Computational systems for single-cell and spatial transcriptomics experiments

About This Grant

Abstract This R35 Maximizing Investigators’ Research Award (MIRA) proposal aims to develop computational systems to address critical challenges in single-cell and spatial transcriptomics experiments. Single-cell and spatial transcriptomics experiments allow us to measure genome-wide gene expression in tens of thousands of individual cells (e.g. from blood, healthy tissue, tumors, or other diseased tissue) or across thousands of tissue spots. As a result, they have emerged as revolutionary tools that allow us to address scientific and clinical questions that were elusive just a few years ago. Computation has likewise been transformed by developments in artificial intelligence (AI). In spite of incredible advances, our ability to obtain genomic measurements continues to outpace our ability to derive useful information from them. This MIRA proposal addresses some of the most critical challenges that are currently limiting the pace at which the scientific community can turn valuable data from high-throughput genomic experiments into meaningful results. In particular, we plan to develop powerful and efficient computational methods to improve the downstream analysis of data from single-cell and spatial transcriptomics experiments. To reduce barriers for non-experts, the methods we develop will be implemented in accessible, interactive software that exploits the power of AI foundation models. Comprehensive benchmarking frameworks will also be developed so that our models and others can be appropriately evaluated. The proposed methods, software, and benchmarking frameworks are required to improve the use of AI single-cell foundation models in genomics, to maximize the information obtained from powerful single-cell and spatial transcriptomics experiments, and to enable biologists and/or clinicians to perform complex analyses without expertise in programming or data science.