PathoEvo 单细胞数据库发布，整合 745 万个细胞覆盖 18 种疾病
PathoEvo: an interactive single-cell database for exploring disease progression and pathological evolution
PathoEvo 是一个以疾病进展为中心的单细胞与单核转录组数据库，整合了来自 981 个样本、59 个数据集的 7 452 971 个细胞或细胞核，覆盖 18 种疾病和 27 套进展分期框架。研究团队通过人工整理、统一处理、元数据协调、细胞类型注释和阶段映射，把原本分散在各研究、采用不同临床、病理或分子分期体系的公共数据整理成标准化资源。数据库的分析功能分三个层次：考察与疾病阶段相关的细胞组成和细胞状态变化，刻画样本层面跨疾病进展的分子变异，以及研究相关细胞群和样本中的基因表达动态。
来源：Nucleic Acids Research · doi.org
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.