Abstract
The advent of mass cytometry has resulted in the generation of high-dimensional, single-cell expression data sets from clinical samples. These data sets cannot be effectively analyzed using traditional approaches. Instead, new approaches using dimensionality reduction and network analysis techniques have been implemented to assess these data. Here, detailed methods are described for analyzing immune cell expression from clinical samples using network analyses. Specifically, details are given for performing SCAFFoLD and CITRUS analyses. The methods described will use immune cell tumor infiltrate as an example.
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References
Ma X, Gao L (2012) Biological network analysis: insights into structure and functions. Brief Funct Genomics 11(6):434–442. https://doi.org/10.1093/bfgp/els045
Spitzer MH, Carmi Y, Reticker-Flynn NE et al (2017) Systemic immunity is required for effective cancer immunotherapy. Cell 168(3):487–502 e415. https://doi.org/10.1016/j.cell.2016.12.022
Spitzer MH, Gherardini PF, Fragiadakis GK et al (2015) IMMUNOLOGY. An interactive reference framework for modeling a dynamic immune system. Science 349(6244):1259425. https://doi.org/10.1126/science.1259425
Grizzle WE, Bell WC, Sexton KC (2010) Issues in collecting, processing and storing human tissues and associated information to support biomedical research. Cancer Biomark 9(1–6):531–549. https://doi.org/10.3233/CBM-2011-0183
Bendall SC, Simonds EF, Qiu P et al (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332(6030):687–696. https://doi.org/10.1126/science.1198704
Irish JM, Doxie DB (2014) High-dimensional single-cell cancer biology. Curr Top Microbiol Immunol 377:1–21. https://doi.org/10.1007/82_2014_367
Leelatian N, Doxie DB, Greenplate AR et al (2017) Single cell analysis of human tissues and solid tumors with mass cytometry. Cytometry B Clin Cytom. https://doi.org/10.1002/cyto.b.21542
Bruggner RV, Bodenmiller B, Dill DL et al (2014) Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A 111(26):E2770–E2777. https://doi.org/10.1073/pnas.1408792111
Volovitz I, Shapira N, Ezer H et al (2016) A non-aggressive, highly efficient, enzymatic method for dissociation of human brain-tumors and brain-tissues to viable single-cells. BMC Neurosci 17(1):30. https://doi.org/10.1186/s12868-016-0262-y
Misharin AV, Morales-Nebreda L, Mutlu GM et al (2013) Flow cytometric analysis of macrophages and dendritic cell subsets in the mouse lung. Am J Respir Cell Mol Biol 49(4):503–510. https://doi.org/10.1165/rcmb.2013-0086MA
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Norton, S., Kemp, R. (2019). Computational Analysis of High-Dimensional Mass Cytometry Data from Clinical Tissue Samples. In: McGuire, H., Ashhurst, T. (eds) Mass Cytometry. Methods in Molecular Biology, vol 1989. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9454-0_19
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DOI: https://doi.org/10.1007/978-1-4939-9454-0_19
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