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

The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.

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

Open access

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

Open this paper in CoChat — free account

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nbt.2594

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Self-citation
RNA sequencing: advances, challenges and opportunities
Ozsolak, F., & Milos, P. M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2934 · Cited by 2,135
Dimensionality Reduction: A Comparative Review
van der Maaten, L. J. P., Postma, E. O., & van den Herik, H. J. (2009) · Cited by 2,062
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,785
Self-citation
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
Mass Cytometry: Technique for Real Time Single Cell Multitarget Immunoassay Based on Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
Bandura, D. R., Baranov, V. I., Ornatsky, O. I., Antonov, A., Kinach, R., Lou, X., Pavlov, S., Vorobiev, S., Dick, J. E., & Tanner, S. D. (2009) · Analytical Chemistry · doi:10.1021/ac901049w · Cited by 1,295
Exploring Strategies for Training Deep Neural Networks
Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,111

Show all 36 references

Sorted by citations

Items 901–1,000 of 1,005

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies
Lucchesi, S., Furini, S., Medaglini, D., & Ciabattini, A. (2020) · Vaccines · doi:10.3390/vaccines8010138 · Cited by 16
Fulminant Myocarditis
Moslehi, J. J., Brinkley, D. M., & Meijers, W. C. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.05.026 · Cited by 17
Engineering unnatural amino acids in peptide linkers enables cathepsin-selective antibody-drug conjugates for HER2-positive breast cancer
Gorzeń, O., Łęcka, M., Ćwilichowska-Puślecka, N., Majchrzak, M., Horbach, N., Wiśniewski, J. Ł., Jakimowicz, P., Szpot, P., Zawadzki, M., Dołęga-Kozierowski, B., et al. (2025) · Journal of Controlled Release · doi:10.1016/j.jconrel.2025.114269 · Cited by 16
Cord blood group 2 innate lymphoid cells are associated with lung function at 6 weeks of age
Gomes, G. M. C., Belinelo, P. D. G., Starkey, M. R., Murphy, V. E., Hansbro, P. M., Sly, P. D., Robinson, P. D., Karmaus, W. J. J., Gibson, P. G., Mattes, J. E., et al. (2021) · Clinical & Translational Immunology · doi:10.1002/cti2.1296 · Cited by 15
Influence of Microplastics on Microbial Structure, Function, and Mechanical Properties of Stream Periphyton
Merbt, S. N., Kröll, A., Tamminen, M. V., Rühs, P. A., Wagner, B., Sgier, L., Sembalova, O., Abel, B., Tlili, A., Schirmer, K., et al. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.928247 · Cited by 17
A Novel Humanized PD-1/PD-L1 Mouse Model Permits Direct Comparison of Antitumor Immunity Generated by Food and Drug Administration–Approved PD-1 and PD-L1 Inhibitors
Barham, W. J., Hsu, M. A., Liu, X., Harrington, S. M., Hirdler, J. B., Gicobi, J. K., Zhu, X., Zeng, H., Pavelko, K. D., Yan, Y., et al. (2023) · ImmunoHorizons · doi:10.4049/immunohorizons.2200054 · Cited by 15
OMIP‐94: Twenty‐four‐color (thirty‐marker) panel for deep immunophenotyping of immune cells in human peripheral blood
Pierzchalski, A., Zenclussen, A. C., & Herberth, G. (2023) · Cytometry Part A · doi:10.1002/cyto.a.24766 · Cited by 15
Lipoprotein (a) integrates monocyte-mediated thrombosis and inflammation in atherosclerotic cardiovascular disease
