![]() All procedures were performed under protocols approved by the Institutional Animal Care and Use Committee at the University of Colorado Anschutz Medical Campus. Pools of two mice were used for LLC tumors, pools of three to five mice were used for CMT167 tumors, and left and right lung lobes were used for uninjected controls. Mice were euthanized at 2.5 (LLC cells) or 3.5 wk (CMT167 cells) postinjection, followed by perfusion of the circulation with PBS/heparin (20 U/ml) and collection of the tumor-containing left lobe for analysis. Following orthotopic injection, incisions were closed using veterinary-grade skin adhesive. fat was removed to completely visualize the left lung, as previously described ( 22). A 4–5-mm incision was made in the skin along the left shoulder and s.c. All surgeries were performed under inhaled isoflurane anesthesia. Both cell lines were routinely tested for mycoplasma and were confirmed negative before orthotopic injection. Mice received firefly luciferase–expressing CMT167 cells ( 21) or luciferase-expressing Lewis lung carcinoma (LLC) cells (Caliper Life Sciences). Second, UBI.GFP mice were orthotopically injected in the left lobe of the lung with 1 × 10 5 cells suspended in HBSS containing 1.35 mg/ml Matrigel (product number 354234 Corning). Mice were euthanized, and lungs were harvested at 9 d postinfection, followed by perfusion with PBS. First, 13-wk-old female C57BL/6J mice (B6, n = 5) or IL-10–knockout (IL-10KO) mice ( n = 4 B6.129P2- Il10 tm1Cgn/J The Jackson Laboratory) were infected with murine gammaherpesvirus 68 (γHV68) by intranasal infection using 4 × 10 5 PFU wild-type virus containing a gene 73-β lactamase fusion protein, as described ( 20). CyTOF data visualization and quantitation continues to be a rapidly evolving field (e.g., 18, 19). These tools are typically developed by computational biologists or by laboratories that are leaders in the field of mass cytometry, using a variety of languages (e.g., R, Matlab, Java, Python), clustering methods (e.g., parametric, nonparametric), and dimensionality reduction approaches ( 15– 17). Many algorithms and software kits have been developed to facilitate analysis of CyTOF datasets, including, but not limited to SPADE ( 6), viSNE ( 7), Wanderlust ( 8), FlowSOM ( 9), PhenoGraph ( 10), Citrus ( 11), Scaffold ( 12), X-shift ( 13), and DensVM ( 14). The technology allows simultaneous quantification of >30 cellular parameters and when integrated with high-dimensional analysis algorithms, it has the potential to reveal extraordinary cellular diversity and heterogeneity ( 2, 4, 5). Since its inception, mass cytometry, or cytometry by time-of-flight (CyTOF), has allowed researchers to gain deep insights into cellular phenotype and function ( 1– 3). 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. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. 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. 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. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. 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. ![]()
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