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Small-Scale Serial Size Exclusion Chromatography (s3SEC) for High Sensitivity Top-Down Proteomics of Large
Holden T Rogers1, Jake A Melby1, Lauren E Ehlers1
1Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Analytical Chemistry
|February 5, 2024
Summary
A new small-scale serial Size Exclusion Chromatography (s3SEC) method enhances top-down mass spectrometry sensitivity for analyzing large proteoforms in minimal samples. This technique improves detection of high molecular weight proteoforms previously missed in standard analyses.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Top-down mass spectrometry (MS) is crucial for proteoform analysis, including mutations and modifications.
- Challenges include analyzing large proteoforms and low sample sensitivity.
- Existing methods struggle with high molecular weight (MW) proteoforms and limited sample amounts.
Purpose of the Study:
- To develop a sensitive method for analyzing large proteoforms from minimal samples.
- To overcome limitations in current top-down proteomics approaches.
- To improve the detection of high MW proteoforms.
Main Methods:
- Developed small-scale serial Size Exclusion Chromatography (s3SEC) coupled with online RPLC-MS/MS.
- Utilized small-scale protein extraction (1 mg tissue) and serial SEC without postfractionation.
- Employed high-sensitivity capillary reversed-phase liquid chromatography tandem MS (RPLC-MS/MS).
Main Results:
- s3SEC-RPLC-MS/MS significantly enhanced sensitivity and reduced proteome complexity.
- Enabled detection of high MW proteoforms previously undetectable by 1D-RPLC.
- Observed improved signal intensity for high MW proteoforms and maintained proteoform abundance for lower MW species.
Conclusions:
- The s3SEC-RPLC-MS/MS method effectively analyzes large proteoforms from limited samples.
- This approach offers improved sensitivity and detection capabilities for high MW proteoforms.
- The method shows broad applicability to various biological systems with minimal sample input.
