Many patients with major depressive disorder (MDD) experience limited treatment effectiveness due to an incomplete understanding of its neurobiological underpinnings. This review integrates neuroimaging and genetic data to examine structural and functional brain changes in MDD, alongside their genetic bases. A PRISMA-guided systematic review of imaging transcriptomics over the past decade was conducted using PubMed and Web of Science. Studies included MRI scans of both MDD patients and healthy controls, as well as brain-wide gene expression data, excluding those that were purely meta-analytical, lacked spatial correlations, or involved transdiagnostic analyses. Of the 206 studies reviewed, 20 met the inclusion criteria. Consistent patterns across studies reveal that key biological processes-such as synaptic signaling, calcium ion binding, neurodevelopment, immune regulation, and neurotransmitter transport-play a central role in brain alterations associated with MDD. Additionally, our findings suggest that electroconvulsive therapy (ECT) may alleviate symptoms by modulating these shared pathways. This review underscores the link between brain changes in MDD and specific gene expression profiles, offering insights that could inform more targeted therapeutic approaches.

Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.

We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.

Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.

These include measuring anhedonia using interest-activity score and employing a cross-sectional design.

This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.

Recent studies have linked branched-chain amino acids (BCAAs) metabolism with the risk of major depressive disorder (MDD). However, it is unclear whether associations of plasma BCAA levels with MDD are causal or driven by reverse causality.

Mendelian randomization (MR) was used to investigate the causal association of genetically determined BCAA levels with the risk of MDD. The large genome-wide association study (GWAS) datasets on plasma BCAA levels (n = 115,051) were obtained from the UK Biobank. The summary GWAS dataset for MDD was obtained from the Psychiatric Genomics Consortium (n = 1,035,760). We applied the inverse variance-weighted (IVW) method to explore the causal relationships between BCAA levels and MDD, followed by multiple pleiotropy and heterogeneity tests.

Our results demonstrated that genetically determined circulating total BCAAs (odds ratio (OR): 1.05, 95 % confidence interval (CI): 1.01-1.10, P = 0.016), leucine (OR: 1.06, 95 % CI: 1.02-1.11, P = 7.22 × 10-3), and isoleucine (OR: 1.08, 95 % CI: 1.01-1.16, P = 0.032) levels were associated with an increased risk of MDD. There was suggestive evidence supporting the causal effect of valine levels on MDD (OR: 1.04, 95 % CI: 1.00-1.08, P = 0.075). Bidirectional MR analysis did not provide evidence of reverse causality.

We report evidence supporting the causal role of BCAAs in the development of MDD. This study offers new insights into the mechanisms and treatment of MDD.

Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identified, functional interpretation of the associated loci becomes the critical next step toward translational applications. The article discusses achievements, challenges, and opportunities in psychiatric genomics associated with complexity and heterogeneity. Brain expression quantitative trait loci, single-cell ribonucleic acid-sequence, and functional genomics technologies are highlighted. It also covers newly developed techniques with improved spatiotemporal resolution, quality and sensitivity, coupled with advanced analytical methods and artificial intelligence. The power of collaborative research and inclusion of diverse populations will ensure a bright future for precision psychiatry.

Chronic stress is a major contributor to the development of major depressive disorder, one of the leading causes of disability worldwide. Using a model of repeated social defeat stress in mice, we and others have reported that neuroinflammation plays a dynamic role in the development of behavioral deficits consistent with social avoidance and impaired reward responses. Animals susceptible to the model also exhibit hypomyelination in the medial prefrontal cortex, indicative of changes in the differentiation pathway of cells of the oligodendroglial lineage (OLN). We computationally confirmed the presence of immune oligodendrocytes, a population of OLN cells, which express immune markers and myelination deficits. In the current study, we report that microglia are necessary to induce expression of antigen presentation markers (and other immune markers) on oligodendroglia. We further associate the appearance of these markers with changes in the OLN and confirm that microglial changes precede OLN changes. Using co-cultures of microglia and OLN, we show that under inflammatory conditions the processes of phagocytosis and expression of MHCII are linked, suggesting potential priming for antigen presentation by OLN cells. Our findings provide insights into the nature of these OLN cells with immune capabilities, their obligatory interaction with microglia, and identify them as a potential cellular contributor to the pathological manifestations of psychosocial stress.

Gut microbiota (GM) alterations have been implicated in depression, potentially mediated via changes in the prefrontal cortex (PFC). However, the causal relationship and underlying biological mechanisms remain unclear.

