CyTOF: We have mass appeal. The technology backed by 1,000+ publications.
CyTOF® is the only high-parameter single-cell analysis system vetted by more than 1,000 publications. Covering a wide spectrum of diseases and research applications, this collection of studies demonstrates the versatility and proven performance of mass cytometry for translational and clinical research around the world.
Explore the publications by research area using the links to the right.
Bibliography
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Browse our complete catalog of over 1,000 CyTOF related publications to see how Helios™ and mass cytometry empower researchers to make breakthrough discoveries in basic, translational and clinical research.
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Mass Cytometry Publications Trending Now
Take a look at the issues of Mass Cytometry Publications Trending Now, a quarterly anthology series covering the most recent and impactful publications that help expand our understanding of human health and disease
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Nov 2020 issue On recent applications of mass cytometry to vaccine development for infectious diseases.
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Overview
Number of papers per research area as of January, 2021Number of cumulative papers as of January, 2021 -
Recent
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Two subsets of stem-like CD8 + memory T cell progenitors with distinct fate commitments in humans
Galletti, G., Simone, G.D., Mazza, E.M.C. Nature Immunology (2020): doi: 10.1038/s41590-020-0791-5 -
Th17-inducing autologous dendritic cell vaccination promotes antigen-specific cellular and humoral immunity in ovarian cancer patients
Block, M. S., Dietz, A.B., Gustafson, M.P. Nature Communications (2020): doi.org/10.1038/s41467-020-18962-z -
Glioma stem-like cells can be targeted in boron neutron capture therapy with boronophenylalanine
Kondo, N., Hikida, M., Nakada , M. Cancers (2020): doi.org/10.3390/cancers12103040 -
Application of an activity-based probe to determine proteolytic activity of cell surface cathepsin G by mass cytometry data acquisition
Gärtner, F., Knippschild, U., Burster, T. ACS Omega (2020): doi.org/10.1021/acsomega.0c04092 -
Salmonella enterica serovar Typhi exposure elicits ex vivo cell-type-specific epigenetic changes in human gut cells
Sztein, M.B., Bafford, A.C., Salerno-Goncalves, R. Scientific Reports (2020): doi.org/10.1101/cshperspect.a029546 -
Agile workflow for interactive analysis of mass cytometry data
Casado, J., Lehtonen, O., Rantanen, V. Bioinformatics (2020): DOI: 10.1093/bioinformatics/btaa946
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Two subsets of stem-like CD8 + memory T cell progenitors with distinct fate commitments in humans
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Research Areas
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Biomarker Screening
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Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases
Phongpreecha, T., Fernandez, R., Mrdjen, D. Science Advances (2020): DOI: 10.1126/sciadv.abd5575 -
CD8+T-bet+ cells as a predominant biomarker for inclusion body myositis
Dzangué-Tchoupou, G., Mariampillai, K., Bolko, L. et al. Autoimmunity Reviews (2019): 325–33 -
A high-sensitivity lanthanide nanoparticle reporter for mass cytometry: tests on microgels as a proxy for cells
Lin, W., Hou, Y., Lu, Y. et al. Langmuir (2014): 3,142–53 -
Mass cytometry identifies distinct lung CD4+ T cell patterns in Löfgren’s syndrome and non-Löfgren’s syndrome sarcoidosis
Kaiser, Y., Lakshmikanth, T., Chen, Y., et al. Frontiers in Immunology (2017): 1,130 -
High dimensional immune biomarkers demonstrate differences in phenotypes and endotypes in food allergy and asthma
Chinthrajah, R.S., Purington, N., Sampath, V. et al. Annals of Allergy, Asthma, and Immunology (2018): 117–9.e1 -
A frameshift in CSF2RB predominant among Ashkenazi Jews increases risk for Crohn's disease and reduces monocyte signaling via GM-CSF
Chuang, L.S., Villaverde, N., Hui, K.Y. et al. Gastroenterology (2016): 710–723.e2 -
Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients
Subrahmanyam, P.B., Dong, Z., Gusenleitner, D. et al. Journal for ImmunoTherapy of Cancer (2018): 18 -
A time to amaze, a time to settle down, and a time to discover
Cosma, A. Cytometry Part A (2015): 795–6 -
Immune cell profiling to guide therapeutic decisions in rheumatic diseases
Ermann, J., Rao, D.A., Teslovich, N.C. et al. Nature Reviews Rheumatology (2015): 541–51 -
Signaling effects of sodium hydrosulfide in healthy donor peripheral blood mononuclear cells
Sulen, A., Gullaksen, S.E., Bader, L. et al. Pharmacological Research (2016): 216–27
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Single-cell peripheral immunoprofiling of Alzheimer’s and Parkinson’s diseases
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Data Analysis Methods
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Prediction of functional markers of mass cytometry data via deep learning
Solís-Lemus, C., Ma, X., HostetterII, M. Statistical Modeling in Biomedical Research (2021): 95-104 -
SwiftReg cluster registration automatically reduces flow cytometry data variability including batch effects.
