Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14−CD19− PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.

248 Word count, text (from the beginning of Introduction to the end of Discussion): 4,467 Reference count: 48 Table count: 3 Figure count: 3 Supplementary online material: 12 figures and 8 tables Disclosure of Potential Conflicts of Interest: M.C.L., J.B., S.O., and J.A.N. are listed as co-inventors on a provisional application for a patent titled "System for and Method of Discovering Spatially-Derived Signatures of Tumor-Immune Cell Interactions through Tumor-Immune Partitioning and Clustering" regarding novel methods for characterizing immune cell distributions in solid tumors that has been filed through Partners Healthcare. J.L.G. is a consultant for GlaxoSmithKline, Codagenix, Array BioPharma, and Verseau Therapeutics; and receives research support from GlaxoSmithKline, Eli Lilly and Array BioPharma for the study of the breast tumor microenvironment. A.T.

Purpose. While abundant myeloid cell populations in the pancreatic ductal adenocarcinoma (PDAC) microenvironment have been postulated to suppress anti-tumor immunity, the composition of these populations, their spatial locations, and how they relate to patient outcomes are poorly understood. Experimental Design. To generate spatially-resolved tumor and immune cell data at single cell resolution, we developed two quantitative multiplex immunofluorescence assays to interrogate myeloid cells (CD15, CD14, ARG1, CD33, HLA-DR) and macrophages [CD68, CD163, CD86, interferon regulatory factor 5 (IRF5), MRC1 (CD206)] in the PDAC tumor microenvironment. Spatial point pattern analyses were conducted to assess the degree of co-localization between tumor cells and immune cells. Multivariable-adjusted Cox proportional hazards regression was used to assess associations with patient outcomes. Results. In a multi-institutional cohort of 305 primary PDAC resection specimens, myeloid cells were abundant, enriched within stromal regions, highly heterogeneous across tumors, and differed by somatic genotype. High densities of CD15 + ARG1 + immunosuppressive granulocytic cells and M2-polarized macrophages were associated with worse patient survival. Moreover, beyond cell density, closer proximity of M2-polarized macrophages to tumor cells was strongly associated with disease-free survival, revealing the clinical significance and biologic importance of immune cell localization within tumor areas. Conclusions. A diverse set of myeloid cells are present within the PDAC tumor microenvironment and are distributed heterogeneously across patient tumors. Not only the densities but also the spatial locations of myeloid immune cells are associated with patient Research.