A Label-free Method to Determine theGlycolytic State of T cells for CAR T Generation1161-B

Keisuke Wagatsuma1, Kaori Matsuyama1, Yuri Murata1, Gregory Schneider2
1ThinkCyte, K.K. Tokyo, Japan and 2ThinkCyte, Inc. San Carlos, CA USA

Abstract

RESULTS

It has become increasingly clear that glycolysis state is an important consideration for the production of Tcells into genetically redirected T Cells. Studies indicate that T cells with lower glycolytic activity yielded
better anti-tumor outcomes and that current methods tend to drive cells toward terminal differentiationand senescencei. Standard FACS with cell sorting required staining the cells with fluorescent analogues of
glucose such as 2-NBDG which render the cells not suitable for downstream culture. Thus, the need for alabel-free method of isolating T cells with a low glycolysis state to ensure better genetically redirected Tcells. Ghost Cytometry with its label-free and AI technology offers a method to ensure the sorting of T cellswith low glycolytic activity and suitable for downstream culture and genetic redirection.

Ghost Cytometry Prediction ResultTNFPFNTP

Test data
Low 20%(pos) : 2000
High 20%(neg) : 2000

iSukumar, M et al, Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function. J Clin Invest. 2013; 123(10) 4479. https://doi.org/10.1172/JCI69589

ACC: 0.942
ROC-AUC : 0.986

Introduction

Modality :FSC+BSC+fsGMI+bsGMI+
dGMI+bfGMI

As the field of T cell genetic manipulation expands, the need to monitor cellular health has become an
important consideration. The need for a sorting technique that can accurately identify and sort cells withlow glycolytic activity without effecting their cellular activity. A standard sorting based on glycolysis would
require fluorescent analogues of glucose that would render the cells unusable for downstream applications
such as culture or introduction to animal models. We show here that Ghost Cytometry provides a reliablesorting of CAR T cells that have low glycolytic activity, without the need for fluorescent labels, providing asource of T cells that are available for downstream applications.

Figure 3: Classification Results of Low Glycolytic vs. High Glycolytic CAR-T Cells Validated the Supervised Ground
Truth Modeling. After Ghost Cytometry was trained, the same set of cells were reintroduced into the GC instrument todetermine how well the predicted label would match the true label; the Ghost Cytometry AI model is validated.

Microscopic Confirmation of Cellular Difference due to Glycolysis

Methods
Ghost Cytometry ground truth label training was accomplished by staining the T cells with a fluorescently
labeled glucose analog (2-NBDG) and using the lowest 20% vs. the highest 20% of 2-NBDG labeling. Thisyielded a very high GC Score of 0.986 and precision scores of 0.94 for both low and high glycolytic activity.

VisionSort

2-NBDG high 80%

2-NBDG low 20%

Figure 4: Microscopic Morphological Characteristic Confirmation of CAR T cells with Different Glycolysis
Levels. Cells with low 2-NBDG staining showed very different cell shape indicating significant cytoskeletal change
corresponding with differential glycolytic activity. Ghost Cytometry was able to determine cells with low and highglycolytic activity with high accuracy. Image Sort can separate one defined cell population at a time. In this casethe Low 20% glycolytic activity were sorted from the remaining 80% of the cells. The cells can then be resorted toattain additional cell populations.

POWERED BY

Ghost Cytometry

TimeTimeTime

Genomic Confirmation of Ghost Cytometry Sorted Cells

dGMI: diffractive GMI(analogous to phasecontrast images)
Light Intensity

bsGMI: backscattering GMI(analogous toback-scatteringdark field images)

bfGMI: bright field GMI(analogous to brightfield images)
Light Intensity

fsGMI: forwardscattering GMI(analogous toforward-scatteringdark field images)

Light Intensity

Light Intensity

Label free Ghost Motion Imaging Signals

Incorporation of
2-NBDG (glucose analog)

SVM modelSort glycolytic low 20%cells (sorted) and others(waste) by GC

2-NBDG low 20% vs others (high 80%)
Modalities: fsGMI, bsGMI, FSC-H, BSC-H
hCD19 targeting
CAR-T cells
Time

PKM – Pyruvate Kinase M – a glycolysis related geneTCF7 - Transcription Factor 7 – high expression associated with naïve or early memory T cells (which correlates with low glycolysis)
PDCD1 – Programmed Cell Death Protein 1 – High expression is associated with activated or exhausted T cells (Low expression correlates with low glycolysis)

Figure 1: ThinkCyte® ImageSort™ – Combining Ghost Cytometry with Traditional Fluorescence Cytometry.
ImageSort™ utilizes standard, fluorescent flow cytometry with ThinkCyte® Ghost Cytometry. Ghost Cytometry utilizes
structured illumination where the laser lite is diffused through a pinhole structure to reduce the laser intensity andincrease the signal to noise ratio. The signals are read by four modalities: forward scatter, back scatter, bright fieldand diffractive Ghost Motion Imaging (GMI) to create waveforms for each mode. The artificial intelligence interprets
the waveforms and uses specific peaks that are predictive of cell populations. Two general modes can be used in
Ghost Cytometry: Supervised learning where cells of interest are defined by existing fluorescently labeled biomarkers
for Ground Truth Labels

PKM

TCF7

PDCD1

Results
sortedClassification of T cells with Low and High Glycolysis activity
Incorporation of2-NBDG (glucose analog)

waste

sortedSort using
fluorescence intensity(control)waste

pre-sort

Sort using
SVM model

sortedwaste

pre-sort

Sort using
SVM model

sortedwaste

pre-sort

Sort using
SVM model

Figure 5: Sort glycolytic low CAR-T cells using GC: Correlation with qPCR Expression of Low Glycolytic Cell
Expression

Conclusions
1.
Train data
Low 20%(pos): 2000
High 20%(neg): 2000

ACC
: 0.942
ROC-AUC : 0.986

Modality :FSC+BSC+fsGMI+bsGMI+dGMI+bfGMI

Low glycolytic CAR T cells could be identified by Ghost Cytometry (GC)

2. Low glycolytic CAR T cells identified by GC correlated very well with standard flow cytometry

techniques using 2-NBDG

3. Low Glycolysis classification also correlated with microscopy morphology dataFigure 2: Ghost Cytometry Ground Truth Label Training Using 2-NBDG Staining Created a Strong AI Model toSort Cells by their Glycolytic Profile. Cells were stained with 2-NBDG and assayed by ImageSort® fluorescentdetection. The low and high 20% 2-NBDG were gated to define the Ghost Cytometry groups. The separation wassignificant with a ROC-AUC value of 0.986 for all T cells. The training set consisted of 2000 cells for both the low andhigh glycolysis (low and high 2-NBDG staining).

4. Low Glycolysis classification correlated with qPCR data of PKM and TCF7 and PDCD1 markers

correlated with low glycolytic activity.

5. Label – free Ghost Cytometry is an effective technique to isolate low glycolytic CAR T cells fordownstream uses