Development of a high-throughput image cytometric screening method as a research tool for immunophenotypic characterization of patient samples from clinical studies

Immunophenotyping has been the primary assay for characterization of immune cells from patients undergoing therapeutic treatments in clinical research, which is critical for understanding disease progression and treatment efficacy. Currently, flow cytometry has been the dominant methodology for characterizing surface marker expression for immunological research. Flow cytometry has been proven to be an effective and efficient method for immunophenotyping, however, it requires highly trained users and a large time commitment. Recently, a novel image cytometry system (Cellaca (R) PLX Image Cytometer, Revvity Health Sciences, Inc., Lawrence, MA) has been developed as a complementary method to flow cytometry for performing rapid and high-throughput immunophenotyping. In this work, we demonstrated an image cytometric screening method to characterize immune cell populations, streamlining the analysis of routine surface marker panels. The T cell, B cell, NK cell, and monocyte populations of 46 primary PBMC samples from subjects enrolled in autoimmune and oncological disease study cohorts were analyzed with two optimized immunophenotyping staining kits: Panel 1 (CD3, CD56, CD14) and Panel 2 (CD3, CD56, CD19). We validated the proposed image cytometry method by comparing the Cellaca (R) PLX and the Aurora (TM) flow cytometer (Cytek Biosciences, Fremont, CA). The image cytometry system was employed to generate bright field and fluorescent images, as well as scatter plots for multiple patient PBMC samples. In addition, the image cytometry method can directly determine cell concentrations for downstream assays. The results demonstrated comparable CD3, CD14, CD19, and CD56 cell populations from the primary PBMC samples, which showed an average of 5% differences between flow and image cytometry. The proposed image cytometry method provides a novel research tool to potentially streamline immunophenotyping workflow for characterizing patient samples in clinical studies.