Depression is a severe mental illness that poses substantial burdens on public health. Given that depression research is still challenged by its multifaceted pathogenesis, depicting the depression associated genetic regulatory networks is essential for understanding its mechanism, optimizing diagnosis, and developing targeted therapies. However, a comprehensive panoramic view of transcriptional alterations in depression remains lacking. By leveraging the available transcriptomic studies from the National Center for Biotechnology Information (NCBI), China National Center for Bioinformatics (CNCB), European Bioinformatics Institute (EBI), and our laboratory, we compiled an extensive set of depression related datasets, encompassing 4 species, 31 types of brain and peripheral tissues, 35 categories of antidepressant interventions, and 6391 samples. Furthermore, a unified pipeline for raw data preprocessing and differential expression analysis was employed to identify differentially expressed genes (DEGs). A total of 631882 molecules entries were obtained, including 190366 entries from humans, 6612 from non-human primates, 332780 from mice, and 102124 from rats. Additionally, 15 single-cell and single-nucleus transcriptomic datasets, including 164 samples, and 78536 molecular entries were also included. Notably, we developed the TranDep database ( http://www.depression-atlas.cn/ ) for the depression research community, which provided a user-friendly web interface for browsing, and searching these molecules. To demonstrate the utility of TranDep, a case study was presented to identify robust DEGs, and explore its biological functions in the prefrontal cortex of patients with depression. Overall, TranDep is a comprehensive cross-species resource developed to provide a transcriptional atlas of depression, which may shed light on the identification and validation of diagnostic and therapeutic markers for depression.
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