Rosenson, R. S., Tate, A. M., Grushko, O. G., Damodaran, D., Chen, Q., Boffa, M. B., Koschinsky, M. L., Narula, J., & Goonewardena, S. N. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100820 · Cited by 24
Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability
Park, J. H., Ogunnaike, B. A., Schwaber, J. S., & Vadigepalli, R. (2014) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2014.11.004 · Cited by 16
High-Dimensional Profiling of Tumor-Specific Immune Responses: Asking T Cells about What They “See” in Cancer
Newell, E. W., & Becht, É. (2018) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-17-0519 · Cited by 14
TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis
Baskar, R., Fienberg, H. G., Khair, Z., Favaro, P. M. B., Kimmey, S. C., Green, D. R., Nolan, G. P., Plevritis, S. K., & Bendall, S. C. (2019) · Life Science Alliance · doi:10.26508/lsa.201900554 · Cited by 14
Self-citation
Phosphoflow cytometry and barcoding in blood platelets: Technical and analytical considerations
Spurgeon, B. E. J., & Naseem, K. M. (2019) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21851 · Cited by 15
Characterization of the tumor immune infiltrate by multiparametric flow cytometry and unbiased high-dimensional data analysis
Araya, R. E., & Goldszmid, R. S. (2019) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2019.11.012 · Cited by 15
IL-7 receptor alpha defines heterogeneity and signature of human effector memory CD8+ T cells in high dimensional analysis
Shin, M. S., Kim, D., Yim, K., Park, H., You, S., Dong, X., Koumpouras, F., Shaw, A. C., Fan, R., Krishnaswamy, S., et al. (2020) · Cellular Immunology · doi:10.1016/j.cellimm.2020.104155 · Cited by 15
Self-citation
TIGIT regulates apoptosis of risky memory T cell subsets implicated in belatacept-resistant rejection
Sun, H. Y., Hartigan, C. R., Chen, C.-W., Sun, Y., Tariq, M., Robertson, J. M., Krummey, S. M., Mehta, A. K., & Ford, M. L. (2021) · American Journal of Transplantation · doi:10.1111/ajt.16571 · Cited by 17
Fractional response analysis reveals logarithmic cytokine responses in cellular populations
Nienałtowski, K., Rigby, R. E., Walczak, J., Zakrzewska, K. E., Głów, E., Rehwinkel, J., & Komorowski, M. (2021) · Nature Communications · doi:10.1038/s41467-021-24449-2 · Cited by 15
PTEN Loss and BRCA1 Promoter Hypermethylation Negatively Predict for Immunogenicity in BRCA-Deficient Ovarian Cancer
Kraya, A., Maxwell, K. N., Eiva, M. A., Wubbenhorst, B. S., Pluta, J. B., Feldman, M. D., Nayak, A., Powell, D. J., Domchek, S. M., Vonderheide, R. H., et al. (2022) · JCO Precision Oncology · doi:10.1200/po.21.00159 · Cited by 15
Behind the Wall—Compartment-Specific Neovascularisation during Post-Stroke Recovery in Mice
Kolbinger, A., Kestner, R. I., Jencio, L., Schäufele, T. J., Vutukuri, R., Pfeilschifter, W., & Scholich, K. (2022) · Cells · doi:10.3390/cells11101659 · Cited by 15
The normal and fibrotic mouse lung classified by spatial proteomic analysis
Ciccimarra, R., Bolognesi, M. M., Zoboli, M., Cattoretti, G., Stellari, F. F., & Ravanetti, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-12738-9 · Cited by 16
Alterations in high‐dimensional T‐cell profile and gene signature of immune aging in HIV ‐infected older adults without viremia
Shin, M. S., Park, H., Salahuddin, S., Montgomery, R. R., Emu, B., Shaw, A. C., & Kang, I. (2022) · Aging Cell · doi:10.1111/acel.13702 · Cited by 14
Single‐cell technologies in reproductive immunology
Vázquez, J., Ong, I. M., & Stanic, A. K. (2019) · American Journal of Reproductive Immunology · doi:10.1111/aji.13157 · Cited by 15
