A technology capable of generating robust protein data across various biological states, with the sensitivity and coverage available to next-generation sequencing, would drastically change our understanding of cellular proteomes and ability to detect rare proteins in limited samples. Mass spectrometry is a powerful tool for proteomics. However, it suffers from limited sensitivity (>106 molecules required) preventing the identification of low-abundance proteins and single-cell proteomics. A high-throughput single-molecule protein identification method remains a key technical challenge for the proteomic community. Addressing this challenge will dramatically improve the ability to discover and assay novel biomarkers, with transformative impact in our understanding of cancer, immunology and brain research. We propose a robust high-throughput strategy for single-molecule protein identification. This approach will be based on our recent technological breakthrough on developing the highly multiplexed (10-plex; Nature Methods 2014), precisely quantitative (>90% precision and accuracy; Nature Methods 2016), and ultra-high resolution (sub-5 nm; Nature Nanotechnology 2016) DNA-PAINT super-resolution imaging method. Using DNA-PAINT to image a DNA-barcoded and stretched protein will provide a unique optical signature for accurate identification of any proteins in a complex mixture. This method will enable parallel identification of proteins with single-molecule sensitivity, resulting in broadly transformative impacts on fundamental and translational biomedical studies.

Public Health Relevance

High-throughput single-molecule protein identification approaches would enable single-cell proteomics, and benefit the development of new and robust biomarker panels, which would improve the early detection of the onset of cancer and neurodegeneration. We propose to solve this challenge by leveraging methods from the fields of super-resolution imaging and DNA nanotechnology. Our project will enable proteomic studies to match the high sensitivity of next-generation sequencing.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
1DP1GM133052-01
Application #
9555222
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sammak, Paul J
Project Start
2018-09-01
Project End
2023-07-31
Budget Start
2018-09-01
Budget End
2019-07-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Harvard University
Department
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code