Transcriptomic profiling of the middle temporal gyrus reveals differential glial/neuronal dysregulation across Alzheimer’s disease and aging

Transcriptomic profiling of the middle temporal gyrus reveals differential glial/neuronal dysregulation across Alzheimer’s disease and aging | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Transcriptomic profiling of the middle temporal gyrus reveals differential glial/neuronal dysregulation across Alzheimer’s disease and aging View ORCID Profile IS Piras , A Bonfitto , S Song , A Aldabergenova , J Sloan , A Trejo , JC Troncoso , C Geula , EJ Rogalski , CH Kawas , M Corrada , TG Beach , View ORCID Profile GE Serrano , PF Worley , CA Barnes , View ORCID Profile MJ Huentelman doi: https://doi.org/10.1101/2025.10.19.683343 IS Piras 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for IS Piras A Bonfitto 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site S Song 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site A Aldabergenova 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site J Sloan 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site A Trejo 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site JC Troncoso 2 Neuropathol Lab., Johns Hopkins University, Sch. of Med ., Baltimore, MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site C Geula 3 Mesulam Cent for Cognitive Neurology & Alzheimer Disease, Northwestern University. Med. Sch ., Chicago, IL Find this author on Google Scholar Find this author on PubMed Search for this author on this site EJ Rogalski 4 Healthy Aging & Alzheimer’s Res. Care (HAARC) Center, Neurol., Univ. of Chicago , Chicago, IL Find this author on Google Scholar Find this author on PubMed Search for this author on this site CH Kawas 5 Univ. of California, Irvine School of Medicine , Irvine, CA Find this author on Google Scholar Find this author on PubMed Search for this author on this site M Corrada 5 Univ. of California, Irvine School of Medicine , Irvine, CA Find this author on Google Scholar Find this author on PubMed Search for this author on this site TG Beach 6 Banner Sun Health. Res. Inst ., Sun City, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site GE Serrano 6 Banner Sun Health. Res. Inst ., Sun City, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for GE Serrano PF Worley 7 Dept Neurosci/Neurol, Johns Hopkins Sch. Med ., Baltimore, MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site CA Barnes 8 Evelyn F. McKnight Brain Institute, Univ. of Arizona , Tucson, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site MJ Huentelman 1 Translational Genomics Research Institute , Phoenix, AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for MJ Huentelman For correspondence: mhuentelman{at}tgen.org Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Alzheimer’s disease (AD), the most common cause of dementia, is characterized by amyloid-β plaques, neurofibrillary tangles, and widespread neuronal dysfunction. Aging, the strongest risk factor for AD, is also associated with some overlapping processes, such as neuronal cell transcriptional downregulation and glial cell activation. The middle temporal gyrus (MTG) is a brain region that supports semantic processing and default-mode connectivity and shows early vulnerability in both aging and AD. Here we profile bulk RNA-seq from 606 postmortem MTG samples with the goal of understanding the transcriptional changes associated with AD and aging. In 217 clinical and neuropathologically confirmed AD versus 290 no-dementia controls donors, we identify 613 differentially expressed genes (390 up, 223 down; |log2 fold change| ≥ 0.5; BH P < 0.05), with NPNT and ADAMTS2 among the top upregulated signals. Cell set enrichment indicates reduced excitatory neuronal signatures together with increased microglial, astrocytic, endothelial, and pericyte programs. Gene-set analyses reveal strong activation of angiogenesis, extracellular-matrix organization, wound response, adaptive immunity, and coordinated suppression of neuronal and mitochondrial processes, including synaptic signaling and respiratory-chain complexes. Multiscale coexpression mapping resolves three disease clusters: a neuron-mitochondrial module suppressed in AD (M5; hub PJA2; key driver GABRB3), a microglial immune module upregulated in AD (M6; hub C1QC; key driver FCER1G), and an increased astrocyte-vascular extracellular-matrix module in AD (M8; hub ESAM; key driver TAGLN). Across 324 non-AD controls aged 24–108 years, aging is associated with declines in gene expression associated with translation, proteostasis, and mitochondrial function and increases in those linked to oligodendrocyte and myelination programs (for example M4; hub CNTN2; key driver MOBP); in a 65+ subset, neuronal and protein-folding modules show the strongest decrements with reduced glial gene expression upregulatio. Our results indicate that late-life aging involves increased glial responses and neuronal/proteostasis suppression, whereas AD is also associated with immune– vascular–ECM activation and suppression of neuronal programs. Introduction Alzheimer’s disease (AD) is the most common form of dementia and is clinically characterized by progressive cognitive decline and neuropathologically defined by amyloid-β plaques and neurofibrillary tangles 1 , 2 . Additionally, multiple cellular phenotypes have been reported, including synaptic and mitochondrial dysfunction, inflammation, vascular alterations, and impaired metabolic signaling 3 – 6 . The number of people with AD continues to