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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia


New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.

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Figure 1: viSNE map of healthy human bone marrow.
Figure 2: viSNE is robust, consistent, and does not require canonical markers.
Figure 3: Cancer samples form contiguous but heterogeneous shapes.
Figure 4: viSNE reveals the progression of cancer from diagnosis to relapse.
Figure 5: A gating scheme for FACS of an AML relapse sample in patient B based on the viSNE map.
Figure 6: Using viSNE to identify synthetic MRD.


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The authors would like to thank N. Friedman, I. Pe'er and O. Litvin for valuable comments. The authors would also like to thank M. Minden (Princess Margaret Hospital), C. Mullighan, J. Downing and I. Radtke (St. Jude Children's Hospital) for generously providing leukemia samples for mass cytometry analysis. This research was supported by the National Science Foundation CAREER award through grant number MCB-1149728, National Institutes of Health Roadmap Initiative, NIH Director's New Innovator Award Program through grant number 1-DP2-OD002414-01 and National Centers for Biomedical Computing Grant 1U54CA121852-01A1. E.D.A. is a Howard Hughes Medical Institute International Student Research Fellow. K.L.D. is supported by Alex's Lemonade Fund Young Investigator Award and St. Baldrick's Foundation Scholar Award. S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09). G.P.N. is supported by the Rachford and Carlota A. Harris Endowed Professorship and grants from U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, CIRM DR1-01477 and RB2-01592, NCI RFA CA 09-011, NHLBIHV-10-05(2), European Commission HEALTH.2010.1.2-1, and the Bill and Melinda Gates Foundation (GF12141-137101). D.P. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund and Packard Fellowship for Science and Engineering.

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Authors and Affiliations



E.D.A., G.P.N. and D.P. conceived the study. E.D.A. and D.P. developed the methods. D.K.S. and M.D.T. implemented parallel t-SNE and cyt, respectively. E.F.S., S.C.B., K.L.D. and G.P.N. designed and performed mass and flow cytometry experiments. E.D.A., J.H.L., E.F.S., S.C.B., K.L.D., S.K. and D.P. performed the biological analysis and interpretation. E.D.A. and M.D.T. performed robustness analysis of the method. E.D.A., J.H.L., K.L.D., E.F.S. and D.P. wrote the manuscript.

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Correspondence to Dana Pe'er.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Methods and Supplementary Figures 1–20 (PDF 4743 kb)

Supplementary Table 1

Antibody sources, metal isotope and staining concentration for all of the antibodies used throughout the various experiments (XLSX 50 kb)

Supplementary Table 2

Experiment details, per figure. Figure and section refers to the location of the figure in the text (XLSX 13 kb)

Supplementary Data (ZIP 5011 kb)

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Amir, Ea., Davis, K., Tadmor, M. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31, 545–552 (2013).

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