Adapting a high-throughput clinical sample pipeline for proteomic characterization of adipose tissue

• Published in: Journal of biomolecular techniques 2020-08, Vol.31 (Suppl), p.S23-S23
• Main Authors: Hutchinson, Chelsea, Piehowski, Paul, Sanford, James, Gritsenko, Marina, Hansen, Joshua, Moore, Ronald, Qian, Wei-Jun, Adkins, Joshua
• Format: Article
• Language: English
• Subjects: Poster Abstracts
• ISSN: 1524-0215; 1943-4731

Adipose tissue has historically been viewed as an energy storage tissue but is now recognized for its active role in metabolic and endocrine functions and related metabolic dysfunctions such as diabetes. Despite fresh interest, characterizing the adipose tissue proteome presents unique obstacles from a sample preparation standpoint due to its high lipid content and low protein concentration. Furthermore, adipose samples have disparate degrees of blood contamination, which skew estimates of protein yield and suppress protein identifications. Using rat white adipose, we sought to simplify sample handling, increase protein yields, and achieve greater peptide and phosphopeptide identifications by modifying our current clinical proteomics pipeline. We tested rinsing agents prior to protein extraction, compared the impact of different protein extraction formulations on protein yield and total identifications, and compared peptide yield after desalting samples with different solid phase extraction products. Using single dimensional LC-MS analysis, we identified an average of 13,000 unique peptides corresponding to 2,300 proteins per sample. After enriching for phosphopeptides with immobilized metal affinity chromatography (IMAC), we identified an average of 8,600 unique phosphopeptides from 2,800 proteins per sample. Finally, to increase coverage and quantification accuracy, we used multiplexed tandem mass tag labeling with offline fractionation. Applying these methods, we were able to quantify more than 8,900 proteins in global samples and 49,000 unique phosphorylated peptides from 6,500 proteins in IMAC-enriched samples. Representing the deepest proteome coverage from adipose reported, this study validates an improved pipeline for robust characterization of the adipose tissue proteome. Altogether, these methods improve our ability to reveal the molecular information contained in adipose tissue and apply this knowledge to enhance our understanding of human health.