(1331-D) Label-free morphometric characterization of T cells for cell and gene therapy research and development

Abstract: Cell and gene therapies (C>) are a novel category of biological medicines where active research and development (R&D) is helping researchers and clinicians understand what makes for the most efficacious therapies. With T cell-based therapies leading the therapeutic product pipeline, identification, characterization, and minimally invasive isolation of T cells is critical for understanding how to best develop new cellular therapies and manage disease. Modern T cell cellular immunotherapy approaches demand new technologies that can isolate and characterize T cells and their functional subsets in their native biological and functional states to improve the quality and efficacy of cell therapy drug products.

Here we used VisionSort, a new label-free cellular analysis and sorting platform built with proprietary optics and artificial intelligence (AI), to characterize and isolate truly untouched human T cell subsets. VisionSort was used to identify T-cells with therapeutically relevant phenotypes, an emerging need in cell therapy R&D. By capturing single-cell digital phenotypes, we characterized human T-cells and generated ‘ground truth’ functional profiles for 1) glycolysis level, 2) exhaustion state 3) activation or resting profiles and 4) viability. A set of machine-learning derived classifiers was generated to identify these phenotypic classes in unlabeled T-cell subsets. The classifiers showed area under the curve (AUC) performance ranges for detecting specific, phenotypically defined T-cell populations between 0.923 and 0.995. Next, we explored the use of VisionSort to identify and isolate functionally distinct T cell subsets based on their morphology, label-free. Using a combination of supervised and unsupervised machine learning approaches, we show that we can identify follicular helper T (Tfh) cells from other CD4+ T cells and as well as differentiate between naive and effector regulatory T cells based on morphology alone. When we analyzed the morphological profiles of T cell subsets by unsupervised machine learning using uniform maniform projection and approximation (UAMP), we found that both gamma delta (γδ) T cells were distinct from alpha beta (αβ) T cells and CD8+ T cells were distinct from CD4+ T cells.

Here we report on the use of a novel, AI-based cytometry platform to characterize and isolate T cells for C> applications. The approach enables label-free isolation of target T cell subsets with defined phenotypic profiles in native states and has practical applications for investigators in basic life science and cell therapy R&D.