Importance of Multiparametric Evaluation of Immune-Related T-Cell Markers in Renal-Cell Carcinoma
Kawashima, A., Uemura, M., & Nonomura, N. (2019) · Clinical Genitourinary Cancer · doi:10.1016/j.clgc.2019.07.021 · Cited by 15
Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review
Rochigneux, P., Garcia, A. J., Chanez, B., Madroszyk, A., Olive, D., & Garon, E. B. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01036 · Cited by 17
An agentic AI framework for ingestion and standardization of single-cell RNA-seq data analysis
Nouri, N., Artzi, R., & Savova, V. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00064-0 · Cited by 22
Gata2-L359V impairs primitive and definitive hematopoiesis and blocks cell differentiation in murine chronic myelogenous leukemia model
Fu, Y., Tan, Y., Wu, B., Dai, Y.-T., Xu, X.-G., Pan, M., Chen, Z., Qiao, N., Wu, J., Jiang, L., et al. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03826-1 · Cited by 14
Proliferation and Self-Renewal Are Differentially Sensitive to NRASG12V Oncogene Levels in an Acute Myeloid Leukemia Cell Line
Kurata, M., Antony, M. L., Noble-Orcutt, K. E., Rathe, S. K., Lee, Y., Furuno, H., Ishibashi, S., Ikeda, M., Yamamoto, K., Kitagawa, M., et al. (2022) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-22-0109 · Cited by 14
Computational Methods for Single-Cell Proteomics
Guldberg, S. M., Okholm, T. L. H., McCarthy, E. E., & Spitzer, M. H. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020422-050255 · Cited by 18
Integrated systems immunology approach identifies impaired effector T cell memory responses as a feature of progression to severe dengue fever
Ioannidis, L. J., Studniberg, S. I., Eriksson, E. M., Suwarto, S., Denis, D., Liao, Y., Shi, W., Garnham, A. L., Sasmono, R. T., & Hansen, D. S. (2023) · Journal of Biomedical Science · doi:10.1186/s12929-023-00916-4 · Cited by 14
Effects of Immune Cell Heterogeneity and Protein Corona on the Cellular Association and Cytotoxicity of Gold Nanoparticles: A Single-Cell-Based, High-Dimensional Mass Cytometry Study
Park, S., Ha, M. K., Lee, Y., Song, J., & Yoon, T. H. (2023) · ACS Nanoscience Au · doi:10.1021/acsnanoscienceau.3c00001 · Cited by 14
Cyclone: an accessible pipeline to analyze, evaluate, and optimize multiparametric cytometry data
Patel, R. K., Jaszczak, R. G., Im, K. W., Carey, N. D., Courau, T., Bunis, D. G., Samad, B., Avanesyan, L., Chew, N. W., Stenske, S., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1167241 · Cited by 11
Research Techniques Made Simple: Mass Cytometry Analysis Tools for Decrypting the Complexity of Biological Systems
Matos, T. R., Liu, H., & Ritz, J. (2017) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2017.03.002 · Cited by 14
Cellular network modeling and single cell gene expression analysis reveals novel hepatic stellate cell phenotypes controlling liver regeneration dynamics
COOK, D. J., Achanta, S., Hoek, J. B., Ogunnaike, B. A., & Vadigepalli, R. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0605-7 · Cited by 15
Microparticle Delivery of Protein Markers for Single‐Cell Western Blotting from Microwells
Kim, J. J., Chan, P. P. Y., Vlassakis, J., Geldert, A., & Herr, A. E. (2018) · Small · doi:10.1002/smll.201802865 · Cited by 14
Immune involvement of the contralateral hemisphere in a glioblastoma mouse model
Crommentuijn, M. H. W., Schetters, S. T. T., Dusoswa, S. A., Kruijssen, L. J. W., García-Vallejo, J. J., & van Kooyk, Y. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2019-000323 · Cited by 15
Does Cancer Biology Rely on Parrondo’s Principles?
