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.

Keywords: LC-MS; MS1 quantification; ion current-based proteomics; large cohorts; reproducible protein measurement.

Figure 1

The general workflow for MS1 ion current‐based quantitative strategy.

Figure 2

Comparison of MS1 and MS2‐based method. Upper: an illustration showing MS1 based methods resulted in much higher signal intensity of a peptide comparing to MS2‐based methods. Therefore, MS1‐based methods can achieve high sensitivity if a high selectivity is realized, for example, by high MS resolution. Lower: an example for quantifying a low‐abundance protein in human tissue samples (group A vs. B, n = 6 per group). MS1 ion current‐based method was able to quantify the protein in all samples without missing data while MS2 spectral counting (SpC) method is not useful owing to the very high missing data, that is, only two MS2 identifications in all runs.

Figure 3

High‐resolution MS measurement of peptide precursor substantially improves selectivity and therefore sensitivity. Upper: the effect of MS resolution on the selectivity for MS1‐based quantification, illustrated via simulated MS spectra. Lower: examples showing higher MS resolution drastically lowered chemical noises for MS1‐based analysis of low‐abundance protein in tissue samples.

Figure 4

Comparison of representative MS1‐based strategies on (a) levels of missing data, as showed by the abundance heat maps of proteins with the lowest 10% abundances in the benchmark sample set (n = 20 in total). White areas indicate missing data. (b) Comparison of valid quantitative feature numbers generated by IonStar vs. a representative PPB method in the same sample set; (c) Reproducibility of the methods as examined by Pearson correlation of two replicate runs from the same sample. The R ² values were separately calculated for proteins in the upper 75% and lower 25% abundance percentiles. (Reprinted with permission from (Shen et al., 2018b), copyright 2018, Proc Natl Acad Sci USA).