DISEASE PROFILINGClassification of Cellular
Diversity and Disease
Assessment in ALL
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
Acute Lymphoblastic Leukemia (ALL) is a malignancy of lymphoid progenitor cells in the bone marrow and blood.
The disease has a bimodal distribution with 80% of ALL patients presenting as children who respond positively
to therapy. However, for adult patients ALL can be a devastating disease, with only 30–40% achieving long–termremission. Chromosomal abnormalities, such as the Philadelphia chromosome, are commonly found in ALL andare a major determinant of prognostic outcome. While long-term survival in pediatric ALL without chromosomal
abnormalities approaches 90%, Philadelphia chromosome positive adult ALL has a five-year survival rate of 5 –20%1. As research into the genomic determinants of ALL disease progression and response to therapy advance,
researchers require a label-free way to isolate disease-associated cells for research and development.

RESULTS
Using PBMC samples from patients with clinically confirmed cases of ALL and healthy
non-diseased donors, we developed a protocol to isolate disease associated PBMCs
from ALL based on morphological differences. Using a supervised machine learning
approach, we generated a classifier model to recognize disease associated cells in ALL.
The model differentiated PBMCs from ALL patients and healthy control well and was
reproducible across six samples with a ROC-AUC for discriminating between PBMCs
from normal healthy controls and ALL of = 0.897 +/- 0.019 and 0.958±0.012 for blast(CD45 low) and non-blast cells (CD45 high) within the ALL (Figure 1). In a parallel assessment, we used an unsupervised machine learning approach on individual cellularmorphological profiles to confirm the models ability to separate disease and non-disease cells (Figure 2). In summary, here we show application of the VisionSort platform
to analyze primary human cells in both directed and unbiased approaches to developmachine learning models for identifying ALL specific cell populations.

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Classification of Cellular Diversity and Disease Assessment in ALL

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Figure 1. Ground truth definition and classifier performance assessment. PBMCs from normal healthy controls (Normal) and ALL Patients were
stained with PKH67 (a red fluorescent dye), PKH26 (a green fluorescent dye), and CD45 (to identify blast cells), mixed, and gated to identify groundtruth populations for Normal and ALL PBMCs. The morphological profiles from gated cells were used to generate machine learning classifiers
which when tested across six independent samples showed an AUC of 0.897 +/- 0.019 for discriminating between PBMCs from normal healthy
controls and ALL and 0.958±0.012 for blast (CD45 low) and non-blast cells (CD45 high) within ALL.

Figure 2. Unbiased assessment of morphological diversity in ALL and healthy
control PBMCs. PBMCs from ALL andNormal patients were analyzed by unsupervised learning based on their unbiased
morphological profiles and plotted byUMAP. Three distinct populations of cells
can be observed, with observable differences in representation between ALL andhealthy controls.

SUMMARYREFERENCES

Here we show the application of the VisionSort platform to identify target cells fromhuman disease states. The classifier model developed in this study can be used
to identify ALL specific subpopulations without the use of surface marker labels,preserving the cells untouched for downstream R&D applications. The approachyielded a classifier model that was reproducible across different donors and yielded a low standard error. In addition, we show that unsupervised machine learning
based on cellular morphological profiles can be used to confirm the identification ofdisease specific cells as well as aid in the discovery of novel disease cell subpopulations in ALL.

1. https://pubmed.ncbi.nlm.nih.gov/8625065/

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