Cited by 1,550Single-cell and spatial transcriptomicsData Visualization and AnalyticsAcute Myeloid Leukemia Research

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

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Amir, El-ad David, Davis, Kara L, Tadmor, Michelle D, Simonds, Erin F, Levine, Jacob H, Bendall, Sean C, Shenfeld, Daniel K, Krishnaswamy, Smita, Nolan, Garry P, Pe'er, Dana·2013·Nature Biotechnology

DOI 10.1038/nbt.2594PMC PMC4076922

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Abstract

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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Sgier, L., Freimann, R., Županič, A., & Kröll, A. (2016) · Nature Communications · doi:10.1038/ncomms11587 · Cited by 116
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Fisher, D. A. C., Malkova, O., Engle, E. K., Miner, C. A., Fulbright, M. C., Behbehani, G. K., Collins, T. B., Bandyopadhyay, S., Zhou, A. W., Nolan, G. P., et al. (2016) · Leukemia · doi:10.1038/leu.2016.377 · Cited by 113
Self-citation
Multicenter Systems Analysis of Human Blood Reveals Immature Neutrophils in Males and During Pregnancy
Blažková, J., Gupta, S., Liu, Y., Gaudilliere, B. L., Ganio, E. A., Bolen, C. R., Dover, R. S., Fragiadakis, G. K., Angst, M. S., Hasni, S., et al. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1601855 · Cited by 113
Pathogenic CD4+ T cells in patients with asthma
Muehling, L. M., Lawrence, M. G., & Woodfolk, J. A. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.02.025 · Cited by 113
Id2 reinforces TH1 differentiation and inhibits E2A to repress TFH differentiation
Shaw, L. A., Bélanger, S., Omilusik, K. D., Cho, S., Scott-Browne, J. P., Nance, J. P., Goulding, J., Lasorella, A., Lu, L., Crotty, S., et al. (2016) · Nature Immunology · doi:10.1038/ni.3461 · Cited by 114
Gating mass cytometry data by deep learning
Li, H., Shaham, U., Stanton, K., Yao, Y., Montgomery, R. R., & Kluger, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx448 · Cited by 111
Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts
Ntranos, V., Kamath, G. M., Zhang, J. M., Pachter, L. S., & Tse, D. N. C. (2016) · Genome biology · doi:10.1186/s13059-016-0970-8 · Cited by 110
Single cell analysis reveals distinct immune landscapes in transplant and primary sarcomas that determine response or resistance to immunotherapy
Wisdom, A. J., Mowery, Y. M., Hong, C. S., Himes, J. E., Nabet, B. Y., Qin, X., Zhang, D., Chen, L., Fradin, H., Patel, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19917-0 · Cited by 113
Cytobank: Providing an Analytics Platform for Community Cytometry Data Analysis and Collaboration
Chen, T. J., & Kotecha, N. (2014) · Current topics in microbiology and immunology · doi:10.1007/82_2014_364 · Cited by 108
Single-Cell Transcriptional Analysis
Wu, A. R., Wang, J., Streets, A., & Huang, Y. (2017) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-061516-045228 · Cited by 107
Trained immunity of alveolar macrophages enhances injury resolution via KLF4-MERTK-mediated efferocytosis
Chakraborty, S., Singh, A., Wang, L. X., Wang, X., Sanborn, M. A., Ye, Z., Maienschein‐Cline, M., Mukhopadhyay, A., Ganesh, B. B., Malik, A. B., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20221388 · Cited by 112
Mass Cytometric Functional Profiling of Acute Myeloid Leukemia Defines Cell-Cycle and Immunophenotypic Properties That Correlate with Known Responses to Therapy
Behbehani, G. K., Samusik, N., Bjornson, Z. B., Fantl, W. J., Medeiros, B. C., & Nolan, G. P. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0298 · Cited by 107
Self-citation
In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment
Shah, A. T., Diggins, K. E., Walsh, A. J., Irish, J. M., & Skala, M. C. (2015) · Neoplasia · doi:10.1016/j.neo.2015.11.006 · Cited by 106
Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
Ding, J., & Regev, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22851-4 · Cited by 110
Current trends in flow cytometry automated data analysis software
Cheung, M., Campbell, J. J., Whitby, L., Thomas, R. J., Braybrook, J. H., & Petzing, J. N. (2021) · Cytometry Part A · doi:10.1002/cyto.a.24320 · Cited by 105
The anatomy of single cell mass cytometry data
Olsen, L. R., Leipold, M. D., Pedersen, C. B., & Maecker, H. T. (2018) · Cytometry Part A · doi:10.1002/cyto.a.23621 · Cited by 108
Killer-like receptors and GPR56 progressive expression defines cytokine production of human CD4+ memory T cells
Truong, K.-L., Schlickeiser, S., Vogt, K., Boës, D., Stanko, K., Appelt, C., Streitz, M., Grütz, G., Stobutzki, N., Meisel, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10018-1 · Cited by 103
Histone H3K27me3 demethylases regulate human Th17 cell development and effector functions by impacting on metabolism