Rebhahn, J.A., Quataert, S.A., Sharma, G. Communications Biology (2021): doi.org/10.5281/zenodo.3733491 -
Brick plots: an intuitive platform for visualizing multiparametric immunophenotyped cell clusters
Norton, S.E., Leman, J. K. H., Khong, T. BMC Bioinformatics (2021): doi.org/10.1186/s12859-020-3469-y -
The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data
Yeo, J.G., Wasser, M., Albani, S. Nature Biotechnology (2020): 679–684 -
Single-cell profiling of Ebola virus disease in vivo reveals viral and host dynamics
Kratochv´ıl, M., Hunewald, O., Heirendt, L. GigaScience (2020): DOI: 10.1093/gigascience/giaa127 -
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions
Culos, A., Tsai, A. S., Aghaeepour, N. Nature Machine Intelligence (2020): 619–628 -
Uncovering the key dimensions of high-throughput biomolecular data using deep learning
Zhang, S., Li, X., Lin, Q. Nucleic Acid Research (2020): 56 -
Learning pathway dynamics from single‐cell proteomic data: a comparative study
Verrou, K.M., Tsamardinos, L., Papoutsoglou, G. Cytometry Part A (2020): doi.org/10.1002/cyto.a.23976 -
VoPo leverages cellular heterogeneity for predictive modeling of single-cell data
Stanley, N., Stelzer, I.A., Aghaeepour, N. Nature Communications (2020): doi.org/10.1182/bloodadvances.2020001565 -
Visualizing Structure and Transitions for Biological Data Exploration
Moon, K.R., van Dijk, D., Wang, Z. et al. Cell (2018): 1,482–92 -
HiPPO and PANDA: Two bioinformatics tools to support analysis of mass cytometry data
Pirrò, S., Spada, F., Gadaleta, E. et al. Journal of Computational Biology (2019): 1,283–94 -
High‐dimensional data analysis algorithms yield comparable results for mass cytometry and spectral flow cytometry data
Ferrer-Font, L., Mayer, J.U., Old, S. Cytometry Part A (2020): 824–31 -
Uncovering axes of variation among single-cell cancer specimens
Chen, W.S., Zivanovic, N., van Dijk, D. et al. Nature Methods (2020): 302–10 -
FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets
Ko, M.E., Williams, C.M., Fread, K.I. et al. Nature Protocols (2020): 398-420. -
Stabilized reconstruction of signaling networks from single-cell cue-response data
Kumar, S., Lun, X-K., Bodenmiller, B. et al. Scientific Reports (2020): 1233 -
Structure-preserving visualisation of high dimensional single-cell datasets
Szubert, B., Cole, J.E., Monaco, C. et al. Scientific Reports (2019): 8914 -
Cell cycle analysis and relevance for single-cell gating in mass cytometry
Rein, I.D., Notø, H. Ø., Bostad, M. et al. Cytometry Part A (2020): DOI: 10.1002/cyto.a.23926 -
CytoFA: Automated gating of mass cytometry data via robust skew factor analyzers
Lee, S.X. Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science (2019): 514–25 -
Learning single‐cell distances from cytometry data
Nguyen, B., Rubbens, P., Kerckhof, F.M. et al. Cytometry Part A (2019): 782–91 -
Visualizing structure and transitions in high-dimensional biological data
Moon, K.R., van Dijk, D., Wang, Z. et al. Nature Biotechnology (2019): 1,482–92 -
Computational analysis of high-dimensional mass cytometry data from clinical tissue samples
