Hybrid Data Acquisition and Processing Strategies with Increased Throughput and Selectivity: pSMART Analysis for Global Qualitative and Quantitative Analysis

Data-dependent acquisition (DDA) and data-independent acquisition strategies (DIA) have both resulted in improved understanding of proteomics samples. Both strategies have advantages and disadvantages that are well-published, where DDA is typically applied for deep discovery and DIA may be used to create sample records. In this paper, we present a hybrid data acquisition and processing strategy (pSMART) that combines the strengths of both techniques and provides significant benefits for qualitative and quantitative peptide analysis. The performance of pSMART is compared to published DIA strategies in an experiment that allows the objective assessment of DIA performance with respect to interrogation of previously acquired MS data. The results of this experiment demonstrate that pSMART creates fewer decoy hits than a standard DIA strategy. Moreover, we show that pSMART is more selective, sensitive, and reproducible than either standard DIA or DDA strategies alone

Proteomic signatures of serum albumin-bound proteins from stroke patients with and without endovascular closure of PFO are significantly different and suggest a novel mechanism for cholesterol efflux

Background: The anatomy of PFO suggests that it can allow thrombi and potentially harmful circulatory factors to travel directly from the venous to the arterial circulation – altering circulatory phenotype. Our previous publication using high-resolution LC-MS/MS to profile protein and peptide expression patterns in plasma showed that albumin was relatively increased in donor samples from PFO-related than other types of ischemic strokes. Since albumin binds a host of molecules and acts as a carrier for lipoproteins, small molecules and drugs, we decided to investigate the albumin-bound proteins (in a similar sample cohort) in an effort to unravel biological changes and potentially discover biomarkers related to PFO-related stroke and PFO endovascular closure. Methods: The method used in this study combined albumin immuno-enrichment with high resolution LC-MS in order to specifically capture and quantify the albumin-bound proteins. Subsequently, we measured cholesterol and HDL in a larger, separate cohort of PFO stroke patients, pre and post closure. Results: The results demonstrated that a number of proteins were specifically associated with albumin in samples with and without endovascular closure of the PFO, and that the protein profiles were very different. Eight proteins, typically associated with HDL were common to both sample sets and quantitatively differently abundant. Pathway analysis of the MS results suggested that enhanced cholesterol efflux and reduced lipid oxidation were associated with PFO closure. Measurement of total cholesterol and HDL in a larger cohort of PFO closure samples using a colorimetric assay was consistent with the proteomic predictions. Conclusions: The collective data presented in this study demonstrate that analysis of albumin-bound proteins could provide a valuable tool for biomarker discovery on the effects of PFO endovascular closure. In addition, the results suggest that PFO endovascular closure can potentially have effects on HDL, cholesterol and albumin-bound ApoA-I abundance, therefore possibly providing benefits in cardioprotective functions. Electronic supplementary material The online version of this article (doi:10.1186/1559-0275-12-2) contains supplementary material, which is available to authorized users

A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes