Biology subjects

Jiang, R.

Publications and source records attributed to Jiang, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Detection of spatial chromatin accessibility patterns with inter-cellular correlations

Recent advances in spatial sequencing technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within intact tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the first and key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here we propose an efficient and iterative model, Descartes, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking for spatially variable peaks identification, we demonstrate the superiority of Descartes in revealing cellular heterogeneity and capturing tissue structure. In terms of computational efficiency, Descartes also outperforms existing methods with spatial assumptions. Utilizing the graph of inter-cellular correlations, Descartes denoises and imputes data via the neighboring relationships, enhancing the precision of downstream analysis. We further demonstrate the ability of Descartes for peak module identification by using peak-peak correlations within the graph. When applied to spatial multi-omics data, Descartes show its potential to detect gene-peak interactions, offering valuable insights into the construction of gene regulatory networks.

bioinformatics↗

bioRxiv2024

Characterization of the SARS-CoV-2 BA.5 Variants in H11-K18-hACE2 Hamsters

This study aims to comprehensively characterize the SARS-CoV-2 BA.5 variants using K18 hACE2 transgenic mice and golden hamsters as model organisms. Previous research on SARS-CoV-2 has utilized both mouse and hamster models, leading to conflicting results concerning the viruss lethality. In our study, the finding suggests that H11-K18 hACE2 golden hamsters closely mimic the disease progression observed in human COVID-19 cases caused by BA.5 variants, demonstrating consistent severity and symptoms comparable to severe infections. Additionally, hamsters exhibit heightened respiratory viral replication, accurately reflecting the clinical viral kinetics observed in humans. The study emphasizes the critical importance of selecting an appropriate animal model for SARS-CoV-2 research, while also providing robust support for the hypothesis that BA.5 variants contribute to fatal outcomes in COVID-19 cases. These findings highlight the pivotal role of the golden hamster model in advancing our understanding of the pathogenic mechanisms underlying SARS-CoV-2 variants, as well as in the development of targeted therapeutic strategies. Significance StatementOur research work explores groundbreaking insights that could reshape our understanding of COVID-19 and pave the way for targeted therapies. We use golden hamster models to express the possibility of different animal models could contribute to human diseases. We hope this finding could clarify some conflicts existed, and help further development of medication for COVID.

immunology↗

bioRxiv2024

A missense variant effect map for the human tumour suppressor protein CHK2

The tumour suppressor CHEK2 encodes the serine/threonine protein kinase CHK2 which, upon DNA damage, is important for pausing the cell cycle, initiating DNA repair and inducing apoptosis. CHK2 phosphorylation of the tumour suppressor BRCA1 is also important for mitotic spindle assembly and chromosomal stability. Consistent with its cell cycle checkpoint role, both germline and somatic variants in CHEK2 have been linked to breast and multiple other cancer types. Over 90% of clinical germline CHEK2 missense variants are classified as variants of uncertain significance, complicating diagnosis of CHK2-dependent cancer. We therefore sought to test the functional impact of all possible missense variants in CHK2. Using a scalable multiplexed assay based on the ability of human CHK2 to complement DNA sensitivity of a S. cerevisiae lacking its ortholog RAD53, we generated a systematic missense variant effect map for CHEK2 missense variation. Map scores reflect known biochemical features of CHK2 and exhibit good performance in separating pathogenic from benign clinical missense variants. Thus, the missense variant effect map for CHK2 offers value in understanding both known and yet-to-be-observed CHK2 variants.

genetics↗

bioRxiv2024

Brain-phenotype predictions can survive across diverse real-world data

Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

neuroscience↗

bioRxiv2023

Potential clinical value and influence of conductivity in conductive cardiac patches for reducing post-MI arrythmia risks