rise globally, and aging remains the strongest risk factor 7 . Aging is characterized by overlapping, but not identical, transcriptomic changes such as downregulation of neuronal and mitochondrial pathways and increased glial gene expression 8 , 9 . High-throughput transcriptomic profiling has been relevant for the characterization of these processes in AD and RNA-seq datasets have highlighted the down-regulation of synaptic and mitochondrial genes with concomitant up-regulation of immune and vascular pathways 10 – 19 . However, the middle temporal gyrus (MTG) has not been extensively characterized with RNA-sequencing 20 . The MTG is a brain region that supports lexical-semantic processing and conceptual knowledge critical for language comprehension 21 , 22 . The MTG also participates in the default mode network 23 , the activity of which was linked to differences between healthy aging and AD over 20 years ago 24 . With typical aging, cortical thinning consistently affects the temporal gyri of the brain, including the MTG, and this has been associated with declines in higher-order cognition 25 . In AD, the MTG shows early atrophy and hypometabolism, and degeneration in this region can predict progression from typical aging to AD 26 , 27 . These findings suggest that the MTG is a key region for semantic cognition whose vulnerability is associated with both cognitive aging and AD-related pathology. In this study, we performed an extensive transcriptomic analysis of more than 600 postmortem samples from the MTG, encompassing individuals with clinical and neuropathologically confirmed AD and no-dementia controls (CTL). We also conducted a detailed investigation of aging in individuals without dementia and significant AD pathology. This large dataset allowed us to examine both AD-associated and aging-related transcriptional changes within the same cortical region. By combining differential gene expression with coexpression network analyses, we mapped the transcriptional programs associated with AD pathology and brain aging. This combined strategy allowed us to identify molecular changes that are shared between aging and AD from those that are unique to each condition. We provided insights into the distinct and overlapping mechanisms driving age-and AD-related transcriptional remodeling in the human brain. Methods Samples Fresh frozen cortical sections from the middle temporal lobe were obtained from four different brain banks: Banner Sun Health Research Institute, the 90+ Study of the University of California Irvine, Johns Hopkins University, the SuperAging Research Initiative and Northwestern University brain bank. After quality controls (sequencing quality and outliers’ removal, see results section for details), we included a total of 606 samples. Neuropathological and demographic variables for the final samples included in the study are reported in Table 1 . Informed written consent was obtained from all participants prior to inclusion in the study. There were two primary studies: 1) the AD study, which focused on identifying transcriptional signatures and co-expression networks associated with AD, and 2) the aging study, which examined transcriptional signatures across aging in no-dementia individuals. View this table: View inline View popup Download powerpoint Table 1. Demographic and neuropathological characteristics of the analyzed samples. For the AD study, we included individuals with a clinical and neuropathological diagnosis of AD dementia and no-dementia controls (CTL) with age at death equal to or greater than 65 years old. All AD cases selected had a clinical diagnosis of AD dementia , Braak Stage III-VI, and/or CERAD plaque density “moderate” or “frequent” (AD high pathology). Using these criteria, we selected a total of 217 AD and 290 CTL participants. For the aging study, we included all the no-dementia controls (CTL) without age cutoff, plus 9 participants from the SuperAging research Initiative 28 – 31 , not pathologically confirmed but classified as “superagers” using cognitive tests. The final sample size for the aging CTL group was 324. This study samples were further stratified into “all-ages” (24-108 years; n = 324) and “65+”, including in the latter only donors aged 65 or older (n = 299). Brain Bank Contributors Banner Sun Health Brain and Body Donation Program (BBDP) The Arizona Study of Aging and Neurodegenerative Disorders and Body Donation Program at Banner Sun Health Research Institute is a longitudinal clinicopathologic cohort initiated in 1987 that recruits mainly from retirement communities in metropolitan Phoenix, Arizona 32 , 33 . Enrollees undergo standardized medical, neurologic, and neuropsychological assessments during life, with rapid autopsy at death (median post-mortem interval ∼3.0 hours) yielding high-quality frozen tissues, including a median brain RNA Integrity Number of 8.9 32 , 33 . Whole-body donation has been available since 2005, and both anatomical and neuropathologic diagnoses are rendered by licensed pathologists using contemporary consensus criteria 33 . A total of 232 samples were included in the AD/CTL study, and 151 in the aging study. 90+ Study The 90+ Study is a community-based longitudinal cohort of adults aged 90 years and older, drawn primarily from surviving members of the Leisure World Cohort in Laguna Woods, California 34 . Participants are evaluated approximately every 6 months with a neurological examination, a standardized neuropsychological battery, and informant questionnaires; remote phone or mail assessments are used when in-person visits are not feasible 34 , 35 . Cognitive status (normal cognition, CIND, or dementia) was assigned after a participant’s death in a multidisciplinary diagnostic conference that used information from all longitudinal evaluations, informants, laboratory tests, and medical records, with conferences blinded to pathological evaluation. 