Multiplexed single-cell morphometry for hematopathology diagnostics

Abstract

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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Fig. 1: Scatterbodies capture morphological differences of major hematopoietic cell populations in healthy human bone marrow.
Fig. 2: Morphometric profiles are consistent across clinical patient samples.
Fig. 3: Individual morphometric features have unique diagnostic capabilities.
Fig. 4: Morphometric maps recapitulate and improve upon side scatter versus CD45 gating.
Fig. 5: Myeloid morphometric maps for visualizing myelopoiesis and quantifying blasts in myeloid leukemias.

Data availability

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.

Code availability

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

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Acknowledgements

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.

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Authors

Contributions

A.G.T. and S.C.B. conceived and designed the study. A.G.T., D.R.G. and F.J.H. performed data generation and analysis. A.G.T., M.J., J.S.O., S.F.-P. and R.S.O. carried out hematopathology assays and consultation. A.G.T., D.R.G. and S.C.B. wrote the original draft. A.G.T. and S.C.B. acquired funding for the study. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Sean C. Bendall.

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The authors declare no competing interests.

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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.

Extended data

Extended Data Fig. 1 Summary of mass cytometry antibodies/reagents.

List of antibodies, clones, reporter isotopes, stock and staining concentrations, manufacturer, and catalog numbers.

Extended Data Fig. 2 Summary of clinical samples.

a, Set 1 comprises the initial 54 samples used for Figs. 15c. b, Set 2 comprises the 17 samples for Fig. 5d–i.

Extended Data Fig. 3 Lamin A/C in T cell neoplasms.

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.

Extended Data Fig. 4 CD45 vs. side scatter in clinical sample gating.

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).

Extended Data Fig. 5 Comparison of SSC vs. CD45 and VAMP-7 vs. CD45.

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.

Extended Data Fig. 6 MM, MD, and BD axis coefficients.

LDA axis coefficients for a, Morphometric (MM) axes in Fig. 4, b, Myeloid Differentiation (MD) axes in Fig. 5a–c and Extended Data Fig. 7, and c, Blast Bifferentiation (BD) axes in Fig. 5d–i and Extended Data Fig. 8.

Extended Data Fig. 7 Applying myeloid differentiation axes to myeloid neoplasms.

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. be, 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).

Extended Data Fig. 8 Applying blast differentiation axes to myeloid neoplasms.

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.

Extended Data Fig. 9 Methanol renders standard PFA fixation inadequate for clinical diagnostics.

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).

Extended Data Fig. 10 Barium improves doublet and non-specific debris/antibody aggregate exclusion.

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.

Supplementary information

Supplementary Information

Supplementary Table 1, Supplementary Figures 1–8 and Supplementary Data: Mass cytometry and flow cytometry plots for 11 in-depth samples.

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Source data

Source Data for Figure 3

Mass cytometry and flow cytometry plots for 11 in-depth samples.

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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

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