Capp, J., Nedelcu, A. M., Dujon, A. M., Roche, B., Catania, F., Újvári, B., Alix‐Panabières, C., & Frédéric Thomas, A. (2021) · Cancers · doi:10.3390/cancers13092197 · Cited by 15
Specific Immune Response and Cytokine Production in CD70 Deficiency
Abolhassani, H. (2021) · Frontiers in Pediatrics · doi:10.3389/fped.2021.615724 · Cited by 14
NK Cell Responses in Zika Virus Infection Are Biased towards Cytokine-Mediated Effector Functions
Maucourant, C., Queiroz, G. A. N., Corneau, A., Gois, L. L., Meghraoui, A., Tarantino, N., Bandeira, A. C., Samri, A., Blanc, C., Yssel, H., et al. (2021) · The Journal of Immunology · doi:10.4049/jimmunol.2001180 · Cited by 14
Mass Cytometry in Hematologic Malignancies: Research Highlights and Potential Clinical Applications
Astle, J. M., & Huang, H. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.704464 · Cited by 14
Towards Mapping Mouse Metabolic Tissue Atlas by Mid‐Infrared Imaging with Heavy Water Labeling
Liu, X., Shi, L., Shi, L., Wei, M., Zhao, Z., & Min, W. (2022) · Advanced Science · doi:10.1002/advs.202105437 · Cited by 14
Determining T-cell specificity to understand and treat disease
Hadrup, S. R., & Newell, E. W. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0143-4 · Cited by 15
Single-Cell Tracking Reveals a Role for Pre-Existing CCR5+ Memory Th1 Cells in the Control of Rhinovirus-A39 After Experimental Challenge in Humans
Muehling, L. M., Turner, R. B., Brown, K. B., Wright, P. W., Patrie, J. T., Lahtinen, S. J., Lehtinen, M. J., Kwok, W. W., & Woodfolk, J. A. (2017) · The Journal of Infectious Diseases · doi:10.1093/infdis/jix514 · Cited by 14
Characterization of TRKA signaling in acute myeloid leukemia
Herbrich, S. M., Kannan, S., Nolo, R. M., Aitken, M. J. L., Chandra, J., & Zweidler‐McKay, P. A. (2018) · Oncotarget · doi:10.18632/oncotarget.25723 · Cited by 15
Elevated regulatory T cells at diagnosis of Coccidioides infection associates with chronicity in pediatric patients
Davini, D. W., Naeem, F., Phong, A., Al-Kuhlani, M., Valentine, K. M., McCarty, J. M., Ojcius, D. M., Gravano, D. M., & Hoyer, K. K. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2018.10.022 · Cited by 14
Multiplex MHC Class I Tetramer Combined with Intranuclear Staining by Mass Cytometry
Simoni, Y., Fehlings, M., & Newell, E. W. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9454-0_11 · Cited by 14
Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells
Zhu, Y. P., Padgett, L. E., Dinh, H. Q., Marcovecchio, P., Wu, R., Hinz, D., Kim, C., & Hedrick, C. C. (2019) · Journal of Visualized Experiments · doi:10.3791/59617 · Cited by 14
Comprehensive Immune Monitoring of Clinical Trials to Advance Human ImmunotherapyPreprint
Hartmann, F. J., Babdor, J., Gherardini, P. F., Amir, E.-A. D., Jones, K. B., Sahaf, B., Marquez, D. M., Krutzik, P. O., O'Donnell, E. A., Sigal, N., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/489765 · Cited by 14
Self-citation
Two new immature and dysfunctional neutrophil cell subsets define a predictive signature of sepsis useable in clinical practicePreprint
Meghraoui, A., Chousterman, B. G., Guillou, N., Barone, S. M., Granjeaud, S., Vallet, H., Corneau, A., Guessous, K., Boissonnas, A., Irish, J. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.29.123992 · Cited by 14
Human induced pluripotent stem cell-derived vessels as dynamic atherosclerosis model on a chipPreprint
Mallone, A., Gericke, C., Hosseini, V., Chahbi, K., Haenseler, W., Emmert, M. Y., von Eckardstein, A., Walther, J. H., Viola Vogel, B., Weber, B., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.27.401034 · Cited by 15