30 Neuropathological evaluation was done blinded to clinical information using the National Institute on Aging-Alzheimer’s Association “ABC” score 36 , 37 . 34 Additional cohort descriptions and epidemiologic findings on dementia prevalence and incidence in this population have been published 38 , 39 . A total of 275 samples were included in the AD/CTL study and 156 in the aging study. SuperAging Research Initiative This longitudinal cohort enrolls community-dwelling adults aged 80 years or older who perform at or above the mean for 50–60-year-olds on Rey Auditory Verbal Learning Test (RAVLT-DR) delayed recall and at least within the average range for their age on non-memory measures; recruitment occurs through the Alzheimer’s Disease Center and community outreach 40 , 41 . It also enrolls cognitively average controls who perform within the average normative range on the RAVLT-DR for their demographics and at least within the average range for their age on non-memory measures. Participants complete standardized neuropsychological evaluations at baseline and follow-up, with optional neuroimaging and biomarker studies and brain donation for neuropathology 41 . This study was expanded in 2021 as a multisite with enrollment across five sites in the US and Canada 28 . A total of 9 samples were included in the aging study. Johns Hopkins Division of Neuropathology Brain Bank Tissues were fresh frozen punches of the left MTG. This autopsy cohort draws brain donations through the Maryland Office of the Chief Medical Examiner and affiliated programs, explicitly including young adults; standardized dissections and region-specific sampling are performed at the Johns Hopkins Division of Neuropathology 42 , 43 . Each case, detailed medical and toxicology histories are abstracted, and tissue is processed for uniform histology, with genotyping and molecular assays performed when applicable 42 , 43 . The cohort has been used to characterize preclinical Alzheimer-type pathology in individuals aged 40–50, informing age-stratified analyses of early disease biology 43 . A total of 8 samples were included in the aging study. Data analysis FASTQ files were analyzed using the Nextflow RNA-seq pipeline, including trimming with Trim Galore, alignment with STAR 44 , and quantification of counts at the gene level with SALMON. Quality control was conducted using MultiQC and Qualimap 45 . Samples were included if they had all covariates (age, sex, RIN, and PMI), at least 20 million sequencing reads, and if 80% of those reads were uniquely mapped to the human transcriptome. Raw counts were imported from DESeq2 46 and, for quality control purposes, transformed using the vst method. We removed genes located on the sex chromosomes, and conducted Principal Component Analysis (PCA) to identify outlier samples, defined as those that were above ± 3 standard deviations from the average of at least one of the two top PCs. The relationship between gene expression and potential confounding variables (RNA integrity number RIN, postmortem interval PMI, sample source, sex and age) was investigated by correlating the top two PCs with each of the potential confounding variables and assessing significance with Pearson’s r (for RIN, age, and PMI) or Wilcoxon Test (source and sex). For normalization and differential expression, we generated three different datasets with DESeq2: AD/CTL, aging-65+ ( ≥ 65 years old) and all-ages. This approach was implemented to minimize bias by excluding samples irrelevant to each specific analysis, thereby reducing their influence on the overall variance. Each dataset was prefiltered for low expressed genes using a minimum count threshold equal to the total sample size. Normalization and differential expression were conducted using DESeq2 46 , including all of the covariates in the model (age, sex, PMI, RIN, source and sequencing batch). The continuous covariates were standardized using the ‘ scale’ function from R, while differential expression p-values were adjusted for multiple testing using the Benjamini & Hochberg (BH) method 47 . Log2 Fold Change (LFC) was estimated using the shrinkage method ( apeglm ) 48 to avoid inflated LFC and p-values due to transcripts with very low counts (these were found to mostly be non-protein coding transcripts). During differential expression, we applied the independent filtering method implemented in DESeq2 to remove lowly expressed genes, optimizing the cutoff for α = 0.05. Genes with BH adjusted p-values (BHP) < 0.05 and absolute log 2 FC (LFC) ≥ 0.25 were considered statistically significant. Boxplots (AD/CTL study) and scatterplots (aging study) were visualized using variance-stabilizing transformed data, adjusted for covariates. The adjustment was conducted using the removeBatchEffect function for the R-package limma 49 . All lists of genes were further analyzed using the Gene Set Enrichment Analysis (GSEA) referenced to the Gene Ontology database, as implemented in the R-package clusterProfiler 50 . Redundant GO functional classes were removed using the ‘ simplify ’ function with the default settings. GO processes with BHP < 0.05 were considered statistically significant. Cell-set enrichment