Norton, S. and Kemp, R. Mass Cytometry: Methods and Protocols (2019): 295–307 -
Exploring single-cell data with deep multitasking neural networks
Amodio, M., van Dijk, D., Srinivasan, K., et al. Nature Methods (2019): 1,139–45 -
Analysis of high-dimensional phenotype data generated by mass cytometry or high-dimensional flow cytometry
Cirovic, B., Katzmarski, N., and Schlitzer, A. Mass Cytometry: Methods and Protocols (2019): 281–94 -
SCENERY: a web application for (causal) network reconstruction from cytometry data
Papoutsoglou, G., Athineou G., Lagani, V. et al. Nucleic Acids Research (2017): W270–W275 -
Automated data cleanup for mass cytometry
Bagwell, C.B., Inokuma, M., Hunsberger, B., et al. Cytometry Part A (2019): 184–198 -
Modeling cell-cell interactions from spatial molecular data with spatial variance component analysis
Arnol, D., Schapiro, D., Bodenmiller, B. et al. Cell Reports (2019): P202–211.E6 -
Visualization and quantification of high-dimensional cytometry data using Cytofast and the upstream clustering methods FlowSOM and Cytosplore
Beyrend, G., Stam, K., Ossendorp, F. et al. Journal of Visual Experiments (2019): DOI: 10.3791/60525 -
A comparison framework and guideline of clustering methods for mass cytometry data
Liu, X., Song, W., Wong, B.Y., et al. Genome Biology (2019): 297 -
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
Belkina, A.C., Ciccolella, C.O., Anno, R., et al. Nature Communications (2019): 5415 -
Challenges in the multivariate analysis of mass cytometry data: The effect of randomization
Papoutsoglou, G., Lagani, V., Schmidt, A., et al. Cytometry Part A (2019): DOI: 10.1002/cyto.a.23908 -
Acquisition, processing, and quality control of mass cytometry data
Lee, B.H. and Rahman, A.H. Mass Cytometry: Methods and Protocols (2019): 13–31 -
Supervised machine learning with CITRUS for single cell biomarker discovery
Polikowsky, H.G. and Drake, K.A. Mass Cytometry: Methods and Protocols (2019): 309–32 -
Analysis of mass cytometry data
Pederson, C.B. and Olsen, L.R. Mass Cytometry: Methods and Protocols (2019): 267–79 -
Automated cell type discovery and classification through knowledge transfer
Lee, H.C., Kosoy, R., Becker, C.E. et al. Bioinformatics (2017): 1,689–95 -
CytoNorm: A normalization algorithm for cytometry data
Van Gas, S., Gaudilliere, B., Angst , M.S. et al. Cytometry Part A (2019): doi: 10.1002/cyto.a.23904 -
Minimizing Batch Effects in Mass Cytometry Data
Schuyler, R.P., Jackson, C., Garcia-Perez, J.E. et al. Frontiers in Immunology (2019): doi: 10.3389/fimmu.2019.02367 -
Predicting cell populations in single cell mass cytometry data
Abdelaal, T., van Unen, V., Höllt T. et al. Cytometry Part A (2019): 769–81 -
Computational approaches for characterizing the tumor immune microenvironment
Liu, C.C., Steen, C.B., and Newman, A.M. Immunology (2019): 70–84 -
SCINA: A semi-supervised subtyping algorithm of single cells and bulk samples
Zhang, Z., Luo, D., Zhong, X. et al. Genes (2019): 531 -
A novel, five-marker alternative to CD16-CD14 gating to identify the three human monocyte subsets
Ong, S.M., Teng, K., Newell, E. et al. Frontiers in Immunology (2019): 1,761 -
