Automation of PRM‐dependent D3‐Leu tracer enrichment in HDL to study the metabolism of apoA‐I, LCAT and other apolipoproteins

We developed an automated quantification workflow for PRM‐enabled detection of D3‐Leu labeled apoA‐I in high‐density lipoprotein (HDL) isolated from humans. Subjects received a bolus injection of D3‐Leu and blood was drawn at eight time points over three days. HDL was isolated and separated into six...

Full description

Saved in:
Bibliographic Details
Published in:Proteomics (Weinheim) 2017-01, Vol.17 (1-2), p.np-n/a
Main Authors: Lee, Lang Ho, Andraski, Allison B., Pieper, Brett, Higashi, Hideyuki, Sacks, Frank M., Aikawa, Masanori, Singh, Sasha A.
Format: Article
Language:English
Subjects:
Apolipoprotein
Apolipoprotein A-I - analysis
Apolipoprotein A-I - chemistry
Automation
Bioinformatics
Computational Biology - methods
Endogenous labeling
Humans
Ion peak intensity
Lipoproteins, HDL - analysis
Lipoproteins, HDL - chemistry
Mass Spectrometry - methods
Metabolism
Neutron encoded mass signatures
Noise removal
Targeted mass spectrometry
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We developed an automated quantification workflow for PRM‐enabled detection of D3‐Leu labeled apoA‐I in high‐density lipoprotein (HDL) isolated from humans. Subjects received a bolus injection of D3‐Leu and blood was drawn at eight time points over three days. HDL was isolated and separated into six size fractions for subsequent proteolysis and PRM analysis for the detection of D3‐Leu signal from ∼0.03 to 0.6% enrichment. We implemented an intensity‐based quantification approach that takes advantage of high‐resolution/accurate mass PRM scans to identify the D3‐Leu 2HM3 ion from non‐specific peaks. Our workflow includes five modules for extracting the targeted PRM peak intensities (XPIs): Peak centroiding, noise removal, fragment ion matching using Δm/z windows, nine intensity quantification options, and validation and visualization outputs. We optimized the XPI workflow using in vitro synthesized and clinical samples of D0/D3‐Leu labeled apoA‐I. Three subjects’ apoA‐I enrichment curves in six HDL size fractions, and LCAT, apoA‐II and apoE from two size fractions were generated within a few hours. Our PRM strategy and automated quantification workflow will expedite the turnaround of HDL apoA‐I metabolism data in clinical studies that aim to understand and treat the mechanisms behind dyslipidemia.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.201600085