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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Flow cytometry and normalized singlet mass cytometry FCS data are openly available as below. Mass cytometry data without normalization or doublet/debris removal are available upon request.
http://flowrepository.org/id/FR-FCM-Z2DM : 54 initial samples, CyTOF
http://flowrepository.org/id/FR-FCM-Z2D3 : 11 in-depth samples, flow cytometry
http://flowrepository.org/id/FR-FCM-Z2DX : 11 in-depth samples, CyTOF, MM axes
http://flowrepository.org/id/FR-FCM-Z2E6 : 11 in-depth samples, CyTOF, MD axes
http://flowrepository.org/id/FR-FCM-Z2E7 : 17 myeloid blast samples, CyTOF, BD axes
Processed flow and mass cytometry source data plots for the 11 in-depth samples are openly available at the Stanford Digital Repository, https://purl.stanford.edu/dq578cx2728 Plots are also available as Supplementary Data in addition to being freely available online.
All code used for analyses was written in the R programming language (r-project.org). Code to reproduce all main figures is available at https://github.com/davidrglass/SCMP
American Cancer Society. Cancer Facts & Figures 2018 (American Cancer Society, 2018).
Font, P. et al. Interobserver variance in myelodysplastic syndromes with less than 5 % bone marrow blasts: unilineage vs. multilineage dysplasia and reproducibility of the threshold of 2 % blasts. Ann. Hematol. 94, 565–573 (2015).
Hodes, A. et al. The challenging task of enumerating blasts in the bone marrow. Semin. Hematol. 56, 58–64 (2019).
Goasguen, J. E. et al. Morphological evaluation of monocytes and their precursors. Haematologica 94, 994–997 (2009).
Swerdlow, S. H. (ed.) WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 4th edn (IARC, 2017).
Xu, Y., McKenna, R. W., Karandikar, N. J., Pildain, A. J. & Kroft, S. H. Flow cytometric analysis of monocytes as a tool for distinguishing chronic myelomonocytic leukemia from reactive monocytosis. Am. J. Clin. Pathol. 124, 799–806 (2005).
Lacy, P. Mechanisms of degranulation in neutrophils. Allergy Asthma Clin. Immunol. 2, 98–108 (2006).
Manley, H. R., Keightley, M. C. & Lieschke, G. J. The neutrophil nucleus: an important influence on neutrophil migration and function. Front. Immunol. 9, 2867 (2018).
Rowat, A. C. et al. Nuclear envelope composition determines the ability of neutrophil-type cells to passage through micron-scale constrictions. J. Biol. Chem. 288, 8610–8618 (2013).
Stern, A. D., Rahman, A. H. & Birtwistle, M. R. Cell size assays for mass cytometry. Cytometry A 91, 14–24 (2017).
Popova, E. Y., Claxton, D. F., Lukasova, E., Bird, P. I. & Grigoryev, S. A. Epigenetic heterochromatin markers distinguish terminally differentiated leukocytes from incompletely differentiated leukemia cells in human blood. Exp. Hematol. 34, 453–462 (2006).
Jamieson, A. R. et al. Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE. Med. Phys. 37, 339–351 (2010).
van Dongen, J. J. M. et al. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 17, 2257–2317 (2003).
Tembhare, P. et al. Flow cytometric immunophenotypic assessment of T-cell clonality by Vβ repertoire analysis: detection of T-cell clonality at diagnosis and monitoring of minimal residual disease following therapy. Am. J. Clin. Pathol. 135, 890–900 (2011).
Nguyen, D. T., Diamond, L. W. & Braylan, R. C. Flow Cytometry in Hematopathology: A Visual Approach to Data Analysis and Interpretation 2nd edn (Humana Press, 2007).
Kalina, T. et al. EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols. Leukemia 26, 1986–2010 (2012).
Van Dongen, J. J. M. et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia 26, 1908–1975 (2012).
Behbehani, G. K. Applications of mass cytometry in clinical medicine: the promise and perils of clinical CyTOF. Clin. Lab. Med. 37, 945–964 (2017).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2001) .
Kussick, S. J. et al. Four-color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. Am. J. Clin. Pathol. 124, 170–181 (2005).
Chung, J. W. et al. A combination of CD15/CD10, CD64/CD33, CD16/CD13 or CD11b flow cytometric granulocyte panels is sensitive and specific for diagnosis of myelodysplastic syndrome. Ann. Clin. Lab. Sci. 42, 271–280 (2012).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–47 (2019).
Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).