Methods for analyzing tellurium Imaging Mass Cytometry data
Bassan, J. and Nitz, M. PLoS One (2019): e0221714 -
VOLARE: visual analysis of disease-associated microbiome-immune system interplay
Siebert, J.C., Preston Neff, C., Schneider, J.M. et al. BMC Bioinformatics (2019): 432 -
Improving the t-SNE algorithms for cytometry and other technologies: Cen-Se′ mapping
Bagwell, C.B., Bray, C.M., Herbert, D.J. et al. Journal of Biometrics & Biostatistics (2019): 430 -
diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering
Weber, L.M., Nowicka, M., Soneson, C. et al. Communications Biology (2019): 183 -
ImaCytE: Visual exploration of cellular microenvironments for Imaging Mass Cytometry data
Somarakis, A., van Unen, V., Koning, F. et al. IEEE Transactions on Visualization and Computer Graphics (2019): doi: 10.1109/TVCG.2019.2931299. -
Advanced imaging technology applications in cytology
Pantanowitz, L., Preffer, F., Wilbur, D.C. Nature Communications (2018): 5–14 -
Mass synaptometry: High-dimensional multi parametric assay for single synapses
Gajera, C.R., Fernandez, R., Postupna, N. et al. Journal of Neuroscience Methods (2018): 73–83 -
histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
Schapiro, D., Jackson, H.W., Raghuraman, S. et al. Nature Methods (2017): 873–6 -
Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data
Fernandez, N.F., Gundersen, G.W., Rahman, A. et al. Scientific Data (2017): 170151 -
CytoBinning: immunological insights from multi-dimensional data
Shen, Y., Chaigne-Delalande, B., Lee, R.W.J. et al. PLoS One (2018): e0205291 -
Compensation of signal spillover in suspension and Imaging Mass Cytometry
Chevrier, S., Crowell, H.L., Zanotelli, V.R.T. et al. Cell Systems (2018): 612–20 -
Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization
Meehan, S., Kolyagin, G.A., Parks, D. et al. Nature Communications Biology (2019): 229 -
Quantitative comparison of conventional and t-SNE-guided gating analyses treatment in cynomolgus monkeys
Eshgi, S.T., Au-Yeung, A., Takahashi, C. et al. Frontiers in Immunology (2019): 1,194 -
Development of a comprehensive antibody staining database using a standardized analytics pipeline
Amir, E.D., Lee, B., Badoual, P. et al. Frontiers in Immunology (2019): 1,315 -
CyTOFmerge: Integrating mass cytometry data across multiple panels
Abdelaal, T., Höllt T., van Unen, V. et al. Bioinformatics (2019): btz180 -
Robust prediction of clinical outcomes using cytometry data
Hu, Z., Glicksberg, B.S., Butte, A.J. Bioinformatics (2018): 1,197–203 -
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
Sidhom, J.W., Theodros, D., Murter, B. et al. Journal of Visualized Experiments (2019): doi: 10.3791/57473. -
CytoBackBone: an algorithm for merging of phenotypic information from different cytometric profiles
Pereira, A.L., Lambotte, O., Le Grand, R. et al. Bioinformatics (2019): btz212 -
High throughput automated analysis of big flow cytometry data
Rahim, A., Meskas, J., Drissler, S. et al. Methods (2018): 164–76 -
GateFinder: projection-based strategy optimization for flow and mass cytometry.