Conductive cardiac patches can help to restore electric signal conduction of the diseased myocardium after myocardial infarction (MI). However, none of the conductive cardiac patches reported in literature has entered clinical trials. Bench-to-bedside translation of conductive patches has long been hindered by the lack of knowledge of the optimal patch conductivity and deep understanding of the potential clinical benefits and risks in patients. Here, we first prepared conductive cardiac patches with conductivities covering 5 orders of magnitude (10-3-101 S/cm). Disagreeing with the mainstream opinion that patch conductivity close to native myocardium (10-3-10-2 S/cm) is most favorable, our results showed that patches with conductivity two orders of magnitudes higher than native myocardium (10-1-100 S/cm) are most effective in restoring cardiac conduction and lowering inducibility quotient. Conduction velocity (CV) is the essence of the observed results. Rat experiments showed that the low-conductivity patch could increase the CV of infarcted myocardium, but did not fully compensate the mismatch in the CVs of infarct and healthy myocardium. Moderate-conductivity patches could increase myocardial CV to the same level of healthy myocardium, while high-conductivity patches further increased myocardial CV, causing a reversed mismatch. The relationship between patch conductivity and improved CVs in myocardium can be explained by monodomain model theory. Based on the theory, 3D finite element simulation of a MI patient heart predicted that a suitable, patch-improved myocardial CV could reduce the number of reentrants, and stabilize the remaining reentry circuits in the myocardium of the MI patient, which indicated its clinical value.

bioengineering↗

bioRxiv2023

Power and reproducibility in the external validation of brain-phenotype predictions

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.

neuroscience↗

bioRxiv2023

Reduced nephron endowment in the common Six2-TGCtg mouse line is due to Six3 misexpression by aberrant enhancer-promoter interactions in the transgene

Lifelong kidney function relies on the complement of nephrons generated during mammalian development from a mesenchymal nephron progenitor cell (NPC) population. Low nephron endowment confers increased susceptibility to chronic kidney disease. We asked whether reduced nephron numbers in the popular Six2TGC transgenic mouse line1 was due to disruption of a regulatory gene at the integration site or to ectopic expression of a gene(s) contained within the transgene. Targeted locus amplification identified integration of the Six2TGC transgene within an intron of Cntnap5a on chr1. We generated Hi-C datasets from NPCs isolated from the Six2TGCtg/+ mice, the Cited1CreERT2/+control mice, and the Six2TGCtg/+;Tsc1+/Flox,2 mice that exhibited restored nephron number compared with Six2TGCtg/+mice, and mapped the precise integration of Six2TGC and Cited1CreERT2transgenes to chr1 and chr14, respectively. No changes in topology, accessibility, or expression were observed within the 50-megabase region centered on Cntnap5a in Six2TGCtg/+ mice compared with control mice. By contrast, we identified an aberrant regulatory interaction between a Six2 distal enhancer and the Six3 promoter contained within the transgene. Increasing the Six2TGCtg to Six2 locus ratio or removing one Six2 allele in Six2TGCtg/+ mice, caused severe renal hypoplasia. Furthermore, CRISPR disruption of Six3 within the transgene (Six2TGC{Delta}Six3CT) restored nephron endowment to wildtype levels and abolished the stoichiometric effect. Data from genetic and biochemical studies together suggest that in Six2TGC, SIX3 interferes with SIX2 function in NPC renewal through its C-terminal domain. SignificanceUsing high-resolution chromatin conformation and accessibility datasets we mapped the integration site of two popular transgenes used in studies of nephron progenitor cells and kidney development. Aberrant enhancer-promoter interactions drive ectopic expression of Six3 in the Six2TGCtgline which was correlated with disruption of nephrogenesis. Disruption of Six3 within the transgene restored nephron numbers to control levels; further genetic and biochemical studies suggest that Six3 interferes with Six2-mediated regulation of NPC renewal.

developmental biology↗

bioRxiv2023

EpiGePT: a Pretrained Transformer model for epigenomics

The inherent similarities between natural language and biological sequences have given rise to great interest in adapting the transformer-based large language models (LLMs) underlying recent breakthroughs in natural language processing (references), for applications in genomics. However, current LLMs for genomics suffer from several limitations such as the inability to include chromatin interactions in the training data, and the inability to make prediction in new cellular contexts not represented in the training data. To mitigate these problems, we propose EpiGePT, a transformer-based pretrained language model for predicting context-specific epigenomic signals and chromatin contacts. By taking the context-specific activities of transcription factors (TFs) and 3D genome interactions into consideration, EpiGePT offers wider applicability and deeper biological insights than models trained on DNA sequence only. In a series of experiments, EpiGePT demonstrates superior performance in a diverse set of epigenomic signals prediction tasks when compared to existing methods. In particular, our model enables cross-cell-type prediction of long-range interactions and offers insight on the functional impact of genetic variants under different cellular contexts. These new capabilities will enhance the usefulness of LLM in the study of gene regulatory mechanisms. We provide free online prediction service of EpiGePT through http://health.tsinghua.edu.cn/epigept/.

