血液病理学
质量细胞仪
分化群
计算生物学
细胞病理学
流式细胞术
免疫分型
生物
细胞
病理
医学
免疫学
细胞遗传学
基因
表型
细胞学
生物化学
遗传学
染色体
作者
Albert G. Tsai,David R. Glass,Marisa M. Juntilla,Felix J. Hartmann,Jean Oak,Sebastian Fernandez‐Pol,Robert S. Ohgami,Sean C. Bendall
出处
期刊:Nature Medicine
[Nature Portfolio]
日期:2020-03-01
卷期号:26 (3): 408-417
被引量:46
标识
DOI:10.1038/s41591-020-0783-x
摘要
The diagnosis of lymphomas and leukemias requires hematopathologists to integrate microscopically visible cellular morphology with antibody-identified cell surface molecule expression. To merge these into one high-throughput, highly multiplexed, single-cell assay, we quantify cell morphological features by their underlying, antibody-measurable molecular components, which empowers mass cytometers to 'see' like pathologists. When applied to 71 diverse clinical samples, single-cell morphometric profiling reveals robust and distinct patterns of 'morphometric' markers for each major cell type. Individually, lamin B1 highlights acute leukemias, lamin A/C helps distinguish normal from neoplastic mature T cells, and VAMP-7 recapitulates light-cytometric side scatter. Combined with machine learning, morphometric markers form intuitive visualizations of normal and neoplastic cellular distribution and differentiation. When recalibrated for myelomonocytic blast enumeration, this approach is superior to flow cytometry and comparable to expert microscopy, bypassing years of specialized training. The contextualization of traditional surface markers on independent morphometric frameworks permits more sensitive and automated diagnosis of complex hematopoietic diseases.
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