Mass cytometry reveals unique subsets of T cells and lymphoid cells in nasal polyps from patients with chronic rhinosinusitis (CRS)
Bartemes, K. R., Choby, G. W., O’Brien, E. K., Stokken, J. K., Pavelko, K. D., & Kita, H. (2020) · Allergy · doi:10.1111/all.14720 · Cited by 13
NK cells in peripheral blood carry trogocytosed tumor antigens from solid cancer cells
Campos‐Mora, M., Jacot, W., Garcin, G., Depondt, M.-L., Constantinides, M., Alexia, C., & Villalba, M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1199594 · Cited by 13
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
Lee, Y., Chen, E. L. Y., Chan, D. C. H., Dinesh, A., Afiuni‐Zadeh, S., Klamann, C., Selega, A., Mrkonjic, M., Jackson, H. W., & Campbell, K. R. (2025) · Nature Communications · doi:10.1038/s41467-024-55214-w · Cited by 13
Novel single-cell technologies in acute myeloid leukemia research
Gupta, S. D., & Sachs, Z. (2017) · Translational research · doi:10.1016/j.trsl.2017.07.007 · Cited by 14
Characterization of Leukocytes From HIV-ART Patients Using Combined Cytometric Profiles of 72 Cell Markers
Pereira, A. L., Tchitchek, N., Lambotte, O., Grand, R. L., & Cosma, A. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01777 · Cited by 13
Visualization of emergency department clinical data for interpretable patient phenotyping
Hurley, N. C., Haimovich, A. D., Taylor, R. A., & Mortazavi, B. J. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100285 · Cited by 13
Multi-Dimensional Flow Cytometry Analyses Reveal a Dichotomous Role for Nitric Oxide in Melanoma Patients Receiving Immunotherapy
Garg†, S. K., Ott, M. J., Mostofa, A., Chen, Z., Chen, Y. A., Kroeger, J. L., Cao, B., Mailloux, A. W., Agrawal, A., Schaible, B., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00164 · Cited by 13
High-dimensional immunotyping of tumors grown in obese and non-obese mice
Wogsland, C. E., Lien, H. E., Pedersen, L., Hanjra, P., Grøndal, S. M., Brekken, R. A., Lorens, J. B., & Halberg, N. (2021) · Disease Models & Mechanisms · doi:10.1242/dmm.048977 · Cited by 11
Long-Term Evolution of the Adaptive NKG2C+ NK Cell Response to Cytomegalovirus Infection in Kidney Transplantation: An Insight on the Diversity of Host–Pathogen Interaction
Ataya, M., Redondo‐Pachón, D., Llinàs-Mallol, L., Yélamos, J., Alari‐Pahissa, E., Pérez‐Sáez, M. J., Altadill, M., Regué, D. R., Vilches, C., Pascual, J., et al. (2021) · The Journal of Immunology · doi:10.4049/jimmunol.2100055 · Cited by 13
Computational prediction of manually gated rare cells in flow cytometry data
Qiu, P. (2015) · Cytometry Part A · doi:10.1002/cyto.a.22654 · Cited by 15
Flow Cytometry Data Preparation Guidelines for Improved Automated Phenotypic Analysis
Jiménez‐Carretero, D., Ligos, J. M., Martínez‐López, M., Sancho, D., & Montoya, M. C. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800446 · Cited by 15
Multidimensional analysis of Gammaherpesvirus RNA expression reveals unexpected heterogeneity of gene expression
Oko, L. M., Kimball, A. K., Kaspar, R. E., Knox, A. N., Coleman, C. B., Rochford, R. A., Chang, T., Alderete, B. E., van Dyk, L. F., & Clambey, E. T. (2019) · PLoS Pathogens · doi:10.1371/journal.ppat.1007849 · Cited by 11
Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization
Meehan, S., Kolyagin, G. A., Parks, D. R., Youngyunpipatkul, J., Herzenberg, L. A., Walther, G., Ghosn, E., & Orlova, D. Y. (2019) · Communications Biology · doi:10.1038/s42003-019-0467-6 · Cited by 13
