Mass Cytometry: Single Cells, Many Features

AbstractMass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful to dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. With a proper computational analysis, timestamped CyTOF data of signaling proteins could help develop predictive and mechanistic models for signaling kinetics. These models would be useful for predicting the effects of perturbations in cells, or comparing signaling networks across cell groups. We propose our Mass cytometry Signaling Network Analysis Code, or McSNAC, a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption breaks down often as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of the protein species that are involved in signaling. We carry out a series of in silico experiments here to show that 1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; 2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from timestamped CyTOF data.

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All Roads Lead to Rome: Phenotypic Channeling to Uniform Differentiation Structures from Diverse Leukemia Genotypes by Mass Cytometry

Blood ◽

10.1182/blood.v122.21.sci-22.sci-22 ◽

2013 ◽

Vol 122 (21) ◽

pp. SCI-22-SCI-22

Author(s):

Garry Nolan

Keyword(s):

Stem Cells ◽

B Cell ◽

Single Cell ◽

Conflicts Of Interest ◽

Single Cells ◽

Lymphoblastic Leukemia ◽

Myelogenous Leukemia ◽

Signaling Networks ◽

Mass Cytometry ◽

Human Cord Blood

Abstract Emerging single-cell technologies have been pivotal in uncovering an extensive degree of heterogeneity between and within tissues (1). Analysis of single-cell data has shed light on many different cellular processes (2-7) and recent technological advances have enabled the study of a large number of parameters in single cells at unparalleled resolution. One such technology, mass cytometry (8), can measure up to 45 parameters simultaneously in tens of thousands of individual cells. Using mass cytometry and genomic sequencing of conventionally sorted subpopulations show that acute myelogenous leukemia (AML) in a given patient can simultaneously occupy multiple stages of differentiation. Occupation of these stages was correlated with the presence, or absence, of unique exonic mutation fingerprints. In another cancer, B-cell acute lymphoblastic leukemia (ALL), outgrowth of tumor at pro and pre-B cell stages was nearly always uniquely at a single stage - contrary to the results in AML. This suggests that evolutionary “niche” searching is not only for physical space in cancers, but also involves utilization of differentiation machinery as an additional elaboration mechanism. Each differentiation stage in both AML and B-cell ALL was characterized by utilization of cognate signaling networks which showed differential susceptibility to drug action. Using such deep profiling and signaling delineation approaches at the single-cell level will allow for fine structured indexing of patient disease and further tailoring of disease management. In addition, it will allow “heterogeneous” tumors to be organized by a maturation index associated with a granular catalog of mutations that drive cells to occupy these pseudo-differentiation niches. 1. Bendall, S.C., et al., A deep profiler's guide to cytometry.Trends Immunol, 2012. 33(7): p. 323-32. 2. Petilla Interneuron Nomenclature Group, et al., Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat Rev Neurosci, 2008. 9(7): p. 557-68. 3. Irish, J.M., et al., Single cell profiling of potentiated phospho-protein networks in cancer cells.Cell, 2004. 118(2): p. 217-28. 4. Sachs, K., et al., Causal protein-signaling networks derived from multiparameter single-cell data.Science, 2005. 308(5721): p. 523-9. 5. Majeti, R., C.Y. Park, and I.L. Weissman, Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell, 2007. 1(6): p. 635-45. 6. Tarnok, A., H. Ulrich, and J. Bocsi, Phenotypes of stem cells from diverse origin.Cytometry A, 2010. 77(1): p. 6-10. 7. O'Brien, C.A., A. Kreso, and J.E. Dick, Cancer stem cells in solid tumors: an overview.Semin Radiat Oncol, 2009. 19(2): p. 71-7. 8. Bandura, D.R., et al., Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem, 2009. 81(16): p. 6813-22. Disclosures: No relevant conflicts of interest to declare.