genomics↗

bioRxiv2023

Cofea: correlation-based feature selection for single-cell chromatin accessibility data

Single-cell sequencing technologies have revolutionized the understanding of cellular heterogeneity at an unprecedented resolution. However, the high-noise and high-dimensional nature of single-cell data poses challenges for downstream analysis, and thus increases the demand for selecting biologically informative features when processing and analyzing single-cell data. Such approaches are mature for single-cell RNA sequencing (scRNA-seq) data, while for single-cell chromatin accessibility sequencing data, the epigenomic profiles at the cellular level, there is a significant gap in the availability of effective methods. Here we present Cofea, a correlation-based framework that focuses on the correlation between accessible chromatin regions, to accurately select scCAS datas features which are highly relevant to biological processes. With various simulated datasets, we quantitively demonstrate the advantages of Cofea for capturing cellular heterogeneity of imbalanced cell populations or differentiation trajectories. We further demonstrate that Cofea outperforms existing feature selection methods in facilitating downstream analysis, particularly in cell clustering, on a wide range of real scCAS datasets. Applying this method to identification of cell type-specific peaks and candidate enhancers, pathway enrichment analysis and partitioned heritability analysis, we show the potential of Cofea to uncover functional biological process and the genetic basis of cellular characteristics.

bioinformatics↗

bioRxiv2023

The effects of data leakage on neuroimaging predictive models

Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.

neuroscience↗

bioRxiv2023

Rescuing missing data in connectome-based predictive modeling

Recent evidence suggests brain-behavior predictions may require very large sample sizes. However, as the sample size increases, the amount of missing data also increases. Conventional methods, like complete-case analysis, discard useful information and shrink the sample size. To address the missing data problem, we investigated rescuing these missing data through imputation. Imputation is the substitution of estimated values for missing data to be used in downstream analyses. We integrated imputation methods into the Connectome-based Predictive Modeling (CPM) framework. Utilizing four open-source datasets--the Human Connectome Project, the Philadelphia Neurodevelopmental Cohort, the UCLA Consortium for Neuropsychiatric Phenomics, and the Healthy Brain Network (HBN)--we validated and compared our framework with different imputation methods against complete-case analysis for both missing connectomes and missing phenotypic measures scenarios. Imputing connectomes exhibited superior prediction performance on real and simulated missing data as compared to complete-case analysis. In addition, we found that imputation accuracy was a good indicator for choosing an imputation method for missing phenotypic measures but not informative for missing connectomes. In a real-world example predicting cognition using the HBN, we rescued 628 individuals through imputation, doubling the complete case sample size and increasing explained variance by 45%. Together, our results suggest that rescuing data with imputation, as opposed to discarding subjects with missing information, improves prediction performance.

neuroscience↗

bioRxiv2023

Consensus peaks of chromatin accessibility in the human genome

The rapid advancement of transposase-accessible chromatin using sequencing (ATAC-seq) technology, particularly with the emergence of single-cell ATAC-seq (scATAC-seq), has accelerated the studies of gene regulation. However, the absence of a generic feature reference for ATAC-seq data limits single-cell analyses and hinders the development of comprehensive cell atlases. To address this, we constructed a generic chromatin accessibility reference by aggregating peaks from 624 high-quality bulk ATAC-seq datasets, defining more than 1 million consensus peaks (cPeaks). Leveraging a deep neural network model, we expanded cPeaks to include previously unobserved regions, enhancing their coverage across diverse tissues and cell types. cPeaks exhibit consistent shapes across tissue types, sequencing technologies, and peak-calling methods, indicating that they represent inherent genomic features. Compared to existing feature defining methods and references, cPeaks show superior performance in scATAC-seq analyses, improving cell annotation and rare cell type identification. Additionally, cPeaks provide insights into chromatin dynamics during cellular differentiation and tumor progression. cPeaks can serve as a robust reference for chromatin accessibility studies to promote cross-dataset consistency and accelerate biological discoveries.

bioinformatics↗

bioRxiv2023

scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data

Single-cell sequencing technology has enabled the characterization of cellular heterogeneity at an unprecedented resolution. To analyze single-cell RNA-sequencing data, numerous tools have been proposed for various analytic tasks, which have been systematically summarized and concluded in a comprehensive database called scRNA-tools. Although single-cell epigenomic data can effectively reveal the chromatin regulatory landscape that governs transcription, the analysis of single-cell epigenomic data presents assay-specific challenges, and an abundance of tools with varying types and functionalities have thus been developed. Nevertheless, these tools have not been well summarized, hindering retrieval, selection, and utilization of appropriate tools for specific analyses. To address the issues, we here proposed scEpiTools database with a multi-functional platform (http://health.tsinghua.edu.cn/scepitools). Specifically, based on the comprehensive collection and detailed annotation of 553 articles, scEpiTools groups articles into 14 major categories and 90 subcategories, provides task-specific recommendation for different emphases, and offers intuitive trend analysis via directed graphs, word clouds, and statistical distributions. For single-cell chromatin accessibility data analysis, we proposed a novel ensemble method named scEpiEnsemble, which, along with multiple methods as built-in kernels, can be used for flexible and efficient online analysis via the scEpiTools platform. We envision that scEpiTools will guide tool usage and development for single-cell epigenomic data and provide valuable resources for understanding regulatory mechanisms and cellular identity. Author summaryCompared to single-cell RNA-sequencing data, single-cell epigenomic data can reflect a set of epigenetic modifications at the cellular level. In general, the analysis of these data is typically divided into several steps: 1) retrieving available tools based on the omics of data and tasks; 2) selecting appropriate tools manually; and 3) utilizing the chosen tools to analyze data. However, due to the rapid development of tools and the unique complexity of the data, each of the above steps is extremely challenging for researchers. To provide researchers with great convenience, we developed scEpiTools (http://health.tsinghua.edu.cn/scepitools), a database with multiple functionalities. For instance, given the omics type and the analytic task, researchers can easily browse all the available tools via the hierarchical categorization of scEpiTools, and get recommendation scores from multiple perspectives. Considering that researchers may encounter difficulties in hardware requirements or environment setup, we also provide online analysis with various commonly used tools, as well as a novel ensemble method named scEpiEnsemble. In summary, scEpiTools represents a valuable resource for the single-cell epigenomics community, facilitating retrieval, selection and utilization of appropriate tools for diverse analyses, and helping to drive future advancements in the field.

bioinformatics↗

bioRxiv2023

Plastome-based Phylogenomic analyses provide insights into the germplasm resource diversity of Cibotium in China

Germplasm resource is the source of herbal medicine production. Cultivation of superior germplasm resources helps to resolve the serious conflict between long-term population persistence and growing market demand by producing materials with high quality consistently. Cibotium barometz is the original plant of cibotii rhizoma ("Gouji"), a traditional Chinese medicine used in the therapy of pain, weakness, and numbness of lower extremity. Long-history use of Cibotium has rendered wild populations of this species declined seriously in China. Without sufficient understanding of species and lineage diversity of Cibotium, it is difficult to propose a targeted conservation scheme at present, let alone selecting high-quality germplasm resources. In order to fill such a knowledge gap, this study sampled C. barometz and relative species throughout their distribution in China, performed genome skimming to obtain plastome data, and conducted phylogenomic analyses. We constructed a well-supported plastome phylogeny of Chinese Cibotium, which showed that three species with significant genetic difference distributed in China, namely C. barometz, C. cumingii, and C. sino-burmaense, a cryptic species endemic to NW Yunnan and adjacent region of NE Myanmar. Moreover, our results revealed two differentiated lineages of C. barometz distributed in the east and west side of a classic phylogeographic boundary that probably shaped by monsoons and landforms in China. We also evaluated the resolution of nine traditional barcode loci, and designed five new DNA barcodes based on the plastome data which can discriminate all these species and lineages of Chinese Cibotium accurately. These novel findings integrated genetic basis will guide conservation planners and medicinal plant breeders to build systematic conservation plans and exploit germplasm resources of Cibotium in China.

evolutionary biology↗

bioRxiv2023

Titering of Chimeric Antigen Receptors on CAR T Cells enabled by a Microfluidic-based Dosage-Controlled Intracellular mRNA Delivery Platform