Aghaeepour, N., Simonds, E.F., Knapp D.J.H.F. et al. Bioinformatics (2018): 4,131–3 -
QFMatch: multidimensional flow and mass cytometry samples alignment
Orlova, D.Y., Meehan, S., Parks D. et al. Scientific Reports (2018): 3,291 -
Cytosplore: interactive immune cell phenotyping for large single-cell datasets
Höllt, T., Pezzotti, N., van Unen, V. et al. Computer Graphics Forum (2016): 171–80 -
A roadmap towards personalized immunology
Delhalle, S., Bode, S.F.N., Balling, R. et al. Systems Biology and Applications (2018): 9 -
Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations
Beyrend, G., Stam, K., Höllt, T. et al. Computational and Structural Biotechnology Journal (2018): 435–42 -
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J. et al. Nature Biotechnology (2018): 38–44 -
Single-cell epigenetics - Chromatin modification atlas unveiled by mass cytometry
Cheung, P., Vallania, F., Dvorak, M. et al. Clinical Immunology (2018): 40–8 -
MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data
Hu, Z., Jujjavarapu, C., Hughey, J.J. et al. Cell Reports (2018): 1,377–88 -
Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternate tuft cell origins in the gut
Pollyea, D.A., Stevens, B.M., Jones, C.L. et al. Cell Systems (2018): 37–51 -
CytoML for cross-platform cytometry data sharing
Finak, G., Jiang, W., Gottardo, R. Cytometry Part A: Journal of the ISAC (2018): 1,189–96 -
Removal of batch effects using distribution-matching residual networks
Shaham, U., Stanton, K.P., Zhao, J. et al. Bioinformatics (2017): 2,539–46 -
Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis
Fonseka, C.Y., Rao, D.A., Teslovich, N.C. et al. Science Translational Medicine (2018): eaaq0305 -
Sensitive detection of rare disease-associated cell subsets via representation learning
Arvaniti, E., Claassen, M. Nature Communications (2017): doi: 10.1007/s12094-018-02004-8 -
Multiparametric analysis of colorectal cancer immune responses
Leman, J.K.H., Sandford, S.K., Rhodes, J.L. et al. World Journal of Gastroenterology (2018): 2,995-3,005 -
Learning time-varying information flow from single-cell epithelial to mesenchymal transition data
Krishnaswamy, S., Zivanovic, N., Sharma, R. et al. PLoS One (2018): e0203389 -
Continuous Immune Cell Differentiation Inferred From Single-Cell Measurements Following Allogeneic Stem Cell Transplantation
Chen, Y., Lakshmikanth, T., Olin, A. et al. Frontiers in Molecular Biosciences (2018): 81 -
Average Overlap Frequency: a simple metric to evaluate staining quality and community identification in high dimensional mass cytometry experiments
Amir, E.D., Guo, X.V., Mayovska, O. et al. Journal of Immunological Methods (2018): 20–29 -
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Qiu, P., Simonds, E.F., Bendall, S.C. et al. Nature Biotechnology (2011): 886–91 -
Bayesian hierarchical models for protein networks in single-cell mass cytometry
Mitra, R., Müller, P., Qiu, P. et al. Cancer Informatics (2014): 79–89 -
Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline
Chen, H., Lau, M.C., Wong, M.T. et al. PLoS Computational Biology (2016): e1005112 -
ImmPort, toward repurposing of open access immunological assay data for translational and clinical research
Bhattacharya, S., Dunn, P., Thomas, C.G. et al. Scientific Data (2018): 180,015 -
Alternatives to current flow cytometry data analysis for clinical and research studies
Gondhalekar, C., Rajwa, B., Patsekin, V. et al. Methods (2018): 113–29 -
Generating quantitative cell identity labels with marker enrichment modeling (MEM)
Diggins, K.E., Gandelman, J.S., Roe, C.E. et al. Current Protocols in Cytometry (2018): 10.21.1–28 -
CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
Rapsomaniki, M.A., Lun, X.K., Woerner, S. et al. Nature Communications (2018): 632 -
Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data
Weber, L.M., Robinson, M.D. Cytometry Part A (2016): 1,084–96 -
Gating mass cytometry data by deep learning
Li, H., Shaham, U., Stanton, K.P. et al. Bioinformatics (2017): 3,423–30 -
Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks
Li, Y.H., Li, D., Samusik, N. et al. PLoS Computational Biology (2017): e1005875 -
A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data
Platon, L., Pejoski, D., Gautreau, G. et al. Methods (2017): 66–75 -
Automated identification of stratifying signatures in cellular subpopulations
Bruggner, R.V., Bodenmiller, B., Dill, D.L. et al. Proceedings of the National Academy of Sciences of the United States of America (2014): E2770–E2777 -
OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis
Finak, G., Frelinger, J., Jiang, W. et al. PLoS Computational Biology (2014): e1003806 -
Conditional density-based analysis of T cell signaling in single-cell data
Krishnaswamy, S., Spitzer, M.H., Mingueneau, M. et al. Science (2014): 1,250,689 -
Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternative tuft cell origins in the gut
Herring, C.A., Banerjee, A., McKinley, E.T. et al. Cell Systems (2018): 37–51.e9 -
Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
Van Unen, V., Höllt, T., Pezzotti, N. et al. Nature Communications (2017): 1,740 -
CyteGuide: visual guidance for hierarchical single-cell analysis
Holt, T., Pezzotti, N., van Unen, V. et al. IEEE Transactions on Visualization and Computer Graphics (2018): 739–48 -
A beginner's guide to analyzing and visualizing mass cytometry data
Kimball, A.K., Oko, L.M., Bullock, B.L. et al. Journal of Immunology (2018): 3–22 -
Integrated inference and analysis of regulatory networks from multi-level measurements
Poultney, C.S., Greenfield, A., Bonneau, R. Methods in Cell Biology (2012): 19–56 -
RchyOptimyx: cellular hierarchy optimization for flow cytometry
Aghaeepour, N., Jalali, A., O'Neill, K. et al. Cytometry, Part A (2012): 1,022–30 -
Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)
Shekhar, K., Brodin, P., Davis, M.M. et al. Proceedings of the National Academy of Sciences (2014): 202–7 -
Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data
O'Neill, K., Jalali, A., Aghaeepour, N. et al. Bioinformatics (2014): 1,329–30 -
FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data
Van Gassen, S., Callebaut, B., Van Helden, M.J. et al. Cytometry Part A (2015): 636–45 -
An Interactive Reference Framework for Modeling a Dynamic Immune System
Spitzer, M.H., Gherardini, P.F., Fragiadakis, G.K. et al. Science (2015): 1,259,425 -
immunoClust—An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets
Sörensen, T., Baumgart, S., Durek, P. et al. Cytometry, Part A (2015): 603–15 -
NetworkPainter: dynamic intracellular pathway animation in Cytobank
Karr, J.K., Guturu, H., Chen, E.Y. et al. BMC Bioinformatics (2015): 172 -
Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms
Zaunders, J., Jing, J., Leipold, M. et al. Cytometry, Part A (2015): 44–58 -
Processing, visualising and reconstructing network models from single-cell data
Woodhouse, S., Moignard, V., Göttgens, B., Fisher, J. Immunology and Cell Biology (2015): 256–65 -
Wishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M.D., Reich-Zeliger, S. et al. Nature Biotechnology (2016): 637–45 -
Automated mapping of phenotype space with single-cell data
Samusik, N., Good, Z., Spitzer, M.H. et al. Nature Methods (2016): 493–6 -
Computational flow cytometry: helping to make sense of high-dimensional immunology data
Saeys, Y., Gassen, S.V., Lambrecht, B.N. Nature Reviews Immunology (2016): 449–62 -
The end of gating? An introduction to automated analysis of high dimensional cytometry data