Heterogeneity and Plasticity of Human Breast Cancer Cells in Response to Molecularly-Targeted Drugs
Bousoik, E., Nabiee, R., Amirrad, F., Nichols, A., Witt, R., Mahdipoor, P., & Aliabadi, H. M. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.01070 · Cited by 13
High‐dimensional single‐cell proteomics analysis reveals the landscape of immune cells and stem‐like cells in renal tumors
Li, Z., Hu, J., Zhao, Q., Tao, Y., Lai, Z., Wang, Q., & Li, T. (2019) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.23155 · Cited by 13
Transformation of multicolour flow cytometry data with OTflow prevents misleading multivariate analysis results and incorrect immunological conclusions
Folcarelli, R., van Staveren, S., Tinnevelt, G. H., Cadot, E., Vrisekoop, N., Buydens, L. M. C., Koenderman, L., Jansen, J. J., & van den Brink, O. F. (2021) · Cytometry Part A · doi:10.1002/cyto.a.24491 · Cited by 18
Novel tools for primary immunodeficiency diagnosis: making a case for deep profiling
Hsieh, E. W. Y., & Hernandez, J. D. (2016) · Current Opinion in Allergy and Clinical Immunology · doi:10.1097/aci.0000000000000319 · Cited by 13
Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering
Li, X., & Wong, K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2906601 · Cited by 13
A regression approach for explaining manifold embedding coordinatesPreprint
Meilă, M., Koelle, S., & Zhang, H. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.20959.71841 · Cited by 13
Single-Cell Map of Diverse Immune Phenotypes in the Metastatic Brain Tumor Microenvironment of Non Small Cell Lung CancerPreprint
Wang, L., Dai, J., Han, R.-R., Dong, L., Feng, D., Zhu, G., Guo, W., Wang, Y., Chao, M., Jin, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.30.890517 · Cited by 14
CX3CR1+ age-associated CD4+ T cells contribute to synovial inflammation in late-onset rheumatoid arthritis
Akiyama, M., Wakasugi, S., Yoshimoto, K., Saito, K., Ishigaki, S., Inukai, R., Matsuno, Y., Alshehri, W., Kondo, Y., & Kaneko, Y. (2025) · Inflammation and Regeneration · doi:10.1186/s41232-025-00367-4 · Cited by 12
Advances in Clinical Mass Cytometry
Koladiya, A., & Davis, K. L. (2023) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2023.05.004 · Cited by 10
Self-citation
Characterization of papillary and clear cell renal cell carcinoma through imaging mass cytometry reveals distinct immunologic profiles
Govindarajan, A., Salgia, N., Li, H., Castro, D. V., Mirzapoiazova, T., Armstrong, B., Zhao, D., Mercier, B. D., Dizman, N., Chawla, N. S., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1182581 · Cited by 12
Monocyte-Mediated Thrombosis Linked to Circulating Tissue Factor and Immune Paralysis in COVID-19
Goonewardena, S. N., Chen, Q., Tate, A. M., Grushko, O. G., Damodaran, D., Blakely, P. K., Hayek, S. S., Pinsky, D. J., & Rosenson, R. S. (2024) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318721 · Cited by 13
Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery
Schlickeiser, S., Streitz, M., & Sawitzki, B. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3139-2_15 · Cited by 12
Current Advances in Highly Multiplexed Antibody‐Based Single‐Cell Proteomic Measurements
Huang, L., Michael, S. A., Chen, Y., & Wu, H. (2017) · Chemistry - An Asian Journal · doi:10.1002/asia.201700404 · Cited by 12
Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
Folcarelli, R., van Staveren, S., Bouman, R., Hilvering, B., Tinnevelt, G. H., Postma, G., van den Brink, O. F., Buydens, L. M. C., Vrisekoop, N., Koenderman, L., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29367-w · Cited by 12
