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      CyTOF protocol for immune monitoring of solid tumors from mouse models

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          Summary

          Techniques for robust immune profiling of mouse tumor and blood are key to understanding immunological responses in mouse models of cancer. Here, we describe mass cytometry (cytometry by time-of-flight) procedures to facilitate high-parameter profiling of low-volume survival blood samples and end-of-study tumor samples. We employ live-cell barcoding systems to mark all cells from each tumor and blood to improve cost-effectiveness and minimize batch effects.

          For complete details on the use and execution of this protocol, please refer to Charmsaz et al. (2021). 1

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          Highlights

          • Facilitating discovery through high-parameter immune profiling in tumor mouse models

          • Design surface and intracellular antibody panels specific to experimental needs

          • Immune and non-immune live-cell barcoding scheme to enable multiplexing of samples

          • Mass cytometry (cytometry by time-of-flight; CyTOF) for mouse tumors and whole blood

          Abstract

          Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

          Abstract

          Techniques for robust immune profiling of mouse tumor and blood are key to understanding immunological responses in mouse models of cancer. Here, we describe mass cytometry (cytometry by time-of-flight) procedures to facilitate high-parameter profiling of low-volume survival blood samples and end-of-study tumor samples. We employ live-cell barcoding systems to mark all cells from each tumor and blood to improve cost-effectiveness and minimize batch effects.

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          Most cited references11

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          diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

          High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 targeted protein markers per cell at the single-cell level. However, data analysis can be difficult, due to the large size and dimensionality of datasets as well as limitations of existing computational methods. Here, we present diffcyt, a new computational framework for differential discovery analyses in high-dimensional cytometry data, based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Our approach provides improved statistical performance, including for rare cell populations, along with flexible experimental designs and fast runtimes in an open-source framework.
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            A Beginner's Guide to Analyzing and Visualizing Mass Cytometry Data.

            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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              Stabilizing Antibody Cocktails for Mass Cytometry

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                Author and article information

                Contributors
                Journal
                STAR Protoc
                STAR Protoc
                STAR Protocols
                Elsevier
                2666-1667
                19 December 2022
                17 March 2023
                19 December 2022
                : 4
                : 1
                : 101949
                Affiliations
                [1 ]Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
                [2 ]Mass Cytometry Facility at Johns Hopkins University, Baltimore, MD 21287, USA
                [3 ]Convergence Institute, Johns Hopkins University, Baltimore, MD 21287, USA
                Author notes
                []Corresponding author wjho@ 123456jhmi.edu
                [4]

                Technical contact: sarahshin@jhmi.edu

                [5]

                Lead contact

                Article
                S2666-1667(22)00829-2 101949
                10.1016/j.xpro.2022.101949
                9794973
                36538397
                7ce4c72d-df21-4ee1-9463-e8dc13faffcf
                © 2022 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Protocol

                flow cytometry/mass cytometry,cancer,immunology,antibody

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