CyTOFmerge: integrating mass cytometry data across multiple panels

Abstract Motivation High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel. Results To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection. Availability and implementation Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge). Supplementary information Supplementary data are available at Bioinformatics online.

[1]  O. Ornatsky,et al.  Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. , 2009, Analytical chemistry.

[2]  Thomas Höllt,et al.  Predicting cell types in single cell mass cytometry data , 2018 .

[3]  E S Costa,et al.  Automated pattern-guided principal component analysis vs expert-based immunophenotypic classification of B-cell chronic lymphoproliferative disorders: a step forward in the standardization of clinical immunophenotyping , 2010, Leukemia.

[4]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[5]  G. Nolan,et al.  Automated Mapping of Phenotype Space with Single-Cell Data , 2016, Nature Methods.

[6]  G. Nolan,et al.  Mass Cytometry: Single Cells, Many Features , 2016, Cell.

[7]  Sean C. Bendall,et al.  Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. , 2012, Immunity.

[8]  Sean C. Bendall,et al.  A deep profiler's guide to cytometry. , 2012, Trends in immunology.

[9]  M. Mearin,et al.  Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune Subsets. , 2016, Immunity.

[10]  Mark M Davis,et al.  Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization , 2013, Nature Biotechnology.

[11]  Elmar Eisemann,et al.  Hierarchical Stochastic Neighbor Embedding , 2016, Comput. Graph. Forum.

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Nima Aghaeepour,et al.  Deep profiling of multitube flow cytometry data , 2015, Bioinform..

[14]  Michael B. Stadler,et al.  An Immune Atlas of Clear Cell Renal Cell Carcinoma , 2017, Cell.

[15]  Elmar Eisemann,et al.  Approximated and User Steerable tSNE for Progressive Visual Analytics , 2015, IEEE Transactions on Visualization and Computer Graphics.

[16]  Elmar Eisemann,et al.  Cytosplore: Interactive Immune Cell Phenotyping for Large Single‐Cell Datasets , 2016, Comput. Graph. Forum.

[17]  A Orfao,et al.  EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes , 2012, Leukemia.

[18]  Sean C. Bendall,et al.  Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum , 2011, Science.

[19]  A. Regev,et al.  Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.

[20]  Elmar Eisemann,et al.  Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types , 2017, Nature Communications.

[21]  Sean C. Bendall,et al.  Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[22]  Sean C. Bendall,et al.  viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia , 2013, Nature Biotechnology.

[23]  N. McGovern,et al.  A High-Dimensional Atlas of Human T Cell Diversity Reveals Tissue-Specific Trafficking and Cytokine Signatures. , 2016, Immunity.

[24]  C. E. Pedreira,et al.  Generation of flow cytometry data files with a potentially infinite number of dimensions , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[25]  William Finn,et al.  Statistical file matching of flow cytometry data , 2010, J. Biomed. Informatics.

[26]  Benjamin D. Greenbaum,et al.  Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses , 2017, Cell.

[27]  Ewold Verhagen,et al.  Nonlinear cavity optomechanics with nanomechanical thermal fluctuations , 2016, Nature Communications.

[28]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[29]  Thomas Höllt,et al.  Predicting Cell Populations in Single Cell Mass Cytometry Data , 2019, Cytometry. Part A : the journal of the International Society for Analytical Cytology.