Multi-omics insights in major depressive disorder: Dysfunction of Neurons

Multi-omics insights in major depressive disorder: Dysfunction of Neurons | medRxiv /* */ /* */ <!-- <!-- /*! * 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-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Multi-omics insights in major depressive disorder: Dysfunction of Neurons View ORCID Profile Lingfeng Zhang , Honggang Lyu , Simeng Ma , Yantao Xu , Xinhui Xie , View ORCID Profile Qian Gong , Lijun Kang , Shijia Chen , Zhongchun Liu doi: https://doi.org/10.1101/2025.05.03.25326369 Lingfeng Zhang 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China M. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lingfeng Zhang Honggang Lyu 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simeng Ma 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China M. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yantao Xu 3 Department of Dermatology, Xiangya Hospital, Central South University , Changsha, Hunan, PR China 4 Xiangya School of Medicine, Central South University , Changsha, Hunan, PR China M. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xinhui Xie 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China 5 Division of Pulmonary, Critical Care Medicine, John T. Milliken Department of Medicine, Washington University School of Medicine in Saint Louis , USA M. D., Ph. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qian Gong 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China M. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qian Gong Lijun Kang 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China M. D, Ph. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shijia Chen 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China M. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhongchun Liu 1 Department of Psychiatry, Renmin Hospital of Wuhan University , Wuhan, Hubei, PR China 2 Taikang Center for Life and Medical Sciences, Wuhan University , Wuhan, Hubei, PR China M. D., Ph. D Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: zcliu6{at}whu.edu.cn Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Major Depressive Disorder (MDD) is a complex psychiatric condition characterized by neuronal and functional disruptions in the dorsolateral prefrontal cortex (dlPFC). We integrated MDD-associated multi-omics data, including two single-cell RNA sequencing (scRNA-seq) datasets, GWAS summary data, and depression-related proteomic data from UK Biobank. We identified 273 MDD-associated eQTL, while the single-cell disease-relevance score (scDRS) algorithm revealed that excitatory neurons, inhibitory neurons, and oligodendrocyte precursor cells (OPCs) are significantly associated with MDD. Non-negative matrix factorization (NMF) identified four meta-programs (MPs) in excitatory and inhibitory neurons, reflecting functional impairments related to synaptic plasticity, neuronal connectivity, and epigenetic regulation. Differentiation trajectory analysis revealed distinct pathological states of neuronal subtypes. We identified CXCL14 + inhibitory neurons and investigated their role in stress perception and intercellular communication. Plasma proteomics data validated 33 risk genes, five of which encoded proteins predictive of patient survival in CoxBoost regression models. These findings provide a comprehensive understanding of the cellular and molecular underpinnings of MDD, offering potential diagnostic and therapeutic targets and advancing the development of precision medicine approaches for MDD. Introduction Major depressive disorder (MDD) is a severe psychiatric condition characterized by pervasive affective disturbances, including persistent sadness, anhedonia, and various cognitive and somatic impairments. Affecting approximately 185 million individuals globally 1 , MDD represents a leading cause of disability, imposing a substantial burden on patients, their families, and society 2 . Despite extensive research, the etiopathogenesis of MDD remains poorly understood and involves complex interplay interactions among genetic, environmental, and neurobiological factors 3 – 5 . A significant challenge in understanding and treating MDD is the absence of robust biomarkers and the considerable heterogeneity inherent in the disorder, underscoring the need for innovative approaches to elucidate its underlying mechanisms. MDD is a complex condition with high heritability and polygenicity 6 . Integrating data from multiple levels is essential to minimize biases in understanding its causes. The largest depression-related GWAS to date identified 243 risk loci for depression through a meta-analysis of over 1.3 million individuals, revealing novel enrichment in neurodevelopmental and functional pathways, prenatal GABAergic neurons, astrocytes, and oligodendrocyte lineages 7 . Whole-transcriptome and bulk RNA-seq have focused on the dorsolateral prefrontal cortex (dlPFC) and boold, revealing the role of brain and peripheral blood inflammation in depression 8 , 9 . In recent years, with the advancement of transcriptomic technologies, single-cell transcriptomics and single-cell ATAC sequencing have yielded numerous findings, scaling down analyses to the cellular and epigenetic levels. These approaches have further elucidated mechanisms involved in depression, including neurogenesis, synaptic plasticity, receptor function, and neuroimmune interactions 10 – 12 . However, few studies have combined multi-omics data We integrated scRNA-seq data with GWAS and eQTL findings from the dlPFC of MDD patients, validating the results via plasma proteomic data. This approach identified functional neuronal expression profiles and genes involved in neuronal development and the pathophysiology of depression. Our findings provide a comprehensive understanding of the molecular mechanisms underlying MDD and identify promising targets for