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Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

10.1101/549592 ◽

2019 ◽

Author(s):

Alexandre Bouzekri ◽

Amanda Esch ◽

Olga Ornatsky

Keyword(s):

Morphological Changes ◽

Single Cells ◽

Quantitative Information ◽

Mass Cytometry ◽

Microtubule Depolymerization ◽

Ki 67 ◽

Pairwise Correlation ◽

Cellular Markers ◽

Mcf 7

AbstractIn pharmaceutical research, high-content screening is an integral part of lead candidate development. Drug responsein vitroover 40 parameters including biomarkers, signaling molecules, cell morphological changes, proliferation indexes and toxicity in a single sample could significantly enhance discovery of new therapeutics. As a proof of concept, we present a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single-cell quantitative information visualized as t-SNE plots and heatmaps. Human breast cancer-derived cell lines SKBR3, HCC1143 and MCF-7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF-7 cells dosed with three target-specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3S28, Ki-67 and p4E-BP1T37/T46in classified mitotic cells and anti-correlation with cell surface markers. Our study demonstrates that IMC data expands the number of measured parameters in single cells and brings higher-dimension analysis to the field of cell-based screening in early lead compound discovery.

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Multi-Dimensional Immuno-Profiling of Drosophila Hemocytes by Single Cell Mass Cytometry

10.1101/2020.06.10.144584 ◽

2020 ◽

Author(s):

József Á. Balog ◽

Viktor Honti ◽

Éva Kurucz ◽

Beáta Kari ◽

László G. Puskás ◽

...

Keyword(s):

Single Cell ◽

Complex Analysis ◽

Single Cells ◽

Cell Mass ◽

Antigen Expression ◽

Bioinformatic Analysis ◽

Multidimensional Analysis ◽

Cell Mediated Immunity ◽

Mass Cytometry ◽

Cell Surface Antigens

AbstractSingle cell mass cytometry (SCMC) combines features of traditional flow cytometry (FACS) with mass spectrometry and allows the measurement of several parameters at the single cell level, thus permitting a complex analysis of biological regulatory mechanisms. We optimized this platform to analyze the cellular elements, the hemocytes, of the Drosophila innate immune system. We have metal-conjugated six antibodies against cell surface antigens (H2, H3, H18, L1, L4, P1), against two intracellular antigens (3A5, L2) and one anti-IgM for the detection of L6 surface antigen, as well as one anti-GFP for the detection of crystal cells in the immune induced samples. We investigated the antigen expression profile of single cells and hemocyte populations in naive, in immune induced states, in tumorous mutants (hopTum bearing a driver mutation and l(3)mbn1 carrying deficiency of a tumor suppressor) as well as in stem cell maintenance defective hdcΔ84 mutant larvae. Multidimensional analysis enabled the discrimination of the functionally different major hemocyte subsets, lamellocytes, plasmatocytes, crystal cell, and delineated the unique immunophenotype of the mutants. We have identified sub-populations of L2+/P1+ (l(3)mbn1), L2+/L4+/P1+ (hopTum) transitional phenotype cells in the tumorous strains and a sub-population of L4+/P1+ cells upon immune induction. Our results demonstrated for the first time, that mass cytometry, a recent single cell technology combined with multidimensional bioinformatic analysis represents a versatile and powerful tool to deeply analyze at protein level the regulation of cell mediated immunity of Drosophila.

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Channel crosstalk correction in suspension and imaging mass cytometry

10.1101/185744 ◽

2017 ◽

Cited By ~ 1

Author(s):

Stéphane Chevrier ◽

Helena Crowell ◽

Vito Zanotelli ◽

Stefanie Engler ◽

Mark D. Robinson ◽

...

Keyword(s):

Fluorescent Dyes ◽

Expert Knowledge ◽

Single Cells ◽

Spillover Effects ◽

Pure Metal ◽

Cell Phenotype ◽

Mass Cytometry ◽

Complex Samples ◽

And Function ◽

Experimental Effort

ABSTRACTMass cytometry enables simultaneous analysis of over 40 proteins and their modifications in single cells through use of metal-tagged antibodies. Compared to fluorescent dyes, the use of pure metal isotopes strongly reduces spectral overlap among measurement channels. Crosstalk still exists, however, caused by isotopic impurity, oxide formation, and mass cytometer properties. Spillover effects can be minimized, but not avoided, by following a set of constraining rules when designing an antibody panel. Generation of such low crosstalk panels requires considerable expert knowledge, knowledge of the abundance of each marker and substantial experimental effort. Here we describe a novel bead-based compensation workflow that includes R-based software and a web tool, which enables correction for interference between channels. We demonstrate utility in suspension mass cytometry and show how this approach can be applied to imaging mass cytometry. Our approach greatly simplifies the development of new antibody panels, increases flexibility for antibody-metal pairing, improves overall data quality, thereby reducing the risk of reporting cell phenotype and function artifacts, and greatly facilitates analysis of complex samples for which antigen abundances are unknown.