Chimeric antigen receptor (CAR) T-cell therapy shows unprecedented efficacy for cancer treatment, particularly in treating patients with various blood cancers, most notably B-cell acute lymphoblastic leukemia (B-ALL). In recent years, CAR T-cell therapies are being investigated for treating other hematologic malignancies and solid tumors. Despite the remarkable success of CAR T-cell therapy, it has unexpected side effects that are potentially life threatening. Here, we demonstrate the delivery of approximately the same amount of CAR gene coding mRNA into each T cell propose an acoustic-electric microfluidic platform to manipulate cell membranes and achieve dosage control via uniform mixing, which delivers approximately the same amount of CAR genes into each T cell. We also show that CAR expression density can be titered on the surface of primary T cells under various input power conditions using the microfluidic platform.

bioengineering↗

bioRxiv2023

Early mitochondrial dysfunction proceeds neuroinflammation, synaptic alteration, and autophagy impairment in hippocampus of App knock-in Alzheimer mouse models

Increased amyloid {beta}-peptide (A{beta}) level is one of the drivers of Alzheimers disease (AD). Amyloid precursor protein (App) knock-in mice recapitulate the human A{beta} pathology, allowing the elucidation of the downstream effects of A{beta} and their temporal appearance upon disease progression. Here we have investigated the sequential onset of AD-like pathologies in the AppNL-F and AppNL-G-F knock-in mouse models by time-course transcriptome analysis of the hippocampus, a region severely affected in AD. Energy metabolism emerged as one of the most significantly altered pathways at an early stage of the development of the pathologies. Functional experiments in mitochondria isolated from AppNL-G-F brain subsequently identified upregulation of oxidative phosphorylation driven by the activity of mitochondrial complexes I, IV and V, combined with higher susceptibility to Ca2+-overload. This was followed by a strong neuroinflammatory response and impaired autophagy. Accumulation of autophagosomes and reduced number of mitochondria content in presynaptic terminals could account for the altered synapse morphology including increased number of synaptic vesicles and lowered thickness of post synaptic density in AppNL-G-F mice. This shows that A{beta}-induced pathways in the App knock-in mice recapitulate some key pathologies observed in AD brain, and our data herein contributes to the understanding of their timewise appearance and potential role in new therapeutic approaches.

neuroscience↗

bioRxiv2023

3D Confinement-enabled Priming of Synaptic Activation Promotes Primary T Cell Expansion

The success of autologous cell therapy, which depends highly on T lymphocyte expansion efficiency, is often hindered by suboptimal interactions between T-cell receptors and peptide-MHC molecules. Here, we demonstrate 3D confinement-enabled priming of T cell-MHC immune synapse junctions based on cytoskeletal forces within minutes, which is 200-fold faster than conventional 24 h bulk shaking method. Using T cell-Dynabead binding skeletons in the starting culture, two- to six-fold greater T cell expansion was achieved over the conventional T cell expansion approach without inducing excessive cell exhaustion. Under 3D force-confinement, T-cell division (G1, S, and G2 phases) was increased to be twice as fast. Creating 3D T cell-Dynabead skeletons as the "booster" material enables highly efficient T cell expansion, without requiring complex surface modification of antigen-presenting cells. This method can be modularly adapted to existing T cell expansion processes for a wide range of applications including adoptive cell therapies. Teaser3D confinement-enabled priming of synaptic activation enables radically faster autologous cell production.

immunology↗

bioRxiv2023

Electroacupuncture prevents astrocyte atrophy to alleviate depression

Astrocyte atrophy is the main histopathological hallmark of the major depressive disorder (MDD) in humans and in animal models of depression. Here we show that electroacupuncture prevents astrocyte atrophy in the prefrontal cortex and alleviates depressive-like behaviour in mice subjected to the chronic unpredictable mild stress (CUMS). Treatment of mice with CUMS induced depressive-like phenotypes as confirmed by sucrose preference test, tail suspension test, and forced swim test. These behavioural changes were paralleled with morphological atrophy of astrocytes in the prefrontal cortex, revealed by analysis of 3D reconstructions of confocal Z-stack images of mCherry expressing astrocytes. This morphological atrophy was accompanied with a decrease in expression of cytoskeletal linker Ezrin, associated with formation of astrocytic leaflets, which form astroglial synaptic cradle. Electroacupuncture at the acupoint ST36 as well as treatment with anti-depressant fluoxetine prevented depressive-like behaviours, astrocytic atrophy and down-regulation of astrocytic ezrin. In conclusion, our data further strengthen the notion of a primary role of astrocytic atrophy in depression and reveal astrocytes as cellular target for electroacupuncture in treatment of depressive disorders.

neuroscience↗