future therapeutic interventions. Methods Datasets GWAS datasets We obtained GWAS summary data for mMDD from The Psychiatric Genomics Consortium (PGC), including 246,363 cases and 561,190 controls 13 . The height GWAS data were obtained from the UKB. All participants were of European ancestry 14 . eQTL Data We obtained cis-eQTL data from multiple brain regions through the MetaBrain consortium, which provides a large-scale eQTL meta-analysis of previously published human brain eQTL datasets 15 . This dataset includes RNA-seq samples from 8,613 individuals, encompassing seven brain tissues (cortex, basal ganglia, hypothalamus, amygdala, hippocampus, cerebellum, and spinal cord). Significant cis-eQTLs were selected on the basis of an FDR threshold of q < 0.05. scRNA-seq Data We accessed 2 databases from the GEO database: GSE144136 and GSE 213982 and generated a gene X barcodes count matrix with 36,588 genes and 160,712 barcodes 11 , 12 . The study included 38 female participants (20 MDD patients (MDD), 18 health control donors (HC)) and 33 male participants (17 MDD, 16 HC) sourced from the GSE144136 and GSE213982 datasets, obtained from the McGill Group for Suicide Studies at McGill University 11 , 12 . Validation Data This study used UKB data and Olink Explore 3072 to analyze 2,923 proteins, standardize variables and impute missing values (Supplementary File 2). Mendelian Randomization We employed Mendelian randomization to identify risk genes across multiple brain tissues, via MDD GWAS summary data and human brain eQTL datasets. For Mendelian randomization, the Wald ratio method was applied when a single variant was available as the instrument, and the inverse-variance weighted (IVW) method with random effects was used for multiple variant instruments 16 . The MR.RAPS robust adjusted profile score (RAPS) method was used as a confirmatory approach to enhance robustness against pleiotropic variant inclusion 17 . Instrument selection adhered to criteria based on protein abundance or expression-associated variants with p < 5 × 10 - 8 within ±1 Mb of the coding gene, clumped at r 2 < 0.01. To account for multiple testing, pFDR values were calculated separately for plasma protein and tissue-specific analyses. MR analyses were performed for brain gene expression (effect sizes were calculated from z scores, assuming var(y) = 1) and were deemed statistically significant at p FDR < 0.05. All analyses were performed using the TwoSampleMR, MendelianRandomization, and MR-RAPS packages. Leveraging single-cell data analysis Quality control To evaluate the quality of the barcodes, low-quality barcodes were filtered via Seurat (version 4.4.0) on the basis of mitochondrial, ribosomal, and hemoglobin gene read proportions 18 , with the following criteria: 350 < nCount_RNA < 25,000, 200 < nFeature_RNA < 5,000, percent_ribo < 30%, and percent_mt < 5%. After filtering, 136,602 nuclei remained. SCTransform was applied to regress out nCount_RNA, percent.ribo, percent.mt, and percent.hb, resulting in 32,186 genes and 144,531 barcodes. We calculated 100 principal components (PCs) and used the first 30 PCs for UMAP to visualize the cell populations. Batch effects were corrected via the Harmony package (version 1.2.1) on the basis of chemical and batch information 19 . Clutsering We optimized clustering parameters for the Seurat FindClusters function using scclusteval subsampling (80% of cells, repeated 100 times) and stability analysis via Jaccard indices. We tested k-values of 20 and 30, resolutions from 0.1 to 1.5, the smart local moving (SLM) algorithm, and Harmony-corrected PCs: 30, 40, and 50. Annotation We used specific markers to preliminarily annotate various cell types in our samples (Supplementary File 2). Correct doublet effect Initially, we identified and eliminated 7,929 high-confidence doublet barcodes via DoubletFinder (version 2.0.4) 20 . Integrate Multi-omics Data We used the R package “scDRS” (version 1.0.2) to associate individual cells in single-cell RNA-seq data with MDD GWAS-summary data and eQTL results 13 , 21 . Downstream analyses utilized single-cell disease-association P -values and raw omics scores, to assess MDD heterogeneity within each cell type. Non-negative matrix factorization for gene program discovery The R package “GeneNMF” (version 0.6.0) was used to decompose the expression matrix of excitatory and inhibitory neurons into submatrices with k values from 2 to 6, identifying 10 MPs per dataset. Based on silhouette and Jaccard scores, MP3 and MP6 were selected for excitatory neurons, and MP4 and MP6 were selected for inhibitory neurons 22 . Pre-ranked gene set enrichment analysis For the L4 ExN 10 and L4 ExN 12, we individually performed pre-ranked Gene Set Enrichment Analysis 23 with clusterProfiler 24 . STRING analysis We used STRING DB (version 12.0) 25 to assess the relationships between the protein products of the overlapping genes in the DEGs, ExN MP3 and MP6. Trajectory Analysis Neuronal differentiation trajectories were analyzed via “Slingshot” (version 2.10.0) 26 and Monocle3 (version 1.3.1) 27 . SlingshotDataSet used to characterize the differentiation trajectories, and fit_models were run to identify genes that were differentially expressed by pseudotime. Survival analysis Cox, random survival forest, and CoxBoost regression analyses were performed via the R packages “survival” (version 3.7-0) 28 , “randomForestRSC” (version 3.3.1) 29 , and “CoxBoost” (version 1.5) 30 to evaluate the correlation and independence of 2,923 proteins from the UKB cohort with MDD survival time. We divided 42,712 samples (1,968 cases and 40,744 controls) into a training set (70%) and a test set (30%) and evaluated the training results using the test set. Analysis of the 33 proteins revealed five significant genes, with an AUC of 0.656 in the Cox model. Reconstructing the model with these genes improved the AUC to 0.662 in