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

By Sean Bendall, Erin F. Simonds, Peng Qiu, E. Amir, Peter O. Krutzik, R. Finck, Robert V. Bruggner, Rachel D. Melamed, Angelica Trejo, O. Ornatsky and 6 more. 2011

2,263 citations recorded by the source corpus.

Simultaneous measurement of more than 30 properties in individual human cells is used to characterize signaling in the immune system. 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.

Subfields: Biophysics, Immunology, Molecular Biology

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Citations in the corpus

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, methodology): “Expert-guided (manual) analysis of cell populations was performed in FlowJo 10.3 – 10.5 as described previously for speci ﬁ c datasets 25,46 (Suppl.”
Single-Cell Metabolomics: Analytical and Biological Perspectives (2013, methodology): “[Reproduced from (92)] (B) Microfluidic single-cell trapping array.”
Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade (2017, positive): “…have been utilized to characterize cellular processes including hematopoiesis, immune-cell differentiation, and leukemic disease progression (Bendall et al., 2011; Spitzer and Nolan, 2016) and, more recently, to analyze the immune infiltrates of solid tumors (Chevrier et al., 2017; Lavin et…”
SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation (2017, methodology): “Multiple antibody-based methods for quantifying proteins in single cells have been recently developed, including CyTOF [7, 8], single-cell Western blots [9], and Proseek Multiplex, an immunoassay readout by PCR [10].”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, methodology): “Emerging methods, such as high-throughput single-cell qPCR (Dalerba et al., 2011; Guo et al., 2010; Han et al., 2017), single-cell mass cytometry (Bendall et al., 2011), and singlecell mRNA-seq (Hashimshony et al., 2012; Ramsköld et al., 2012; Shalek et al., 2013; Tang et al., 2009; Treutlein et…”
Sensitive detection of rare disease-associated cell subsets via representation learning (2016, background): “It is becoming routine to measure thousands of DNA, RNA 3 and dozens of protein 4 species in thousands of single cells, optionally including their spatial context 5–7 .”
Data-analysis strategies for image-based cell profiling (2017, methodology): “We therefore recommend the following options to make code and data available publicly online.”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, methodology): “This technology has been successfully applied in a number of areas including mapping phenotypic heterogeneity of leukemia [5], inferring cellular progression and hierarchies [6], assessing drug effects on immune cells [7,8] and uncovering mechanisms of cellular reprogramming [9].”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, neutral): “The number of layers was ﬁ xed at ten for both the variational in ﬂ uence neural networks and the model neural networks; the number of units in each layer was set to 8, 16, 32, 64, and 128.”