# Correlating mechanical and genetic data at high-throughput and single cell levels to investigate metastasis > **NIH NIH F31** · GEORGIA INSTITUTE OF TECHNOLOGY · 2020 · $45,520 ## Abstract Metastasis is the cause of 90% of cancer-related deaths, a statistic that has changed little over the past 50 years. During that time, cancer researchers have recognized that changes in cells’ mechanical phenotypes dictate their ability to generate force, invade through tissues and migrate throughout the body. Several studies have implicated that as a cell’s metastatic potential increases, cell stiffness decreases. More generally, the relationship between disease state and cell mechanics suggests that changes in cell stiffness are correlated with phenotypes of invasiveness, migration, epithelial-to-mesenchymal transition (EMT), and metastasis and are controlled through various cell-signaling networks. Yet while certain genes that affect cell mechanics have been studied, a genome-wide study of genes and gene networks that modulate cell biophysical properties has not been attempted. The use of genome-wide CRISPR knockout (GeCKO) pooled screens has allowed researchers to start exploring the connection between a cell’s genotype and various phenotypes. To understand gene networks that control cell mechanics and their role in metastatic potential, we will need to uncover the genetic molecular mechanisms that allow cells to change their mechanical properties to successfully form a metastatic tumor. The long-term goal of this research is to understand the molecular and mechanical mechanisms driving metastasis that will lead to the discovery of new diagnostics and therapeutic targets to find and stop key processes of metastatic cells. To reach this goal, we will leverage a novel microfluidics approach for cell sorting based upon biophysical properties for the high-throughput discovery of genes linked to cell mechanics and metastasis. We will use this approach to determine how cellular mechanics are regulated within the context of networks of cytoskeletal and structural proteins in addition to various transcription factors and signaling proteins associated with increased metastatic potential. I will investigate this intersection with the following aims: 1) Identify genes related to mechanical changes in cancer cells through GeCKO high- throughput mechanical screen and 2) Validate phenotypic and mechanotypic importance of genes of interest. We hypothesize that there is a link between cell softening and mesenchymal, migratory phenotypes that is controlled by the expression of a network of genes of interest. The proposed studies will represent the first attempt to evaluate the entire genome for its role in directing cell mechanics to understand the connection between cancer cell genotype, phenotype and mechanotype both across the whole genome and on the single cell level. Being able to collect information about the combination of gene expression data and cell stiffness measurements in a high throughput fashion and an in-depth exploration of genes related to mechanics and metastatic potential will lead to great advancement of our understanding of the metasta... ## Key facts - **NIH application ID:** 9969043 - **Project number:** 5F31CA243345-02 - **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY - **Principal Investigator:** Katherine M Young - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $45,520 - **Award type:** 5 - **Project period:** 2019-07-01 → 2021-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9969043 ## Citation > US National Institutes of Health, RePORTER application 9969043, Correlating mechanical and genetic data at high-throughput and single cell levels to investigate metastasis (5F31CA243345-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9969043. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*