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| Funder | NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES |
|---|---|
| Recipient Organization | State University New York Stony Brook |
| Country | United States |
| Start Date | Feb 01, 2024 |
| End Date | Jan 31, 2029 |
| Duration | 1,826 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10763760 |
SUMMARY: MIRA Title: Advanced Single-Cell Protein Analysis with Multiplex in Situ Tagging Array Technology Single-cell analysis has been the essential approach in understanding cellular machinery, organism development, and disease mechanisms. Proteins are naturally one of the main targets in single-cell analysis since proteins are
responsible for nearly every task for the cellular life. Unfortunately, proteins are not amplifiable like DNA, and thus the minuscule amount of proteins on single cells poses a great challenge to the detection techniques. To date, there is still no single-cell proteome technology available yet that has sufficient sensitivity and scale. We
have innovated a few single-cell protein detection techniques on an ultrahigh-density multiplex in situ tagging (MIST) array which demonstrates its power for not only analyzing the full spectrum of function proteome in animal and human cells, but also rapidly detecting cluster of surface proteins on smaller microbial pathogens. The MIST
array is a large-scale monolayer of small-size microbeads with a density much higher than most genome chips, and the microbeads carry various probes for protein detection. The single-cell MIST (scMIST) technology has exhibited it ability to quantify in single cells up to 465 functional proteins, which is the highest multiplexity assay
and the most comprehensive mapping of T cell protein markers. The smaller scale of the assay proved its utility in prediction of sepsis outcomes by analyzing large quantity of primary samples. In the proposed research, we will extend scMIST to spatially analyze solid tissue specimens by quantifying ~500 functional proteins to
thoroughly assess cellular features, physiological status and functions within a tissue microenvironment. In addition, we will optimize the scMIST and combine it with artificial intelligence algorithms to precisely prognose disease occurrence and progression. Last, an explosion-like biomolecular chain reaction will be developed on
the MIST array to rapidly detect few protein copies on single bacteria or viral particles by specifically amplifying signals for millions to billions of times, with the goal of visually detecting single pathogens within a minute. The implementation of these advanced single-cell protein detection technologies will catalyze the revolution of
biomedical sciences and fundamentally enhance the precision of disease prognosis. Visual detection of single pathogens within a minute will bring enormous opportunities in broadly many areas concerned with pathogens.
State University New York Stony Brook
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