Summary statistics from genome-wide association studies (GWAS) of 473 GM (n = 5959), depression (43,280 cases, 329,192 controls), and 3935 brain imaging-derived phenotypes (IDPs) (n = 33,224) were utilized. Univariable Mendelian randomization (MR) analyses examined the associations between (i) GM and depression, (ii) depression and GM, (iii) brain IDPs and depression, and (iv) GM and PFC-related IDPs. Multivariable MR (MVMR) assessed whether PFC structural phenotypes mediated the effect of GM on depression risk. Summary-data-based MR (SMR) identified potential genes and therapeutic targets. Single-cell RNA sequencing and bulk RNA data from GEO database about PFC tissue of depression patients were analyzed for biomarker expression and regulatory mechanisms. Target genes were validated in mice model through behavioral tests, RT-qPCR, and immunofluorescence analyses. Drug prediction and molecular docking explored potential therapeutic targets and traditional Chinese medicine.

The analysis identified 15 positive and 16 negative causal effects between GM and depression. Reverse MR analysis revealed 36 GM traits with reverse causality to depression. After excluding results with heterogeneity and pleiotropy, 11 PFC-related IDPs exhibited causal relationships with depression. MVMR adjustment for genetically predicted GM levels showed that the effects of PFC-related IDPs remained significant, indicating mediation of GM's effect on depression. Integration of GWAS and eQTL data identified 14 genes passing the SMR test. Differential expression of these genes was observed across cell types and between patients with depression and healthy controls. FARS2 and SLC25A21 were identified as potential therapeutic targets for depression, with protective effects shown in MR analysis (FARS2: OR: 0.892, 95 % CI: 0.813-0.971, P = 0.005; SLC25A21: OR: 0.924, 95 % CI: 0.851-0.997, P = 0.035). Reduced expression of FARS2 and SLC25A21 was validated in CUMS induced mice, along with depressive-like behaviors confirmed through behavioral tests. Molecular docking predicted Betanidin, Aloenin B, Rehmannioside D, and Vicenin-2 as potential drugs targeting both FARS2 and SLC25A21.

This study supports causal relationships between specific GM and depression, with PFC structures potentially mediating these effects. The findings provide insights into personalized depression treatment strategies and identify promising candidate targets and drugs for future research.

Adolescent depression is increasingly recognized as a serious mental health disorder with distinct clinical and molecular features. Using single-nucleus RNA sequencing, we identified cell-specific transcriptomic changes in the nucleus accumbens (NAc), particularly in astrocytes, of adolescent macaques exhibiting depressive-like behaviors. The level of diacylglycerol kinase beta was significantly reduced in neurons and glial cells of depressed macaques, while FKBP5 levels increased in glial cells. Disruption of GABAergic synapses and disruption of D-glutamine and D-glutamate metabolism were linked to depressive phenotypes in medium spiny neurons (MSNs) and subtypes of astrocytes. Communication pathways between astrocytes and D1/D2-MSNs were also disrupted, involving factors like bone morphogenetic protein-6 and Erb-B2 receptor tyrosine kinase-4. Bulk transcriptomic and proteomic analyses corroborated these findings, and FKBP5 upregulation was confirmed by qRT-PCR, western blotting, and immunofluorescence in the NAc of rats and macaques with chronic unpredictable mild stress. Our results highlight the specific roles of different cell types in adolescent depression in the NAc, offering potential targets for new antidepressant therapies.

Previous studies have largely overlooked cellular differential alterations across differentially affected brain regions in both disease mechanisms and therapeutic development of Alzheimer's disease (AD). This study aimed to compare the differential cellular and transcriptional changes in the prefrontal cortex (PFC) and entorhinal cortex (EC) of AD patients through an integrated single-cell transcriptomic analysis. We integrated three single-cell RNA sequencing (scRNA-seq) datasets comprising PFC and EC samples from AD patients and age-matched healthy controls. A total of 124,658 nuclei and 31 cell clusters were obtained and classified into eight major cell types, with EC exhibiting much more pronounced transcriptional alterations than PFC. Through network analysis, we pinpointed hub regulatory genes that form interconnected networks driving AD pathogenesis, findings validated by RT-qPCR showing more pronounced expression changes in EC versus PFC of AD mice. Moreover, dysregulation of the LINC01099-associated regulatory networks in the PFC and EC, showing correlation with AD progression, may present new therapeutic targets for AD. Together, these results suggest that effective AD biomarkers and therapeutic strategies may require simultaneous, precise targeting of specific cell populations across multiple brain regions.

Psychiatric research has shifted from a neuroncentric view to understanding mental disorders as disturbances of heterogeneous brain networks. Oligodendrocyte precursor cells (OPCs)- actively involved in the modulation of neuronal functions - are altered in psychiatric patients, but the extent and related consequences are unclear. This review explores canonical and non-canonical OPC-related pathways in schizophrenia, bipolar disorder, post-traumatic stress disorder, and depression in humans, highlighting potential mechanisms shared across diagnostic entities.

