Sample multiplexing has been the catalyst for many recent large-scale proteomics initiatives. The advent of isobaric tagging, as popularized by iTRAQ (isobaric tags for relative and absolute quantitation) and TMT (tandem mass tag) reagents, has become the quintessential methodology for multiplexed protein expression profiling. Two major data acquisition methods exist each with its own advantages and disadvantages. First, the MS2-only method (?MS2-IDQ? herein) can identify (ID) and quantify (Q) a peptide in a single spectrum. Second, the synchronous precursor selection (SPS)-MS3 method identifies the precursor in the MS2 stage, but then selects a series of fragment ions from the MS2 stage that are fragmented further and read out as an MS3 spectrum for quantification measurements. MS2-IDQ suffers from the co-isolation and co-fragmentation of precursor ions (?interference?), and although SPS-MS3 helps to alleviate interference, it is at the expense of speed, a direct result from the acquisition of long MS3 scans. Here we aim to develop, evaluate, and apply a novel data acquisition platform that merges the benefits of current methods and alleviates their major caveats. A recent development on ThermoFisher Scientific's Orbitrap Fusion and Lumos instruments has been the implementation of an instrument application programming interface (iAPI) that allows for expanded control of the instrumentation beyond the manufacturer's built-in functionality. Using this interface, the Gygi Lab and others have begun to create custom on-the-fly real-time search (RTS) algorithms. RTS enables an MS2 spectrum to be searched in real-time and decisions to be made as to whether an MS3 scan is likely to result in a significant peptide quantification measurement. By omitting MS3 scans, more MS2 spectra can be collected and new peptides may be identified. Using the iAPI, functions can be added including targeted lists and limits set for the number of peptides quantified per protein (in the case of very abundant and/or large proteins), which is useful in translational research, such as the interrogation of plasma samples and other body fluids.
Our Specific Aims are geared toward developing further the methodology for successful application of RTS- MS3.
In Specific Aim 1, we will benchmark emerging algorithms for RTS-MS3 using both the TKO and HYPER (human-yeast peptide resource) standards for TMT-based proteome profiling.
In Specific Aim 2, we will evaluate the RTS-MS3 platform across several sample types (bacterial cultures, mouse tissues, blood, cerebral spinal fluid, human cell lines, and yeast cultures) against traditional MS2-IDQ and SPS-MS3 methods (Specific Aim 2). Finally, in Specific Aim 3 we will apply the RTS-MS3 platform to analyze an entire Yeast Deletion Strain Collection under two growth conditions, which will produce the largest yeast protein expression profiling data set to date. Accomplishing these three Specific Aims will establish the RTS-MS3 platform as a disruptive technology to current isobaric tag-based multiplexing methodology and will mark a paradigm shift in isobaric tag-based quantitative proteomics.

Public Health Relevance

Isobaric tag-based quantitative proteomics is well-accepted as a leading methodology for multiplexed protein expression profiling. Current data acquisition strategies, however, lack either accuracy or depth, both of which are essential to high-throughput protein expression profiling. Here, we develop, evaluate, and apply a new real- time database searching platform (RTS-MS3) to alleviate the caveats associated with current methods and advance the technology further to significantly impact research across all aspects of human health.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM132129-02
Application #
10018062
Study Section
Enabling Bioanalytical and Imaging Technologies Study Section (EBIT)
Program Officer
Gindhart, Joseph G
Project Start
2019-09-15
Project End
2024-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
2
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Anatomy/Cell Biology
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115