba-1\u003c/em\u003e/\u003cem\u003eSOX10\u003c/em\u003e double positive cells were significantly increased in the PFC of wild-type old mice. In addition, we found that \u003cem\u003eIba-1\u003c/em\u003e/\u003cem\u003eSOX10\u003c/em\u003e double positive cells were significantly elevated in the PFC of AD mice of the same age. These results suggest that ARM is not only a pathological feature of individual brain aging, but may also be involved in the pathogenesis of AD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eOligodendrocyte lineage transcriptomic alteration in postnatal human PFC\u003c/h2\u003e\u003cp\u003eThe oligodendrocyte lineage mainly includes oligodendrocyte precursor cells (OPC), committed/new-formed oligodendrocytes (COP/NFOL) and mature oligodendrocytes (MOL), which are mainly responsible for the formation and maintenance of myelin [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In this study, we identified three distinct oligodendrocyte lineages, each of which performs specific biological functions \u003cb\u003e(Supplementary Fig.\u0026nbsp;5A-C)\u003c/b\u003e. The corrected cell counts indicate that OPC and NFOL significantly decreased at 50s, while MOL exhibited a significant increase \u003cb\u003e(Supplementary Fig.\u0026nbsp;5D and Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Research shows that although the number of MOLs increases, the continuity of myelin structure deteriorates in the elderly group, presenting as interrupted or isolated myelin [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To investigate the changes in the transcriptome during the aging process of OPCs and oligodendrocytes, we analyzed the trends of DEGs in OPCs and oligodendrocytes throughout the lifespan as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cb\u003e(Supplementary Fig.\u0026nbsp;5E and F)\u003c/b\u003e. Based on functional enrichment analysis, we found that the OPC-C2 and MOL-C5 gene sets related to synaptic transmission and synaptic organization were continuously downregulated with age at 40s \u003cb\u003e(Supplementary Fig.\u0026nbsp;5E and F)\u003c/b\u003e. In OPCs, there was a group of gene sets related to insulin response (OPC-C3) that gradually increased with age \u003cb\u003e(Supplementary Fig.\u0026nbsp;5E)\u003c/b\u003e. In MOLs, there was a group of gene sets related to axon development and glial cell differentiation (MOL-C6) that gradually increased with age \u003cb\u003e(Supplementary Fig.\u0026nbsp;5F)\u003c/b\u003e. Genes related to neuronal myelination, axonal myelination, and oligodendrocyte differentiation were significantly downregulated at 30s and then showed fluctuating changes with age \u003cb\u003e(Supplementary Fig.\u0026nbsp;5F)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTranscriptomic differences of endothelial cells and pericytes in the PFC during the aging process\u003c/h3\u003e\n\u003cp\u003eThe vascular system in the brain forms a special blood-brain barrier (BBB), which regulates the transport of nutrients, molecules and cells from the blood to the brain. The aging characteristics of the brain's vascular system are changes in vascular morphology and stiffness, as well as dysregulation of cerebral blood flow (CBF) and tissue oxygenation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Traditionally, it was believed that the BBB begins to disintegrate with aging, allowing molecules that cause cognitive impairment to leak [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As one of the most important constituent cells of the BBB, endothelial cells and pericytes are crucial to understand their changes during aging. Through temporal analysis of the differentially expressed genes in Endo, we found that genes related to BBB transport (Endo-C2) were significantly downregulated with age, while genes related to viral invasion (Endo-C1) were significantly upregulated in the elderly \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u003cb\u003eand Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Further subpopulation analysis of Endo identified seven transcriptionally distinct and functionally specific cell subpopulations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-D\u003cb\u003e)\u003c/b\u003e. For instance, Endo1, which is associated with BBB transport and vascular transport, was significantly decreased at 60s \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e, while Endo2, related to cytokine-mediated signaling pathways, and Endo4, related to T cell activation and lipid transport, were significantly increased at 60s \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. In the pseudo-temporal sequence of Monocle analysis, we found that Endo1 showed significant differences in differentiation trajectories compared to Endo2 and Endo4 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-H\u003cb\u003e)\u003c/b\u003e, with Endo1 appearing in the early stage and Endo2 and Endo4 in the late stage. Functional enrichment of time-dependent differentially expressed genes revealed that genes related to the differentiation of Endo2 and Endo4 were associated with T cell immune responses \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eNext, we conducted a temporal analysis of the differentially expressed genes in Peri and found that genes related to functions such as amino acid transport, neurotransmitter reuptake, and synaptic transmission (Peri-C1) were significantly downregulated with age, while genes related to T cell differentiation (Peri-C2) showed fluctuating increases with age \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eScreening of plasma markers related to brain aging based on aging-related plasma proteomics data\u003c/h2\u003e\u003cp\u003eTo further screen plasma markers that can