The gut-brain axis is dysregulated as a consequence of alterations in the gut microbiota. These alterations increase toxic microbial metabolites, endotoxemia, and the release of immune mediators and contribute to the development of depression. Cubebin is a dibenzyl butyrolactone lignan, and its stem is also known as Agaru in Tibetan areas, it is commonly used as a sedative and tranquilizing medicine. This study aimed to investigate the effects of cubebin on chronic stress-induced depression-like behavior in mice. Cubebin was observed to mitigate depressive-like behavior in chronic unpredictable mild stress (CUMS) mice, influence the restoration of their cerebral cortex and hippocampal tissue morphology, and enhance the abundance of relevant intestinal flora in depression model mice, particularly by decreasing the abundance of Clostridium, Dorea, and Ruminococcus. The final protein function expression was normalized by regulating depression-related metabolic pathways. Concomitantly, the concentrations of neurotransmitters serotonin (5-HT), norepinephrine (NE), and dopamine (DA) in the brains of mice in the model group were enhanced, and their depressive symptoms were mitigated. Our study findings suggest that cubebin may ameliorate CUMS-induced depression in mice by modulating the microbe-gut-brain axis, elucidating the key effect of gut metabolites on depressive symptoms.

Human brain development requires generating diverse cell types, a process explored by single-cell transcriptomics. Through parallel meta-analyses of the human cortex in development (seven datasets) and adulthood (16 datasets), we generated over 500 gene co-expression networks that can describe mechanisms of cortical development, centering on peak stages of neurogenesis. These meta-modules show dynamic cell subtype specificities throughout cortical development, with several developmental meta-modules displaying spatiotemporal expression patterns that allude to potential roles in cell fate specification. We validated the expression of these modules in primary human cortical tissues. These include meta-module 20, a module elevated in FEZF2+ deep layer neurons that includes TSHZ3, a transcription factor associated with neurodevelopmental disorders. Human cortical chimeroid experiments validated that both FEZF2 and TSHZ3 are required to drive module 20 activity and deep layer neuron specification but through distinct modalities. These studies demonstrate how meta-atlases can engender further mechanistic analyses of cortical fate specification.

Post-stroke depression (PSD) is a common but severe mental complication after stroke. However, the cellular and molecular understanding of PSD is still yet to be illustrated. In current study, we prepared PSD rat model (MD) via unilateral middle cerebral artery occlusion (MCAO) and chronic stress stimulation (DEPR), and isolated hippocampal tissues for single cell sequencing of 10x Genomics Chromium. First, we determined the presence of the increased cell population of endothelium and microglia and the compromised oligodendrocytes in MD compared to NC, MCAO and DEPR. The enriched functions of highly variable genes (HVGs) of endothelium and microglia suggested a reinforced blood-brain barrier in MD. Next, cell clusters of endothelium, microglia and oligodendrocytes were individually analyzed, and the subtypes with distinct functions were identified. The presence of expression profiles, intercellular communications and signaling pathways of these three cell populations of PSD displayed a similar but more aggressive appearance with DEPR compared to MCAO and NC. Taken together, this study characterized the specific gene profile of endothelium, microglia and oligodendrocytes of hippocampal PSD by single cell sequencing, emphasizing the crosstalk among them to provide theoretical basis for the in-depth mechanism research and drug therapy of PSD.

Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. While there exist algorithms to estimate the cell type proportions in tissues, a major challenge is the algorithms can show reduced performance if using tissues that have varying cell sizes, such as in brain tissue. In this way, without adjusting for differences in cell sizes, computational algorithms estimate the relative fraction of RNA attributable to each cell type, rather than the relative fraction of cell types, leading to potentially biased estimates in cellular composition. Furthermore, these tools were built on different frameworks with non-uniform input data formats while addressing different types of systematic errors or unwanted bias.

We present lute, a software tool to accurately deconvolute cell types with varying sizes. Our package lute wraps existing deconvolution algorithms in a flexible and extensible framework to enable easy benchmarking and comparison of existing deconvolution algorithms. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition.

Our software ( https://bioconductor.org/packages/lute ) can be used to enhance and improve existing deconvolution algorithms and can be used broadly for any type of tissue containing cell types with varying cell sizes.

Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery.

Bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia share genetic architecture, yet their molecular mechanisms remain elusive. Both common and rare genetic variants contribute to neural dysfunction, impacting cognition and behavior. This study investigates the molecular effects of genetic variants on human cortical single-cell types using a single-exon analysis approach. Integrating exon-level eQTLs (common variants influencing exon expression) and joint exon eQT-Scores (combining polygenic risk scores with exon-level gene expression) from a postmortem psychiatric cohort (BD = 15, MDD = 24, schizophrenia = 68, controls = 62) with schizophrenia-focused rare variant data from the SCHEMA consortium, we identified 110 core genes enriched in pathways including circadian entrainment (FDR = 0.02), cortisol synthesis and secretion (FDR = 0.026), and dopaminergic synapse (FDR = 0.038). Additional enriched pathways included hormone signaling (FDRs < 0.0298, including insulin, GnRH, aldosterone, and growth hormone pathways) and, notably, adrenergic signaling in cardiomyocytes (FDR = 0.0028). These pathways highlight shared molecular mechanisms in the three disorders. Single-nuclei RNA sequencing data from three cortical regions revealed that these core set genes are predominantly expressed in excitatory neuron layers 2-6 of the dorsolateral prefrontal cortex, linking molecular changes to cell types involved in cognitive dysfunction. Our results demonstrate the power of integrating multimodal genetic and transcriptomic data at the exon level. This approach moves beyond symptom-based diagnoses toward molecular classifications, identifying potential therapeutic targets for psychiatric disorders.

Stress-induced demyelination resulting from oligodendrocyte (OLG) dysfunction is one of the key pathological mechanisms of depression, yet its dynamic regulatory network remains unclear. This study integrates single-cell transcriptomics, lineage tracing, and functional interventions to uncover a temporally disordered OLG cholesterol metabolism in a restraint stress mouse model: After 3 days of stress, upregulation of efflux genes Abca1/Abcg1 triggers a compensatory response; however, by day 14, persistent suppression of transport genes (Apoe, Apod) and homeostatic regulatory genes (Dhcr24, Srebf2, etc.) leads to intracellular accumulation of "ineffective cholesterol", with compensatory activation of the AMPK pathway unable to restore cholesterol conversion into myelin. Pseudotime analysis further reveals that stress alters OLG differentiation trajectories, decreasing the proportion of mature OLGs and causing immature precursors to abnormally stall at the late pre-differentiation stage, resulting in myelin regeneration failure. Moreover, an immune OLG_C10 subpopulation expressing complement component C3 and P2ry12 is identified, indicating that OLGs may contribute to neuroinflammatory cascades through immune reprogramming. In summary, these findings reveal a novel mechanism from the dynamic perspective of OLGs, in which the interplay of "metabolic imbalance, differentiation blockade, and immune activation" collaboratively drives stress-induced demyelination, providing a theoretical foundation for depression treatment targeting OLG functional restoration.

Focal cortical dysplasia type II (FCDII) is a major cause of drug-resistant epilepsy, but genetic factors explain only some cases, suggesting other mechanisms. In this study, we conduct a molecular analysis of brain lesions and adjacent areas in FCDIIb patients. By analyzing over 217,506 single-nucleus transcriptional profiles from 15 individuals, we find significant changes in smooth muscle cells (SMCs) and astrocytes. We identify abnormal vascular malformations and a unique type of SMC that we call "Firework cells", which migrate from blood vessels into the brain parenchyma and associate with VIM+ cells. These abnormalities create localized ischemic-hypoxic (I/H) microenvironments, as confirmed by clinical data, further impairing astrocyte function, activating the HIF-1α/mTOR/S6 pathway, and causing neuronal loss. Using zebrafish models, we demonstrate that vascular abnormalities resulting in I/H environments promote seizures. Our results highlight vascular malformations as a factor in FCDIIb pathogenesis, suggesting potential therapeutic avenues.

Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD), that are caused by multiple etiologies, including genetic heterogeneity, epigenetic dysregulation, and aberrant morphological and biochemical conditions. Psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have been renewed as fascinating treatment options and have gradually demonstrated potential therapeutic effects in mental disorders. However, the multifaceted conditions of psychiatric disorders resulting from individuality, complex genetic interplay, and intricate neural circuits impact the systemic pharmacology of psychedelics, which disturbs the integration of mechanisms that may result in dissimilar medicinal efficiency. The precise prescription of psychedelic drugs remains unclear, and advanced approaches are needed to optimize drug development. Here, recent studies demonstrating the diverse pharmacological effects of psychedelics in mental disorders are reviewed, and emerging perspectives on structural function, the microbiota-gut-brain axis, and the transcriptome are discussed. Moreover, the applicability of deep learning is highlighted for the development of drugs on the basis of big data. These approaches may provide insight into pharmacological mechanisms and interindividual factors to enhance drug discovery and development for advanced precision medicine.

