Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Cell. 2021 Oct 14;184(21):5482-5496.e28. doi: 10.1016/j.cell.2021.09.014. Epub 2021 Sep 30.
Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.
确定细胞如何随其局部信号环境变化并组织成不同的细胞群落对于理解从发育、衰老到癌症等多样化的过程至关重要。在这里，我们介绍了 EcoTyper，这是一个用于从批量、单细胞和空间分辨基因表达数据中大规模识别和验证细胞状态和多细胞群落的机器学习框架。当应用于 16 种人类癌中的 12 种主要细胞谱系时，EcoTyper 鉴定出 69 个转录定义的细胞状态。大多数状态是肿瘤组织特有的，在肿瘤类型中普遍存在，并且具有显著的预后意义。通过分析细胞状态的共现模式，我们发现了十个具有惊人保守性的临床独特的多细胞群落，其中包括三个与髓样和基质成分相关的不良生存群落，一个富含正常组织的群落，以及两个与早期癌症发展相关的群落。这项研究阐明了人类癌中的基本细胞组织单元，并为在任何组织中大规模分析细胞生态系统提供了框架。
