CELL THERAPY

T cell Activation

About VisionSort
ThinkCyte’s VisionSort platform combines the strengths of conventional flow cytometry fluorescencesignals with a novel morphometric cellular analysis measure. The dual-mode analytical capability
can be used to identify and sort phenotypically defined cell populations, label free, using machinelearning approaches.

INTRODUCTION
T cell activation is a critical step in the adaptive immune response. Studying
the mechanisms of T cell activation is an important part of modern
immunology research and in vitro activation is a requirement for celltherapy drug development programs. However, current methods to isolate
activated T cells require labeling with T-cell markers (e.g. CD8) and key
extracellular molecular activation indicators (such as CD38, CD45RO, andHLA-DR) with fluorescently tagged antibodies; a process that can interferewith downstream R&D assays and requires label removal in cell therapy
development. The VisionSort platform enables label-free sorting of T cells,giving investigators and drug developers activated T cells, untouched byexternal labels, for use in downstream assays and cell therapy development
programs. In this research collaboration with the University of Tokyo, weuse a mouse model of T cell activation to show how the VisionSort platform
can be used to achieve label-free sorting of activated cells.

RESULTS
Two cell populations were used to train a machine learning classifier on VisionSort,
T cells from Pmel-1 TCR transgenic mice and RMA cells. Pmel-1 mouse T cells
express the hgp 100 peptide-specific TCR and RMA cell is a tumor cell line, which
can present exogenous peptides with major histocompatibility complex class I molecules on its cell surface. After priming Pmel-1 T-cells with bone marrow-derived

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dendritic cells (BMDCs) in the presenceof hgp 100 peptide, Pmel-1 T-cells wereco-cultured with RMA cells that weretreated with hgp 100 peptide for activation or given sham treatment as controls. T cells in each treatment groupwere stained with different fluorescentmolecules (FITC and APC/Cy-7) conjugated to anti-CD8 antibodies, mixed,
and used as ground truth populations
to train a machine learning model on
VisionSort (Figure 1). The classifier
showed excellent performance, discriminating between activated cytotoxic T cells and non-activated cytotoxic T
cells with a ROC-AUC value of 0.917. In
a parallel analysis, labeled T-cells wereanalyzed by an unsupervised machinelearning algorithm (Uniform Manifold
Approximation and Projection, (UMAP))
to reveal different populations basedon morphological differences between
the cells. Stimulated and unstimulated
cell populations were then visualized bybackplotting CD8 intensity (Figure 2).

Figure 1. Workflow for training and evaluating the machine learning classifier for CD8+ Tcells. T cells from each treatment group were stained with different fluorescent molecules(FITC and APC/Cy-7) conjugated to anti-CD8 antibodies, mixed, and used as ground truth
populations to train a machine learning model. The resulting classifier could discriminate
between activated cytotoxic T cells and non-activated cytotoxic T cells with a ROC-AUC valueof 0.917SUMMARY
Here we describe a high throughput
method to identify activated murine
cytotoxic T cells without the use of external labels. When combined with cellsorting, the approach described here
has practical implications for immunology research and cell therapy development programs, where label-freeisolation of activated T cells is a criticalrequirement.

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Figure 2. Unsupervised machine learning on morphological profiles of mouse lymphocytes.
T-cells analyzed by UMAP with CD8 intensity shown on the overlay. Distinct populations ofCD8+ cells in stimulated and unstimulated conditions were detected.

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