# Fully automated and ultra-high-throughput platform for in-depth single-cell proteomics

> **NIH NIH R01** · BRIGHAM YOUNG UNIVERSITY · 2020 · $330,525

## Abstract

PROJECT SUMMARY/ABSTRACT
The development of effective therapies to advance human health requires an in-depth molecular-level
understanding of cellular processes and dynamic interactions between individual cells. Conventional population-
based biochemical measurements provide limited utility, as contributions from individual cells are averaged and
crucial information is lost. Direct measurements of the biochemical makeup of single cells are thus needed to
characterize cellular transitions, regulatory mechanisms and the contribution of the microenvironment. Single-
cell RNA sequencing is making a tremendous impact on biological research, but proteins mediate the bulk of
cellular function and the correlation between RNA and protein abundance is often poor. In addition, RNA
measurements are unable to inform on important posttranslational modifications that are readily measured by
mass spectrometry. Current efforts to directly quantify targeted proteins in single cells such as CyTOF and
immunohistochemistry share common shortcomings in that only a limited number of proteins can be analyzed.
There is thus an urgent unmet need for technologies capable of directly generating unbiased and in-depth single-
cell protein profiles to provide a more complete picture of cellular processes. We recently developed a proof-of-
concept platform termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples) that effectively
downscales sample processing volumes to the nanoliter scale to reduce sample losses. In combination with
ultrasensitive liquid chromatography-mass spectrometry (LC-MS), nanoPOTS enables global proteome profiling
of ~1000 protein groups in individual dissociated cells isolated by cell sorting or small regions of tissue sections
isolated by microdissection. Building upon this proof-of-concept platform, our overall objective is to develop a
fully automated prototype that yields far greater proteome coverage and throughput than is currently achievable,
providing a capability for direct, in-depth and large-scale protein quantification that is analogous to single-cell
RNA-seq. Studies in Aim 1 will focus on fully automating sample preparation and decreasing sample processing
volumes at least tenfold to further reduce sample losses and increase proteome coverage. Aim 2 will automate
sample transfer to the analytical platform and develop a fully automated and ultrasensitive LC-MS workflow with
100% MS utilization efficiency. Aim 3 will extend these advances in sensitivity, throughput and automation to the
multiplexed analysis of single cells based on barcoding with unique isobaric labels. We will combine two distinct
multiplexing approaches to enable simultaneous analysis of up to 32 samples in a single run. The completed
platform will be fully automated, capable of highly quantitative label-free and multiplexed single cell proteome
profiling to a depth of >3000 proteins per cell, and will achieve an unprecedented measurement throughput of
>300 sin...

## Key facts

- **NIH application ID:** 10034850
- **Project number:** 1R01GM138931-01
- **Recipient organization:** BRIGHAM YOUNG UNIVERSITY
- **Principal Investigator:** Ryan T Kelly
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $330,525
- **Award type:** 1
- **Project period:** 2020-09-05 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10034850

## Citation

> US National Institutes of Health, RePORTER application 10034850, Fully automated and ultra-high-throughput platform for in-depth single-cell proteomics (1R01GM138931-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10034850. Licensed CC0.

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