title: 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 (∼10%). 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 ≈90% accurate differentiation of individual samples by means of a single LC-MS/MS analysis. This method improves the precision of measurements and increases the accuracy of predictive models based on the measurements. The post-acquisition nature of the correction technique and its generality promise its widespread application in LC-MS/MS-based methods such as proteomics and metabolomics.
authors.affiliation: Division of Physiological Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-17 177 Stockholm, Sweden.
authors.forename: Yaroslav
authors.initials: Y
authors.lastname: Lyutvinskiy
authors.forename: Hongqian
authors.initials: H
authors.lastname: Yang
authors.forename: Dorothea
authors.initials: D
authors.lastname: Rutishauser
authors.forename: Roman A
authors.initials: RA
authors.lastname: Zubarev
mesh_terms: D002853:Chromatography, Liquid; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein; D053719:Tandem Mass Spectrometry / Q000379:methods