Recent Machine Learning Approaches for Single-Cell RNA-seq Data Analysis
Vrahatis, A. G., Tasoulis, S. K., Maglogiannis, I. G., & Plagianakos, V. P. (2020) · Studies in computational intelligence · doi:10.1007/978-3-662-61114-2_5 · Cited by 11
Training Novices in Generation and Analysis of High‐Dimensional Human Cell Phospho‐Flow Cytometry Data
Roe, C. E., Hayes, M., Barone, S. M., & Irish, J. M. (2020) · Current Protocols in Cytometry · doi:10.1002/cpcy.71 · Cited by 12
Systematic Study of Immune Cell Diversity in ischemic postconditioning Using High-Dimensional Single-Cell Analysis with Mass Cytometry
Yao, Y., Li, Y., Ni, W., Li, Z., Feng, L., Wang, Y., Meng, J., & Zhao, H. (2021) · Aging and Disease · doi:10.14336/ad.2020.1115 · Cited by 12
Intratumoural administration of an NKT cell agonist with CpG promotes NKT cell infiltration associated with an enhanced antitumour response and abscopal effect
Prasit, K., Ferrer‐Font, L., Burn, O. K., Anderson, R. J., Compton, B. J., Schmidt, A., Mayer, J. U., Chen, C. J., Dasyam, N., Ritchie, D. S., et al. (2022) · OncoImmunology · doi:10.1080/2162402x.2022.2081009 · Cited by 12
Distinct populations of eosinophils in the human thymus with capacity to modulate thymocyte maturation
Albinsson, S., Lingblom, C., Lundqvist, C., Hennings, V., Telemo, E., Ekwall, O., & Wennerås, C. (2022) · Immunology · doi:10.1111/imm.13610 · Cited by 12
IL-15 enhances HIV-1 infection by promoting survival and proliferation of CCR5+CD4+ T cells
Li, Y., Gao, H., Clark, K. M., & Shan, L. (2023) · JCI Insight · doi:10.1172/jci.insight.166292 · Cited by 12
Development of Spectral Imaging Cytometry
Vorobjev, I. A., Kussanova, A., & Barteneva, N. S. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3020-4_1 · Cited by 11
Human PLCG2 haploinsufficiency results in a novel natural killer cell immunodeficiency
Alinger, J. B., Mace, E. M., Porter, J. R., Mah-Som, A. Y., Daugherty, A. L., Li, S., Throm, A. A., Pingel, J. T., Saucier, N., Yao, A., et al. (2023) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.09.002 · Cited by 11
Novel strategies to assess cytokine release mediated by chimeric antigen receptor T cells based on the adverse outcome pathway concept
Alb, M., Reiche, K., Rade, M., Sewald, K., Loskill, P., Cipriano, M., Maulana, T. I., van der Meer, A. D., Weener, H. J., Clerbaux, L., et al. (2024) · Journal of Immunotoxicology · doi:10.1080/1547691x.2024.2345158 · Cited by 14
Characterization by mass cytometry of different methods for the preparation of muscle mononuclear cells
Spada, F., Fuoco, C., Pirro, S., Paoluzi, S., Castagnoli, L., Gargioli, C., & Cesareni, G. (2016) · New Biotechnology · doi:10.1016/j.nbt.2015.12.007 · Cited by 12
High‐dimensional single cell mapping of cerium distribution in the lung immune microenvironment of an active smoker
Rahman, A. H., Lavin, Y., Kobayashi, S., Leader, A. M., & Mérad, M. (2017) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21545 · Cited by 12
Advancing systems immunology through data-driven statistical analysis
Fong, L. E., Muñoz-Rojas, A. R., & Miller‐Jensen, K. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.03.009 · Cited by 12
SRC/ABL inhibition disrupts CRLF2-driven signaling to induce cell death in B-cell acute lymphoblastic leukemia
Sarno, J., Savino, A. M., Buracchi, C., Palmi, C., Pinto, S., Bugarin, C., Jager, A., Bresolin, S., Barber, R. C., Silvestri, D., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.25089 · Cited by 12
