Nonlinear Regression Improves Accuracy of Characterization of Multiplexed Mass Spectrometric Assays

Abstract

AbbreviationsDIA data-independent acquisition LOB limit of blank LOD limit of detection LLOQ lower limit of quantitation/quantification PRM parallel reaction monitoring SRM selected reaction monitoring 2 SummaryThe need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Among many assay characteristics, the limit of blank (LOB) and limit of detection (LOD) are two particularly useful figures of merit. LOB and LOD are determined by repeatedly quantifying the observed intensities of peptides in samples with known peptide concentrations, and deriving an intensity versus concentration response curve. Most commonly, a weighted linear or logistic curve is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Here we argue that these methods inaccurately characterize assays where observed intensities level off at low concentrations, which is a common situation in multiplexed systems. This manuscript illustrates the deficiencies of these methods, and proposes an alternative approach based on non-linear regression that overcomes these inaccuracies. We evaluated the performance of the proposed method using computer simulations, and using eleven experimental datasets acquired in Data-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), and Selected Reaction Monitoring (SRM) mode. When the intensity levels off at low concentrations, the non-linear model changes the estimates of LOB/LOD upwards, in some datasets by 20-40%. In absence of a low concentration intensity leveling off, the estimates of LOB/LOD obtained with non-linear statistical modeling were identical to those of weighted linear regression. We implemented the non-linear regression approach in the open-source R-based software MSstats, and advocate its general use for characterization of mass spectrometry-based assays.3

Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics

“…However large-scale studies on the order of 1,000’s to 10,000’s of peptides like most DIA/SWATH-MS experiments do not evaluate peptide response. Calibration curves for up to 30 stable isotope-labeled internal standard peptides have been collected using DIA/SWATH-MS methods [8], but it is cost-prohibitive to synthesize stable isotope-labeled peptides for the number of targets detected in DIA. In this work, we propose a framework for discriminating between peptides that are only detectable and those which are both detectable and quantitative in a mass spectrometry experiment.…”

“…To fit calibration curves to this novel data, we developed a computational model (Fig 1b) which extends the work described previously by Galitizine et al . [8] to accommodate the sparseness of matrix-matched calibration curve data and to determine the LLOQ for each detected analyte. Briefly, the model first fits a piece-wise linear regression to the noise and the signal segments of the curve data, then bootstraps the observed data, refits the piece-wise regression to the bootstrapped data to predict signal over the range of quantities measured.…”

Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows

“…Peak areas in each LC-MS/MS run were normalized by integrated peak areas of a set of peptides from the background yeast matrix to correct for run-to-run variation in, e.g., LC column performance, ion source cleanliness, and sample load volume (see “ Peak Area Normalization ”). MSStats version 3.10.2 was used to calculate the lower limit of quantification from spike-in curve data based on the shape of the spike-in curve and reproducibility of replicate measurements [ 29 ].…”

In silicoapproach toward the identification of unique peptides from viral protein infection: Application to COVID-19

“…The most common example is that untargeted assays which may observe thousands of peptide ions per experiment invariably have a lower limit of detection (LOD) and quantification (LOQ) compared to assays where a smaller number of ions are targeted. [21][22][23] Improvements in each subsequent generation of hardware can mitigate this compromise, but the improvement is limited. Today, the only way to truly offset this rule is to increase the total LCMS run time.…”