Computational systems for single-cell and spatial transcriptomics experiments

The National Institute of General Medical Sciences awarded the University of Wisconsin - Madison $413,001 on April 1, 2026, under the Biomedical Research and Research Training program (CFDA 93.859) to develop computational systems for analyzing single-cell and spatial transcriptomics experiments.

The recipient will create computational methods to improve downstream analysis of gene-expression data from single-cell and spatial transcriptomics experiments, which measure genome-wide expression in tens of thousands of individual cells or across thousands of tissue spots. The work includes implementing these methods in accessible, interactive software that leverages artificial intelligence foundation models, and developing comprehensive benchmarking frameworks to evaluate AI-driven single-cell analysis tools. These deliverables aim to reduce computational barriers for biologists and clinicians without programming or data science expertise, enabling complex genomic analyses that currently outpace the field's ability to extract meaningful information from high-throughput experimental data.

This is an R35 Maximizing Investigators' Research Award (MIRA) project grant. The period of performance runs through February 28, 2031, with work performed in Madison, Wisconsin.

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.