analysis (CSEA) was conducted using as gene sets the lists of cell-specific markers from the single nucleus RNA sequencing study from Mathys et al. 18 , obtained using our deconvolution method as previously described 51 . The enrichment analysis was conducted using the fast enrichment analysis (fGSEA) method 52 . Coexpression analysis was conducted using the Multiscale Embedded Gene Expression Network Analysis (MEGENA) algorithm 53 only selecting the protein coding genes after annotation with Biomart . As input, we used the data matrices previously generated for the boxplot and scatterplot visualizations (see above). The datasets were further filtered to include only the top 50% of genes with the highest median absolute deviation. For coexpression module generation, we first calculated signed pairwise gene correlations using Pearson’s method with 1,000 permutations, retaining correlations that were significant at the 5% FDR level (function: calculate.correlation). Significantly correlated gene pairs (FDR < 0.05) were ranked and iteratively tested for planarity, leading to the development of a planar filtered network using the planar maximally filtered graph technique (function: calculate.PFN). Subsequently, we conducted a multiscale clustering analysis to identify coexpression modules at varying network scale topologies and their respective hub genes (function: do.MEGENA). Coexpression modules deemed significant (with a permuted P < 0.01 and module of 50 genes or more) were carried forward for further analysis. Next, we extracted module eigengenes (the first principal component obtained from the module genes) using the function moduleEigengenes from the WGCNA R package 54 . Pairwise differential expression between diagnostic groups (AD vs CTL) and across aging in CTL subjects was computed using the limma R-package. Modules with significant associations were annotated for GO functional classes using a hypergeometric testing as implemented in clusterProfiler . Additionally, we investigated the cell-specific enrichment of each associated modules by hypergeometric statistics ( bc3net R-package) using brain-specific cell markers as described in our previous study 51 . Weighted Key Driver Analysis (wKDA) was conducted using the Mergeomics webtool 55 , applying the following parameters: search depth of 1, edge type undirected, min hub overlaps 0.33 and edge factor 0. We used the available Bayesians networks from cortex (GTEx v8) and the ‘legacy’ brain network. Large Language Model Use A large language model (LLM) was used during manuscript preparation to check spelling and grammar, improve readability, and broaden the manuscript’s accessibility to scientific disciplines beyond those specializing in the article’s main topics. Results Filtering and quality controls We sequenced a total of 621 samples, with an average number of 31.8 million reads (range: 16.7 million – 52.2 million; SD: 5.8 million) ( Fig. S1 ). We removed two samples with fewer than 20 million reads. The average mapping rate, as determined by STAR (uniquely mapped reads), was 93.7% (range: 67.9% - 95.8%; SD: 1.85) ( Fig. S2 ). We removed two samples with a mapping rate below 80%, obtaining a final sample size of 617. Data were prefiltered to remove lowly expressed genes (genes with total counts across all samples < 617), resulting in 38,603 genes. Then, counts were transformed using the variance-stabilizing transformation (VST) method, and we extracted the top two principal components, classifying 11 samples as outliers ( Figs S3 and S4 ). After we removed the outliers, the final dataset consisted of 606 samples. We explored the relationship between gene expression and potential confounding factors that correlated with the top two principal components, detecting a significant association of one of the two components with the following variables: age, postmortem interval (PMI), RNA integrity number (RIN), sample source (brain bank), and sex ( Table S1 ). Among the variables tested, only the sequencing run demonstrated a lack of significant association with gene expression (P > 0.221). All of these confounding variables were included in the downstream analyses as covariates and were adjusted for in the model. Transcriptomic changes in the AD MTG are associated with down-regulation of synaptic and mitochondrial pathways as well up-regulation of immune, vascular and extracellular matrix processes For the AD study, we included samples that met the criteria described in the Methods section and excluded those with missing covariates or neuropathological variables, obtaining a final sample size of 217 AD and 290 CTL participants. Differential expression analysis identified a total of 613 significant genes (LFC ≥ 0.5 and BHP < 0.05), with 390 overexpressed and 223 underexpressed in AD ( Fig. 1A , Table S2 ). Among the top protein-coding genes, we identified NPNT , ADAMTS2 , GFAP and ECM2 ( Fig. 1B ). CSEA analysis revealed a significant enrichment of excitatory neuronal genes (underexpressed in AD), along with microglia, astrocyte, endothelial, and pericyte genes (overexpressed in AD) ( Fig 1C ; Table 3S) . GSEA analysis revelated a strong upregulation of processes associated with angiogenesis, extracellular matrix organization, wound response, and adaptive immune activation (NES > 2.0; BHP < 1.0 × 10 - 12 ). In contrast, neuronal and mitochondrial processes, including synaptic signaling, synaptic membrane organization, and respiratory chain/ATP synthesis complexes, were significantly underexpressed in AD (NES < -2.0, BHP = 