MS1 ion current‐based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

Abstract

The rapidly‐advancing field of pharmaceutical and clinical research calls for systematic, molecular‐level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large‐cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large‐cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false‐positive discovery of altered proteins when sample size increases. MS1 ion current‐based methods, which have become an important class of label‐free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1‐based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high‐resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post‐feature‐generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1‐based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Analysis of the Barley Malt Rootlet Proteome

“…Exploring recently developed MS1 ion current-based proteome analysis will lead to a more comprehensive repertoire of the proteins, although this study has reported on more than 800 proteins in the rootlet proteome [48]. One of the most important limitations in analyzing omics dataset is the availability of well-annotated genomes.…”

IceR improves proteome coverage and data completeness in global and single-cell proteomics

“…Its greatly enhanced sensitivity and specificity could be demonstrated for a broad spectrum of published and newly generated data sets demonstrating its universal applicability. Although it had been previously shown that DICE-based data analysis has highly attractive performance features 1 , IceR is the first to offer this as a comprehensive yet userfriendly R-package. In addition, since DDA-based proteomics is widely used and firmly established, having undergone many rounds of methodological and hardware optimization over the last decade, and since IceR can be readily integrated in existing workflows, we expect that IceR can be easily adopted and that it will be of great value for the proteomics community.…”

“…In addition, false transfers cannot be excluded, requiring specific attention 23 . In contrast, ion-based PIP applies direct ion current extraction (DICE), and only requires the existence of ions within a given retention time and mass-to-charge window, thus enabling sensitive identity propagation 1 . Ion-based PIP has been implemented in DeMix-Q 8 and IonStar 12 , both achieving highly reduced missing value rates and improved sensitivity to detect differentially abundant proteins compared to MaxQuant.…”

Defeating the trypanosomatid trio: proteomics of the protozoan parasites causing neglected tropical diseases

“…Conventional 2-DE is now obsolete, with quantitative proteomics MS techniques offering a far better sensitivity of detection, the capability for high-throughput whole proteome analysis in a single MS run and the quantitative comparison of multiple experimental conditions in a single experiment. Quantitative proteomics MS techniques are broadly divided into label-free quantification (LFQ) methods, such as MS1-intensity based methods 50 and spectral counting-based methods, 51 and several different labelling-based methods. Popular labelling strategies include TMT (tandem mass tag), 52 iTRAQ (isobaric tagging for relative and absolute quantitation), 53 SILAC (stable isotope labelling by amino acids in cell culture) 54 and stable isotope dimethyl labelling.…”

“…Both SAMS and SAHH play key roles in the pathways upstream of the biosynthesis of trypanothione, a unique thiol metabolite of Kinetoplastida that is implicated in antimony resistance and the parasite's response to oxidative stress. 48 50 and spectral counting-based methods, 51 and several different labelling-based methods. Popular labelling strategies include TMT (tandem mass tag), 52 iTRAQ (isobaric tagging for relative and absolute quantitation), 53 SILAC (stable isotope labelling by amino acids in cell culture) 54 and stable isotope dimethyl labelling.…”