The spatial structure of cells is highly organized at multiscale levels from global spatial domains to local cell type heterogeneity. Existing methods for analyzing spatially resolved transcriptomics (SRT) are separately designed for either domain alignment across multiple slices or deconvoluting cell type compositions within a single slice. To this end, a novel deep learning method, SMILE, is proposed which combines graph contrastive autoencoder and multilayer perceptron with local constraints to learn multiscale and informative spot representations. By comparing SMILE with the state-of-the-art methods on simulation and real datasets, the superior performance of SMILE is demonstrated on spatial alignment, domain identification, and cell type deconvolution. The results show SMILE's capability not only in simultaneously dissecting spatial variations at different scales but also in unraveling altered cellular microenvironments in diseased conditions. Moreover, SMILE can utilize prior domain annotation information of one slice to further enhance the performance.

Major depressive disorder (MDD) is a complex psychiatric illness, with synaptic plasticity playing a key role in its pathology. Our study aims to investigate the molecular basis of MDD by analyzing synaptic plasticity-related gene expression at the single-cell level. Utilizing a published snRNA-seq dataset (GSE144136), we identified Excitatory.neurons_1 as the cell cluster most associated with MDD and synaptic plasticity through cell clustering, gene set enrichment analysis (GSEA), and pseudotime analysis. Integrating the bulk RNA-seq data (GSE38206), we identified CASKIN1 and CSTB as hub genes via differential expression analysis and machine learning methods. Further exploration of the relevant mechanisms was performed via cell-cell communication and ligand-receptor interaction analysis, functional enrichment analysis, and the construction of molecular regulatory networks, highlighting miR-21-5p as a key biomarker. We propose that elevated miR-21-5p in MDD downregulates CASKIN1 in Excitatory.neurons_1 cells, resulting in decreased neural connectivity and altered synaptic plasticity. As our analyzed snRNA-seq dataset consists solely of male samples, these findings may be male-specific. Our findings shed light on potential mechanisms underlying synaptic plasticity in MDD, offering novel insights into the disorder's cellular and molecular dynamics.

Recent studies have increasingly utilized gradient metrics to investigate the spatial transitions of brain organization, enabling the conversion of macroscale brain features into low-dimensional manifold representations. However, it remains unclear whether alterations exist in the cortical morphometric similarity (MS) network gradient in patients with schizophrenia (SCZ). This study aims to examine potential differences in the principal MS gradient between individuals with SCZ and healthy controls and to explore how these differences relate to transcriptional profiles and clinical phenomenology.

MS network was constructed in this study, and its gradient of the network was computed in 203 patients with SCZ and 201 healthy controls, who shared the same demographics in terms of age and gender. To examine irregularities in the MS network gradient, between-group comparisons were carried out, and partial least squares regression analysis was used to study the relationships between the MS network gradient-based variations in SCZ, and gene expression patterns and clinical phenotype.

In contrast to healthy controls, the principal MS gradient of patients with SCZ was primarily significantly lower in sensorimotor areas, and higher in more areas. In addition, the aberrant gradient pattern was spatially linked with the genes enriched for neurobiologically significant pathways and preferential expression in various brain regions and cortical layers. Furthermore, there were strong positive connections between the principal MS gradient and the symptomatologic score in SCZ.

These findings showed changes in the principal MS network gradient in SCZ and offered potential molecular explanations for the structural changes underpinning SCZ.

The study investigates how Sphingosine-1-phosphate receptor 3 (S1PR3) and the Chronic Unpredictable Mild Stress (CUMS) affects depression-like behaviors. The S1P/S1PR3 signaling pathway is known to play a role in mood regulation, but it is not yet fully understood how it is connected to depression. This study looks to further explore this topic. To investigate the effect of CUMS on S1PR3 expression in hippocampus neurons and the synaptic plasticity, we observed animals' behavior with Sucrose Preference Test (SPT), Forced Swim Test (FST) and Open Field Test (OFT). Combining molecular and histological analysis, we investigated the S1PR3 expression, the change in synapse density, and synaptic structure change in the hippocampus. The CUMS caused a significant decrease in the S1PR3 expression, the density of the synaptic spine and synaptic ultrastructure change in mice. On the other hand, over-expression of S1PR3 by adeno-associated virus (AAV) in hippocampal neurons alleviated the depressive-like behaviors and synaptic deficits observed in stress-susceptible animals. Furthermore, the depressive-like phenotype and synaptic impairments were normalized by the expression of RhoA, implicating the RhoA/ROCK1 pathway in S1PR3 actions. Collectively, our findings provide strong evidence that S1PR3 plays a key role in hippocampal synaptic plasticity and depression and that modulation of S1PR3/RhoA/ROCK1 signaling may offer a novel therapeutic strategy for MDD. This study not only underscores the therapeutic potential of S1PR3 but also provides novel insights into the molecular mechanisms underlying depression.

