Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum

Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA.
Science (Impact Factor: 33.61). 05/2011; 332(6030):687-96. DOI: 10.1126/science.1198704
Source: PubMed


Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell “mass cytometry”
to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability,
DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was
monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors.
The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing
a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis
revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined
cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet
tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling
in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic

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    • "High-throughput single-cell qPCR is a dynamic approach for quantifying a set of target genes in systems of interest (Buganim et al., 2012;Dalerba et al., 2011;Guo et al., 2010Guo et al., , 2013Moignard et al., 2013). Single-cell mass cytometry constitutes a complementary system for multiplexed gene expression analysis at the protein level (Bendall et al., 2011). Single-cell mRNAsequencing strategies, which enable whole-transcriptome analysis from individual cells, have become increasingly mature and capable (Fan et al., 2015;Hashimshony et al., 2012;Islam et al., 2011;Jaitin et al., 2014;Klein et al., 2015;Macosko et al., 2015;Ramskö ld et al., 2012;Sasagawa et al., 2013;Shalek et al., 2013;Tang et al., 2009Tang et al., , 2010Treutlein et al., 2014;Xue et al., 2013;Yan et al., 2013). "
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    ABSTRACT: Variation in gene expression is an important feature of mouse embryonic stem cells (ESCs). However, the mechanisms responsible for global gene expression variation in ESCs are not fully understood. We performed single-cell mRNA-seq analysis of mouse ESCs and uncovered significant heterogeneity in ESCs cultured in serum. We define highly variable gene clusters with distinct chromatin states and show that bivalent genes are prone to expression variation. At the same time, we identify an ESC-priming pathway that initiates the exit from the naive ESC state. Finally, we provide evidence that a large proportion of intracellular network variability is due to the extracellular culture environment. Serum-free culture reduces cellular heterogeneity and transcriptome variation in ESCs.
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    • "Flow cytometry is a tractable method for detecting and quantifying signal transduction information at single-cell resolution (Irish et al, 2004; Krutzik et al, 2004). CyTOF, where the limitation of fluorescence spectral overlap is overcome by the resolution of metal-labeled reagents by mass spectrometry, allows for multiplex sampling of protein signals at a network scale and at single-cell resolution (Bendall et al, 2011, 2014). In parallel, newly developed fluorescent dyes and compensation algorithms allow 15–20 parameters to be measured simultaneously using multicolor fluorescent flow cytometry (O'Donnell et al, 2013). "
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    ABSTRACT: Understanding heterogeneous cellular behaviors in a complex tissue requires the evaluation of signaling networks at single-cell resolution. However, probing signaling in epithelial tissues using cytometry-based single-cell analysis has been confounded by the necessity of single-cell dissociation, where disrupting cell-to-cell connections inherently perturbs native cell signaling states. Here, we demonstrate a novel strategy (Disaggregation for Intracellular Signaling in Single Epithelial Cells from Tissue - DISSECT) that preserves native signaling for Cytometry Time-of-Flight (CyTOF) and fluorescent flow cytometry applications. A 21-plex CyTOF analysis encompassing core signaling and cell-identity markers was performed on the small intestinal epithelium after systemic tumor necrosis factor-alpha (TNF-α) stimulation. Unsupervised and supervised analyses robustly selected signaling features that identify a unique subset of epithelial cells that are sensitized to TNF-α-induced apoptosis in the seemingly homogeneous enterocyte population. Specifically, p-ERK and apoptosis are divergently regulated in neighboring enterocytes within the epithelium, suggesting a mechanism of contact-dependent survival. Our novel single-cell approach can broadly be applied, using both CyTOF and multi-parameter flow cytometry, for investigating normal and diseased cell states in a wide range of epithelial tissues. © 2015 The Authors. Published under the terms of the CC BY 4.0 license.
    Full-text · Article · Oct 2015 · Molecular Systems Biology
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    • "In addition, the proposed conflict resolution technique's superiority is demonstrated over several other alternative conflict resolution methods. Finally, we present a proof-of-concept computational experiment by applying the algorithm on 5 heterogeneous data sets from Bendall et al. (2011) and Bodenmiller et al. (2012) measuring overlapping variable sets under 3 different manipulations. The data sets measure protein concentrations in thousands of human cells of the autoimmune system using mass-cytometry technologies. "
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    ABSTRACT: Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The result is a collection of heterogeneous data sets coming from different data distributions. In this work, we present algorithm COmbINE, which accepts a collection of data sets over overlapping variable sets under different experimental conditions; COmbINE then outputs a summary of all causal models indicating the invariant and variant structural characteristics of all models that simultaneously fit all of the input data sets. COmbINE converts estimated dependencies and independencies in the data into path constraints on the data- generating causal model and encodes them as a SAT instance. The algorithm is sound and complete in the sample limit. To account for conflicting constraints arising from statistical errors, we introduce a general method for sorting constraints in order of confidence, computed as a function of their corresponding p-values. In our empirical evaluation, COmbINE outperforms in terms of efficiency the only pre-existing similar algorithm; the latter additionally admits feedback cycles, but does not admit conflicting constraints which hinders the applicability on real data. As a proof-of-concept, COmbINE is employed to co- analyze 4 real, mass-cytometry data sets measuring phosphorylated protein concentrations of overlapping protein sets under 3 different interventions.
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