Hartmann, F. J., Simonds, E. F. & Bendall, S. C. A universal live cell barcoding-platform for multiplexed human single cell analysis. Sci. Rep. 8, 1–10 (2018).
Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Carr, J. H. & Rodak, B. F. Clinical Hematology Atlas 2nd edn (Elsevier, 2004).
Mei, H. E., Leipold, M. D. & Maecker, H. T. Platinum-conjugated antibodies for application in mass cytometry. Cytometry A 89, 292–300 (2016).
Rahman, A. H., Tordesillas, L. & Berin, M. C. Heparin reduces nonspecific eosinophil staining artifacts in mass cytometry experiments. Cytometry A 89, 601–607 (2016).
Kleinsteuber, K. et al. Standardization and quality control for high-dimensional mass cytometry studies of human samples. Cytometry A 89, 903–913 (2016).
Boone, E., Verhaaf, B. & Langerak, A. W. PCR-based analysis of rearranged immunoglobulin or T-cell receptor genes by GeneScan analysis or heteroduplex analysis for clonality assessment in lymphoma diagnostics. Methods Mol. Biol. 971, 65–91 (2013).
Langerak, A. W. et al. EuroClonality/BIOMED-2 guidelines for interpretation and reporting of Ig/TCR clonality testing in suspected lymphoproliferations. Leukemia 26, 2159–2171 (2012).
Groenen, P. J. T. A., Langerak, A. W., van Dongen, J. J. M. & van Krieken, J. H. J. M. Pitfalls in TCR gene clonality testing: teaching cases. J. Hematop. 1, 97–109 (2008).
Langerak, A. W. et al. Polymerase chain reaction-based clonality testing in tissue samples with reactive lymphoproliferations: usefulness and pitfalls. A report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 21, 222–229 (2007).
Kotrova, M. et al. Next-generation amplicon TRB locus sequencing can overcome limitations of flow-cytometric Vβ expression analysis and confirms clonality in all T-cell prolymphocytic leukemia cases. Cytometry A 93, 1118–1124 (2018).
de Swart, L. et al. Cytomorphology review of 100 newly diagnosed lower-risk MDS patients in the European LeukemiaNet MDS (EUMDS) registry reveals a high inter-observer concordance. Ann. Hematol. 96, 1105–1112 (2017).
Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).
A.T. was supported by a Damon Runyon Cancer Research Foundation (DRCRF) award (DRG-118-16) and a Stanford Department of Pathology Seed Grant. D.G. was supported by a Stanford Graduate Fellowship, and a Bio-X Stanford Interdisciplinary Graduate Fellowship. F.H. was supported by the EMBO organization (EMBO Long-Term Fellowship), the Novartis Foundation for Medical-Biological Research and the Swiss National Science Foundation (SNF Early Postdoc Mobility). S.B. was supported by the DRCRF Fellowship (DRG-2017-09), NIH awards 1DP2OD022550-01, 1R01AG056287–01, 1R01AG057915-01, 1-R00-GM104148-01, 1U24CA224309-01, 5U19AI116484-02 and U19 AI104209, The Bill and Melinda Gates Foundation, and a Translational Research Award from the Stanford Cancer Institute. We also thank L. Keren for her helpful comments and suggestions about the manuscript during its preparation.
The authors declare no competing interests.
Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
List of antibodies, clones, reporter isotopes, stock and staining concentrations, manufacturer, and catalog numbers.
a, Lamin A/C vs. CD3 for 10 samples, gated on the population designated at the top of each box, colored by density except TCL SS 2Ar2, which is colored by CD7 according to the rainbow scale to the right to show aberrant loss of CD7. Neoplastic populations are designated by red arrowheads. b, Four cases of Sézary syndrome stained by another protocol during preliminary work show neoplastic cells highlighted by lamin A/C, with aberrantly dim or bright CD3, CD7, CD26, and/or CD52. In A and B, each sample was only run once.
a, In clinical flow cytometry, a laser beam is shot through each individual cell as it passes through the cytometer. Granules – especially within neutrophils, eosinophils, and monocytes – refract and reflect some of the light perpendicularly, which is measured by a side scatter (SSC) detector. When plotted on a “map” of SSC vs. CD45, a neutrophil (top cell) plots high on the SSC axis (purple shaded region), while a lymphocyte (bottom cell) plots low (green shaded region). b, Events from the regions defined on the SSC vs. CD45 plot are then graphed on daughter biaxial plots. Typical monocytes express CD38 but not CD5 or CD19 (blue event cluster, top row). Lymphocytes include various subsets with various combinations of expression (green events, bottom row).
