High-throughput Genome Wide CRISPR Knock Out mechanical sort identifies genes driving metastatic cancer cell softening

Abstract

Cell mechanics can serve as an important biomarker for cell state and phenotype, such as metastatic ability. While some molecular mechanisms underlying cell mechanical properties have been investigated through targeted analyses, a genome-wide study of human genes and gene networks that modulate cell biophysical properties has not been attempted. In this work, we combined a microfluidic stiffness-based sorting device with a genome-scale CRISPR knockout (GeCKO) screen in order to investigate the effect of individual gene knockouts on cell stiffening and cell softening across the entire protein-coding genome. We processed approximately 150 million Cas9-expressing ovarian cancer cells that had been transduced with a library of 76,000 single guide RNAs (sgRNAs) against the 19,000 protein-coding genes in the genome. The cells were sorted into 5 mechanical subsets. We identified 7 gene knockouts that were significantly depleted in the softer subsets and over 700 gene knockouts that were significantly enriched in the stiffer subsets. Of these significant genes of interest, we selected 3 genes that were highly expressed in our ovarian cancer cell line with greater than 100-fold enrichment in the stiff outlet and resulted in significant changes in ovarian cancer patient survival. These genes, PIK3R4, CCDC88A, and GSK3B, when knocked out result in a significant and predicted increase in cell stiffness. This study is the first to explore the relation between human gene expression and cell mechanics at the genome-scale to generate datasets at the intersection between cell genotype, mechanotype, and phenotype for metastatic cancer cells. The method could also be applied to study the effect of genes on other biophysical cell processes as well as for identifying pathways for the control of cellular mechanics across many cell types.