1.0 × 10 -06 ) ( Figs. 1D - 1F, Table S4 ). We then constructed a multiscale coexpression network using only protein coding genes and, after eigenvalue extraction, identified 61 modules significantly associated with AD ( Fig 1G , Table S5; see Table S6 for the module specific GO functional classes; see Table S7 for the list of significant key drivers). These modules were grouped into three main functionally distinct clusters, including the top modules M5, M6 and M8 ( Fig. 1H ). The largest cluster included module M5 (n = 3,786; hub gene: PJA2 ) as a top-level module, underexpressed in AD and enriched for ribosomal and mitochondrial functions and showing additional signals from synaptic and ion transport processes ( Fig. 1I ). Additionally, module M5 was particularly expressed in excitatory neurons (BHP = 7.8 × 10 -64 ) with some signal in inhibitory neurons (BHP = 3.1 × 10 -06 ). The top key driver of this module was GABRB3 (FDR = 6.4 × 10 -08 ), also showing an under-expression trend in AD (LFC = -0.080; P = 1.5 × 10 -02 ; BHP = 1.0 × 10 -01 ). The second cluster included module M6 (n = 635; hub gene: C1QC ), overexpressed in AD and enriched for immune system-related GO terms and microglia-expressed genes (BHP = 2.0 × 10 -242 ) ( Fig. 1L ). The top key driver was FCER1G (FDR = 5.1 × 10 -32 ), also upregulated in AD (LFC = 0.148; P = BHP = 6.6 × 10- 02 ). The third cluster included module M8 (n = 758; hub gene: ESAM ), overexpressed in AD, enriched for extracellular matrix organization and blood vessel development. This module was primarily associated with astrocyte-expressed genes (p = 2.2 × 10 -66 ), with TAGLN identified as the top key driver included in the module (FDR = 2.1 × 10 -20 ). This gene showed a non-significant upregulation in AD (LFC = 0.036; p = 1.7 × 10-01; BHP = 4.6 × 10 -01 ). Download figure Open in new tab Figure 1. Transcriptomic alterations in the middle temporal gyrus (MTG) of AD compared to control (CTL) brains. (A) Volcano plot of differential expression analysis. Genes significantly overexpressed and underexpressed in AD are shown in red and blue, respectively (n = 613 DEGs, BH-adjusted p < 0.05, |log ₂ FC| ≥ 0.25). (B) Top four protein-coding differentially expressed genes identified. (C) Cell set enrichment analysis showing reduced expression of excitatory neuronal genes (Ex) and increased expression of microglial (Mic), astrocytic (Ast), endothelial *End), and pericyte-associated genes in AD. (D–F) Gene set enrichment analysis (GSEA) results using Gene Ontology for (D) Biological Processes, (E) Cellular Components, and (F) Molecular Functions. (G) Overview and relationships of coexpression modules identified in MTG. Each node represents a coexpression module; the border color indicates cell-specific enrichment, while the core reflects the log 2 FC of the module eigenvector differential expression between AD and CTL. (H) Differential expression of top-level, functionally relevant coexpression modules associated with AD compared to CTL. (I) Synaptic/mitochondrial/ribosomal module Mt5 coexpression network; node colors represent differential expression (log ₂ FC) in AD vs. CTL. The top key driver gene ( GABRB3 ) is indicated. (L) Immune-related module M6 coexpression network; node colors represent log ₂ FC direction in AD vs. CTL. The top key driver gene ( FCER1G ) is indicated. Aging stages are characterized by differential cell-specific expression patterns For the aging study, we explored the relationship between gene expression and aging in a sample of 324 non-AD controls aged between 24 to 108 years (referred to as the “all-ages” group), followed by an analysis of donors aged 65 years and older (“65+” group; n = 299). The goal was to identify aging-stage-specific expression patterns. We identified 47 genes significantly associated with chronological age in the all-ages group (LFC ≥ 0.25; BHP < 0.05), 25 of which were downregulated ( Fig. 2A , Table S8A ). The same analysis conducted in the 65+ group revealed only nine differentially expressed genes, most of which were downregulated ( Table S8B ). Only two genes overlapped between the two groups: GPR26 and NEB ( Fig 2B ) . The top protein-coding genes significantly associated in the all-ages group were: EDN3 , TTR , TNFRSF19 and PRLR , whereas in the 65+ group, we identified GPR26 , FGF18 , HSPA6 and NEB. CSEA analysis ( Fig 1C ) in the all-ages group confirmed the downregulation of excitatory neuronal genes and revealed increased expression of astrocyte- and oligodendrocyte-associated genes ( Table S9A ). In contrast, the 65+ group showed a stronger neuronal signature, with significant downregulation of both excitatory and inhibitory neuronal genes associated with increased age, with minimal glial-related gene upregulation ( Table S9B ). GO-GSEA analysis in the all-ages group indicated strong downregulation of pathways related to ribosomes, protein-folding, proteostasis, mitochondria, and hormone signaling, suggesting impaired translation and proteostasis in this group. The only positively enriched process among the 74 significant categories was “intracellular chloride channel activity” ( Table S10; Figs D-E ). In the 65+ group, the same analysis revealed a large proportion of functional categories with negative enrichment scores ( Table S11 ). The strongest decreases were linked to “blood microparticle” and proteostasis/heat-shock GO classes, including unfolded-protein binding and protein-folding chaperones. Additionally, neuronal/synaptic functional classes were also underexepressed with