Multiplexed single-cell morphometry for hematopathology diagnostics

• Published in: Nature medicine Vol. 26; no. 3; pp. 408 - 417
• Main Authors: Tsai, Albert G., Glass, David R., Juntilla, Marisa, Hartmann, Felix J., Oak, Jean S., Fernandez-Pol, Sebastian, Ohgami, Robert S., Bendall, Sean C.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-03-2020; Nature Publishing Group
• Subjects: 631/114; 631/1647/1407; 692/53/2421; 692/699/1541; 692/699/67/1990; Antibodies; Biomedical and Life Sciences; Biomedicine; Cancer; Cancer Research; Cell surface; Cytology; Diagnosis; Diagnostic systems; Enumeration; Flow cytometry; Hematopoietic stem cells; Hematopoietic Stem Cells - pathology; Humans; Identification and classification; Infectious Diseases; Lamins - metabolism; Learning algorithms; Leukemia; Leukemia - diagnosis; Leukemia - pathology; Leukocyte Common Antigens - metabolism; Lymphocytes; Lymphocytes T; Lymphoma; Lymphoma - diagnosis; Lymphoma - pathology; Lymphomas; Machine learning; Markers; Metabolic Diseases; Molecular Medicine; Morphology; Morphometrics (Biology); Morphometry; Multiplexing; Myeloid Cells - pathology; Neurosciences; Oncology, Experimental; Properties; R-SNARE Proteins - metabolism; Single-Cell Analysis - methods; Surface markers; United States
• ISSN: 1078-8956; 1546-170X
• DOI: 10.1038/s41591-020-0783-x

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.
A scalable mass cytometry-based method for morphometrically classifying hematopoietic cells demonstrates diagnostic utility when applied to clinical samples.