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 cell count and viability of human peripheral blood mononuclear cells (PBMCs) and identified non-apoptotic live cells and early apoptotic/dead cells in PBMCs (ROC-AUC: area under receiver operating characteristic curve = 0.975), T cells and non-T cells in white blood cells (ROC-AUC = 0.969), activated T cells and quiescent T cells in PBMCs (ROC-AUC = 0.990), and particulate impurities in PBMCs (ROC-AUC ≧ 0.998). The results 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.