aging. Only two categories were overexpressed: “regulation of cell communication by electrical coupling” and “proximal/distal pattern” formation. Download figure Open in new tab Figure 2. Transcriptomic alterations in the middle temporal gyrus (MTG) of aging (A) Volcano plot of differential expression analysis in the all-ages cohort (24-108). Genes significantly overexpressed and underexpressed with aging are shown in red and blue, respectively (n = 47 DEGs, BHP < 0.05, |log ₂ FC| ≥ 0.25). Labels indicate the two genes that also overlapped with the 65+ group. (B) Genes significant in both all-ages and 65+ groups. (C) Cell set enrichment analysis showing reduced neuronal gene expression in the 65+ group and an increase of glial signatures in the all-ages group. (D–F) Gene set enrichment analysis (GSEA) results using Gene Ontology for (D) Biological Processes, (E) Cellular Components, and (F) Molecular Functions. (G) Overview and hierarchical relationships of coexpression modules identified in the all-ages group. (H) Differential expression of the two top-level, functionally relevant coexpression modules associated with aging in the all-ages group. (I) Translation/synaptic/oxidative phosphorylation module M5 coexpression network; node colors represent differential expression across aging. The top key driver gene ( GABRB3 ) is indicated. (M) Myelination/oligodendrocyte-related module M4 coexpression network; node colors represent log ₂ FC direction in aging. The top key driver gene ( MOBP ) is indicated. MEGENA analysis in the all-ages group identified age-related modules, which we grouped into two main clusters ( Fig. 2G and Fig 2H , Table S12 ; see Table S13 for the GO classes associated with modules; see Table S14 for the list of significant key drivers). The main cluster was centered around module M5 (n = 3,418; hub gene PJA2 ), which was downregulated with aging and enriched for excitatory neuron expression and translation/ribosome functional classes ( Fig. 2I ). The key driver was GABRB3 (FDR = 7.4 × 10 -12 ), which showed a non-significant downregulation trend with age (LFC = 0.018; P = 2.1 × 10 -01 ). The M5-related modules were associated with synaptic signaling and oxidative phosphorylation and were enriched for excitatory neuron-expressed genes. The second largest cluster was centered around the top-level module M4 (n = 1,326; hub gene: CNTN2 ), which was upregulated with aging and enriched for myelination processes and oligodendrocyte signatures ( Fig. 2L ). The top key driver of M4 was MOBP (FDR = 9.3 × 10 -35 ), which also showed a non-significant upregulation trend with age (LFC = 0.012; P = 1.1 × 10 -01 ). All M4 related modules were enriched for oligodendrocyte genes, and the largest of these (M14; n = 217) was associated with myelin sheath. The same analysis conducted in the 65+ aging group identified a total of 17 modules associated with age ( Table S15; see Table S16 for the GO classes associated with modules; see Table S17 for the list of significant key drivers). The largest group of related modules included M2 (n = 599, hub gene: HGSNAT), which was underexpressed with aging and enriched for protein-folding GO functional classes. The second group included M112 (n = 298; hub gene: SULT4A1 ), which was downregulated with age and enriched for synaptic processes as well as for inhibitory neuron genes. Discussion In this study, we characterized MTG from a large sample of postmortem brains, first investigating AD signatures and then exploring their relationship with normal aging (range: 24-108 years old). We analyzed 217 AD and 290 CTL brains, detecting 613 DEGs, the majority of which (63.6%) were overexpressed in AD. The top gene identified was NPNT (LFC = 0.625; BHP = 1.9 × 10 -19 ). NPNT (nephronectin) encodes an extracellular-matrix glycoprotein containing and RGD integrin-binding motif 56 and plays a known role in matrix organization and cell adhesion. Notably, this gene was recently reported as the top differentially expressed gene in a cross-ancestry transcriptomic meta-analysis 57 . The second most significant gene was ADAMTS2 (LFC = 0.594; BHP = 2.7 × 10 -14 ) (ADAM metallopeptidase with thrombospondin type 1 motif 2), which encodes a zinc metalloprotease. In agreement with our results, increased expression of this gene has been associated with cognitive decline in the anterior cingulate cortex and reported in a multiethnic transcriptome AD meta-analysis 58 , 59 . The other top genes, ECM2 and GFAP, were associated with increased ECM signatures and astrocyte activity, respectively. Overall, our differential expression results revealed a stronger deregulation of excitatory than inhibitory, neuronal genes. Accordingly, we observed suppression of synaptic and mitochondrial respiratory/ATP synthases functional classes, suggesting neurodegeneration, impaired neurotransmission and disrupted bioenergetic mechanisms. Conversely, we found a larger number of overexpressed non-neuronal genes in AD, primarily associated with microglia, astrocyte, endothelial cells and pericytes. Consistently, we observed increased immune, vascular and ECM transcriptional programs linked to tissue repair/remodeling and neuroinflammation. These phenomena have also been reported in previous bulk tissue and single-nuclei RNA-seq data 13 , 18 , 60 – 62 . Changes in astrocyte and ECM upregulated in AD are compatible with synaptic dysfunction. Astrocytes reside in the neurovascular-synaptic interface, and the ECM regulates synaptic integrity and plasticity. Our data suggest an excess of ECM