the CoxBoost model, enabling accurate prediction of the MDD survival time. Results Identification of cell types in the dPFC We employed scRNA-seq to identify and characterize different cell types in the adult human dlPFC. Fig. 1A presents a flowchart outlining the design of our study. All the cells are illustrated and annotated with confident markers in Fig. 1B-C . Download figure Open in new tab Fig. 1. Overview and expression of MDD-related genes across different cell types in suicide MDD donors’ BA9 in the dlPFC. (A) Schematic representation of participant demographics and data sources. (B) UMAP plots depicting the identified cell types in the prefrontal cortex. (C) Heatmap of reference gene markers for each identified cell type. (D) Cell proportions by participant group (HC and MDD) and gender. (E) Disease relevance scores for the MDD-GWAS, MDD-eQTL, AD, and height groups. As a positive control, we used Alzheimer’s disease (AD) GWAS results and observed that high-risk cell subpopulations were concentrated in excitatory neurons and microglia, which is consistent with existing findings, with height-associated individuals used as controls. The cell type proportions in the dlPFC revealed significant differences between the MDD patients and healthy controls. Specifically, the proportion of neurons in the dlPFC was significantly lower in MDD patients compared to healthy controls. This trend was observed in both male and female patients, with a more pronounced difference in female patients. Fig. 1D illustrates these differences, showing the cell proportion distributions for the healthy control and MDD groups, separated by gender. We observed that the total number of neurons in the MDD patients was significantly lower than that in the HCs. eQTL analysis and disease relevance Scores to identify risk cell types On the basis of previous GWASs of MDD populations, we used cis-eQTLs as instrumental variables for Mendelian randomization (MR) analysis, identifying 273 MDD-associated genes derived from the basal ganglia, cerebellum, cortex, hippocampus, and spinal cord. We identified 150 risk genes derived from the cortex. We applied a disease-relevance score at single-cell resolution via GWAS summary and eQTL results to evaluate which cells play a critical role in the pathogenesis of MDD 21 . Using this method, we evaluated prefrontal cortex cells and found that high-scoring cell subpopulations were predominantly found in excitatory neurons, inhibitory neurons, and the OPC, as shown in Fig. 1E . The scDRS analysis revealed that excitatory neurons (FDR = 0.024, Z = 1.985), inhibitory neurons (FDR = 0.001, Z = 4.773), and OPC (FDR = 0.050, Z = 1.846) were positively correlated with MDD, and these correlations remained significant after multiple testing correction. To identify the most critical genes in MDD, we conducted an analysis of associated genes, and the Pearson’s correlation are listed in Supplementary File 1. Clustering and characterization of neurons and MPs We applied NMF for dimensionality reduction to refine the neuronal subtypes ( Fig. 2A ), enhancing interpretability and ensuring that biologically meaningful metaprograms (MPs) were selected ( Fig. 2B ). We identified ExN MP3 and MP6 in excitatory neurons, and InN MP4 and MP6 in inhibitory neurons as key functional modules associated with MDD. Download figure Open in new tab Fig. 2. NMF embedding and identification of metaprograms in neurons of MDD patients. (A) Excitatory and inhibitory neurons, colored according to specific neuronal markers and marker gene expression. (B) NMF clustering results, showing distinct clusters of excitatory and inhibitory neuron MPs. (C) Distribution of identified metaprograms (MP1-MP10) across excitatory and inhibitory neurons. (D, E) MP functions in excitatory and inhibitory neurons. Distinct patterns of MP expression emerged across neuronal subtypes ( Fig. 2C ). In excitatory neurons, MP3 and MP6 were predominantly expressed in deep-layer neurons (L4 and L5/6), suggesting their involvement in MDD pathogenesis. Among inhibitory neurons, those in the CXCL14 + , LAMP5 + , and VIP + subtypes primarily expressed MP4, whereas those in the PVALB + and SST + subtypes aligned more with MP6 expression, indicating distinct mechanisms through which these subtypes contribute to MDD. To investigate the role of MPs in MDD development, the enrichment analysis results are presented in Fig. 2D-E . Identifying the subtypes and DEGs of neurons Building on our previous clustering analysis, we subdivided excitatory neurons into 38 subtypes and inhibitory neurons into 20 subtypes, as shown in Fig. 3A . From this refined clustering, we conducted differential gene expression analysis for each neuronal subtype, identifying 61 differentially expressed genes (DEGs) in excitatory neurons and 7 DEGs in inhibitory neurons ( Fig. 3B ). Download figure Open in new tab Fig. 3. Differential gene expression and functional analysis of neuronal subtypes in MDD patients. (A) Subtypes of neurons identified in the dlPFC. (B) DEGs between MDD patients and HCs within excitatory and inhibitory neuron subtypes. (C) Functions of MDD high-risk MDD subtypes. (D) Genes that are shared across eQTLs and different metaprograms in neurons. (E) PPI network constructed from DEGs and MPs in excitatory neurons. We identified 24 neuronal subtypes associated with MDD, including 15 excitatory and 9 inhibitory neuron subtypes. Among these, the subtypes with the most DEGs in MDD patients were L4 ExN 12, with 23 DEGs (13 upregulated and 10 downregulated), and L4 ExN 10, with 20 DEGs (all downregulated). Furthermore, we explored functions of these genes via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) ( Fig. 3C ). Overlap genes in DEGs, eQTL Analysis and MPs We identified significant overlaps between DEGs, eQTLs and MPs ( Fig. 3D ). Integration of DEGs, eQTL analysis, and ExN MP3 highlighted two