Mair, F., Hartmann, F.J., Mrdjen et al. European Journal of Immunology (2015): 34–43 -
Mapping the effects of drugs on the immune system
Kidd, B.A., Wroblewska, A., Boland, M.R. et al. Nature Biotechnology (2015): 47–54 -
densityCut: an efficient and versatile topological approach for automatic clustering of biological data
Ding, J., Shah, S., Condon, A. Bioinformatics (2016): 2,567–76 -
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding
Cheng, Y., Wong, M.T., van der Maaten, L., Newell, E.W. Journal of Immunology (2016): 924–32 -
AirLab: a cloud-based platform to manage and share antibody-based single-cell research
Catena, R., Özcan, A., Jacobs, A. et al. Genome Biology (2016): 142 -
destiny: diffusion maps for large-scale single-cell data in R
Angerer, P., Haghverdi, L., Büttner, M. et al. Bioinformatics (2016): 1,241–3 -
Visualization and cellular hierarchy inference of single-cell data using SPADE
Anchang, B., Hart, T.D., Bendall, S.C. et al. Nature Protocols (2016): 1,264–79 -
Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
Triantafillou, S., Lagani, V., Heinze-Deml, C. et al. Scientific Reports (2017): 12,724 -
Toward deterministic and semiautomated SPADE analysis
Qiu, P. Cytometry Part A (2017): 281–9 -
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets
Nowicka, M., Krieg, C., Weber, L.M. et al. F1000 Research (2017): 748 -
Cluster stability in the analysis of mass cytometry data
Melchiotti, R., Gracio, F., Kordasti, S. et al. Cytometry Part A (2017): 73–84 -
Testing for differential abundance in mass cytometry data
Lun, A.T.L., Richard, A.C., Marioni, J.C. Nature Methods (2017): 707–9 -
SPADEVizR: an R package for visualization, analysis and integration of SPADE results
Gautreau, G., Pejoski, D., Le Grand, R. et al. Bioinformatics (2017): 779–81 -
Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling
Diggins, K.E., Greenplate, A.R., Leelatian, N. et al. Nature Methods (2017): 275-8 -
Exploring Glucocorticoid Receptor Agonists Mechanism of Action through Mass Cytometry and Radial Visualizations
Abraham, Y., Gerrits, B., Ludwig, M.G. et al. Cytometry Part B Clinical Cytometry (2016): 42–56
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Prediction of functional markers of mass cytometry data via deep learning
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Immuno-oncology
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Partial absence of PD‐1 expression by tumor‐infiltrating EBV‐specific CD8+ T cells in EBV‐driven lymphoepithelioma‐like carcinoma
Simoni, Y., Becht, E., Li, S. Clinical & Translational Immunology (2021): doi.org/10.1002/cti2.1175 -
High‐dimensional analyses reveal a distinct role of T‐cell subsets in the immune microenvironment of gastric cancer
Wang, M., Huang, Y. K., Kong, J. CH. Clinical Translational Immunology (2021): doi: 10.1002/cti2.1127 -
Identification of the immune checkpoint signature of multiple myeloma using mass cytometry‐based single‐cell analysis
Wang, J., Zheng, Y., Tu, C. Clinical & Translational Immunology (2021): doi.org/10.1002/cti2.1132 -
Integrated RNA Sequencing and Single-Cell Mass Cytometry Reveal a Novel Role of LncRNA HOXA-AS2 in Tumorigenesis and Stemness of Hepatocellular Carcinoma
Lu, Q., Gao, J., Tang, S. OncoTargets and Therapy (2020): doi: 10.2147/OTT.S272717 -
The identification and functional analysis of CD8+PD-1+CD161+ T cells in hepatocellular carcinoma
Li, Z., Zheng, B., Qiu, X. npj Precision Oncology (2020): doi.org/10.17632/ttccnvj -
The PD-1 expression balance between effector and regulatory T cells predicts the clinical efficacy of PD-1 blockade therapies
Kumagai, S., Togashi, Y., Kamada, T. Nature Immunology (2020): DOI: 10.1038/s41590-020-0769-3 -
IL-31 induces antitumor immunity in breast carcinoma
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