Development of a high-throughput proteomic, transcriptomic, and metabolomic platform for multiomic profiling of same single-cells

The National Institute of General Medical Sciences awarded Battelle Memorial Institute's Pacific Northwest National Laboratory $492,167 on August 15, 2026, under the Biomedical Research and Research Training program (CFDA 93.859) to develop a high-throughput multiomic platform for simultaneous single-cell profiling of proteins, transcripts, and metabolites.

The platform integrates novel chemistry, advanced mass spectrometry, and automated nanodroplet processing to analyze thousands of individual cells. Central innovations include masked amino tags that double multiplexing capacity in mass spectrometry to enable analysis of up to 70 single cells per run, high-density nanodroplet arrays, automated droplet splitting for same-cell mRNA transcript capture, and ion-mobility-enhanced LC-MS for isobaric-labeled proteomic and metabolomic measurements. The project will deliver two new R packages—METABOR for metabolite quantification and COVARR for cross-modality covariation analysis—to integrate transcript, protein, and metabolite data. The platform will be applied to human peripheral blood mononuclear cells and pancreatic islet cells, with focus on amino acid signaling in immune activation and diabetes pathogenesis.

Work is performed in Richland, Washington. The period of performance runs from August 15, 2026, through May 31, 2031.

Abstract

Systems biology seeks to build predictive models of cell behavior by integrating information across multiple molecular layers. However, many models rely on bulk measurements, which obscure underlying regulatory mechanisms at the single-cell level. While transcriptomic assays are increasingly routine in single-cell experiments, global single-cell proteomics and metabolomics remain technically limited, preventing complete multiomic analysis of the same cell. The proposed work addresses this GAP through the development of a high- throughput platform that combines novel chemistry, advanced mass spectrometry, and automated nanodroplet processing to simultaneously profile proteins, transcripts, and metabolites from thousands of individual cells. Central to this effort is the introduction of masked amino tags, a chemical innovation that doubles the multiplexing capacity of mass spectrometry analyses, enabling up to 70 single cells to be analyzed in a single run. This chemistry will be paired with new high-density nanodroplet arrays, automated droplet splitting for same-cell mrna transcript capture, and ION-mobility-enhanced LC-MS for isobaric-labeled proteomic and metabolomic measurements. Parallel informatic advances will include two new r packages: metabor for metabolite quantification and covarr for cross-modality covariation analysis, providing unified integration of transcript, protein, and metabolite data. This platform will be applied to human peripheral blood mononuclear cells and pancreatic islet cells, with a focus on amino acid signaling in immune activation and diabetes. By generating deep, multiomic single-cell datasets, the project will advance both fundamental biology and translational research. The resulting technology will accelerate the development of predictive single-cell models, provide powerful new tools for systems biology, while creating broad opportunities for studying cellular heterogeneity in health and disease.