D R U G D I S C OV E R YHigh Throughput CRISPRBased Phenotypic Screening
in Flow for Complex
Intracellular Phenotypes
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 of novellife sciences applications1. In drug discovery, researchers have harnessed the precision of selective gene knockouts
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 of novel druggabletargets. 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 simplephenotypes by binary fluorescence signals. To fully realize the potential of CRISPR-based phenotypic screening,
here show application of the VisionSort platform to a pooled high throughput CRISPR screening methodology
targeting nuclear translocation as the target phenotype. As a proof-of-concept, we show a small scale screen toidentify genes involved in regulating a well-characterized nuclear translocation pathway.

RESULTS
THP-1 cells expressing Cas9 protein were transduced with pooled CRISPR lentivirallibraries containing 60 gRNAs for loss-of-function gene sets to create the pooled
knockout cell library. The cell library was treated with LPS, a TLR4 agonist known
to induce nuclear translocation of NF-kB2 (Figure 1). Cells exhibiting nuclear NF-kB
and cells showing cytoplasmic NF-kB were mixed and used to generate a machinelearning classifier on VisionSort. The resulting classifier was able to distinguishbetween the two populations with an AUC score of 0.987 (Figure 2).
Next, using test samples, cells exhibiting cytoplasmic NF-kB were identified andsorted using the classifier and processed for downstream gRNA readouts. Enrichment of gRNAs relating to genes known to be involved in the TLR4 signaling pathway including MYD88, MAP3K7, IRAK4 and IKBKB was observed, demonstrating theutility of the approach (Figure 3).

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High Throughput Crispr-Based Phenotypic Screening in Flow for Complex Intracellular Phenotypes

2 OF 2Figure 1. Workflow for pooled phenotypic CRISPR screening with VisionSort. A
gene-knockout cell library was prepared via
the CRISPR-Cas9 system with a lentiviralgRNA library containing 60 gRNAs. Cells
were stimulated with LPS to induce nuclear
translocation NF-kB and then analyzed
and sorted with VisionSort. Cells exhibiting
cytoplasmic NF-kB (phenotype positive) as
the target phenotype were collected from thepool and processed for downstream readoutsof gRNAs by NGS.

Figure 2. Validation of phenotype detection. Nuclear translocationof NF-kB in THP-1 cells induced by LPS stimulation was visualized
and confirmed using fluorescence microscopy. A mixed populationof cells was used for training the classifier. The classifier achieved
outstanding performance for the detection of the target phenotype
with an AUC of 0.987.

Figure 3. Biological validation of phenotype detection. gRNA fromsorted cells were sequenced to identify enriched genes involved in thetarget phenotype (nuclear translocation of NF-kB (green box)). As confirmation of the approach, genes known to be involved in the TLR4 signaling pathway upstream of NF-kB nuclear translocation (e.g., MYD88(red box), IKBKB (blue box)) were enriched in the phenotype positive
fraction. Volcano plot visualization of statistical significance (y-axis) andmagnitude of the change (x-axis) in gGNA enrichment before and aftercell sorting. Statistical significance was calculated with Mann-Whitney
U test. Dashed lines: cutoff for ‘hit’ gene calls (FDR = 0.01). Images
and nuclear translocation scores were taken and calculated using theFlowSight system (Amnis).

SUMMARYREFERENCES

We used VisionSort to perform a novel high throughput CRISPR screening approach and show validation using a simple,
well-characterized phenotype3. The approach can be adapted
to screen for more complex phenotypes requiring intracellular spatial resolution, giving researchers a powerful new toolfor drug screening.

1. https://www.science.org/doi/10.1126/science.1231143

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2. https://pubmed.ncbi.nlm.nih.gov/16924467/
3. https://www.biorxiv.org/content/10.1101/2023.01.26.525784v1

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