High‐Dimensional Single‐Cell Analysis with Mass Cytometry

Brodie, Tess M.; Toševski, Vinko

doi:10.1002/cpim.31

Cited by 19 publications

(11 citation statements)

References 32 publications

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“…Cells were then stained with cisplatin for 5 minutes at 37°C for live-dead cell discrimination and processed further for long-term storage at -80°C until analysis, as described in ref. 44. Serum was isolated from blood by centrifuging the tubes at 300 g for 10 minutes and stored at -80˚C.…”

Section: Methodsmentioning

confidence: 99%

Lymphocyte mass cytometry identifies a CD3-CD4+ cells subset with a potential role in psoriasis

et al. 2019

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Section: Methodsmentioning

confidence: 99%

Lymphocyte mass cytometry identifies a CD3-CD4+ cells subset with a potential role in psoriasis

et al. 2019

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“…Such an approach enables simultaneous quantitative analyses and efficient resource utilization by reducing time and reagent costs. Several reference protocols are available on how to perform single‐cell mass cytometry assays , including in solid tissues and tumors . General computational methods for analysis and interpretation of mass cytometry data have been recently reviewed .…”

Section: Advantages Of Mass Cytometry For Single‐cell Protein Analysesmentioning

confidence: 99%

Beyond the message: advantages of snapshot proteomics with single‐cell mass cytometry in solid tumors

et al. 2019

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Single‐cell technologies that can quantify features of individual cells within a tumor are critical for treatment strategies aiming to target cancer cells while sparing or activating beneficial cells. Given that key players in protein networks are often the primary targets of precision oncology strategies, it is imperative to transcend the nucleic acid message and read cellular actions in human solid tumors. Here, we review the advantages of multiplex, single‐cell mass cytometry in tissue and solid tumor investigations. Mass cytometry can quantitatively probe nearly any cellular feature or target. In discussing the ability of mass cytometry to reveal and characterize a broad spectrum of cell types, identify rare cells, and study functional behavior through protein signaling networks in millions of individual cells from a tumor, this review surveys publications of scientific advances in solid tumor biology made with the aid of mass cytometry. Advances discussed include functional identification of rare tumor and tumor‐infiltrating immune cells and dissection of cellular mechanisms of immunotherapy in solid tumors and the periphery. The review concludes by highlighting ways to incorporate single‐cell mass cytometry in solid tumor precision oncology efforts and rapidly developing cytometry techniques for quantifying cell location and sequenced nucleic acids.

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“…The invention and application of mass cytometry transformed single-cell analysis, allowing deeper understanding of cellular heterogeneity among single cells at the molecular and phenotypic level (3). Beyond the technical aspects of performing a mass cytometry experiment (recently reviewed in (28)), there remain significant challenges in how to effectively analyze these complex data, particularly for the new user (17). The development of multiple algorithms, which utilize different data analysis methods and visualization approaches has enabled new insights, but has also made it difficult to know where to begin for CyTOF analysis.…”

Section: Discussionmentioning

confidence: 99%

A Beginner’s Guide to Analyzing and Visualizing Mass Cytometry Data

Kimball

Oko

Bullock

et al. 2018

The Journal of Immunology

142

138

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Mass cytometry has revolutionized the study of cellular and phenotypic diversity, significantly expanding the number of phenotypic and functional characteristics that can be measured at the single-cell level. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. For the beginner, however, the large number of algorithms that have been developed, as well as the lack of consensus on best practices for analyzing these data, raise multiple questions: Which algorithm is the best for analyzing a dataset? How do different algorithms compare? How can one move beyond data visualization to gain new biological insights? In this article, we describe our experiences as recent adopters of mass cytometry. By analyzing a single dataset using five cytometry by time-of-flight analysis platforms (viSNE, SPADE, X-shift, PhenoGraph, and Citrus), we identify important considerations and challenges that users should be aware of when using these different methods and common and unique insights that can be revealed by these different methods. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. In total, these analyses emphasize the benefits of integrating multiple cytometry by time-of-flight analysis algorithms to gain complementary insights into these high-dimensional datasets.

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High‐Dimensional Single‐Cell Analysis with Mass Cytometry

Cited by 19 publications

References 32 publications

Lymphocyte mass cytometry identifies a CD3-CD4+ cells subset with a potential role in psoriasis

Lymphocyte mass cytometry identifies a CD3-CD4+ cells subset with a potential role in psoriasis

Beyond the message: advantages of snapshot proteomics with single‐cell mass cytometry in solid tumors

A Beginner’s Guide to Analyzing and Visualizing Mass Cytometry Data

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