Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore.

The complexity of data generated by mass cytometry has necessitated new tools to rapidly visualize analytic outcomes. Clustering methods like Cytosplore or FlowSOM are used for the visualization and identification of cell clusters. For downstream analysis, a newly developed R package, Cytofast, can generate a rapid visualization of results from clustering methods. Cytofast takes into account the phenotypic characterization of cell clusters, calculates the cell cluster abundance, then quantitatively compares groups. This protocol explains the applications of Cytofast to the use of mass cytometry data based on modulation of the immune system in the tumor microenvironment (i.e., the natural killer [NK] cell response) upon tumor challenge followed by immunotherapy (PD-L1 blockade). Demonstration of the usefulness of Cytofast with FlowSOM and Cytosplore is shown. Cytofast rapidly generates visual representations of group-related immune cell clusters and correlations with immune system composition. Differences are observed in the clustering analysis, but separation between groups are visible with both clustering methods. Cytofast visually shows the patterns induced by PD-L1 treatment that include a higher abundance of activated NK cell subsets, expressing a higher intensity of activation markers (i.e., CD54 or CD11c).

[1]  Sean C. Bendall,et al.  An interactive reference framework for modeling a dynamic immune system , 2015, Science.

[2]  Ramon Arens,et al.  Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations , 2018, Computational and structural biotechnology journal.

[3]  Jelle J. Goeman,et al.  A global test for groups of genes: testing association with a clinical outcome , 2004, Bioinform..

[4]  Fabian J. Theis,et al.  Diffusion maps for high-dimensional single-cell analysis of differentiation data , 2015, Bioinform..

[5]  Elmar Eisemann,et al.  Hierarchical Stochastic Neighbor Embedding , 2016, Comput. Graph. Forum.

[6]  A. Vilanova,et al.  PD-L1 blockade engages tumor-infiltrating lymphocytes to co-express targetable activating and inhibitory receptors , 2019, Journal of Immunotherapy for Cancer.

[7]  G. Nolan,et al.  Automated Mapping of Phenotype Space with Single-Cell Data , 2016, Nature Methods.

[8]  Piet Demeester,et al.  FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data , 2015, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[9]  Sean C. Bendall,et al.  Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[10]  H. Hotelling Analysis of a complex of statistical variables into principal components. , 1933 .

[11]  Eli R. Zunder,et al.  A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. , 2015, Cell stem cell.

[12]  Garry P Nolan,et al.  Visualization and cellular hierarchy inference of single-cell data using SPADE , 2016, Nature Protocols.

[13]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[14]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.