Multiparametric flow cytometry panels for analyses of whole blood samples as a tool for drug screening and immunotherapy-based applications

Researcher(s)

  • Emma Bertolino, Biological Sciences, University of Delaware

Faculty Mentor(s)

  • Arit Ghosh, Delaware Biotechnology Institute, University of Delaware

Abstract

Multicolor flow cytometry analysis is a powerful method to decipher the underpinnings of various immune disorders and hematologic malignancies such as multiple myeloma and acute myeloid leukemia. Flow cytometry is a laser- and hydrodynamic focusing-based technique used to detect and analyze the chemical and physical characteristics of live or fixed cell suspensions. The UDEL Flow Cytometry core has expanded its capabilities in multicolor analyses by establishing a working procedure for immunophenotyping whole blood for various (T cell, platelet and macrophage) multiparametric panels. We have developed a standard operating procedure to efficiently analyze T-cells from whole blood through a 10-color panel. Additionally, we have developed a standard operating procedure for platelets and macrophages by establishing a 12- color panel. In terms of disease phenotyping – by looking at various cell surface and intracellular markers, we can better understand these diseases at a more detailed level. In addition, we have utilized recent advances in cytometric data analyses to efficiently visualize high parametric data via algorithms such as AutoSpill, t-SNE, UMAP and FlowSOM. We have also established a cell- based assay which can utilize a drug screen to look for potential increments in T-cell (Tregs, T helper, T suppressors, NK and DC populations) therapeutic markers in patient (and donor) blood. As future work – we are looking to extend our work into Treg-sort-mediated cell-based assays (TRSA) to look for potential small molecules which can modulate Treg function and thereby inhibit tumor progression. In summary, the cell-based assays and multicolor panels established at the UDEL Flow Cytometry Core will help researchers find patterns in their data, better understand immunological disorders, and potentially find new treatments.