Chronic rhinosinusitis (CRS) and depression are both common conditions with significant socioeconomic impact. The high co-occurrence of depression in CRS patients suggests a common pathophysiology, but the mechanisms are unclear. This study aimed to identify potential molecular links between the two conditions. We retrieved gene expression datasets for CRS and depression from the GEO database. Using differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA), we identified co-expression genes associated with CRS and depression. Enrichment analyses including GO, KEGG, and GSEA were performed to explore biological pathways. Machine learning algorithms including random forest and LASSO regression were engaged to screen for shared hub genes predictive of CRS and depression. Single-cell RNA sequencing (scRNA-seq) data were analyzed to delineate the expression profiles of the shared hub genes across different cell types. Animal experiments were employed to validate the role of core genes in CRS-related depression. We identified five shared hub genes: CHRDL1, DIO2, HSD17B6, PDE3A, and PLA2G5, with the TGF-β signaling, cytokine-cytokine interaction receptors, and cell adhesion as key biological pathways. DIO2, as identified by machine learning, is a promising diagnostic biomarker for CRS and depression. The scRNA-seq analysis showed that DIO2 is primarily expressed in neurons and astrocytes. Animal experiments showed that overexpression of DIO2 improved the depressive-like behaviors in CRS mice. This study sheds new light on the molecular basis of the comorbidity between CRS and depression. DIO2 is a potential diagnostic and therapeutic target for CRS patients with comorbid depression.

Astrocytes are closely linked to depression, and the prefrontal cortex (PFC) is an important brain region involved in major depressive disorder (MDD). However, the underlying mechanism by which astrocytes within PFC contribute to MDD remains unclear. Using single-nucleus RNA sequencing analyses, we show a significant reduction in astrocytes and attenuated pleiotrophin-protein tyrosine phosphatase receptor type Z1 (PTN-PTPRZ1) signaling in astrocyte-to-excitatory neuron communication in the PFC of male MDD patients. We find reduced astrocytes and PTN in the dorsomedial PFC of male mice with depression induced by chronic restraint and social defeat stress. Knockdown of astrocytic PTN induces depression-related responses, which is reversed by exogenous PTN supplementation or overexpression of astrocytic PTN. The antidepressant effects exerted by astrocytic PTN require interaction with PTPRZ1 in excitatory neurons, and PTN-PTPRZ1 activates the AKT signaling pathway to regulate depression-related responses. Our findings indicate the PTN-PTPRZ1-AKT pathway may be a potential therapeutic target for MDD.

Social isolation (SI) is a prevalent issue in modern society, particularly exacerbated during the COVID-19 pandemic, and it is a significant contributor to depressive disorders. Inflammation-related markers are upregulated in patients with major depressive disorder (MDD) unresponsive to first-line selective serotonin reuptake inhibitor (SSRI) antidepressants. This study investigates the role of formyl peptide receptor 2 (FPR2), a G-protein coupled receptor expressed in central and peripheral immune cells, in SI-induced depression. We developed a mouse model of SI by housing mice individually for three weeks. SI mice exhibited increased capillary-associated microglia (CAMs) with upregulated FPR2 expression in the prefrontal cortex (PFC) and hippocampus compared to group-housed controls. Notably, subcutaneous administration of the FPR2 antagonist WRW4 alleviated depressive and anxiety-like behaviors in SI mice, reducing microglial activation and neuronal damage. WRW4 treatment decreased CAM numbers and their FPR2 expression. RNA sequencing revealed that SI primarily induced changes in genes associated with blood-brain barrier (BBB) function, followed by alterations in genes related to hormone activity, immune activation, and neuronal function. Transcriptomic changes in brain endothelial cells from SI mice resembled those observed in animal models of several neurological disorders and in MDD patients. WRW4 treatment partially reversed these transcriptomic alterations and restored compromised BBB integrity. Additionally, intracerebroventricular (ICV) injection of WRW4 also alleviated depressive and anxiety-like behaviors in SI mice. Finally, our analysis of public transcriptome databases indicates FPR2 upregulation in the orbital ventral PFC of MDD patients and peripheral blood mononuclear cells of those in severe depressive episodes. These findings suggest that the pharmacological targeting of FPR2 may rescue SI-induced pathology in mice by protecting BBB integrity.

Psychiatric disorders like schizophrenia, bipolar disorder, and major depressive disorder exhibit substantial genetic and clinical overlap. However, their molecular architecture remains elusive due to their polygenic nature and complex brain cell interactions. We integrated clinical data with genetic susceptibility to investigate gene expression and chromatin accessibility in the orbitofrontal cortex of 92 postmortem human brain samples at the single-nucleus (sn) level. Using snRNA-seq and snATAC-seq, we analyzed ~800,000 and 400,000 nuclei, respectively. We observed cell-type-specific dysregulation related to clinical diagnosis and genetic risk. Dysregulation in gene expression and chromatin accessibility associated with diagnosis was pronounced in excitatory neurons. Conversely, genetic risk predominantly affected glial and endothelial cells. Notably, INO80E and HCN2 genes exhibited dysregulation in excitatory neurons' superficial layers 2/3 influenced by schizophrenia polygenic risk. This study unveils the complex genetic and epigenetic landscape of psychiatric disorders, emphasizing the importance of cell-type-specific analyses in understanding their pathogenesis and contrasting genetic predisposition with clinical diagnosis.

Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to premature senescence. However, the relationship between senescence-related genes (SRGs) and MDD remains underexplored.

We analyzed 144 MDD samples and 72 healthy controls from the GEO database to compare SRGs expression. Using Random Forest (RF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), we identified five hub SRGs to construct a logistic regression model. Consensus cluster analysis, based on SRGs expression patterns, identified subclusters of MDD patients. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules strongly linked to each cluster. Single-cell RNA sequencing was used to analyze MDD SRGs functions.

The five hub SRGs: ALOX15B, TNFSF13, MARCH 15, UBTD1, and MAPK14 showed differential expression between MDD patients and controls. Diagnostics models based on these hub genes demonstrated high accuracy. The hub SRGs correlated positively with neutrophils and negatively with T lymphocytes. SRGs expression pattern revealed two distinct MDD subclusters. WGCNA identified significant gene modules within these subclusters. Additionally, individual endothelial cells with high senescence scores were found to interact with astrocytes via the Notch signaling pathway, suggesting a specific role in MDD pathogenesis.

This comprehensive study elucidates the significant role of SRGs in MDD, highlighting the importance of the Notch signaling pathway in mediating senescence effects.

Major depressive disorder (MDD) is a widespread psychiatric condition, recognized as the third leading cause of global disease burden in 2008. In the context of MDD, alterations in synaptic transmission within the prefrontal cortex (PFC) are associated with PFC hypoactivation, a key factor in cognitive function and mood regulation. Given the high energy demands of the central nervous system, these synaptic changes suggest a metabolic imbalance within the PFC of MDD patients. However, the cellular mechanisms underlying this metabolic dysregulation remain not fully elucidated. This study employs single-nucleus RNA sequencing (snRNA-seq) data to predict metabolic alterations in the dorsolateral PFC (DLPFC) of MDD patients. Our analysis revealed cell type-specific metabolic patterns, notably the disruption of oxidative phosphorylation and carbohydrate metabolism in the DLPFC of MDD patients. Gene set enrichment analysis based on human phenotype ontology predicted alterations in serum lactate levels in MDD patients, corroborated by the observed decrease in lactate levels in MDD patients compared to 47 age-matched healthy controls (HCs). This transcriptional analysis offers novel insights into the metabolic disturbances associated with MDD and the energy dynamics underlying DLPFC hypoactivation. These findings are instrumental for comprehending the pathophysiology of MDD and may guide the development of innovative therapeutic strategies.

Psychiatric disorders such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are characterized by altered cognition and mood, brain functions that depend on information processing by cortical microcircuits. We hypothesized that psychiatric disorders would display cell type-specific transcriptional alterations in neuronal subpopulations that make up cortical microcircuits: excitatory pyramidal (PYR) neurons and vasoactive intestinal peptide- (VIP), somatostatin- (SST), and parvalbumin- (PVALB) expressing inhibitory interneurons. Using laser capture microdissection followed by RNA sequencing (LCM-seq), we performed cell type-specific molecular profiling of subgenual anterior cingulate cortex, a region implicated in mood and cognitive control. We sequenced libraries from 130 whole cells pooled per neuronal subtype (VIP, SST, PVALB, superficial and deep PYR) in 76 subjects from the University of Pittsburgh Brain Tissue Donation Program, evenly split between MDD, BD and SCZ subjects and healthy controls (totaling 380 bulk transcriptomes from ~50,000 neurons). We identified hundreds of differentially expressed (DE) genes and biological pathways across disorders and neuronal subtypes, with the vast majority in interneurons, particularly PVALB. While DE genes were unique to each cell type, there was a partial overlap across disorders for genes involved in the formation and maintenance of neuronal circuits. We observed coordinated alterations in biological pathways between select pairs of microcircuit cell types, also partially shared across disorders. Finally, DE genes coincided with known risk variants from psychiatric genome-wide association studies, suggesting cell type-specific convergence between genetic and transcriptomic risk for psychiatric disorders. Our study suggests transdiagnostic cortical microcircuit pathology in SCZ, BD, and MDD and sets the stage for larger-scale studies investigating how cell circuit-based changes contribute to shared psychiatric risk.

Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Previous studies of AUD used underpowered single-cell analysis or bulk homogenates of postmortem brain tissue, which obscure gene expression changes in specific cell types. Therefore, we sought to conduct the largest-to-date single-nucleus RNA sequencing (snRNA-seq) postmortem brain study in AUD to elucidate transcriptomic pathology with cell type-specific resolution.

