DIANA--algorithmic improvements for analysis of data-independent acquisition MS data

Streptococcus pyogenes Infection and the Human Proteome with a Special Focus on the Immunoglobulin G-cleaving Enzyme IdeS

Infectious diseases are characterized by a complex interplay between host and pathogen, but how these interactions impact the host proteome is unclear. Here we applied a combined mass spectrometry-based proteomics strategy to investigate how the human proteome is transiently modified by the pathogen Streptococcus pyogenes, with a particular focus on bacterial cleavage of IgG in vivo. In invasive diseases, S. pyogenes evokes a massive host response in blood, whereas superficial diseases are characterized by a local leakage of several blood plasma proteins at the site of infection including IgG. S. pyogenes produces IdeS, a protease cleaving IgG in the lower hinge region and we find highly effective IdeS-cleavage of IgG in samples from local IgG poor microenvironments. The results show that IdeS contributes to the adaptation of S. pyogenes to its normal ecological niches. Additionally, the work identifies novel clinical opportunities for in vivo pathogen detection.

Quantitative proteomic characterization of lung-MSC and bone marrow-MSC using DIA-mass spectrometry

Mesenchymal stromal cells (MSC) are ideal candidates for cell therapies, due to their immune-regulatory and regenerative properties. We have previously reported that lung-derived MSC are tissue-resident cells with lung-specific properties compared to bone marrow-derived MSC. Assessing relevant molecular differences between lung-MSC and bone marrow-MSC is important, given that such differences may impact their behavior and potential therapeutic use. Here, we present an in-depth mass spectrometry (MS) based strategy to investigate the proteomes of lung-MSC and bone marrow-MSC. The MS-strategy relies on label free quantitative data-independent acquisition (DIA) analysis and targeted data analysis using a MSC specific spectral library. We identified several significantly differentially expressed proteins between lung-MSC and bone marrow-MSC within the cell layer (352 proteins) and in the conditioned medium (49 proteins). Bioinformatics analysis revealed differences in regulation of cell proliferation, which was functionally confirmed by decreasing proliferation rate through Cytochrome P450 stimulation. Our study reveals important differences within proteome and matrisome profiles between lung- and bone marrow-derived MSC that may influence their behavior and affect the clinical outcome when used for cell-therapy.

Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry

Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.SWATH-mass spectrometry consists of a data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics on the scale of thousands of proteins. Here, using data generated by eleven groups worldwide, the authors show that SWATH-MS is capable of generating highly reproducible data across different laboratories.

Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

Liquid chromatography coupled to tandem mass spectrometry is the main method for high-throughput identification and quantification of peptides and inferred proteins. Within this field, data-independent acquisition (DIA) combined with peptide-centric scoring, exemplified by SWATH-MS, emerged as a scalable method to achieve deep and consistent proteome coverage across large-scale datasets. Here we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies and provide guidance on their application. We show that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library. We propose the application of a global analyte constraint to prevent accumulation of false positives across large-scale datasets. Furthermore, to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported for detected peptide queries, peptides and inferred proteins.

Enhanced differential expression statistics for data-independent acquisition proteomics

We describe a new reproducibility-optimization method ROPECA for statistical analysis of proteomics data with a specific focus on the emerging data-independent acquisition (DIA) mass spectrometry technology. ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression. Using a ‘gold standard’ spike-in data and a hybrid proteome benchmark data we show the competitive performance of ROPECA over conventional protein-based analysis as well as state-of-the-art peptide-based tools especially in DIA data with consistent peptide measurements. Furthermore, we also demonstrate the improved accuracy of our method in clinical studies using proteomics data from a longitudinal human twin study.

Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine

Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.

Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS

Consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is a prerequisite for functional analysis of biological processes. Data-independent acquisition (DIA) is a bottom-up mass spectrometry approach that provides complete information on precursor and fragment ions. However, owing to the convoluted structure of DIA data sets, confident, systematic identification and quantification of peptidoforms has remained challenging. Here, we present inference of peptidoforms (IPF), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by the DIA method SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. IPF reduced false site-localization by more than sevenfold compared with previous approaches, while recovering 85.4% of the true signals. Using IPF, we quantified peptidoforms in DIA data acquired from >200 samples of blood plasma of a human twin cohort and assessed the contribution of heritable, environmental and longitudinal effects on their PTMs.

Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics

In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.

Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification

Host cell proteins (HCPs) are process-related impurities of biopharmaceuticals that remain at trace levels despite multiple stages of downstream purification. Currently, there is interest in implementing LC-MS in biopharmaceutical HCP profiling alongside conventional ELISA, because individual species can be identified and quantitated. Conventional data dependent LC-MS is hampered by the low concentration of HCP-derived peptides, which are 5-6 orders of magnitude less abundant than the biopharmaceutical-derived peptides. In this paper, we present a novel data independent acquisition (DIA)-MS workflow to identify HCP peptides using automatically combined targeted and untargeted data processing, followed by verification and quantitation using parallel reaction monitoring (PRM). Untargeted data processing with DIA-Umpire provided a means of identifying HCPs not represented in the assay library used for targeted, peptide-centric, data analysis. An IgG1 monoclonal antibody (mAb) purified by Protein A column elution, cation exchange chromatography, and ultrafiltration was analyzed using the workflow with 1D-LC. Five protein standards added at 0.5 to 100 ppm concentrations were detected in the background of the purified mAb, demonstrating sensitivity to low ppm levels. A calibration curve was constructed on the basis of the summed peak areas of the three highest intensity fragment ions from the highest intensity peptide of each protein standard. Sixteen HCPs were identified and quantitated on the basis of the calibration curve over the range of low ppm to over 100 ppm in the purified mAb sample. The developed approach achieves rapid HCP profiling using 1D-LC and specific identification exploiting the high mass accuracy and resolution of the mass spectrometer.

Optimization of Acquisition and Data-Processing Parameters for Improved Proteomic Quantification by Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectrometry

Proteomic analysis with data-independent acquisition (DIA) approaches represented by the sequential window acquisition of all theoretical fragment ion spectra (SWATH) technique has gained intense interest in recent years because DIA is able to overcome the intrinsic weakness of conventional data-dependent acquisition (DDA) methods and afford higher throughout and reproducibility for proteome-wide quantification. Although the raw mass spectrometry (MS) data quality and the data-mining workflow conceivably influence the throughput, accuracy and consistency of SWATH-based proteomic quantification, there lacks a systematic evaluation and optimization of the acquisition and data-processing parameters for SWATH MS analysis. Herein, we evaluated the impact of major acquisition parameters such as the precursor mass range, isolation window width and accumulation time as well as the data-processing variables including peak extraction criteria and spectra library selection on SWATH performance. Fine tuning these interdependent parameters can further improve the throughput and accuracy of SWATH quantification compared to the original setting adopted in most SWATH proteomic studies. Furthermore, we compared the effectiveness of two widely used peak extraction software PeakView and Spectronaut in discovery of differentially expressed proteins in a biological context. Our work is believed to contribute to a deeper understanding of the critical factors in SWATH MS experiments and help researchers optimize their SWATH parameters and workflows depending on the sample type, available instrument and software.

Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms

Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics

Large scale, quantitative proteomic studies have become essential for the analysis of clinical cohorts, large perturbation experiments and systems biology studies. While next-generation mass spectrometric techniques such as SWATH-MS have substantially increased throughput and reproducibility, ensuring consistent quantification of thousands of peptide analytes across multiple LC-MS/MS runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we have developed the TRIC software which utilizes fragment ion data to perform cross-run alignment, consistent peak-picking and quantification for high throughput targeted proteomics. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate peak elution information from all LC-MS/MS runs acquired in a study. When compared to state-of-the-art SWATH-MS data analysis, the algorithm was able to reduce the identification error by more than 3-fold at constant recall, while correcting for highly non-linear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem (iPS) cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups and substantially increased the quantitative completeness and biological information in the data, providing insights into protein dynamics of iPS cells. Overall, this study demonstrates the importance of consistent quantification in highly challenging experimental setups, and proposes an algorithm to automate this task, constituting the last missing piece in a pipeline for automated analysis of massively parallel targeted proteomics datasets.

Advances in targeted proteomics and applications to biomedical research

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

Systems-level Proteomics of Two Ubiquitous Leaf Commensals Reveals Complementary Adaptive Traits for Phyllosphere Colonization

Plants are colonized by a diverse community of microorganisms, the plant microbiota, exhibiting a defined and conserved taxonomic structure. Niche separation based on spatial segregation and complementary adaptation strategies likely forms the basis for coexistence of the various microorganisms in the plant environment. To gain insights into organism-specific adaptations on a molecular level, we selected two exemplary community members of the core leaf microbiota and profiled their proteomes upon Arabidopsis phyllosphere colonization. The highly quantitative mass spectrometric technique SWATH MS was used and allowed for the analysis of over two thousand proteins spanning more than three orders of magnitude in abundance for each of the model strains. The data suggest that Sphingomonas melonis utilizes amino acids and hydrocarbon compounds during colonization of leaves whereas Methylobacterium extorquens relies on methanol metabolism in addition to oxalate metabolism, aerobic anoxygenic photosynthesis and alkanesulfonate utilization. Comparative genomic analyses indicates that utilization of oxalate and alkanesulfonates is widespread among leaf microbiota members whereas, aerobic anoxygenic photosynthesis is almost exclusively found in Methylobacteria. Despite the apparent niche separation between these two strains we also found a relatively small subset of proteins to be coregulated, indicating common mechanisms, underlying successful leaf colonization. Overall, our results reveal for two ubiquitous phyllosphere commensals species-specific adaptations to the host environment and provide evidence for niche separation within the plant microbiota.

Dinosaur: A Refined Open-Source Peptide MS Feature Detector

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur.