accumulation, which may constrain activity dependent remodeling and microglial pruning that support cognition 63 , 64 . Unlike normative aging, AD shows coordinated immune-vascular ECM activation along with suppression of neural and synaptic programs. Our multiscale coexpression analysis recapitulate these molecular features in AD. Synaptic and mitochondrial suppression were detected in the large module M5, which was downregulated in AD. The key driver of this module was GABRB3 , encoding for the β3 subunit of the GABA-A receptor, which showed a downregulation trend in AD. The gene has been consistently reported as downregulated in AD 65 . The increase in immune processes was reflected in M6-related modules, which were upregulated and had C1QC as a hub gene. The key driver of this module was FCER1G (FDR = 5.1 × 10 - 32 ), upregulated in AD (LFC = 0.144; BHP = 6.6 × 10 -02 ). C1QC encodes the C1q subcomponent subunit C, part of the C1q complex, which is the recognition component of the classical complement pathway, associated with the downstream inflammatory response. The C1q complex interacts with TREM2 66 and has been implicated in synapse elimination mediated by astrocyte and microglia in AD 67 . Additionally, it enhances microglia activation through a positive feedback loop, increasing neuroinflammation 68 , 69 . FCER1G, which encodes the Fc receptor γ chain, recruits Spleen Tyrosine Kinase to activate key innate immune effector functions such as phagocytosis and cytokine production. It has been described as hub-gene in microglia enriched modules associated with aging and neurodegeneration, including AD 70 . Finally, module M8 summarized the endothelial/ECM component. This module was upregulated in AD, with ESAM as a hub gene (significantly upregulated in AD: LFC = 0.229; BHP = 6.9 × 10 -04 ), enriched for astrocyte genes. The top key driver was TAGLN (FDR = 1.5 × 10 -10 ), which showed a slight increase in AD. ESAM (Endothelial Cell Adhesion Molecule) encodes a transmembrane immunoglobulinl1llike adhesion protein located at endothelial tight junctions and expressed on platelets. Its function is related to maintaining endothelial barrier integrity and regulating leukocyte transmigration, as well as playing a role in angiogenesis 71 . This gene may be involved in the vasculature remodeling and maintenance of the blood brain barrier following AD-related injury. The aging study highlighted two genes GPR26 (G protein–coupled receptor 26) and NEB (nebulin) – that were decreased in expression with advancing age in both the all-ages (24-108 years old) and 65+ groups. GPR26 , is primarily expressed in the brain and has been implicated in the maintenance of neuronal excitability and energy balance. NEB, a large actin-binding protein, might be involved in broader cytoskeletal remodeling processes. Notably, we identified distinct patterns at the cellular level across different age stages. In the all-ages group, the major shift in transcriptomic dysregulation appears to be associated with a decrease of excitatory neuronal gene expression and an increase in astrocyte and oligodendrocyte gene expression. These patterns, characterized by reduced neuronal and decreased glial increased expression, have been previously reported in human transcriptomics aging studies 9 . In the all-ages group, GSEA analysis revealed strong downregulation in pathways related to ribosomes, protein-folding, proteostasis, mitochondrial, and hormones, highlighting impaired translation and proteostasis 72 . These processes, especially mitochondrial and ribosomal functions, and the decreased expression in excitatory neuronal regions were summarized by the module M5, which included GABRB3 as a key driver gene, showing a downregulation trend in aging. Notably, we identified a similar large module with GABRB3 as a key driver, suggesting shared mechanisms of vulnerability between AD and aging. In addition, coexpression analysis highlighted a set of modules upregulated across aging and enriched for oligodendrocyte genes. The top module among these was M4 (key driver: MOBP ), which showed an upregulation trend with age. This result suggests active compensatory remodeling of the myelin in the aging brain. MOBP is a myelin-specific structural protein mostly expressed in oligodendrocytes, playing a key role in myelin stabilization and integrity of myelin in the central nervous system 73 . This gene has been reported to be a key gene downregulated in Multiple System Atrophy 74 . Among the top DEGs we identified EDN3 (Endothelin-3) and TTR (Transthyretin), both of which were downregulated with aging. EDN3 is a vasoactive peptide crucial for vascular function and cardiovascular aging, although its role in brain aging remains unknown. TTR transports thyroxine and retinol in the cerebrospinal fluid and plasma. Its levels decline with age, likely due to the deterioration of the choroid plexus integrity and disrupted hormone transport 75 . Other studies suggest that lower circulating TTR levels are associated with all-cause mortality and risk of heart failure, making it a robust biomarker of systematic aging 76 . In the 65+ group, transcriptomic dysregulation was much lower, with only nine genes significantly associated with aging, eight of which were downregulated. Similarly to the all-ages groups, we observed a decrease of both inhibitory and excitatory neuron gene expression, with lower changes in astrocyte and not significant enrichment for oligodendrocyte gene expression. This was confirmed by GO analysis, which showed