PathoEvo: an interactive single-cell database for exploring disease progression and pathological evolution
6 affiliations
- 1School of Medical Science and Engineering, Beijing Institute of Technology , Beijing 100081 ,
- 2School of Artificial Intelligence, Beijing Institute of Technology , Beijing 100081 ,
- 3School of Computer Science & Technology, Beijing Institute of Technology , Beijing 100081 ,
- 4School of Computer Science & Technology, Beijing Jiaotong University , Beijing 100044 ,
- 5School of Physical Science and Engineering, Beijing Jiaotong University , Beijing 100044 ,
- 6Gansu Provincial Key Laboratory of Wearable Computing , Lanzhou 730000 ,
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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BibTeX
@article{feng2026pathoevo,
title = {{PathoEvo: an interactive single-cell database for exploring disease progression and pathological evolution}},
author = {Shuang Feng and Lifeng Zhang and Shuxiang Yang and Xin Wang and Yugang Li and Boran Jing and Xinyue Hu and Bin Hu and Xiaohua Wan and Fa Zhang},
journal = {Nucleic Acids Research},
year = {2026},
month = oct,
doi = {10.1093/nar/gkag1075},
url = {https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1075/9005758},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - PathoEvo: an interactive single-cell database for exploring disease progression and pathological evolution
AU - Shuang Feng
AU - Lifeng Zhang
AU - Shuxiang Yang
AU - Xin Wang
AU - Yugang Li
AU - Boran Jing
AU - Xinyue Hu
AU - Bin Hu
AU - Xiaohua Wan
AU - Fa Zhang
PY - 2026
DA - 2026/10/31
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag1075
UR - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1075/9005758
AB - 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.
SN - 0305-1048
SN - 1362-4962
ER -