diagnose PFC aging, we conducted an integrated analysis with aging-related plasma proteomics data [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We identified 10 genes/proteins that showed significant age-related changes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Among them, 4 genes/proteins (\u003cem\u003eRBM39, STAT3, DGKB\u003c/em\u003e, and \u003cem\u003eFUT9\u003c/em\u003e) showed significant age-related changes in both men and women \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Further analysis of other CNS regions and species revealed that \u003cem\u003eFUT9\u003c/em\u003e also showed significant changes in aging MOLs in other areas such as ACC, HIP, and SC. Additionally, \u003cem\u003eFUT9\u003c/em\u003e exhibited significant changes in aging MOLs in the HIP of macaques and tree shrews \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. \u003cem\u003eFUT9\u003c/em\u003e underwent significant changes during the aging process in both MOL and ExN. To further investigate the function of \u003cem\u003eFUT9\u003c/em\u003e, we performed \u003cem\u003eFUT9\u003c/em\u003e gene knockout in MOL and ExN, respectively. The results showed that the knockout of \u003cem\u003eFUT9\u003c/em\u003e affected the expression of 101 and 140 genes in MOL and ExN, respectively \u003cb\u003e(Supplementary Fig.\u0026nbsp;6 and Supplementary Table\u0026nbsp;5)\u003c/b\u003e. The functions of these genes were also different. For example, in MOL, the genes affected by \u003cem\u003eFUT9\u003c/em\u003e were mainly related to neurodegenerative diseases, neurofiber development, axon development, and neurofiber bundle formation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. In ExN, the genes affected by \u003cem\u003eFUT9\u003c/em\u003e were mainly related to \u003cem\u003eGABA\u003c/em\u003e metabolism, glutamate metabolism, neuroprojection development, trans-synaptic signaling, neuronal migration, and memory \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Then, to further verify whether \u003cem\u003eFUT9\u003c/em\u003e could serve as a CNS aging marker, we collected plasma from 20 adult individuals and 20 elderly individuals and conducted ELISA experiments. The results indicated that \u003cem\u003eFUT9\u003c/em\u003e showed age-related downregulation in peripheral plasma \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptional dysregulation of cell subpopulation-specific in the prefrontal cortex of AD individuals\u003c/h2\u003e\u003cp\u003eTo investigate the changes of the above-mentioned cell subpopulations under disease conditions, we conducted a comparative analysis of snRNA-seq data from 427 participants (including 189 normal controls and 238 AD cases) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-E\u003cb\u003e)\u003c/b\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. We found that, apart from astrocytes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, the cell subpopulations that underwent significant changes during aging showed even more pronounced alterations in disease. For instance, microglia subpopulations ARM2, CPM, HM1 and HM2 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, endothelial cell subpopulations Endo2 and Endo7 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, excitatory neuron subpopulations L3_5_RORB_PCP4 and ExN_NRGN \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e, and inhibitory neuron subpopulation PVALB_SST_InN \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Further, by constructing in vitro aging and AD models of microglia, WB experiments showed that \u003cem\u003eIL-1β\u003c/em\u003e, which is significantly associated with activated microglia, was significantly elevated in both the aging group and the AD group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e, which further verified the results of snRNA-seq.\u0026nbsp;In summary, our data contribute to understanding the cellular diversity in the brain tissue of AD patients and identifying specific cell subpopulations that may have a higher susceptibility to the disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, to clarify the cellular composition and transcriptome changes of the PFC throughout an individual's life course, we conducted an integrated analysis of snRNA-seq data from 158 healthy individuals in 15 datasets. Based on this, we constructed the largest single-cell transcriptome atlas of PFC aging to date and elucidated the specific and consistent changes of different cell types and their subtypes with age through differential gene analysis and subpopulation analysis. Additionally, by integrating peripheral plasma proteomics data related to aging, we screened for potential markers of brain aging. This study provides specific time windows and targets for the diagnosis and treatment of CNS aging and aging-related diseases in the future.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch on PFC aging across the life course clarifies the conservation and differences among cell types.