PathoEvo: an interactive single-cell database for exploring disease progression and pathological evolution

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ID: 337879
2026
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Abstract
Disease progression is driven by coordinated changes in tissue microenvironments, cellular composition, cell states, and transcriptional programmes. Single-cell and single-nucleus RNA sequencing enable these changes to be studied at cellular resolution. However, public disease-associated datasets are often organized within individual studies and annotated using heterogeneous clinical, pathological, or molecular staging systems, limiting comparison across disease stages and datasets. Here, we present PathoEvo (https://biomedlab-ai.com/pathoevo/), a disease-associated single-cell and single-nucleus transcriptomic database centred on disease progression. PathoEvo integrates 7 452 971 cells or nuclei from 981 samples and 59 datasets, covering 18 diseases and 27 progression frameworks. Through manual curation, unified processing, metadata harmonization, cell-type annotation, and stage mapping, PathoEvo organizes heterogeneous datasets into standardized resources organized around disease progression. Its data and analytical functions span three complementary levels, allowing users to examine changes in cellular composition associated with disease stage and cell states, characterize sample-level molecular variation across disease progression, and investigate gene expression dynamics across relevant cell populations and samples. Structured search and browsing further facilitate dataset discovery and navigation of stage-resolved information across diseases and staging systems. PathoEvo provides an integrative resource for investigating pathological evolution and comparing cellular and molecular patterns of disease progression across datasets at single-cell resolution.
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Authors Shuang Feng, Lin Zhang, Shuxiang Yang, Xin Wang, Yugang Li, Boran Jing, Xinyue Hu, Bin Hu, Xiaohua Wan, Fa Zhang
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1075
URL
https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkag1075/72639837/gkag1075.pdf
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