downregulation of neuronal and synaptic processes, in addition to proteostasis and heat-shock functional classes, and in the coexpression analysis, which demonstrated an absence of glial-driven modules. These finding suggest that glial gene expression changes are more prominent in early aging than in later life, where neuronal gene expression downregulation is still the dominant pattern. However, these results should be interpreted with caution, as the small proportion of donors under 65 years old (7.7% of the sample) limits statistical power and highlights the need for confirmation in larger, more age-balanced cohorts. It is important to note several potential limitations of our work. We acknowledge the differences between brain bank sources (e.g., characteristics of participants, staining methods, tissue sampling and assessment methods) and the possibility that those may influence the statistical outcomes of our analyses. We attempted to address this by investigating key variables that could be confounding, such as brain bank, RIN, PMI, sequencing run, age at death and sex, and by including those variables that were significantly correlated with the top two principal components in our statistical modeling. Secondly, we were able to include MTG specimens from donors spanning eight decades of age, however, brains from individuals under the age of 65 were much less prevalent in our study cohort. Therefore, some of the age-related findings that we describe could be artificially enhanced by the smaller number of cases examined from younger ages. We divided our cohort into AD cases and controls wherein the control samples were from individuals who did not have a dementia diagnosis while living, however, it is important to remember that cognitive performance is a continuum and our control cohort included individuals with mild cognitive impairment as well as individuals who had much higher than typical cognitive performance, such as the SuperAger donors from the Northwestern brain bank. Lastly, bulk-based transcriptome analysis is unable to fully discern cell-specific alterations and therefore our findings should be considered a starting point for understanding the changes happening in the MTG during aging and AD. Future work should include the use of single cell/nucleus RNA sequencing as well as spatial transcriptomic investigations, both of which will serve to further add additional information to what we report on here using bulk RNA sequencing. In conclusion, our analysis of post-mortem MTG reveals a broad downregulation of excitatory neuronal, synaptic and mitochondrial processes, along with an upregulation of immune, vascular, and ECM pathways. The results are mostly confirmatory of prior published work, but no other in-depth analyses have been conducted on the MTG using high-throughput RNA sequencing. Additionally, we highlighted a few key genes associated with large coexpression networks perturbed in AD, such as GABRB3 and FCER1G . The aging study revealed partly overlapping features, with more pronounced glial activation in early stages, driven by MOBP , followed by neuronal deregulation in later stages, driven by GABRB3 , the same key driver identified in a large AD coexpression network. Finally, we highlighted the immune and vascular transcriptional programs in AD, which are not found to be significantly associated with aging in general in our dataset. Funding support This study was supported by the NIH Research grants R01AG072643-02 (CAB, PFW, MJH) and McKnight Brain Research Foundation (CAB). The Brain and Body Donation Program has been supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30 AG019610 and P30AG072980, Arizona Alzheimer’s Disease Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson and Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research. EJR was supported by NIH Research Grants U19AG073153, R01AG045571 R56AG045571, 5R01AG067781, and the McKnight Brain Research Foundation (MBRF). MC and CHK were supported by NIH grants R01AG021055, UF1AG057707 and P30AG066519. 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Share Transcriptomic profiling of the middle temporal gyrus reveals differential glial/neuronal dysregulation across Alzheimer’s disease and aging IS Piras , A Bonfitto , S Song , A Aldabergenova , J Sloan , A Trejo , JC Troncoso , C Geula , EJ Rogalski , CH Kawas , M Corrada , TG Beach , GE Serrano , PF Worley , CA Barnes , MJ Huentelman bioRxiv 2025.10.19.683343; doi: https://doi.org/10.1101/2025.10.19.683343 Share This Article: Copy Citation Tools Transcriptomic profiling of the middle temporal gyrus reveals differential glial/neuronal dysregulation across Alzheimer’s disease and aging IS Piras , A Bonfitto , S Song , A Aldabergenova , J Sloan , A Trejo , JC Troncoso , C Geula , EJ Rogalski , CH Kawas , M Corrada , TG Beach , GE Serrano , PF Worley , CA Barnes , MJ Huentelman bioRxiv 2025.10.19.683343; doi: https://doi.org/10.1101/2025.10.19.683343 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7619) Biochemistry (17641) Bioengineering (13865) Bioinformatics (41860) Biophysics (21408) Cancer Biology (18545) Cell Biology (25434) Clinical Trials (138) Developmental Biology (13357) Ecology (19863) Epidemiology (2067) Evolutionary Biology (24288) Genetics (15587) Genomics (22466) Immunology (17701) Microbiology (40301) Molecular Biology (17142) Neuroscience (88441) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4814) Physiology (7633) Plant Biology (15108) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9811) Zoology (2268)