生物
RNA序列
免疫
核糖核酸
计算生物学
疾病
细胞
基因
遗传学
免疫系统
转录组
基因表达
医学
病理
作者
Peter van Galen,Volker Hovestadt,Marc H. Wadsworth,Travis K. Hughes,Gabriel K. Griffin,Sofia Battaglia,Julia A. Verga,Jason Stephansky,Timothy J. Pastika,Jennifer Lombardi Story,Geraldine S. Pinkus,Olga Pozdnyakova,Ilene Galinsky,Richard M. Stone,Timothy A. Graubert,Alex K. Shalek,Jon C. Aster,Andrew A. Lane,B Bernstein
出处
期刊:Cell
[Elsevier]
日期:2019-02-28
卷期号:176 (6): 1265-1281.e24
被引量:815
标识
DOI:10.1016/j.cell.2019.01.031
摘要
Summary
Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies. Video Abstract
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