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Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE

10.1101/396135 ◽

2018 ◽

Author(s):

Axel Theorell ◽

Yenan Troi Bryceson ◽

Jakob Theorell

Keyword(s):

Single Cell ◽

Open Source ◽

Rna Sequencing ◽

Biological Markers ◽

State Of The Art ◽

Single Cells ◽

Data Mining Algorithm ◽

Mass Cytometry ◽

Sequencing Data ◽

Biologically Relevant

AbstractTechnological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make this highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi-and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better as the best performing state-of-the-art clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. An open source R implementation of DEPECHE is available at https://github.com/theorell/DepecheR.Author summaryDEPECHE-a data-mining algorithm for mega-variate dataModern experimental technologies facilitate an array of single cells measurements, e.g. at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.

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Improving oligo-conjugated antibody signal in multimodal single-cell analysis

10.1101/2020.06.15.153080 ◽

2020 ◽

Author(s):

Terkild Brink Buus ◽

Alberto Herrera ◽

Ellie Ivanova ◽

Eleni Mimitou ◽

Anthony Cheng ◽

...

Keyword(s):

High Throughput Sequencing ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Number ◽

Surface Proteins ◽

Biological Information ◽

Antibody Concentration ◽

Mass Cytometry ◽

Background Signal ◽

High Background

AbstractSimultaneous measurement of surface proteins and gene expression within single cells offers high resolution snapshots of complex cell populations. These methods rely on staining cells with oligo-conjugated antibodies analogous to staining for flow- and mass cytometry. Unlike flow- and mass cytometry, signal from oligo-conjugated antibodies is not hampered by spectral overlap or limited by the number of metal isotopes, making it a highly sensitive and scalable approach. Signal from oligo-conjugated antibodies is quantified by counting reads from high-throughput sequencing. Consequently, cost of sequencing is strictly dependent on the signal intensities and background from the pool of antibodies used in analysis. Thus, considering the “cost-of-signal” as well as optimizing “signal-to-noise”, makes titration of oligo-conjugated antibody panels more complex and even more important than for flow- and mass cytometry. In this study, we investigated the titration response of a panel of oligo-conjugated antibodies towards four variables: Antibody concentration, staining volume, cell number at staining, and tissue of origin. We find that staining with high antibody concentrations recommended by published protocols and commercial vendors cause unnecessarily high background signal and that concentrations of many antibodies can be drastically reduced without loss of biological information. Reducing staining volume only affects antibodies targeting highly abundant epitopes used at low concentrations and can be counteracted by reducing cell numbers at staining. We find that background signal from empty droplets can account for a major fraction of the total sequencing reads and is primarily derived from antibodies used at high concentrations. Together, this study provides new insight into the titration response and background signal of oligo-conjugated antibodies and offers concrete guidelines on how such panels can be improved.

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High-Dimensional Single-Cell Quantitative Profiling of Skeletal Muscle Cell Population Dynamics during Regeneration

Cells ◽

10.3390/cells9071723 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1723

Author(s):

Lucia Lisa Petrilli ◽

Filomena Spada ◽

Alessandro Palma ◽

Alessio Reggio ◽

Marco Rosina ◽

...

Keyword(s):

Skeletal Muscle ◽

Single Cells ◽

Cell Types ◽

Interstitial Space ◽

Cell Populations ◽

Wild Type ◽

Mass Cytometry ◽

Pathological Conditions ◽

Comprehensive Picture ◽

Quantitative Profiling

The interstitial space surrounding the skeletal muscle fibers is populated by a variety of mononuclear cell types. Upon acute or chronic insult, these cell populations become activated and initiate finely-orchestrated crosstalk that promotes myofiber repair and regeneration. Mass cytometry is a powerful and highly multiplexed technique for profiling single-cells. Herein, it was used to dissect the dynamics of cell populations in the skeletal muscle in physiological and pathological conditions. Here, we characterized an antibody panel that could be used to identify most of the cell populations in the muscle interstitial space. By exploiting the mass cytometry resolution, we provided a comprehensive picture of the dynamics of the major cell populations that sensed and responded to acute damage in wild type mice and in a mouse model of Duchenne muscular dystrophy. In addition, we revealed the intrinsic heterogeneity of many of these cell populations.

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