Cribbs, A. P., Terlecki-Zaniewicz, S., Philpott, M., Baardman, J., Ahern, D., Lindow, M., Obad, S., Oerum, H., Sampey, B. P., Mander, P. K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919893117 · Cited by 102
Single-cell RNA-seq analysis of human CSF microglia and myeloid cells in neuroinflammation
Esaulova, E., Cantoni, C., Shchukina, I., Zaitsev, K., Bucelli, R. C., Wu, G. F., Artyomov, M. N., Cross, A. H., & Edelson, B. T. (2020) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000000732 · Cited by 107
Characterization of humoral and SARS-CoV-2 specific T cell responses in people living with HIV
Alrubayyi, A., Gea‐Mallorquí, E., Touizer, E., Hameiri‐Bowen, D., Kopycinski, J. T., Charlton, B. G., Fisher-Pearson, N., Muir, L., Rosa, A., Roustan, C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26137-7 · Cited by 107
Visualization and cellular hierarchy inference of single-cell data using SPADE
Anchang, B., Hart, T. D. P., Bendall, S. C., Qiu, P., Bjornson, Z. B., Linderman, M. D., Nolan, G. P., & Plevritis, S. K. (2016) · Nature Protocols · doi:10.1038/nprot.2016.066 · Cited by 96
Self-citation
Single cell analysis of human tissues and solid tumors with mass cytometry
Leelatian, N., Doxie, D. B., Greenplate, A. R., Mobley, B. C., Lehman, J. M., Sinnaeve, J., Kauffmann, R. M., Werkhaven, J. A., Mistry, A. M., Weaver, K. D., et al. (2016) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21481 · Cited by 102
Identification of dynamic undifferentiated cell states within the male germline
La, H. M., Mäkelä, J., Chan, A.-L., Rossello, F. J., Nefzger, C. M., Legrand, J. M. D., Seram, M. D., Polo, J. M., & Hobbs, R. M. (2018) · Nature Communications · doi:10.1038/s41467-018-04827-z · Cited by 102
Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line
Lü, Y., Ko, M. E., Cheng, H., Zhu, R., Xue, M., Wang, J. K., Lee, J. W., Frankiw, L., Xu, A. M., Wong, S. K., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15956-9 · Cited by 102
Self-citation
Tumor-promoting macrophages prevail in malignant ascites of advanced gastric cancer
Eum, H. H., Kwon, M., Ryu, D., Jo, A., Chung, W., Kim, N., Hong, Y., Son, D., Kim, S. T., Lee, J., et al. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00538-y · Cited by 104
Studying hematopoiesis using single-cell technologies
Ye, F., Huang, W., & Guo, G. (2017) · Journal of Hematology & Oncology · doi:10.1186/s13045-017-0401-7 · Cited by 111
Single-Cell Analysis of CAR-T Cell Activation Reveals A Mixed T H 1/T H 2 Response Independent of Differentiation
Xhangolli, I., Dura, B., Lee, G., Kim, D., Xiao, Y., & Fan, R. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.03.002 · Cited by 105
Pre-operative exercise therapy triggers anti-inflammatory trained immunity of Kupffer cells through metabolic reprogramming
Zhang, H., Chen, T., Ren, J., Xia, Y., Onuma, A. E., Wang, Y., He, J., Wu, J., Wang, H., Hamad, A. B., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00402-x · Cited by 102
Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation
Chisholm, R. H., Lorenzi, T., & Clairambault, J. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.06.009 · Cited by 99
Mass cytometry: a powerful tool for dissecting the immune landscape
Simoni, Y., Chng, M. H. Y., Li, S., Fehlings, M., & Newell, E. W. (2018) · Current Opinion in Immunology · doi:10.1016/j.coi.2018.03.023 · Cited by 97
Mass cytometry analysis reveals a distinct immune environment in peritoneal fluid in endometriosis: a characterisation study
Guo, M., Bafligil, C., Tapmeier, T. T., Hubbard, C., Manek, S., Shang, C. A., Martínez, F. O., Schmidt, N., Obendorf, M., Hess‐Stumpp, H., et al. (2020) · BMC Medicine · doi:10.1186/s12916-019-1470-y · Cited by 96
T Cells Expressing Checkpoint Receptor TIGIT Are Enriched in Follicular Lymphoma Tumors and Characterized by Reversible Suppression of T-cell Receptor Signaling
Josefsson, S. E., Huse, K., Kolstad, A., Beiske, K., Pende, D., Steen, C. B., Inderberg, E. M., Lingjærde, O. C., Østenstad, B., Smeland, E. B., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2337 · Cited by 98
Mass cytometry deep phenotyping of human mononuclear phagocytes and myeloid-derived suppressor cells from human blood and bone marrow
Roussel, M., Ferrell, P. B., Greenplate, A. R., Lhomme, F., Le Gallou, S., Diggins, K. E., Johnson, D. B., & Irish, J. M. (2017) · Journal of Leukocyte Biology · doi:10.1189/jlb.5ma1116-457r · Cited by 93
Algorithmic Tools for Mining High-Dimensional Cytometry Data
Chester, C., & Maecker, H. T. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1500633 · Cited by 94
OMIP‐044: 28‐color immunophenotyping of the human dendritic cell compartment
Mair, F., & Prlic, M. (2018) · Cytometry Part A · doi:10.1002/cyto.a.23331 · Cited by 96

