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Michael Olivier
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Recent Grants
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North Carolina Diabetes Research Center
Open Date: 2020-04-07
Close Date: 2025-03-31
Grant: Close
Integrated Omics Analysis of Pain: Omics Data Generation Center
Open Date: 2019-09-01
Close Date: 2023-07-31
Grant: Close
Womb to Tomb: Developmental Programming and Aging Interactions in Primates
Open Date: 2018-09-30
Close Date: 2023-05-31
Grant: Close
HyCCAPP: A new method for the functional analysis of regulatory SNPs
Open Date: 2018-09-18
Close Date: 2022-08-31
Grant: Close
Functional characterization of regulatory sequence variants in complex diseases
Open Date: 2014-07-24
Close Date: 2019-06-30
Articles (6)
Year:
2026
Year:
2026
Year:
2025
The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low abundances in the plasma. Accurate detection and quantification of low abundance proteins by standard mass spectrometry approaches remain challenging. In addition, it is difficult to link low abundance plasma proteins back to their specific tissues or organs of origin with confidence. To address these challenges, we developed a mass spectrometry approach based on the use of tandem mass tags (TMT) and a tissue reference sample. By applying this approach to nonhuman primate plasma samples, we were able to identify and quantify 820 proteins by using a kidney tissue homogenate as reference. On average, 643 ± 16 proteins were identified per plasma sample. About 58% of proteins identified in replicate experiments were identified both times. A ratio of 50 μg kidney protein to 10 μg plasma protein, and the use of the TMT label with the highest molecular weight (131) for the kidney reference yielded the largest number of proteins in the analysis, and identified low abundance proteins in plasma that are prominently found in the kidney. Overall, this methodology promises efficient quantification of plasma proteins potentially released from specific tissues, thereby increasing the number of putative disease biomarkers for future study.
Year:
2023
Year:
2022
Year:
2022
Collaborators (2)
Sujoy Ghosh
Professor and Director, Bioinformatics and Computational Biology
Pennington Biomedical Research Center
Xuewen Wang
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