CELL THERAPY

Identification and Isolation ofCAR-T Cells with Enhanced
Therapeutic Efficacy
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
Chimeric antigen receptor T-cells (CAR-T) have emerged as an exciting new approach for the treatment of cancer,
with over 500 CAR-T worldwide clinical trials underway in 2021.1 Conventional thinking was that all CAR-T cells
have similar therapeutic potential, however new research has found that not all CAR-T cells are created equal. A
recent study showed that CAR-T cells with lower levels of glycolysis led to better clinical responses in leukemia
patients.2 Here we use VisionSort to identify metabolically distinct CAR-T cell subsets without the use of molecularlabels or tags as an approach to identify cells with the potential for higher therapeutic efficacy.

RESULTSTo develop a label-free machine learning classifier using VisionSort, CD19-targeted
CAR-T cells were treated with fluorescently labeled deoxyglucose analog (2-NBDG)
as a proxy for glycolysis levels to define ‘ground truth’ metabolic activity in CAR-Tcells. After gating on live cells, populations of low and high glycolysis levels weredefined based on 2-NBDG intensity (bottom 20% = low glycolytic activity and top 20%= high glycolytic activity, (Figure 1). Using the optical profiles generated by VisionSort
from the gated populations, a classifier was built by supervised machine learning todistinguish between the metabolic extremes (top 20% and bottom 20%). The classifier
showed outstanding classification accuracy, with an AUC of 0.986 (Figure 1).

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Identification and Isolation of CAR-T Cells with Enhanced Therapeutic Efficacy

For validation, CAR-T cells were re-analyzed and sorted withVisionSort using the pretrained classifier and sorted fractions
were characterized by 1) microscopy and 2) molecular profiling.
The cells were classified correctly with only minimal false
positive and negative cells detected (Figure 2). When sorted
cells from the metabolic extremes were examined under amicroscope, clear morphological differences between thepopulations were observed (Figure 2). For molecular validation,
the expression of genes regulating metabolic function andT-cell differentiation were assessed by QPCR. Gene expression
levels from sorted cell fractions containing glycolytically low
CAR-T cells showed a profile of less differentiated and lessmetabolically active cells compared to unsorted input cell
populations (Figure 3).

Figure 1. Development and assessment of a morphometric classifier for metabolic activity. CAR-T cell populations exhibiting different levels of 2-NBDG intensity were defined as having ‘low’ (lower
20% of cells, orange bar) and ‘high’ (upper 20% of cells, purplebar) glycolysis levels. An AI-derived classifier for these populations
showed high classification accuracy in discriminating between metabolically distinct cell subsets (AUC score of 0.986). SVM=Support
Vector machine, a.u.=arbitrary units.

Figure 2. Validation of the approach by microscopy. Morphologicaldifferences are clearly observed in microscopic images of low (leftpanel, orange) and high glycolytic activity (right panel, purple) cell
populations.

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SUMMARY
VisionSort can be used to identify and classify metabolicallydistinct subsets of CAR-T cells without the use of external
molecular markers or tags. The label-free approach highlighted here can significantly contribute to the identificationand isolation of higher quality CAR-T therapy products.

REFERENCES
1. https://www.nature.com/articles/s41434-021-00246-w)
2. Nat Med. 2018 May; 24 (5): 563-571).

Figure 3. Validation of the approach by molecular profiling. QPCR
gene expression assessing enolase 1 (ENO1), Transcription Factor 7(TCF7), and Programmed Cell Death Protein 1 (PDCD1) expression
levels in presorted and sorted fractions of low glycolytic activity
cell populations. Expression levels for ENO1 (blue), a key regulator of glycolysis (Ref: Huang CK, Sun Y, Lv L, Ping Y. ENO1 andCancer. Mol Ther Oncolytics. 2022 Jan 3;24:288-298. doi: 10.1016/j.
omto.2021.12.026. PMID: 35434271; PMCID: PMC8987341), wasreduced in sorted fractions of low glycolytic CAR-T cells. Expressionof PDCD1 (green), which is highly expressed on more differentiatedeffector T cells and exhausted T cells (Ref: Utzschneider, D.T., Gabriel, S.S., Chisanga, D. et al. Early precursor T cells establish andpropagate T cell exhaustion in chronic infection. Nat Immunol 21,
1256–1266 (2020). https://doi.org/10.1038/s41590-020-0760-z), wasreduced in the sorted fraction. Conversely, TCF7 (red), which is highly expressed on less differentiated T cells (Ref: Pais Ferreira D, Silva
JG, Wyss T, Fuertes Marraco SA, Scarpellino L, Charmoy M, MaasR, Siddiqui I, Tang L, Joyce JA, Delorenzi M, Luther SA, Speiser DE,
Held W. Central memory CD8+ T cells derive from stem-like Tcf7hi
effector cells in the absence of cytotoxic differentiation. Immunity.
2020 Nov 17;53(5):985-1000.e11. doi: 10.1016/j.immuni.2020.09.005.
Epub 2020 Oct 30. PMID: 33128876) was elevated in the sorted
fraction.

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