# Next-generation spatial -omics: High-throughput, single-molecule proteomic imaging with subcellular resolution

> **NIH NIH R01** · CALIFORNIA INSTITUTE OF TECHNOLOGY · 2023 · $3,702,779

## Abstract

PROJECT SUMMARY
Deep proteomic and metabolomic profiling of biological tissues is an overarching goal of modern biological and
biomedical research. It remains a key – and conspicuous – missing component from the full spectrum of -omics
that mainly capitalize on next-generation sequencing of DNA and RNA. Today's existing methodologies for
proteomic and metabolomic analysis currently lag far behind the capabilities of tools for genomics and
transcriptomics, both in terms of depth-of-coverage and throughput. The proposed effort will meet this challenge
by advancing revolutionary new methods for label-free single-molecule proteomic and metabolomic profiling and
combining them with novel methods for sub-cellular spatial analysis. Protein concentrations within a mammalian
cell span ~8 orders of magnitude; in human blood serum this increases to ~11 orders. Yet, within these
immensely complex milieus, even the most sparsely expressed proteins are important. Cellular signaling, gene
regulation, early responses to exogenous biological stimuli, and disease onset all generally result in the
expression of small copy numbers of proteins. It is thus essential both to discover rare cellular proteins and to
attain holistic proteomic maps – but these goals remain far beyond present technological capabilities. Further,
deciphering the instantaneous state of an organism's proteome – and, especially, observing its post-translational
modifications (PTMs) as they dynamically evolve in response to cellular function, stress, and disease – will
provide transformational knowledge for many fields.
Deep proteome discovery will tackle the cellular proteome's complexity, allowing identification of proteins over
its entire dynamic range of concentration – from the most prolifically expressed cellular proteins to those only
sparsely expressed with a few copies per cell. This project's success will enable deep spatial profiling of the
cellular proteome and metabolome with high throughput and, thereby, discovery of rare cellular proteins and
metabolites. It will fundamentally change the resolution of protein analysis down to the level of individual
molecules in subcellular compartments. Its achievement will complete the constellation of single-cell -omics,
thereby broadly advancing research worldwide in fields that span from fundamental biology to the frontiers of
clinical medicine.
In the proposed effort, existing and well-validated techniques for spatially-resolved tissue sampling will be
pushed downward into the sub-cellular realm. Scaling these methods downward is feasible now solely because
of the single-molecule resolution of the proposed approach. This project builds upon a significant body of recent
efforts focused upon creating instrumentation for deep profiling of the single-cell proteome. The effort proposed
here will further advance these achievements – and will incorporate high-resolution tissue-sampling methods to
deliver, with minimal loss, biological analytes to in...

## Key facts

- **NIH application ID:** 10695792
- **Project number:** 1R01MH136394-01
- **Recipient organization:** CALIFORNIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** MICHAEL L ROUKES
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $3,702,779
- **Award type:** 1
- **Project period:** 2023-09-18 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10695792

## Citation

> US National Institutes of Health, RePORTER application 10695792, Next-generation spatial -omics: High-throughput, single-molecule proteomic imaging with subcellular resolution (1R01MH136394-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10695792. Licensed CC0.

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