D R U G D I S C OV E R Y

Intracellular Protein Aggregation
as a High Content Readout in FlowBased Phenotypic Drug Discovery
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
Phenotypic drug discovery (PDD) is a powerful approach for identifying new drug targets and elucidating diseasemechanisms. Current approaches for PDD mainly rely on either microscopy-based or flow cytometry-based technologies
for analyzing readouts. Microscopy-based screening platforms are well established, but are limited by their low throughput
and resource-intensive infrastructure. On the other hand, conventional flow-based screening platforms allow for higher
efficiencies and large-scale screening, but only work for a limited number of simple phenotypic readouts with definedmarkers. For many diseases, including cancer, infectious diseases, neurodegenerative diseases, etc., complex phenotypes
that involve morphological transformation or intracellular dynamics are important readouts for drug screening.

Here we introduce a new approach for flow-based high content phenotypic screening
by combining ThinkCyte’s AI-enabled VisionSort platform powered by Ghost Cytometry (GC) with PhoreMost’s PROTEINi® technology, targeting intracellular protein aggregation as the target readout. GC is a novel flow cytometry technology that enables
analysis and sorting of cells based on both label-free morphological features and conventional fluorescence parameters with high intracellular spatial resolution. PROTEINi is capable of probing the entire proteome to systematically unmask new and unanticipated druggable sites, directly linking them to useful therapeutic functions. Whenused together, GC and PROTEINi have the potential to rapidly screen for a range ofnew, complex phenotypes and unmask truly novel drug targets for treating diseases.

Visit ThinkCyte.com to learn
more about VisionSort,
the world’s first dual mode,
AI-driven cell characterizationand sorting platform.

Intracellular Protein Aggregation as a High Content Readout in Flow-Based Phenotypic Drug Discovery2 OF 3

Figure 1. Workflow for unsupervised classification and isolation of target cells using the VisionSort platform. Morphological information of each cellis acquired as a compressed temporal waveform (GMI waveforms) using Ghost Cytometry. Individual cells were mapped onto a two-dimensional space
using dimensional reduction methods on the waveforms for visualization and identification of subpopulations of interest. Target subpopulations weregated in the UMAP space and used to create classification models. Target cells isolated by this approach can be used for downstream analysis, such asmulti-omics analyses and functional assays.

Figure 2. Unsupervised learning approach & sorting validation. MCF10a cell lines expressing Dox-inducible GFP-tagged proteins were mixed indifferent proportions of aggregation and diffuse phenotypes (Mix A in green and Mix B in magenta). Mix B contained two distinct forms of the diffuse
phenotype (Diffuse 1 and 2). Unsupervised machine learning classification of the cell populations was performed on VisionSort. Multiple cell populations
were identified in the UMAP space as observed by distinct clustering patterns. Target cells based on phenotype (red gate for the aggregated phenotype)were identified by gating, collected for enrichment and validated using NGS readouts of DNA barcodes.

Intracellular Protein Aggregation as a High Content Readout in Flow-Based Phenotypic Drug Discovery

3 OF 3RESULTS

SUMMARY

We prepared an MCF10a cell line model that expressed Dox-inducible aggregation of GFP-tagged proteins. Cells were labeled with unique DNA barcodes based on their intracellular
protein aggregation phenotypes, either diffuse or aggregated, and were mixed together for analysis via GC (Figure 1). To
identify and classify the different phenotypes, we captured theintracellular spatial distribution of the GFP signals as an optical waveform and performed dimensionality reduction using
UMAP to visualize and identify unique clusters represented by
the phenotypes. 20 million cells were analyzed and sorted in 2
hours based on their UMAP clustering with maintenance of cell
viability and integrity. Downstream deep sequencing analysis
validated that the enriched cells indeed contained DNA barcodes for the aggregated phenotype (Figure 2).

VisionSort was able to identify and differentiate cells expressing different intracellular protein aggregation phenotypes inhigh throughput. The approach was able to screen cells withhigh purity and maintain viability for downstream analysis andvalidation, demonstrating the applicability of VisionSort forhigh content phenotypic screening drug discovery programs.

ACKNOWLEDGEMENTS
This work was performed in collaboration with PhoreMost Ltd. For more information on PhoreMost and their SITESEEKER®
drug target identification platform visit phoremost.com.

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