Clinical side scatter vs. CD45 (left plot of each pair) side-by-side with mass cytometry VAMP-7 vs. CD45 (right of each pair) for the initial 54 clinical samples. Each sample was run only once and none discarded.
a, Myeloid cells from a healthy control (left column) and four clinical samples with myeloid neoplasms (columns 2-5) plotted on MD axes. Plots are z-colored by expression of the marker in the row label, according to the scale at the bottom left. MD axes are scaled individually by column. Cells are randomly subsampled to the same number of cells in each sample. b–e, Comparison of SSC vs. CD45 (left plots) to MD axes (right plots) for the four patient samples, with populations shaded according to the key between the plots. f, Neoplastic blasts and mature myeloids (monocytes and neutrophils) from the four samples in D on MD axes, colored by sample (left). Density plot of MD2, which largely tracks monocyte differentiation, colored by sample (right).
a, Workflow for generation of blast differentiation (BD) axes. b, Myeloid cells from a normal marrow and nine AMLs visualized on the blast differentiation (BD) axes and z-colored by surface markers according to the scale at the upper left. Red arrows indicate “typical” blast populations. The green arrow indicates a purely monocytic blast population. Red-green braces indicate mixed typical-monocytic blast populations. The light blue arrow indicates a normal CD56- granulocyte population, distinct from a dysplastic CD56+ granulocyte population indicated by the orange arrow. c, Myeloid cells visualized on rRNA vs. BD2, z-colored by lactoferrin (top) or density (middle), with the blast gate (left and on top of lactoferrin+ granulocytes) in the blue outline and scaled blast count (bottom). d, Light microscopic blast count. e, Flow cytometry blast and monocyte gates.
Normal marrow was surface stained without methanol permeabilization, fixed in paraformaldehyde (PFA), and shows the expected patterns of CD10 vs. CD20 expression on B cells/hematogones (top left) and CD13 vs. CD16 expression on maturing granulocytes (bottom left). However, after methanol permeabilization CD10 and CD13 are almost completely lost (middle panels). Instead, fixing with glutaraldehyde (GA) renders the antibodies resistant to methanol (right panels). Trials of various conditions resulted in 0.8% glutaraldehyde fixation for 10 minutes on ice. The above results were formally demonstrated three times (n=3 biologically independent specimens) as part of protocol optimization, but loss of CD10 and CD13 staining (due to methanol) was also observed in multiple pilot experiments (n>3).
A diagnostically normal bone marrow sample was stained using our modified protocol, including permeabilization with methanol (Sigma-Aldrich 34860), and run on the CyTOF. For a-d, the left column is ungated. Columns 2 and 3 are gated by event length vs. DNA (ELvD) – the current standard – for singlets and doublets, respectively. Columns 4 and 5 are gated by barium vs. DNA (BvD) for singlets and doublets. Column 6 shows debris/aggregates gated by BvD. a, Gating. b, Granulocyte-granulocyte doublets (green arrow), granulocyte-erythroid doublets (yellow arrow), and erythroid-erythroid doublets (red arrow) are present in the ungated and doublet plots. Non-specific debris/antibody aggregate reactivity (blue arrow) can also be discerned. These are far less evident in BvD singlets than in ELvD singlets. c, 2.4% of ELvD singlets are identified as doublets by BvD, far outnumbering the 0.12% of BvD singlets identified as doublets by ELvD. d, Non-specific debris/antibody aggregate reactivity (blue arrows) shows a diagonal pattern in multiple channels with a nonsensical antigen combination and is inconsistent from run to run. This artifact is removed from BvD but not ELvD singlets. Gating the BvD debris (column 6) shows that it accounts for most of this artifact in the ELvD singlets. Antibody channels are as follows: CD45 Sm 154, CD15 Yb 171, CD71 In 115, IgK Eu 153, CD61 La 139. e, The barium vs. time plot shows that barium signal is decreased at the leading edge of a sample push. One may gate around these regions in the matter demonstrated (right panel). f, The cleanup strategy on another of the normal marrows, with the limitation to 30,000 events optional depending on the analysis. The above analysis was formally demonstrated on one marrow sample (n=1), but the superiority of barium to event length in doublet and debris removal was observed throughout the study.
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Tsai, A.G., Glass, D.R., Juntilla, M. et al. Multiplexed single-cell morphometry for hematopathology diagnostics. Nat Med 26, 408–417 (2020). https://doi.org/10.1038/s41591-020-0783-x
OMIP‐069 : Forty‐Color Full Spectrum Flow Cytometry Panel for Deep Immunophenotyping of Major Cell Subsets in Human Peripheral Blood
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