CELL THERAPYLabel-free Assessmentof Cell Viability
About VisionSort
ThinkCyte’s VisionSort platform combines the strengths ofconventional flow cytometry fluorescence signals with a novel
morphometric cellular analysis measure. The dual-mode analytical
PBMC

VisionSort

LIVEEARLY
APOPTOTIC

DEAD

capability can be used to identify and sort phenotypically definedcell populations, label free, using machine learning approaches.

INTRODUCTION
Characterizing cell viability is critical for many life science research and development studies. Viability is typicallyassessed using fluorescent markers such as propidium iodine (PI) or 7-AAD, that exploit the loss of membrane
integrity in dead cells which allows these markers to permeate into nuclei, bind double stranded DNA, and thuslabel cells in the late stages of death. In earlier stages of apoptosis where membrane integrity has not yet been
lost, other fluorescent markers such as Annexin V, Apotracker, JC-1, Caspase-3/7 are commonly used to label
activation of the apoptotic pathway. However, the use of fluorescent labels to identify cell viability states can hinder
downstream research efforts and therefore label-free approaches for characterizing cell viability is a major unmet
research need. Here we used VisionSort to acquire morphological profiles for three cell viability phenotypes (live,dead, and apoptotic). These profiles were used to build AI-based classifiers via supervised machine learning in
VisionSort and then used to identify cell viability phenotypes without the need for conventional fluorescent markers.

RESULTS
Morphology-based classifiers for cell viability were developed using T cells isolated
from commercial PBMCs. PI and Annexin V were used as ‘ground truth’ labels to define live, dead, and apoptotic cells. Morphological profiles from cells within the gatedpopulations were used to build a machine learning classifier. The resulting classifier
showed outstanding performance in discriminating different cell viability phenotypes(both live versus dead and live versus early apoptotic cells), with area under the curve(AUC) scores greater than 0.92 (Figure 1).

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To generate a complementary view of cell viability phenotypes, 7-AAD and Caspase-3/7 were used as ground truth labels forthe development of AI-based classifiers. These independent classifiers also showed high accuracy for the discrimination of cellviability phenotypes in T cells, with an AUC score of 0.976 (Figure 2).
This approach to assess cell viability states in several other cell types, including human mesenchymal stem cells (MSCs),
was also performed, demonstrating the cell-type independence and flexibility of label-free cell viability assessment using
VisionSort.

Figure 1. Live (blue), early apoptotic (green), and dead (orange) T
cell populations were defined by
the ground truth labels propidium
iodide (PI) and Annexin V. Groundtruth labeled-cells were then used totrain a label-free machine learningclassifier based only on morphological profiles by supervised machinelearning. The resulting classifier
showed excellent performance inresolving cell viability phenotypes
AUCs of 0.987 for differentiating
live (blue) and dead (orange) cells
and 0.923 for differentiating live andearly apoptotic (green) cells. a.u. =arbitrary units.

Figure 2. Classifying live versus apoptotic / dead Tcells. A supervised machine learning classifier wasused to discriminate different viability states using
7-AAD and Caspase-3/7 as ground truth labels. The
resulting label-free morphological classifier showed
high accuracy in resolving cell viability phenotypesof live (blue) and dead or early apoptotic (light green)with an AUC of 0.976. a.u. = arbitrary units.

SUMMARY
Live, dead, and apoptotic cells exhibit morphological differences that can be used to identify them. By using an AI-driven approach and developing classifiers based on ground truth labels, morphology-based classifiers with outstanding phenotypic
discrimination accuracy can be built using VisionSort. The approach enables label-free identification and sorting of cells by viability status, which has significant practical applications in the production of cell therapy products and drug discovery research.

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