D R U G D I S C OV E R YLabel-Free High Throughput
CRISPR-Based Screeningfor Morphological Phenotypes
in Flow
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
The development of CRISPR-Cas9 genome editing technology has led to the emergence of a new generation ofnovel life sciences applications.1,2 In drug discovery, researchers have harnessed the precision of selective geneknockouts by CRISPR to enable genome-wide drug screening. By mapping genotypes to phenotypes, CRISPR-based
phenotypic screens can enable a better understanding of drug mechanism of actions (MOAs) and identification ofnovel druggable targets. However, current phenotypic CRISPR screening approaches rely heavily on microscopic
imaging of target phenotypes, a process that imposes throughput limitations and restricts screening to only
a handful of simple phenotypes based on binary fluorescence signals. To fully realize the potential of CRISPRbased phenotypic screening, here show application of the VisionSort platform to a pooled high throughput, labelfree CRISPR screening methodology targeting morphological phenotypes. As a proof-of-concept, we used thismethodology to screen for genes involved in macrophage polarization.

RESULTSTo rapidly screen for genes involved in complex phenotypes, a workflow for CRISPR-based pooled screening in flow was developed (Figure 1).3 In this implementation, macrophages derived from THP-1 cells expressing Cas9 protein were transduced with pooled CRISPR lentiviral libraries containing 7,290 sgRNAs targeting 279
kinase genes for loss-of-function gene sets to create a pooled knockout cell library. Amachine learning classifier was trained to identify different macrophage polarization
states. For training the machine learning model, M0 and M1 macrophages were separately prepared. After staining only the M0 macrophages with a fluorescent marker
to define a ground truth phenotype, the two populations were mixed. Together with

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Label-Free High Throughput Crispr-Based Screening for Morphological Phenotypes in Flow

2 OF 2fluorescent ground truth definitions, label-free morphological profiles for individual cells were generated by Ghost
Cytometry and used to train a machinelearning classifier on VisionSort. The resulting classifier was able to distinguishbetween the two polarized macrophage
populations with an AUC score of 0.89
(Figure 2).
Next, using test samples, we screenedfor cells exhibiting inhibition of M1 polarization following CRISPR-based geneknockouts. Cells with the target phenotype were identified and sorted using
the pre-trained classifier and processedfor downstream gRNA readouts. By analyzing the enrichment of gRNAs aftersorting, we identified several genes, including potentially novel mediators, that
influence M1 macrophage polarization(Figure 2).

Figure 1. General workflow for pooled high-content CRISPR screening for complex phenotypes in flow. A gene-knockout cell library prepared using the CRISPR-Cas9 gene editingplatform is treated with a compound or other reagents to induce the desired phenotype. A
pre-trained machine learning model selectively enriches cells with the target phenotype fordownstream analysis of target genes involved. In this proof-of-concept, sgRNA regions inserted in genomic DNA of target cells were amplified and sequenced by NGS to determine theenriched/depleted genes involved in macrophage polarization. For live cell analysis, proteomic, transcriptomics, and cell-based functional assays are also possible.

SUMMARY
We developed a novel high throughput
CRISPR screening method and evaluated the approach using a complex morphological phenotype.3 The approach
can be adapted to screen for a wide
range of simple molecular and complex
phenotypes, giving researchers a new
powerful tool for drug screening.

REFERENCES
1. https://pubmed.ncbi.nlm.nih.gov/28333914/
2. https://pubmed.ncbi.nlm.nih.gov/25430774/
3h ttps://www.biorxiv.org/content/10.1101/202
3.01.26.525784v1

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Figure 2. Validation of phenotype detection and screening. Bright-field images of THP-1 derived M0 and M1 macrophages in flow were obtained with a commercial imaging flow cytometer. Scale bars = 10 um. A mixed population of M0 and M1 polarized macrophages was used
to train a machine learning classifier on VisionSort. The classifier achieved excellent performance for the detection of the target phenotype with an AUC of 0.89. sgRNAs from sorted cells
were sequenced to identify enriched genes involved in the target phenotype of macrophage
polarization. Volcano plot visualization of statistical significance (y-axis) and magnitude of thechange (x-axis) before and after the cell sorting. Statistical significance was calculated withMann-Whitney U test. Dashed lines: cutoff for hit genes (FDR = 0.01).

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