MassIVE MSV000096453

Partial Public PXD058001

Depletion of Abundant Plasma Proteins in Human Serum via in situ Protein@ZIF-8 Synthesis for Integrated Proteomics

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Description

Protein biomarkers in human serum provide critical insights into various physiological conditions and diseases, enabling early diagnosis, prognosis, and personalized treatment. However, detecting low-abundance protein biomarkers is challenging due to the presence of highly abundant proteins that make up ~99% of the plasma proteome. Here, we report for the first time the use of in situ metal-organic-framework (MOF) growth in serum to effectively deplete highly abundant serum proteins for integrated proteomic analysis. Through biomolecule-mediated nucleation of a zeolitic imidazolate framework (ZIF-8), abundant plasma proteins are selectively encapsulated within ZIF-8 and removed from serum via centrifugation, leaving a depleted protein fraction in the supernatant. Bottom-up proteomics analysis confirmed significant depletion of the topmost abundant proteins, many at depletion levels exceeding 95%. Such depletion enabled the identification of 277 total proteins in the supernatant (uncaptured) fraction in a single-shot analysis, including 54 uniquely identified proteins, 12 drug targets, and many potential disease biomarkers. Top-down proteomics characterization of the captured and uncaptured protein fractions at the proteoform-level confirmed this method is not biased toward any specific proteoform of individual proteins. These results demonstrate that in situ MOF growth can selectively and effectively deplete high-abundance proteins from serum in a simple, low-cost, one-pot synthesis to enable integrated top-down and bottom-up proteomic analysis of serum protein biomarkers. [doi:10.25345/C52F7K34K] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: metal-organic framework ; human serum proteomics ; protein separation ; bottom-up proteomics ; top-down proteomics ; biomarker discovery ; depletion technology ; DatasetType:Proteomics

Contact

Principal Investigators:
(in alphabetical order) 		Ying Ge, University of Wisconsin-Madison, United States of America
Submitting User: rubyhjchan
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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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