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe aging of the human brain is a cause of cognitive decline in the elderly and a major risk factor for various neurodegenerative diseases. However, when the brain begins to age has always been a research hotspot. In 2004, Lu et al. defined a group of genes that decreased in expression after the age of 40 by analyzing the transcriptional profiles of the prefrontal cortex of 30 individuals aged 26 to 106. These genes play a core role in synaptic plasticity, vesicle transport and mitochondrial function. At the same time, the induction of stress response, antioxidant and DNA repair genes occurs in the aging cortex [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In 2011, Colantuoni et al.'s RNA-seq study on the PFC of 268 subjects indicated that the transcriptome of brain tissue undergoes significant changes at the age of 50. The DEGs significantly associated with age are mainly related to synapses, axons, ATP synthesis, and cell cycles [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In 2023, Hahn et al. conducted spatiotemporal RNA sequencing of the mouse brain and discovered a gliocyte senescence atlas across the entire brain [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, current research has not clarified the changes in different cell types in human brain tissue with age. Therefore, based on single-nucleus transcriptome sequencing, we analyzed the changes in different cell types in the human brain's prefrontal cortex throughout the life course.\u003c/p\u003e\u003cp\u003eInitially, our analysis of DEGs across various cell types revealed that neurons experienced a significant transcriptomic alteration as early as the age of 30, followed by a second notable change at the age of 50. These findings are largely consistent with the transcriptome sequencing results reported by Colantuoni et al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Cross-brain region studies in mice also indicate that the time when the transcriptome undergoes significant changes occurs around 15 months (equivalent to 50 years old in humans) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Further analysis was conducted on the specificity and consistency of DEGs between cells. Although different cell types have a large number of DEGs with specific changes, there are also consistent DEGs. Targeting the treatment of consistent DEGs can improve the state and function of these cells as a whole during aging. Targeting those cell-specific DEGs can more precisely improve the function of cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSynapse-related functional genes undergo aging-associated alterations in different cell types.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThrough the analysis of age-related differential gene expression across various cell types, we identified conserved functional gene alterations among these cell types, with particular emphasis on the significant changes observed in synaptic-related functional genes. For instance, genes related to synaptic integration and transmission in ExN and InN were significantly downregulated at 30 seconds, those in OPC, MOL and Micro were significantly downregulated at 40 seconds, and those in Astro were significantly downregulated at 60 seconds. One of the hallmarks of the brain as the most complex organ is its large-scale synapses. In the brain tissue of normal healthy aging, there is no extensive loss of nerve cells. The most common age-related structural changes that nerve cells undergo are a reduction in the number and length of dendrites, loss of dendritic spines, a decrease in the number of axons, a significant loss of synapses, and a decline in synaptic connections and synaptic function. These changes may have a significant relationship with behavioral disorders and cognitive decline that accompany normal aging [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, we also found that a group of genes related to dendritic spine development and synaptic organization were elevated in elderly individuals. Similarly, the study of life space synaptome architecture (LSA) indicated that different subtypes of synapses changed differently at different stages. For instance, in the brain tissue of the elderly, subtypes 2, 27, and 34 were increased in most brain regions; however, subtypes 12, 14, 15, and 16 were decreased in the olfactory area and thalamus [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. We speculate that this significantly increased group of genes may lead to abnormal development of dendritic spines and synapses, and these abnormally developed dendritic spines may cause abnormal activation of microglia [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Analysis of microglial cell differential genes has demonstrated that the time of gene changes related to synaptic pruning and leukocyte activation overlaps and is slightly later than the time of significant increase in synaptic-related genes. Activated microglial cells release more inflammatory factors, and over-activated microglial cells may phagocytize normal synapses and dendritic spines, thus forming a vicious cycle. Additionally, at 70s, the significant upregulation of genes related to programmed cell death and inflammatory factor production in astrocytes may be associated with the significant increase in synaptic-related genes. This is because studies have shown that in the developing brain, astrocytes can eliminate excess synapses through phagocytic receptors \u003cem\u003eMertk\u003c/em\u003e and \u003cem\u003eMegf10\u003c/em\u003e, as well as indirectly by inducing the expression of complement cascade components in neurons [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicroglia undergo a transformation from a homeostatic state to an activated state during the aging process.