genes ( PEBP1 and STMN1 ) as key molecular contributors to MDD pathophysiology. Additionally, six genes related to neuronal connectivity and epigenetic regulation consistently overlapped with the MP6 module. In inhibitory neurons, fewer DEGs were identified. ADARB2 and CXCL14 are implicated in epigenetic modifications and neuronal development, whereas ASIC2 isd linked to epigenetic regulation and cellular connectivity through overlap in the InN MP4 and MP6 modules. PPI clustering analysis identified key pathways associated with MDD To understand the interactions between these overlapping genes, we conducted STRING proteinlJprotein interaction (PPI) analysis ( Fig. 3E ). Findings in excitatory neurons indicate the translocation of SLC2A4 (GLUT4) to the plasma membrane, the regulation of synaptic vesicle endocytosis, G protein-coupled glutamate receptor binding, the glutamate neurotransmitter release cycle, and the regulation of commissural axon pathfinding by SLIT and ROBO . Characterizing Neuronal Differentiation Trajectories As shown in Fig. 4A , pseudotime analysis of neurons revealed that differentiation trajectories contributed to MDD development. While overall gene expression remained stable, some neurons transitioned from healthy to pathological states, which were absent in healthy controls. Using MDD-associated metaprograms, pathological states were labeled ( Fig. 4B ), and differential gene expression was visualized ( Fig. 4C ). Download figure Open in new tab Fig. 4. Trajectories of neuronal differentiation, metaprogram expression, and pseudotime analysis in MDD neurons. (A) Differentiation trajectories of excitatory and inhibitory neurons in MDD patients, colored by neuronal subtype, with arrows indicating progression from early to terminal states. (B) NMF metaprogram expression in excitatory and inhibitory neurons. (C) MP-associated gene expression in excitatory and inhibitory neurons. (D) Pseudotime gene expression in different subtypes. Analysis of the top six DEGs revealed MP3 in excitatory neurons and MP4 in inhibitory neurons as intermediate pathological states, suggesting transitional dysfunction, whereas MP6 in both types was associated with terminal states ( Fig. 4D ). Intermediate excitatory neurons were primarily L4, and inhibitory neurons were LAMP5 + and VIP + , indicating their role in early synaptic changes in MDD. Conversely, terminal states involve L5/6 excitatory neurons and PVALB + and SST + inhibitory neurons, contributing to MDD chronicity through sustained dysfunction. Cell Interaction Analysis and Validation in Plasma Proteomic Data Analysis of neurotransmitter transmission ( Fig. 5A ) revealed increased outgoing signals from CXCL14 + inhibitory neurons in MDD, with enhanced input from excitatory neurons and LAMP5 + inhibitory neurons. Differentiation trajectory analysis suggested that reduced neurotransmitter transmission in deep excitatory neurons contributes to MDD onset and persistence. Download figure Open in new tab Fig. 5. Interactions between neurons and survival analysis. (A) Neuron linked network disorders in MDD patients. (B) Differences in the signal strength of neurons. (C) The signaling network in CXCL14 + neurons highlights a strong association between NTN4 and NTRK2. (D) Survival analysis of plasma proteomic data from the UKB cohort. Cox regression was performed on 33 proteins from the UKB dataset to identify 5 significant proteins, which were subsequently used for CoxBoost regression. With respect to total neuronal interactions ( Fig. 5B ), L4 excitatory neurons presented increased interactions, whereas L2/3 neurons presented significant reductions, and L5/6 neurons presented decreased incoming signals. CXCL14 + neurons in MDD patients also showed enhanced influence via the NTN4-NTRK2 pathway ( Fig. 5C ). By integrating eQTLs, metaprograms, DEGs, and plasma proteomics, we identified 33 proteins ( Fig. 5D ). Regression analysis highlighted five MDD-associated proteins, with the CoxBoost model demonstrating the best prognostic performance. Discussion In this study, we integrated brain region-specific eQTL data, GWAS data, and scRNA-seq data to elucidate the genetic basis of MDD. Our analysis revealed significant associations between MDD and specific neuronal populations within the dlPFC, including neurons and OPCs, at the transcriptomic level. Notably, MDD-related genes and the identified cell types presented distinct characteristics across multiple omics levels, such as differential gene expression patterns and regulatory network alterations. However, the analysis of high-dimensional scRNA-seq data poses significant challenges, particularly in identifying key biological modules. Here we employ NMF as a dimensionality reduction technique to identify biologically relevant gene expression modules and investigate their roles in MDD patients. NMF is an embedding technique that decomposes a matrix into two lower-dimensional matrices, commonly used to uncover patterns in large datasets 31 . The raw gene counts were organized into different MPs, each representing distinct biological modules or pathways. MPs are characterized by the consistent coexpression of specific gene sets, corresponding to cellular functions, processes, or conditions, facilitating a deeper understanding of specific biological processes or disease states 22 . Consequently, we focused on neuronal dysfunction in the dlPFC, and identified four MPs implicated in neuronal plasticity, connectivity, and epigenetic regulation. We mapped the differentiation landscape to uncover the developmental transitions involved. We revealed vital cell interactions and revealed that CXCL14 + , PVALB + , and SST + inhibitory neurons in MDD patients exhibited abnormal hyperactivity in the NTN4-NTRK2 pathway. These findings underscore the pivotal role of specific neuronal subtypes in pathogenesis. Our results indicate that both