ERI: Integrating Non-Viral CRISPR Gene Activation and Deep Learning for Biomanufacturing Precision Stem Cell Products

Cell engineering often focuses on a small number of genes. CRISPR technology has helped sharpen that focus. The secondary effects of cell engineering are more widespread and difficult to anticipate or measure. These effects are critical to efforts to design and optimize biomanufacturing processes and to produce safer therapeutic cells. The main objective is to understand the impact of gene activation on stem cell metabolism. Outreach to increase retention of students from underrepresented and at-risk backgrounds will strengthen the STEM career workforce. CRISPR application to stem cell biomanufacturing is constrained by suboptimal delivery vectors. These vectors cause unexpected off-target effects. Preliminary data demonstrates the potential of CRISPR-mediated activation (CRISPRa) to upregulate key regulatory genes of mesenchymal stromal cells (MSCs). A fundamental gap in knowledge remains: the effect of specific gene upregulation on the gene network interactions that dictate the completion of complex cellular functions such as differentiation. The goal is to integrate non-viral CRISPRa and artificial intelligence to understand the impact of cell manipulation and gene activation on stem cell differentiation. Two specific objectives are proposed: (1) Decouple the effects of cell transfection and CRISPRa on MSC therapeutic capacity, and (2) bridge the gap between single-gene manipulation and complex cellular behaviors by predicting the downstream effects of CRISPRa on gene expression profiles. The completion of these objectives will be fundamental for establishing standardized high-throughput protocols to enhance the therapeutic output of engineered stem cell products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.