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMicroglia, as the resident immune cells of the CNS, provide immune surveillance for the body and are widely recognized as playing a crucial role in neural development, homeostasis and neuroinflammation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Although microglia are generally regarded as playing a protective role in the nervous system, in the microenvironment of aging-induced glial activation, increased complement factors and inflammatory mediators, microglia may reveal their evil side. Aging, as a key risk factor for many neurological diseases, an increasing amount of evidence indicates that microglia are associated with age-related neurofunctional disorders. [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Through the analysis of microglial cell subgroups, we discovered the activation of microglia associated with aging and disease. Research shows that the activation of microglia has neurotoxic effects on neurodegenerative diseases, while in some aspects, it is also an important defender against many neurodegenerative diseases [\u003cspan additionalcitationids=\"CR54 CR55\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In this study, we found that the steady-state related microglia were significantly downregulated at 60s, while ARM1 was significantly upregulated at 70s and remained at a high level thereafter. ARM2 may be a protective activated state of microglia because it also maintained a relatively high level before the age of 40. Disease association analysis demonstrated a significant association between ARM1 and AD and MS. Additionally, we discovered that the expression of microglial \u003cem\u003eFCGR3A\u003c/em\u003e was dysregulated during aging. Studies have shown that \u003cem\u003eFCGR3A\u003c/em\u003e is involved in antibody-dependent cell-mediated cytotoxicity and antibody-dependent viral infection enhancement and other reactions, and is related to NK cell activation and the production of pro-inflammatory cytokines [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In this study, we found that \u003cem\u003eFCGR3A\u003c/em\u003e was significantly elevated in aging and activated microglia. Gene knockout demonstrated that \u003cem\u003eFCGR3A\u003c/em\u003e was significantly associated with functions such as antigen presentation, ferroptosis, ion homeostasis, synaptic pruning, leukocyte migration, and the production of inflammatory factors. Previous studies have shown that dysregulation of \u003cem\u003eFCGR3A\u003c/em\u003e expression in peripheral immune cells is related to diseases such as AD and SCZ [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These results suggest that \u003cem\u003eFCGR3A\u003c/em\u003e may influence brain aging and aging-related diseases from different perspectives, such as peripheral immune cells and central innate immune cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEndothelial cells mediate the activation and infiltration of T cells during the aging process.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBy conducting subpopulation analysis on PFC endothelial cells, we discovered that two endothelial cell subpopulations were significantly elevated in elderly individuals. The functions of these two subpopulations are mainly related to cytokine-mediated signaling pathways and the activation and migration of T cells. Studies have shown that in the tau pathological regions of mice and the brains of AD patients, the number of T cells (especially cytotoxic T cells) has significantly increased, and the number of T cells is correlated with the degree of neuronal loss [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The alterations of the relevant microenvironment in the brain parenchyma may have guiding significance for the recruitment and guidance of T cell transformation. Although studies have shown that there is communication between T cells and activated microglia in brain tissue, the mechanism of T cell infiltration into brain tissue remains unclear. In this study, we found that senescent endothelial cells may provide a microenvironment for T cell activation and infiltration. Further, reactive astrocytes and microglia promote the chemotaxis and migration of T cells. Anti-aging measures targeting brain endothelial cells may be one of the important directions for future anti-brain aging and related diseases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFUT9 can serve as a potential plasma biomarker for CNS aging.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn recent years, scientists have delved increasingly deeper into the research of biomarkers for quantifying biological aging, especially those based on \"omics\" [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Biomarkers of aging are crucial tools for the identification and evaluation of human longevity intervention measures within a realistic time frame [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These biomarkers have the potential to predict outcomes related to aging and can function as surrogate endpoints for assessing interventions aimed at promoting healthy aging and longevity [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Moreover, recent aging biomarkers have focused more on predicting biological age and age-related health outcomes rather than chronological age. An ideal aging biomarker should have a moderate to strong correlation with age and be able to predict multiple aging-related outcomes other than death, such as functional decline, frailty, chronic diseases and disabilities, as well as (multiple) morbidities [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In this study, through integrated analysis with the plasma proteomics data related to aging, we identified 10 genes/proteins that showed significant changes with age. Among them, 4 genes/proteins (\u003cem\u003eRBM39, STAT3, DGKB\u003c/em\u003e, and \u003cem\u003eFUT9\u003c/em\u003e) demonstrated significant age-related changes in both men and women. \u003cem\u003eRBM39\u003c/em\u003e has been proven to be associated with lifespan and age in multiple species [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Studies have shown that \u003cem\u003eSTAT3\u003c/em\u003e in the spinal cord of aging mice shows an age-related increase, accompanied by an increase in the expression of \u003cem\u003eP16\u003c/em\u003e [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The \u003cem\u003eSTAT3\u003c/em\u003e-ubiquitin aggregates formed by lysine-48 and lysine-63 bonds significantly increase in the spinal cord of aging mice [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Research indicates that \u003cem\u003eDGKB\u003c/em\u003e is associated with cognitive complexity, anxiety, depression and eating disorders [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. The expression of Lex carbohydrate structure in the brain is developmentally regulated and is believed to play a role in intercellular interactions during neuronal development. \u003cem\u003eFUT9\u003c/em\u003e is the most important enzyme for the synthesis of Lex in the brain [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. We found that \u003cem\u003eFUT9\u003c/em\u003e showed significant age-related down-regulation and underwent significant changes in multiple CNS regions (PFC, ACC, HIP and SC) and in multiple species (human, macaque and tree shrew) MOL. Gene knockout demonstrated that the function of \u003cem\u003eFUT9\u003c/em\u003e is related to neurodegenerative diseases, neurofilament development, axon development and nerve fiber bundle formation. ELISA experiments demonstrated significant changes in \u003cem\u003eFUT9\u003c/em\u003e in peripheral plasma. These findings indicate that \u003cem\u003eFUT9\u003c/em\u003e may serve as a potential plasma biomarker for the aging of the central nervous system.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we focused on PFC as our research subject and integrated snRNA-seq data from 158 healthy individuals across 15 datasets. With a decade-long interval, we categorized all samples into eight groups to analyze the transcriptomic changes of various cell types in the PFC throughout an individual's life course. Through temporal analysis, we detailed the transcriptomic alterations of eight major cell types during different stages of life. Our findings indicate that synaptic development, integration, and transmission are generally downregulated with aging; furthermore, distinct subpopulations within these cell types exhibit age-related changes and contribute to brain aging at different time points. The increase in apoptotic signals and inflammatory factor production in astrocytes among older adults accelerates brain aging processes. Microglia predominantly maintain a steady state during early life stages but transition to an activated state later on, characterized by increased release of inflammatory factors and chemotaxis. This activation may be associated with abnormal synapse development and dendritic spine formation as well as irregular myelination patterns. Abnormally activated microglia are implicated in the onset and progression of AD and MS in elderly populations. Additionally, transport functions across the BB in endothelial cells significantly decline with age while pericyte regulatory functions concerning neurotransmitters show a decreasing trend over time.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePFC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprefrontal cortex\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eExN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eexcitatory neurons\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eInN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einhibitory neurons\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMOL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emature oligodencyte\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoligodendrocyte precursor cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAstro\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eastrocytes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMicro\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emicroglia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEndo\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eendothelial cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePeri\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epericytes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUMIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eunique molecular identifiers.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAnimal ethics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures, involving animal care and testing conformed to the Animal Care and Use Committee of Kunming Medical University (approval number: KMMU2019058).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Institutional Research Ethics guidelines and ethical principle involving human participation (Helsinki Declaration) and approved by the Medical Ethics Committee of Kunming Medical University (approval number: KMMU2022MEC092). All volunteers for peripheral blood collection were informed of the purpose of the study and signed the informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets (GSE140231, GSE141552, GSE144136, GSE157827, GSE168408, GSE174367, GSE213982, PMID34582785, PRJNA434002, PRJNA544731, syn18485175, syn21125841, syn38120890, EGAD00001008287) analysed during the current study are available in the Gene Expression Omnibus (GEO), Synapse, ArrayExpress, European Nucleotide Archive (ENA), European Genome-phenome Archive (EGA), and National Genomics Data Center (NGDC). The processed expression matrices and corresponding code used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Kunming Health Science and Technology Talent Training Project (thousand project, 2024-SW (Reserve)-72; 2024-SW (Reserve)-73), Yunnan Education Department Fund (2025J0246), National Natural Science Foundation of China (Grant number 82160269; 82360275; 82160272), Yunnan Provincial Department of Science and Technology Science and Technology Plan Project (202405AC350104), and Scientific research project of the Provincial Clinical Medical Center of Yunnan Province (2024YNLCYXZX0280).