excitatory and inhibitory neurons in the dlPFC are critically involved in MDD development. The observed reduction in neuronal numbers, particularly in female patients, along with differential gene expression across neuronal subtypes, highlights the importance of targeting specific neuronal populations in pathology. We identified a novel neuronal subtype, CXCL14 + inhibitory neurons, which exhibited significant alterations in MDD patients. CXCL14 , also known as BRAK , BMAC , or Mip-2 γ , is a chemokine that belongs to the small secreted protein superfamily and plays an essential role in regulating cell migration. CXCL14 is implicated in immune surveillance, inflammation, and tumor development 32 , 33 . As a non-ELR chemokine, CXCL14 is synthesized as a 99 amino acid precursor and subsequently processed into a mature protein with a molecular weight of 9.4 kDa 34 . Its highly conserved sequence suggests a vital role in growth and development 35 . CXCL14 expression was significantly downregulated in the CXCL14 + inhibitory neurons of the MDD group, indicating a marked decline in the functionality of these neurons in MDD patients. Cellular communication analysis further demonstrated that changes in CXCL14 expression in CXCL14 + neurons mirrored those observed in PVALB + and SST + neurons, which was consistent with findings from the DEG analysis. CXCL14 + inhibitory neurons may be involved in modulating stress responses and maintaining neuronal homeostasis. Dysregulation of this chemokine in MDD could contribute to aberrant immune signaling and pathological processes that disrupt neuronal circuits, emphasizing its potential role in both the onset and maintenance of MDD. To further investigate the roles of CXCL14 + inhibitory neurons, we identified four MPs and characterized their respective functions. ExN MP3 represents the synaptic transmission and neuroplasticity model and is associated with neurodegenerative diseases, such as Alzheimer’s disease, suggesting overlapping mechanisms of synaptic dysfunction underlying both MDD and neurodegenerative conditions. ExN MP6 corresponds to the neuronal connectivity and epigenetic regulation model. InN MP4 and MP6 are associated with neuron projection, receptor signaling, and epigenetic dysfunctions, revealing the function of CXCL14 + inhibitory neurons. We observed strong enrichment of PRC2-related pathways in InN MP4, including targets of SUZ12 and EED . These pathways regulate gene expression through chromatin modification 36 , which is crucial for the stress response and neuronal stability. Polycomb repressive complex 2 (PRC2), a conserved epigenetic regulator of H3K27me3, plays a fundamental role in MDD pathophysiology, underscoring its potential as a therapeutic target 36 – 39 . (2S,6S)-Hydroxynorketamine (HNK), the major metabolite of S-ketamine, exerts antidepressant effects through PRC2-related pathways 37 . In rodent models, (2S,6S)-HNK modulates SUZ12 expression, leading to antidepressant outcomes, particularly in the anterior paraventricular nucleus of the thalamus (aPVT) 37 . These findings suggest that targeting the aPVT region may be critical for therapeutic success. The effectiveness of (2S,6S)-HNK varies across rodent models, such as the repeated tail suspension test (rTST) and chronic social defeat stress (CSDS) model, highlighting the complexity of depression mechanisms and the need for tailored treatment approaches 37 , 40 . Stress, a significant risk factor for MDD, induces long-term epigenetic changes that disrupt neuronal function by altering the expression of gens involved in synaptic plasticity. (2S,6S)-HNK counteracts these changes by restoring expression of normal gene, including BDNF and CAMK2A , to mitigate the detrimental effects of stress. Stress enhances EZH2 activity, increasing H3K27me3 modification and stronger transcriptional repression by PRC2, which disrupts neuronal plasticity. (2S,6S)-HNK upregulates SUZ12 , restoring PRC2 function and normalizing H3K27me3 levels 40 . Specifically, the observed increase in outgoing signals from CXCL14 + inhibitory neurons, coupled with increased input from all excitatory neurons and LAMP5 + inhibitory neurons, suggests a critical role for CXCL14 in the disrupted signaling observed in MDD. The differentiation trajectory analysis points to reduced neurotransmitter transmission in deep excitatory neurons as a potential mechanism for the onset and persistence of MDD. Additionally, the distinct changes in interaction patterns among different layers of excitatory neurons imply that L2/3 and L5/6 excitatory neurons may be subjected to heightened inhibition from inhibitory neurons, contributing to a pathological circuit that enhances self-communication within these neurons, potentially forming a feedback loop linked to MDD. The Netrin pathway is involved in mediating the influence of CXCL14 + inhibitory neurons and other inhibitory neurons, including PVALB + and SST + neurons . We further elucidated the significant contribution of the NTN4-NTRK2 interaction in neurons in the MDD patients. NTRK2 , the gene encoding TrkB, is a critical neurotrophic receptor that regulates synaptic plasticity, neuronal survival, and development by interacting with BDNF and other neurotrophic factors 41 . Psychedelic compounds can promote significant structural plasticity in neurons by activating the TrkB, mTOR, and 5-HT2A pathways, mirroring the effects seen with rapid-acting antidepressants such as ketamine 42 . Previous studies in the CSDS model revealed that PVALB + inhibitory neurons in the thalamus exert similar effects through the BDNFlJTrkB pathway 43 . The stress-induced plasticity enhancement in CXCL14 + , PVALB + , and SST + inhibitory neurons represents an adaptive response to stress. However, in MDD patients, the quantity and function of CXCL14 + , PVALB + , and SST + inhibitory neurons are significantly reduced. This imbalance in