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 intervention.

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    • "We ran PhenoGraph on each sample individually, defining subpopulations based on expression of the 16 surface markers. For each sample, all ex vivo conditions were pooled, as we previously demonstrated that surface marker distributions are not altered by these short-term perturbations (Bendall et al., 2011). PhenoGraph was run on the normalized surface phenotype matrices for each sample, with the parameter k = 50. "
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    ABSTRACT: Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell 06/2015; 162(1). DOI:10.1016/j.cell.2015.05.047 · 32.24 Impact Factor
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    • "However, this approach has many problems and limitations, including variability between different analysts, non-standardization and non-reproducibility of results, an unrealistic assumption that biological relationships between the markers exist only in the projected low-dimensional space and, most importantly, non-scalability to high-dimensional analysis, especially involving a large number of samples. With the advancement of technology, modern day flow cytometers allow simultaneous measurements of many markers on millions of cells, with some latest revolutionary mass cytometers capable of extending this up to 100 simultaneous parameters [1] [2]. As the number of markers increases, the number of bivariate projections increase rapidly. "
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    ABSTRACT: We consider the use of the Joint Clustering and Matching (JCM) procedure for the supervised classification of a flow cytometric sample with respect to a number of predefined classes of such samples. The JCM procedure has been proposed as a method for the unsupervised classification of cells within a sample into a number of clusters and in the case of multiple samples, the matching of these clusters across the samples. The two tasks of clustering and matching of the clusters are performed simultaneously within the JCM framework. In this paper, we consider the case where there is a number of distinct classes of samples whose class of origin is known, and the problem is to classify a new sample of unknown class of origin to one of these predefined classes. For example, the different classes might correspond to the types of a particular disease or to the various health outcomes of a patient subsequent to a course of treatment. We show and demonstrate on some real datasets how the JCM procedure can be used to carry out this supervised classification task. A mixture distribution is used to model the distribution of the expressions of a fixed set of markers for each cell in a sample with the components in the mixture model corresponding to the various populations of cells in the composition of the sample. For each class of samples, a class template is formed by the adoption of random-effects terms to model the inter-sample variation within a class. The classification of a new unclassified sample is undertaken by assigning the unclassified sample to the class that minimizes the Kullback-Leibler distance between its fitted mixture density and each class density provided by the class templates.
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    • "Thus CyTOF studies can combine ~40 labels in a sample. CyTOF has recently been employed to characterize peripheral blood cells in detail (Bendall et al., 2011) as well as NK cells (Horowitz et al., 2013), γδ cells in Celiac disease (Han et al., 2013), responding phenotypes in cancer (Irish and Doxie, 2014), and even holds the promise of examining solid tumors (Giesen et al., 2014). "
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    ABSTRACT: Analysis of immune cell states is paramount to our understanding of the pathogenesis of a broad range of human diseases. Immunologists rely on fluorescence cytometry for cellular analysis, and while detection of 8 markers is now well established, the overlap of fluorescent signals limits efficiency. Mass cytometry or CyTOF (Cytometry by Time-Of-Flight) is a new technology for multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry and can routinely detect as many as 40 markers per sample. This technology provides tremendous detail for cellular analysis of multiple cell populations simultaneously and is a powerful technique for translational investigations. Here we present reproducible detection of immune cell subsets starting with as few as 10,000 cells. Our study provides methods to employ CyTOF for small samples, which is especially relevant for investigation of limited patient biopsies in translational and clinical research.
    Journal of Immunological Methods 11/2014; 415. DOI:10.1016/j.jim.2014.10.010 · 1.82 Impact Factor
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