visualization-quantification-high-dimensional-cytometry-data-using

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 ...

Leiden University Medical Center

Primary cytomegalovirus (CMV) infection leads to strong innate and adaptive immune responses against the virus, which prevents serious disease. However, CMV infection can cause serious morbidity and mortality in individuals who are immunocompromised. The adaptive immune response to CMV is characterized by large populations of effector-memory (EM) T cells that are maintained lifelong, a process termed memory inflation. Recent findings indicate that infection with CMV leads to continuous differentiation of CMV-specific EM-like T cells and that high-dose infection accelerates this progression. Whether measures that counteract CMV infection, such as anti-viral drugs, targeting of latently infected cells, adoptive transfer of CMV-specific T cells, and vaccination strategies, are able to impact the progressive differentiation of CMV-specific EM-like cells is discussed.

Cytomegalovirus infection and progressive differentiation of effector-memory T cells.

Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. A large panel of computational tools have been developed to analyze the high-dimensional landscape of the data generated. Analysis frameworks such as FlowSOM or Cytosplore incorporate clustering and dimensionality reduction techniques and include algorithms allowing visualization of multi-parametric cytometric analysis. To additionally provide means to quantify specific cell clusters and correlations between samples, we developed an R-package, called , for further downstream analysis. Specifically,  enables the visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or physiology. We used  on mass and flow cytometry datasets based on the modulation of the immune system upon immunotherapy. With  we rapidly generated visual representations of group-related immune cell clusters and showed correlations with the immune system composition. We discovered macrophage subsets that significantly decrease upon cancer immunotherapy and distinct prime-boost effects of prophylactic vaccines on the myeloid compartment.  is a time-efficient tool for comprehensive cytometric analysis to reveal immune signatures and correlations.  is available at Bioconductor.

: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations.

The clinical benefit of immunotherapeutic approaches against cancer has been well established although complete responses are only observed in a minority of patients. Combination immunotherapy offers an attractive avenue to develop more effective cancer therapies by improving the efficacy and duration of the tumor-specific T-cell response. Here, we aimed at deciphering the mechanisms governing the response to PD-1/PD-L1 checkpoint blockade to support the rational design of combination immunotherapy.

Article	Year
Cytomegalovirus infection and progressive differentiation of effector-memory T cells. F1000Research\| PubMed ID: 30345004	2018
: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations. Computational and structural biotechnology journal\| PubMed ID: 30450167	2018
PD-L1 blockade engages tumor-infiltrating lymphocytes to co-express targetable activating and inhibitory receptors. Journal for immunotherapy of cancer\| PubMed ID: 31412943	2019

Guillaume Beyrend

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