MassIVE MSV000085465

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Analysis of differential strategies to enhance detection of low abundance proteins in the bovine serum proteome

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Description

Serum-based biomarkers hold propitious applications for addressing livestock health, management, and welfare challenges. However, discovery of protein biomarkers in complex biological fluids like serum is wholly intractable due to the large dynamic range of protein concentrations; that is, ~10 abundant proteins constitute >90% of the total protein content. Consequently, the detection of lower-abundant serum proteins that may be valuable biomarkers is effectively masked in proteomic detection. To overcome this barrier, specific tools/methods have been developed for enriching and identifying low-abundance proteins/biomarkers in human samples. In species like cattle for which such tools do not exist, the detectable serum proteome remains seven-fold lower and biased towards high-abundance proteins, precluding possibilities of biomarker identification. Towards addressing this limitation, we test a continuous elution size-based fractionation method, and two approaches that use affinity interaction-based separation of proteins in preparing bovine serum, and compare LC-MS/MS protein identification to neat serum. Our results identify the high-abundance proteins in bovine serum, and demonstrate dynamic range compression and improved protein identification with the different enrichment methods. Although these findings indicate the highest protein number identified in bovine serum (444 proteins, all methods combined), and by any single sample processing method (312 proteins) till date, they remain lower than thresholds deemed necessary for biomarker discovery. As such, this investigation also revealed the limitations to resolving the bovine serum proteome, and the need for species-specific tools for depleting high-abundance proteins. In concert, this study represents a step towards advancing sample preparation methods for bovine serum biomarker identification. [doi:10.25345/C5VX2B] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: serum ; bovine

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Principal Investigators:
(in alphabetical order) 		Vimal Selvaraj, Cornell, USA
Submitting User: awherren
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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.

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