Self-citation
BRAF and MEK inhibitor therapy eliminates Nestin‐expressing melanoma cells in human tumors
Doxie, D. B., Greenplate, A. R., Gandelman, J. S., Diggins, K. E., Roe, C. E., Dahlman, K. B., Sosman, J. A., Kelley, M. C., & Irish, J. M. (2018) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.12712 · Cited by 12
Immune cell profiling in the age of immune checkpoint inhibitors: implications for biomarker discovery and understanding of resistance mechanisms
Lim, S. Y., & Rizos, H. (2018) · Mammalian Genome · doi:10.1007/s00335-018-9757-4 · Cited by 12
Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches
Kravchenko‐Balasha, N. (2020) · PROTEOMICS · doi:10.1002/pmic.201900227 · Cited by 12
Imidazo[1,2-b]pyrazole-7-Carboxamide Derivative Induces Differentiation-Coupled Apoptosis of Immature Myeloid Cells Such as Acute Myeloid Leukemia and Myeloid-Derived Suppressor Cells
Kotogány, E., Balog, J. Á., Nagy, L. I., Alföldi, R., Bertagnolo, V., Brugnoli, F., Demjén, A., Kovács, A. K., Batár, P., Mezei, G., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21145135 · Cited by 12
Highly Sensitive and Multiplexed In Situ RNA Profiling with Cleavable Fluorescent Tyramide
Xiao, L., LaBaer, J., & Guo, J. (2021) · Cells · doi:10.3390/cells10061277 · Cited by 12
Development of a B-cell maturation antigen-specific T-cell antigen coupler receptor for multiple myeloma
Bezverbnaya, K., Moogk, D., Cummings, D. T., Baker, C. L., Aarts, C. W., Denisova, G. F., Sun, M. T., McNicol, J. D., Turner, R. C., Rullo, A. F., et al. (2021) · Cytotherapy · doi:10.1016/j.jcyt.2021.05.007 · Cited by 13
Mass Cytometry to Decipher the Mechanism of Nongenetic Drug Resistance in Cancer
Fienberg, H. G., & Nolan, G. P. (2014) · Current topics in microbiology and immunology · doi:10.1007/82_2014_365 · Cited by 12
Self-citation
Compromised DNA Repair Promotes the Accumulation of Regulatory T Cells With an Aging-Related Phenotype and Responsiveness
Pieren, D. K. J., Smits, N. A. M., Imholz, S., Nagarajah, B., van Oostrom, C. T., Brandt, R. M. C., Vermeij, W. P., Dollé, M. E. T., & Guichelaar, T. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.667193 · Cited by 12
Ultrasensitive and Multiplexed Protein Imaging with Cleavable Fluorescent Tyramide and Antibody Stripping
Pham, T. H., Nazaroff, C. D., LaBaer, J., & Guo, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22168644 · Cited by 11
Influence of Self–MHC Class I Recognition on the Dynamics of NK Cell Responses to Cytomegalovirus Infection
Potempa, M., Aguilar, O. A., Gonzalez-Hinojosa, M. D. R., Tenvooren, I., Marquez, D. M., Spitzer, M. H., & Lanier, L. L. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2100768 · Cited by 11
Time series clustering of T cell subsets dissects heterogeneity in immune reconstitution and clinical outcomes among MUD-HCT patients receiving ATG or PTCy
Leserer, S., Graf, T., Franke, M., Bogdanov, R., Arrieta‐Bolaños, E., Buttkereit, U., Leimkühler, N. B., Fleischhauer, K., Reinhardt, H. C., Beelen, D. W., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1082727 · Cited by 12
Opportunities in optical and electrical single-cell technologies to study microbial ecosystems
Mermans, F., Mattelin, V., den Eeckhoudt, R. V., García‐Timermans, C., Landuyt, J. V., Guo, Y., Taurino, I., Tavernier, F., Kraft, M. D., Khan, H., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1233705 · Cited by 13

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks