Label-free morphometric characterization of T cells for cell and gene therapy research and development.

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Presentation Time: 11:30 AM - 12:45 PM
Poster Board Number: B897
Abstract ID: 4982

Presenting Author:
Kazuki Teranishi , Product Manager at ThinkCyte

Abstract:

Identification, characterization, and minimally invasive isolation of T cells is critical for developing cellular therapies. Here we used VisionSort, an artificial intelligence (AI)-driven cellular analysis and sorting platform, to characterize and isolate human T cell subsets with therapeutically relevant phenotypes, label-free. 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 label-free. The classifiers showed area under the curve (AUC) performance ranges for detecting specific, phenotypically defined T-cell populations between 0.923 and 0.995. Using a combination of supervised and unsupervised machine learning approaches, we also show identification of follicular helper T (Tfh) cells from other CD4+ 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 (UMAP), 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 AI-based cytometry to characterize and isolate phenotypically defined T cell subsets label-free, with applications in basic life science and cell therapy R&D.