In Silico Instrumental Response Correction Improves Precision of Label-free Proteomics and Accuracy of Proteomics-based Predictive Models

Abstract

In the analysis of proteome changes arising during the early stages of a biological process (e.g. disease or drug treatment) or from the indirect influence of an important factor, the biological variations of interest are often small (ϳ10%). The corresponding requirements for the precision of proteomics analysis are high, and this often poses a challenge, especially when employing label-free quantification. One of the main contributors to the inaccuracy of label-free proteomics experiments is the variability of the instrumental response during LC-MS/MS runs. Such variability might include fluctuations in the electrospray current, transmission efficiency from the air-vacuum interface to the detector, and detection sensitivity. We have developed an in silico post-processing method of reducing these variations, and have thus significantly improved the precision of label-free proteomics analysis. For abundant blood plasma proteins, a coefficient of variation of approximately 1% was achieved, which allowed for sex differentiation in pooled samples and Ϸ90% accurate dif- Label-free proteomics is sensitive, comprehensive, and versatile (1, 2). The "lyse, digest, and analyze" approach requires the least sample preparation and wet chemistry of all quantitative proteomics approaches (3). The label-free quantification technique is equally applicable to the analysis of peptide mixtures, protein complexes, body fluid proteomes, whole organisms, organs, and organelles. It also does not impose a limitation on the number of samples, which makes it best suited for the requirements of clinical proteomics in which analyses of large cohorts are common. Additionally, a single LC-MS/MS run can identify and quantify several thousand proteins, which makes the cost of analysis per quantified peptide, protein, or proteome very low (4).A significant drawback of label-free proteomics has been its limited precision in the determination of relative changes in peptide abundance, even when the area of the extracted chromatographic peak is used as the peptide abundance. It has been estimated that such label-free quantification gives abundance ratio results that are on average two to three times less accurate than the "gold standard" in global proteomics, stable isotope labeling of amino acids in cell culture (SILAC) 1 (4). There are several reasons for such a performance gap. Unlike in SILAC, where proteins in all samples under comparison are extracted and digested simultaneously (5, 6), in the label-free method, each sample is prepared independently, and therefore variations in sample preparation conditions can cause abundance fluctuations. However, these variations are a subjective factor that can be reduced by training the personnel and/or by employing sample preparation robots. The main objective contributor to the imprecision of a label-free LC-MS/MS experiment is the fluctuation of the instrumental response during the LC-MS/MS run or series of runs. A major component of the instrumental response fluctuation is the variation in the current...

Release of Active Peptidyl Arginine Deiminases by Neutrophils Can Explain Production of Extracellular Citrullinated Autoantigens in Rheumatoid Arthritis Synovial Fluid

“…Data were quantified using Quanti work flow 21. P values were calculated using Student's t ‐test, and expectation values were calculated by multiplying the P values by the number of observations (for further details, see Supplementary Methods, available on the Arthritis & Rheumatology web site at http://onlinelibrary.wiley.com/doi/10.1002/art.39313/abstract).…”

DeMix-Q: Quantification-Centered Data Processing Workflow

“…due to sample loading, column temperature, ESI current stability, etc.) can greatly affect analytical accuracy in labelfree experiments (21,44,45). In order to correct fold-changes induced by systematic biases, a rescaling of feature abundances was performed, using a time-dependent median-shift approach.…”

“…The accurate mass and time tag performs the feat of "peptide identity propagation" (PIP) from the LC-MS/MS runs with valid MS/MS information to those runs where such information is lacking. Today, one or another variant of the accurate mass and time tag-based PIP is employed in many MS 1 -based LFQ algorithms for analyzing DDA data (6,21). MS feature matching (22,23) and targeted extraction of ion chromatograms (XIC) (24,25) represent examples of such variants.…”

Phosphorylation of Leukotriene C4 Synthase at Serine 36 Impairs Catalytic Activity

“…In addition, phosphorylation led to an increased frequency of H-bonding with Arg 34 in the same subunit. However, because Ser 36 and Arg 34 are very close (both spatially and in the primary sequence), this interaction is not likely to have significant functional effects. In addition, Arg 34 is not known to be important for catalysis or substrate binding.…”