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRZN, THB, and JL conceptualized, acquired funding, and supervised this study. Data were processed, analyzed and visualized by RZN, YYZ and ZLY. The manuscript was drafted by NRZ, MYZ and CHY, and was reviewed and edited by THB, and XFZ. All authors discussed results and commented on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to xiyoucloud for providing computational infrastructure. We are also grateful to Li Chen for the comments and suggestions on the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLongo, V.D. and R.M. Anderson, \u003cem\u003eNutrition, longevity and disease: From molecular mechanisms to interventions\u003c/em\u003e. Cell, 2022. 185(9): p. 1455\u0026ndash;1470.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorgoulis, V., et al., \u003cem\u003eCellular Senescence: Defining a Path Forward\u003c/em\u003e. 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Glycobiology, 2003. 13(6): p. 445\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"npj-aging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Aging](https://www.nature.com/npjamd/)","snPcode":"41514","submissionUrl":"https://submission.springernature.com/new-submission/41514/3","title":"npj Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prefrontal cortex, Aging, Single-nucleus RNA sequencing, Synapsis, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7406880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7406880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain aging is a major risk factor for numerous diseases, including cerebrovascular diseases and neurodegenerative disorders, posing a significant threat to human health. Currently, the continuous changes of different cell types in human brain tissue throughout an individual's life course have not been fully elucidated. Here we describe the continuous changes in the transcriptomes of different cell types and their subpopulations in the prefrontal cortex (PFC) across the postnatal lifespan. We integrated single-nucleus RNA sequencing (snRNA-seq) data of the PFC from 158 healthy individuals aged 19\u0026ndash;101 years across 15 datasets and constructed a PFC aging atlas of 587,878 nuclei. We found that the ages of 30s and 50s are the two most significant periods of brain transcriptome changes in adulthood. Synaptic development, integration, and transmission are generally downregulated during aging. Different subpopulations of various cell types undergo age-related transitions and participate in the brain aging process at different time points. The increase in apoptotic signals and the production of inflammatory factors in astrocytes of elderly individuals accelerate brain aging. Microglia are mainly in a homeostatic state in the early stage, which is beneficial to the normal function of the CNS, and mainly in an activated state in the later stage, showing an increase in the release of inflammatory factors and chemotaxis. The activation of microglia may be related to the abnormal development of synapses and dendritic spines, as well as the abnormal myelination. Abnormally activated microglia are involved in the occurrence and development of Alzheimer's disease (AD) and multiple sclerosis (MS) in elderly individuals. The function of trans-blood-brain-barrier transport in endothelial cells is significantly downregulated with age. Based on aging-related plasma proteomics data, \u003cem\u003eFUT9\u003c/em\u003e was identified as a plasma biomarker related to brain aging. Our study clarifies the temporal differences and potential connections in the aging of different cell types in the PFC, providing a reference for the selection of specific cell types and time windows for future anti-CNS aging interventions.\u003c/p\u003e","manuscriptTitle":"A single-cell transcriptome atlas of cell diversity in human prefrontal cortex across the postnatal lifespan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 01:28:32","doi":"10.21203/rs.3.rs-7406880/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T15:25:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T22:21:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234437675637493255149805013583684171162","date":"2025-11-12T18:58:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T06:41:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306397986281246991543358908379648734502","date":"2025-09-26T16:06:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156018417223900740514142365063522705401","date":"2025-09-14T23:43:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-14T16:36:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-04T20:40:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-22T14:24:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Aging","date":"2025-08-19T09:11:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-aging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Aging](https://www.nature.com/npjamd/)","snPcode":"41514","submissionUrl":"https://submission.springernature.com/new-submission/41514/3","title":"npj Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a87c43de-f5e7-453f-b15c-be853fbfa5a6","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55029115,"name":"Health sciences/Biomarkers"},{"id":55029116,"name":"Health sciences/Neurology"},{"id":55029117,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-11T08:25:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 01:28:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7406880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7406880","identity":"rs-7406880","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}