SWATH2stats: An R/Bioconductor Package to Process and Convert Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools

SWATH-MS is an acquisition and analysis technique of targeted proteomics that enables measuring several thousand proteins with high reproducibility and accuracy across many samples. OpenSWATH is popular open-source software for peptide identification and quantification from SWATH-MS data. For downstream statistical and quantitative analysis there exist different tools such as MSstats, mapDIA and aLFQ. However, the transfer of data from OpenSWATH to the downstream statistical tools is currently technically challenging. Here we introduce the R/Bioconductor package SWATH2stats, which allows convenient processing of the data into a format directly readable by the downstream analysis tools. In addition, SWATH2stats allows annotation, analyzing the variation and the reproducibility of the measurements, FDR estimation, and advanced filtering before submitting the processed data to downstream tools. These functionalities are important to quickly analyze the quality of the SWATH-MS data. Hence, SWATH2stats is a new open-source tool that summarizes several practical functionalities for analyzing, processing, and converting SWATH-MS data and thus facilitates the efficient analysis of large-scale SWATH/DIA datasets.

Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics

The plasma proteome is highly dynamic and variable, composed of proteins derived from surrounding tissues and cells. To investigate the complex processes that control the composition of the plasma proteome, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in murine plasma. The strategy relies on the construction of a comprehensive protein tissue atlas from cells and highly vascularized organs using shotgun mass spectrometry. The protein tissue atlas was transformed to a spectral library for highly reproducible quantification of tissue-specific proteins directly in plasma using SWATH-like data-independent mass spectrometry analysis. We show that the method can determine drastic changes of tissue-specific protein profiles in blood plasma from mouse animal models with sepsis. The strategy can be extended to several other species advancing our understanding of the complex processes that contribute to the plasma proteome dynamics.

Sepsis can lead to multiple organ failure that could potentially be reflected by change in plasma protein abundance. Here the authors describe a proteomics strategy that allows the determination of plasma proteins tissue origin in a quantitative manner for use as biomarkers—illustrated in a mouse model of sepsis.

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS

Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which, in turn, is detrimental for accurate quantification. The nonoutlier fragment ion (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high-priority fragment ions, these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative data set (i.e., the SWATH Gold Standard) indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to that with the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest data set, NOFI properly assigns low-priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80, against 0.76 for the Spectronaut interference detection algorithm.

Quantitative proteogenomics of human pathogens using DIA-MS

The increasing number of bacterial genomes in combination with reproducible quantitative proteome measurements provides new opportunities to explore how genetic differences modulate proteome composition and virulence. It is challenging to combine genome and proteome data as the underlying genome influences the proteome. We present a strategy to facilitate the integration of genome data from several genetically similar bacterial strains with data-independent analysis mass spectrometry (DIA-MS) for rapid interrogation of the combined data sets. The strategy relies on the construction of a composite genome combining all genetic data in a compact format, which can accommodate the fusion with quantitative peptide and protein information determined via DIA-MS. We demonstrate the method by combining data sets from whole genome sequencing, shotgun MS and DIA-MS from 34 clinical isolates of Streptococcus pyogenes. The data structure allows for fast exploration of the data showing that undetected proteins are on average more amenable to amino acid substitution than expressed proteins. We identified several significantly differentially expressed proteins between invasive and non-invasive strains. The work underlines how integration of whole genome sequencing with accurately quantified proteomes can further advance the interpretation of the relationship between genomes, proteomes and virulence. This article is part of a Special Issue entitled: Computational Proteomics.

Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry

Accurate knowledge of retention time (RT) in liquid chromatography-based mass spectrometry data facilitates peptide identification, quantification, and multiplexing in targeted and discovery-based workflows. Retention time prediction is particularly important for peptide analysis in emerging data-independent acquisition (DIA) experiments such as SWATH-MS. The indexed RT approach, iRT, uses synthetic spiked-in peptide standards (SiRT) to set RT to a unit-less scale, allowing for normalization of peptide RT between different samples and chromatographic set-ups. The obligatory use of SiRTs can be costly and complicates comparisons and data integration if standards are not included in every sample. Reliance on SiRTs also prevents the inclusion of archived mass spectrometry data for generation of the peptide assay libraries central to targeted DIA-MS data analysis. We have identified a set of peptide sequences that are conserved across most eukaryotic species, termed Common internal Retention Time standards (CiRT). In a series of tests to support the appropriateness of the CiRT-based method, we show: (1) the CiRT peptides normalized RT in human, yeast, and mouse cell lysate derived peptide assay libraries and enabled merging of archived libraries for expanded DIA-MS quantitative applications; (2) CiRTs predicted RT in SWATH-MS data within a 2-min margin of error for the majority of peptides; and (3) normalization of RT using the CiRT peptides enabled the accurate SWATH-MS-based quantification of 340 synthetic isotopically labeled peptides that were spiked into either human or yeast cell lysate. To automate and facilitate the use of these CiRT peptide lists or other custom user-defined internal RT reference peptides in DIA workflows, an algorithm was designed to automatically select a high-quality subset of datapoints for robust linear alignment of RT for use. Implementations of this algorithm are available for the OpenSWATH and Skyline platforms. Thus, CiRT peptides can be used alone or as a complement to SiRTs for RT normalization across peptide spectral libraries and in quantitative DIA-MS studies.