Here, we performed snRNA-seq and high-dimensional network analysis of 73 postmortem samples from individuals with AUD (n = 36, nnuclei = 248,873) and neurotypical control individuals (n = 37, nnuclei = 210,573) in the dorsolateral prefrontal cortex from both male and female donors. Additionally, we performed analysis for cell type-specific enrichment of aggregate genetic risk for AUD as well as integration of the AUD proteome for secondary validation.

We identified 32 distinct cell clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glial populations. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and coexpression modules linked to AUD. Differential gene expression was secondarily validated by integration of a publicly available AUD proteome. Finally, analysis for aggregate genetic risk for AUD identified subtypes of glia as potential key players not only affected by but also causally linked to the progression of AUD.

These results highlight the importance of cell type-specific molecular changes in AUD and offer opportunities to identify novel targets for treatment on the single-nucleus level.

Spatial transcriptomics (ST) enables the study of gene expression in spatial context, but many ST technologies face challenges due to limited resolution, leading to cell mixtures at each spot. We present LETSmix to deconvolve cell types by integrating spatial correlations through a tailored LETS filter, which leverages layer annotations, expression similarities, image texture features, and spatial coordinates to refine ST data. Additionally, LETSmix employs a mixup-augmented domain adaptation strategy to address discrepancies between ST and reference single-cell RNA sequencing data. Comprehensive evaluations across diverse ST platforms and tissue types demonstrate its high accuracy in estimating cell-type proportions and spatial patterns, surpassing existing methods (URL: https://github.com/ZhanYangen/LETSmix ).

We assessed the reproducibility of differentially expressed genes (DEGs) in previously published Alzheimer's (AD), Parkinson's (PD), Schizophrenia (SCZ), and COVID-19 scRNA-seq studies. While transcriptional scores from DEGs of individual PD and COVID-19 datasets had moderate predictive power for case-control status of other datasets (AUC=0.77 and 0.75), genes from individual AD and SCZ datasets had poor predictive power (AUC=0.68 and 0.55). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power (AUC=0.88, 0.91, 0.78, and 0.62). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate BCAT1 as down-regulated in AD mouse oligodendrocytes. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

Major depressive disorder (MDD) is associated with disrupted brain structural integration. Morphometric similarity offers a means to capture the coordinated patterns of various structural features. However, it remains unknown whether MDD-related changes can be detected in cortical morphometric similarity through the Morphometric Inverse Divergence (MIND) network. Additionally, the role of brain connectivity in shaping these alterations, and their links to neuroreceptors and gene expression, have yet to be investigated.

Using the T1-weighted MRI data from 71 patients with first-episode, treatment-naïve MDD and 69 healthy controls, we constructed the MIND network for all participants. We then performed between-group comparisons to investigate abnormalities in the network and spatial relationships between the observed patterns of MIND disruption and the patterns of neuroreceptors were estimated. Network-based spreading was utilized to explore the abnormalities constrained by brain connectivity based on structural, functional, and transcriptional connectome architecture and to further identify potential epicenters of MDD. In addition, partial least squares regression was conducted to examine the associations of gene expression profiles with MIND changes in MDD.

Patients with MDD showed significantly increased MIND in regions associated with sensation and cognition compared with healthy controls, with this altered pattern being influenced by a combination of transcriptional and structural connectivity, and potential epicenters of MDD were identified in the frontal, parietal, and paracentral cortices. Furthermore, the cortical map of case-control differences in MIND was spatially correlated with the cannabinoid CB1 receptor and the brain-wide expression of a weighted combination of genes. These genes were enriched for neurobiologically relevant pathways and preferentially expressed in different cell classes and cortical layers.

These results highlight the abnormal pattern of morphometric similarity observed in MDD, shedding light on the complex interplay between disrupted macroscale coordinated morphology and microscale molecular organization in MDD.

The dynamics of chromatin conformation involve continuous and reversible changes within the nucleus of a cell, which participate in regulating processes such as gene expression, DNA replication, and damage repair. Here, SEE is introduced, an artificial intelligence (AI) method that utilizes autoencoder and transformer techniques to analyze chromatin dynamics using single-cell RNA sequencing data and a limited number of single-cell Hi-C maps. SEE is employed to investigate chromatin dynamics across different scales, enabling the detection of (i) rearrangements in topologically associating domains (TADs), and (ii) oscillations in chromatin interactions at gene loci. Additionally, SEE facilitates the interpretation of disease-associated single-nucleotide polymorphisms (SNPs) by leveraging the dynamic features of chromatin conformation. Overall, SEE offers a single-cell, high-resolution approach to analyzing chromatin dynamics in both developmental and disease contexts.