negative emotion regulation may be a key factor in the onset of MDD ( Fig. 6 .). Download figure Open in new tab Fig. 6. Stress-induced neuroplasticity and epigenetic regulation may play important roles in the pathophysiology of depression. Under stress, CXCL14 + , PVALB + , and SST + inhibitory neurons may regulate XIST and the PRC2 complex, transitioning into a depressive state while enhancing synaptic plasticity in other neurons via the NTN4-NTRK2 pathway. This pathological process is initially an adaptive mechanism to resist stress and counteract depression. However, in MDD patients, the quantity and function of CXCL14 + , PVALB + , and SST + inhibitory neurons are significantly reduced, ultimately leading to the onset of depression. Antidepressants, such as S-ketamine, restore synaptic transmission and neuroplasticity, highlighting the critical roles of stress regulation, epigenetic modulation, and synaptic function in the pathogenesis and treatment of depression. We also investigated differential neuronal subtypes and DEGs. Our findings highlight that XIST is highly expressed in six MDD-related excitatory neuron subtypes and five MDD-related inhibitory neuron subtypes, suggesting that XIST may play a significant role in the development of MDD. XIST , a long non-coding RNA involved in X-chromosome inactivation, recruits epigenetic regulators such as PRC2 to silence gene expression 44 – 46 . While essential for dosage compensation, XIST also plays a broader role in the CNS, particularly in response to stress or conditions such as MDD 47 . In L4 ExN 10, downregulated genes impaired synaptic vesicle cycling, axonogenesis, and synaptic membrane adhesion, disrupting synaptic function and neurotransmitter release. Similarly, in L4 ExN 12, downregulation affected synapse organization, celllJcell adhesion, and connectivity. GSEA revealed MP3 module enrichment in both subtypes, highlighting synaptic dysfunction and disrupted neuroplasticity as critical factors in MDD. To validate our findings, we utilized plasma proteomic data from the UKB cohort to further investigate the functional roles of the identified risk genes. We identified 33 protein-coding risk genes in the UKB cohort and applied Cox proportional hazard models to assess their associations with survival. Our analysis revealed five proteins linked to MDD outcomes: PKD1, NEFL, and TMSB10 were associated with increased MDD risk, whereas NCAM2 and CNTN5 demonstrated protective effects against MDD. PKD1 is an integral membrane protein that functions as a calcium-permeable cation channel and a regulator of intracellular calcium homeostasis. It also plays a role in celllJcell and celllJmatrix interactions and may modulate G proteinlJcoupled signaling pathways 48 . The NEFL protein maintains axonal stability and supports intracellular transport. It also participates in MAPK signaling cascades and modulates NMDA receptor activity, including glutamate binding and activation 49 . NEFL is linked to neuronal damage in psychiatric conditions such as schizophrenia and bipolar disorder, indicating the occurrence of neurodegenerative processes 50 . TMSB10 is involved in cytoskeletal dynamics and may influence neuroinflammatory pathways 51 . In contrast, NCAM2 and CNTN5 appear to confer protective effects through their roles in promoting synaptic plasticity and enhancing neural connectivity. NCAM2 likely contributes to selective fasciculation and targeted zone-to-zone projection of primary olfactory axons 52 . CNTN5, an immunoglobulin superfamily member, mediates cell interactions and promotes neurite growth in cortical neurons during nervous system development 53 . Our study, while providing insights into MDD pathogenesis, has certain limitations. The dataset, primarily derived from Caucasian populations, limits generalizability to other ethnic groups. We used multiple datasets for intersections, which may have increased the likelihood of false negatives. Sex-specific differences in dlPFC-derived cells during MDD progression remain underexplored, despite the identification of 2 X-linked genes, XIST and IL1RAPL2 . The identified MPs presented suboptimal clustering indices, reflecting the complexity of neuronal dysfunction in MDD. Furthermore, while mapping differentiation trajectories, pseudotime analysis requires experimental validation to confirm causal relationships among MPs. Owing to the cost limitations of UKB sequencing, plasma proteomics validation may be restricted to a few proteins, and neuronal-derived extracellular vesicles (NDEVs) might serve as a more specific validation method. These limitations underscore the need for further research across diverse populations and targeted investigations into the temporal dynamics of MDD pathology. This study integrates multi-omics data to identify 273 eQTL loci, 24 differentially expressed genes in neurons, 4 MDD-associated functional modules, and 1 key pathogenic pathway, providing a comprehensive analysis of the underlying mechanisms of MDD. Validation via plasma proteomics data further highlights potential biomarkers for clinical prognosis in MDD patients. Declarartions Availability of data and materials The MDD snRNA sequencing data can be freely accessed via the GEO database: GSE144136 and GSE 213982. Brain-region-specific eQTL results can be accessed via Supplementary File S1. The GWAS summary data can be accessed via martinjzhang/scDRS . Proteomics data can be accessed via UK Biobank at https://www.ukbiobank.ac.uk/ . All software used in this study is freely available and can be accessed through the provided URLs or references. The custom code developed for generating the results presented in this research is available at lancelotzhang0124/SingleCell . Conflict of interests The authors declare no competing interests. Data Availability All data produced in the present study are available upon reasonable request to the authors. Download figure Open in new tab Acknowledgements We extend our thanks to Prof. Gustavo Turecki, Dr. Corina Nagy and the McGill Group for Suicide Studies for providing the snRNA-seq data, which were crucial for our analysis. We gratefully acknowledge the support and resources provided by the Department of Psychiatry at McGill University, which were instrumental in making this research possible. This work was supported by grants from the National Natural Science Foundation of China (U21A20364) and National Key Research and Development Project of China (Grant No. 2024YFC3308400). References ↵ Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019, ( 2021 ). ↵ World Health Organization . World Mental Health Report: Transforming Mental Health for All: Executive Summary . ( 2022 ). ↵ Tomasik , J. et al. Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression . JAMA Psychiatry 81 , 101 – 106 ( 2024 ). doi: 10.1001/jamapsychiatry.2023.4096 OpenUrl CrossRef PubMed Abi-Dargham , A. et al. Candidate biomarkers in psychiatric disorders: state of the field . World Psychiatry 22 , 236 – 262 ( 2023 ). doi: 10.1002/wps.21078 OpenUrl CrossRef PubMed ↵ Zhou , E. et al. Psychosocial factors associated with anxious depression . J Affect Disord 322 , 39 – 45 ( 2023 ). doi: 10.1016/j.jad.2022.11.028 OpenUrl CrossRef PubMed ↵ Marx , W. et al. Major depressive disorder . Nat Rev Dis Primers 9 , 44 ( 2023 ). doi: 10.1038/s41572-023-00454-1 OpenUrl CrossRef ↵ Als , T. D. et al. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses . Nat Med 29 , 1832 – 1844 ( 2023 ). doi: 10.1038/s41591-023-02352-1 OpenUrl CrossRef PubMed ↵ Sforzini , L. et al. Transcriptomic profiles in major depressive disorder: the role of immunometabolic and cell-cycle-related pathways in depression with different levels of inflammation . Mol Psychiatry ( 2024 ). doi: 10.1038/s41380-024-02736-w OpenUrl CrossRef ↵ Sun , S. et al. Brain and blood transcriptome profiles delineate common genetic pathways across suicidal ideation and suicide . Mol Psychiatry 29 , 1417 – 1426 ( 2024 ). doi: 10.1038/s41380-024-02420-z OpenUrl CrossRef PubMed ↵ Kim , S. S. et al. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types . Nat Commun 15 , 563 ( 2024 ). doi: 10.1038/s41467-024-44742-0 OpenUrl CrossRef ↵ Nagy , C. et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons . Nat Neurosci 23 , 771 – 781 ( 2020 ). doi: 10.1038/s41593-020-0621-y OpenUrl CrossRef ↵ Maitra , M. et al. Cell type specific transcriptomic differences in depression show similar patterns between males and females but implicate distinct cell types and genes . Nat Commun 14 , 2912 ( 2023 ). doi: 10.1038/s41467-023-38530-5 OpenUrl CrossRef PubMed ↵ Howard , D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions . Nature Neuroscience 22 , 343 – 352 ( 2019 ). doi: 10.1038/s41593-018-0326-7 OpenUrl CrossRef PubMed ↵ Sakaue , S. et al. A cross-population atlas of genetic associations for 220 human phenotypes . Nat Genet 53 , 1415 – 1424 ( 2021 ). doi: 10.1038/s41588-021-00931-x OpenUrl CrossRef PubMed ↵ de Klein , N. et al. Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases . Nature Genetics 55 , 377 – 388 ( 2023 ). doi: 10.1038/s41588-023-01300-6 OpenUrl CrossRef ↵ Burgess , S. , Butterworth , A. & Thompson , S. G . Mendelian randomization analysis with multiple genetic variants using summarized data . Genet Epidemiol 37 , 658 – 665 ( 2013 ). doi: 10.1002/gepi.21758 OpenUrl CrossRef PubMed ↵ Qingyuan , Z. , Jingshu , W. , Gibran , H. , Jack , B. & Dylan , S. S . Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score . The Annals of Statistics 48 , 1742 – 1769 ( 2020 ). doi: 10.1214/19-AOS1866 OpenUrl CrossRef ↵ Hao , Y. et al. Integrated analysis of multimodal single-cell data . Cell 184 , 3573 – 3587 e3529 ( 2021 ). doi: 10.1016/j.cell.2021.04.048 OpenUrl CrossRef PubMed ↵ Korsunsky , I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony . Nature Methods 16 , 1289 – 1296 ( 2019 ). doi: 10.1038/s41592-019-0619-0 OpenUrl CrossRef PubMed ↵ McGinnis , C. S. , Murrow , L. M. & Gartner , Z. J . DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors . Cell Syst 8 , 329 – 337 e324 ( 2019 ). doi: 10.1016/j.cels.2019.03.003 OpenUrl CrossRef PubMed ↵ Zhang , M. J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data . Nature Genetics 54 , 1572 – 1580 ( 2022 ). doi: 10.1038/s41588-022-01167-z OpenUrl CrossRef ↵ Yerly , L. et al. Wounding triggers invasive progression in human basal cell carcinoma . bioRxiv , 2024.2005.2031.596823 ( 2024 ). doi: 10.1101/2024.05.31.596823 OpenUrl Abstract / FREE Full Text ↵ Subramanian , A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles . Proc Natl Acad Sci U S A 102 , 15545 – 15550 ( 2005 ). doi: 10.1073/pnas.0506580102 OpenUrl Abstract / FREE Full Text ↵ Wu , T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data . Innovation (Camb ) 2 , 100141 ( 2021 ). doi: 10.1016/j.xinn.2021.100141 OpenUrl CrossRef PubMed ↵ Szklarczyk , D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest . Nucleic Acids Res 51 , D638 – d646 ( 2023 ). doi: 10.1093/nar/gkac1000 OpenUrl CrossRef PubMed ↵ Street , K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics . BMC Genomics 19 , 477 ( 2018 ). doi: 10.1186/s12864-018-4772-0 OpenUrl CrossRef PubMed ↵ Cao , J. et al. The single-cell transcriptional landscape of mammalian organogenesis . Nature 566 , 496 – 502 ( 2019 ). doi: 10.1038/s41586-019-0969-x OpenUrl CrossRef PubMed ↵ T, T. A Package for Survival Analysis in R ., ( 2024 ). ↵ Kogalur, H. I. a. U. B. Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) , ( 2023 ). ↵ Binder , H. Cox Models by Likelihood Based Boosting for a Single Survival Endpoint or Competing Risks , ( 2020 ). ↵ Lee , D. D. & Seung , H. S . Learning the parts of objects by non-negative matrix factorization . Nature 401 , 788 – 791 ( 1999 ). doi: 10.1038/44565 OpenUrl CrossRef PubMed Web of Science ↵ Iannone , A. F. et al. The chemokine Cxcl14 regulates interneuron differentiation in layer I of the somatosensory cortex . Cell Rep 43 , 114531 ( 2024 ). doi: 10.1016/j.celrep.2024.114531 OpenUrl CrossRef ↵ Ray , P. R. et al. RNA profiling of human dorsal root ganglia reveals sex differences in mechanisms promoting neuropathic pain . Brain 146 , 749 – 766 ( 2023 ). doi: 10.1093/brain/awac266 OpenUrl CrossRef PubMed ↵ Lu , J. , Chatterjee , M. , Schmid , H. , Beck , S. & Gawaz , M . CXCL14 as an emerging immune and inflammatory modulator . J Inflamm (Lond ) 13 , 1 ( 2016 ). doi: 10.1186/s12950-015-0109-9 OpenUrl CrossRef ↵ Zhang , Y. et al. CXCL14 as a Key Regulator of Neuronal Development: Insights from Its Receptor and Multi-Omics Analysis . Int J Mol Sci 25 ( 2024 ). doi: 10.3390/ijms25031651 OpenUrl CrossRef ↵ Torres-Berrío , A. et al. Mono-methylation of lysine 27 at histone 3 confers lifelong susceptibility to stress . Neuron 112 , 2973 – 2989 .e2910 ( 2024 ). doi: 10.1016/j.neuron.2024.06.006 OpenUrl CrossRef PubMed ↵ Kawatake-Kuno , A. et al. Sustained antidepressant effects of ketamine metabolite involve GABAergic inhibition-mediated molecular dynamics in aPVT glutamatergic neurons . Neuron 112 , 1265 – 1285 .e1210 ( 2024 ). doi: 10.1016/j.neuron.2024.01.023 OpenUrl CrossRef PubMed Xu , M. et al. A repressive H3K36me2 reader mediates Polycomb silencing . Nat Commun 15 , 7287 ( 2024 ). doi: 10.1038/s41467-024-51789-6 OpenUrl CrossRef PubMed ↵ Jiang , L. , Huang , L. & Jiang , W . H3K27me3-mediated epigenetic regulation in pluripotency maintenance and lineage differentiation . Cell Insight 3 , 100180 ( 2024 ). doi: 10.1016/j.cellin.2024.100180 OpenUrl CrossRef PubMed ↵ López-Erauskin , J. et al. Stathmin-2 loss leads to neurofilament-dependent axonal collapse driving motor and sensory denervation . Nat Neurosci 27 , 34 – 47 ( 2024 ). doi: 10.1038/s41593-023-01496-0 OpenUrl CrossRef ↵ Lüningschrör , P. & Sendtner , M . Connecting reticulophagy and neuronal NTRK2/TrkB signaling . Autophagy 20 , 692 – 693 ( 2024 ). doi: 10.1080/15548627.2023.2276630 OpenUrl CrossRef PubMed ↵ Ly , C. et al. Psychedelics Promote Structural and Functional Neural Plasticity . Cell Rep 23 , 3170 – 3182 ( 2018 ). doi: 10.1016/j.celrep.2018.05.022 OpenUrl CrossRef PubMed ↵ Li , H. Y. et al. A thalamic-primary auditory cortex circuit mediates resilience to stress . Cell 186 , 1352 – 1368 .e1318 ( 2023 ). doi: 10.1016/j.cell.2023.02.036 OpenUrl CrossRef PubMed ↵ Lee , Y. & Lee , J. T . PRC2-RNA interactions: Viewpoint from YongWoo Lee and Jeannie T. Lee . Mol Cell 84 , 3586 – 3592 ( 2024 ). doi: 10.1016/j.molcel.2024.09.006 OpenUrl CrossRef PubMed Ferrer , J. & Dimitrova , N . Transcription regulation by long non-coding RNAs: mechanisms and disease relevance . Nat Rev Mol Cell Biol 25 , 396 – 415 ( 2024 ). doi: 10.1038/s41580-023-00694-9 OpenUrl CrossRef ↵ Loda , A. , Collombet , S. & Heard , E . Gene regulation in time and space during X-chromosome inactivation . Nat Rev Mol Cell Biol 23 , 231 – 249 ( 2022 ). doi: 10.1038/s41580-021-00438-7 OpenUrl CrossRef PubMed ↵ Ji , B. , Higa , K. K. , Kelsoe , J. R. & Zhou , X . Over-expression of XIST, the Master Gene for X Chromosome Inactivation, in Females With Major Affective Disorders . EBioMedicine 2 , 909 – 918 ( 2015 ). doi: 10.1016/j.ebiom.2015.06.012 OpenUrl CrossRef PubMed ↵ Ali , H. et al. PKD1 Truncating Mutations Accelerate eGFR Decline in Autosomal Dominant Polycystic Kidney Disease Patients . Am J Nephrol 55 , 380 – 388 ( 2024 ). doi: 10.1159/000536165 OpenUrl CrossRef PubMed ↵ Arrambide , G. et al. Neurofilament light chain level is a weak risk factor for the development of MS . Neurology 87 , 1076 – 1084 ( 2016 ). doi: 10.1212/wnl.0000000000003085 OpenUrl CrossRef PubMed ↵ Ceylan , M. F. et al. Changes in neurofilament light chain protein (NEFL) in children and adolescents with Schizophrenia and Bipolar Disorder: Early period neurodegeneration . J Psychiatr Res 161 , 342 – 347 ( 2023 ). doi: 10.1016/j.jpsychires.2023.03.027 OpenUrl CrossRef PubMed ↵ Inoue , M. et al. Tmsb10 triggers fetal Leydig differentiation by suppressing the RAS/ERK pathway . Commun Biol 5 , 974 ( 2022 ). doi: 10.1038/s42003-022-03941-5 OpenUrl CrossRef ↵ Paoloni-Giacobino , A. , Chen , H. & Antonarakis , S. E . Cloning of a novel human neural cell adhesion molecule gene (NCAM2) that maps to chromosome region 21q21 and is potentially involved in Down syndrome . Genomics 43 , 43 – 51 ( 1997 ). doi: 10.1006/geno.1997.4782 OpenUrl CrossRef PubMed Web of Science ↵ Kamei , Y. et al. Human NB-2 of the contactin subgroup molecules: chromosomal localization of the gene (CNTN5) and distinct expression pattern from other subgroup members . Genomics 69 , 113 – 119 ( 2000 ). doi: 10.1006/geno.2000.6310 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted May 05, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Multi-omics insights in major depressive disorder: Dysfunction of Neurons Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Multi-omics insights in major depressive disorder: Dysfunction of Neurons Lingfeng Zhang , Honggang Lyu , Simeng Ma , Yantao Xu , Xinhui Xie , Qian Gong , Lijun Kang , Shijia Chen , Zhongchun Liu medRxiv 2025.05.03.25326369; doi: https://doi.org/10.1101/2025.05.03.25326369 Share This Article: Copy Citation Tools Multi-omics insights in major depressive disorder: Dysfunction of Neurons Lingfeng Zhang , Honggang Lyu , Simeng Ma , Yantao Xu , Xinhui Xie , Qian Gong , Lijun Kang , Shijia Chen , Zhongchun Liu medRxiv 2025.05.03.25326369; doi: https://doi.org/10.1101/2025.05.03.25326369 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 Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (381) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5431) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff0db957b3d593a',t:'MTc3OTMzNzk1MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();