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Characterization and label-free isolation of phenotypicallydefined human immune cells using Ghost Cytometry, a novel
AI-powered high-content flow cytometry technology
Keisuke Wagatsuma1, Kazuki Teranishi1, Asako Tsubouchi1, Hiroaki Adachi1, Ryo Tamoto1, Satoru Akai1, Kaori Matsuyama1, Yuri An1, Andy Wu1, Sadao Ota1,2
1ThinkCyte Inc., Tokyo Japan and San Carlos, CA USA, 2Research Center for Advanced Science and Technology, University of Tokyo, Japan

Abstract

Label-Free T-Cell Analytical Workflow

Identification and isolation of specific human immune cell
populations with technologies that are minimally disruptive tothe native biological states are critical requirements in modern
cellular immunotherapy approaches. Such technologies canenable the improvement of product quality as well as clinicalefficacy of cell therapy drug products, including both autologous

Machine Learning:
Classifier training and sample analysis workflow

and allogeneic cellular therapeutics. Here we applied Ghost
Cytometry (GC), a novel method utilizing proprietary optics
designs in combination with artificial intelligence to generatehigh content cellular structural information and enable analysis
and sorting of cells in flow without the use of molecular labels(i.e., antibodies). We demonstrate GC’s application incharacterizing human pan T-cells, where the system was able toidentify (i) early apoptosis, (ii) glycolysis level, (iii) exhaustion
state, and (iv) activation state with high accuracy in a label-freemanner. These results described here highlight a new promising
approach for isolation of functionally distinct cells, untouched byexternal labels, for investigators and drug developers inimmunology and immunotherapy research and development.

• Ground truth labelingusing known markers
for phenotypes ofinterest for initialclassifier training. AIlearns to map theGMI-based cellular
fingerprints to thetarget phenotype

Characterizing human pan T-cellswith representative phenotypes
Target application

Ground truth labels

Classification

Viability

Annexin V / PI

Live vs. Dead / Apoptotic

Glycolysis

2-NBDG uptake

Low vs. High Glycolytic

Exhaustion

PD1 / LAG3 expression

Exhausted vs. Non-exhausted

Activation

CD3 / CD25 expression

Activated vs. Resting

Ghost Cytometry

Results

Ghost Cytometry: Design and concept

T-cell viability• Single cell morphologicalinformation is collected as cells
pass through a structured
illumination and recorded as
temporal waveforms (GhostMotion Image (GMI) signals)• AI models analyze GMI signals
directly for high-speed analysis
and sorting

GMI signals

Live Cells:

Early Apoptotic Cells:
AUC : 0.987
Dead Cells:

Ground truthlabeling• Target cells are gently collectedusing fluid pressure

GMI Signals:

Label-free high-content data from scattered light profiles• Subsequent runs andanalysis of samples
are conductedcompletely label-freeby applying thedeveloped classifierDeadMachinelearning

Morphological differences between liveand early apoptotic cells are difficult toobserve by eye
Early apoptoticLive

AUC : 0.923

Interpretation of classification resultsTimeTimeTimeTime

GMI signals are high content and contain more information about a cell’s structural morphology
than conventional scattered light signals, such as forward and side scatter (FSC/SSC). The
collection of GMI signals from a given cell represents its ‘cellular fingerprint’.
S. Ota et al,, Science 2018. | M. Ugawa et al,, eLife 2021

Support Vector Machine Score (SVM score) : A score given to each cell which represents
the confidence in (or certainty of) the prediction by a binary classifier trained with an SVM.
Area Under the ROC Curve (AUC): A value used to compare the classification ability ofmachine learning models. A score closer to 1.0 represent better classification performance.

Results

T-cell glycolysis
2-NBDG is a fluorescent glucose analog that has been used to monitor glucose uptake in live cells. Low glycolytic cells have less metabolic
demands thus lower 2-NBDG uptake.
GMI Signals

Ground truth labelingLow glycolytic

Low Glycolytic Cells:

Low glycolytic

High glycolytic

High Glycolytic Cells:

Lowglycolytic

Highglycolytic

High glycolytic

Machinelearning

Pan-T cells were cultured with 2-NBDG andincubated for 15 mins to observe uptake

AUC : 0.935

T-cell exhaustion
PD1 and LAG3 are commonly accepted markers for T-cell exhaustion. Here we define double positive in PD1 and LAG3 as exhausted cells, anddouble negative as non-exhausted cells.
GMI signals

Ground truth labeling

Exhausted Cells:
Exhausted

Exhausted

Non-exhausted Cells:
Machinelearning

NonexhaustedNon-exhausted

Pan-T cells stimulated with CD3 / CD28
Dynabeads and 25 U/mL of IL-2 for 3 days,
then cultured in IL-2 for 11 days

T-cell activation
CD3 and CD25 double positive cells were defined as activated T-cells, while cells expressing CD3 positive and CD25 negative weredefined as resting T-cells.
GMI signals

Activated Cells:

Ground truth labeling
Activated

Resting Cells:

Resting

Machinelearning

PBMCs were stimulated with CD3 and CD28
for 5 days, then labeled with CD25 as theground truth label for activation.

Conclusions• In this study, we demonstrate the useof Ghost Cytometry for label-free
analysis of human pan-T cells incharacterizing cell health

For more information:• Combined with sorting capabilities,

Ghost Cytometry can enable
researchers access to truly untouched
T-cells, avoiding unwanted stimulationor activation by antibody-based labels

Business inquiries: Andy Wu <a.wu@thinkcyte.com>• Practical applications include

development of better cell-based
immunotherapies, discovery of new
drug targets in immunotherapy, andautomated or semi-automated QC incell therapy manufacturing.
General inquiries: contact@thinkcyte.com