Authors: Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D.
Affiliations: Columbia University (US)
Stanford University (US)
ORCID: Davis, Kara L
Simonds, Erin F
Bendall, Sean C
Krishnaswamy, Smita
Nolan, Garry P
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2013-05-19
Volume: 31
Issue: 6
Pages: 545-552
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.2594
PMC: PMC4076922
OpenAlex: W1982729887
Cited by: 1,550
References: 36
Citations by year: 2026: 24 · 2025: 59 · 2024: 70 · 2023: 84 · 2022: 132 · 2021: 181 · 2020: 177 · 2019: 208 · 2018: 192 · 2017: 178 · 2016: 112 · 2015: 83 · 2014: 44 · 2013: 5
FWCI: 36.40
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Howard Hughes Medical Institute
National Institutes of Health (DP2OD002414)
National Heart, Lung, and Blood Institute (HV-10-05_)
National Cancer Institute (U54CA121852, R01CA130826, P01CA034233, CA 09011, R01CA164729)
National Institute of General Medical Sciences (T32GM008798)
National Institute of Allergy and Infectious Diseases (U19AI100627, HHSN272200700038C, U19AI057229)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
Keywords: mass cytometry, leukemia, phenotypic heterogeneity, bone marrow, minimal residual disease, leukemia relapse, dimensionality reduction
MeSH terms: Humans, Leukemia, Neoplasm Recurrence, Local, Recurrence, Biomarkers, Tumor, Immunophenotyping, Image Cytometry, Bone Marrow Neoplasms, Cell Lineage, Single-Cell Analysis

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

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