Label-free ghost cytometry for manufacturing of cell therapy products

Abstract

Automation and quality control (QC) are critical in manufacturing safe and effective cell and gene therapy products. However, current QC methods, reliant on molecular staining, pose difficulty in in-line testing and can increase manufacturing costs. Here we demonstrate the potential of using label-free ghost cytometry (LF-GC), a machine learning-driven, multidimensional, high-content, and high-throughput flow cytometry approach, in various stages of the cell therapy manufacturing processes. LF-GC accurately quantified T cells in human peripheral blood mononuclear cells (PBMCs) and discriminated between live and dead cells, non-apoptotic live cells and early apoptotic/dead cells, T cells and non-T cells, activated T cells and quiescent T cells, and particulate impurities in PBMCs. The data support that LF-GC is a non-destructive label-free cell analytical method that can be used to monitor cell numbers, assess viability, identify specific cell subsets or phenotypic states, and remove impurities during cell therapy manufacturing. Thus, LF-GC holds the potential to enable full automation in the manufacturing of cell therapy products with reduced cost and increased efficiency.