Data-dependent Acquisition Data Research Articles

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines.

Selection of proteomic workflows for a given project can be a daunting task. This research provides a guide outlining the impact on protein identification of different steps such as chromatographic separation, data acquisition strategies, and bioinformatic pipelines. The data presented here will help experts and nonexpert proteomic users to increase proteome coverage and peptide identification. HeLa protein digests were analyzed through different C18 chromatographic columns (15 and 50 cm in length), using top 12 data-dependent acquisition (DDA), top 20 DDA, and data-independent acquisition (DIA) with a nanospray source in positive mode in a Thermo Q Exactive instrument. The raw data were analyzed using different search engines, rescoring approaches, and multi-engine searches. The results were analyzed in the context of peptide and protein identifications, precursor properties, and computation requirements to understand the differences between methods. Our results showed that higher column lengths and top N DDA approaches were able to significantly increase protein identifications. The use of multiple search engines yielded limited gains, whereas the use of rescoring methods clearly outperformed other strategies. Finally, DIA approaches, although successful at generating new identifications, had a limited performance influenced by the previous collection of DDA data, which could prohibitively increase instrument time. Nonetheless, the use of library-free methods showed promising results. Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step.

Comparison of Compound Identification Tools Using Data Dependent and Data Independent High-Resolution Mass Spectrometry Spectra.

Liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS) is a frequently applied technique for suspect screening (SS) and non-target screening (NTS) in metabolomics and environmental toxicology. However, correctly identifying compounds based on SS or NTS approaches remains challenging, especially when using data-independent acquisition (DIA). This study assessed the performance of four HRMS-spectra identification tools to annotate in-house generated data-dependent acquisition (DDA) and DIA HRMS spectra of 32 pesticides, veterinary drugs, and their metabolites. The identification tools were challenged with a diversity of compounds, including isomeric compounds. The identification power was evaluated in solvent standards and spiked feed extract. In DDA spectra, the mass spectral library mzCloud provided the highest success rate, with 84% and 88% of the compounds correctly identified in the top three in solvent standard and spiked feed extract, respectively. The in silico tools MSfinder, CFM-ID, and Chemdistiller also performed well in DDA data, with identification success rates above 75% for both solvent standard and spiked feed extract. MSfinder provided the highest identification success rates using DIA spectra with 72% and 75% (solvent standard and spiked feed extract, respectively), and CFM-ID performed almost similarly in solvent standard and slightly less in spiked feed extract (72% and 63%). The identification success rates for Chemdistiller (66% and 38%) and mzCloud (66% and 31%) were lower, especially in spiked feed extract. The difference in success rates between DDA and DIA is most likely caused by the higher complexity of the DIA spectra, making direct spectral matching more complex. However, this study demonstrates that DIA spectra can be used for compound annotation in certain software tools, although the success rate is lower than for DDA spectra.

Recent advances of data‐independent acquisition mass spectrometry‐based proteomics

Recent advances of data‐independent acquisition mass spectrometry‐based proteomics

Data Processing of Product Ion Spectra: Quality Improvement by Averaging Multiple Similar Spectra of Small Molecules.

In metabolomics studies using high-resolution mass spectrometry (MS), a set of product ion spectra is comprehensively acquired from observed ions using the data-dependent acquisition (DDA) mode of various tandem MS. However, especially for low-intensity signals, it is sometimes difficult to distinguish artifact signals from true fragment ions derived from a precursor ion. Inadequate precision in the measured m/z value is also one of the bottlenecks to narrowing down the candidate compositional formula. In this study, we report that averaging multiple product ion spectra can improve m/z precision as well as the reliability of fragment ions that are observed in such spectra. A graph-based method was applied to cluster a set of similar spectra from multiple DDA data files resulting in creating an averaged product-ion spectrum. The error levels for the m/z values declined following the central limit theorem, which allowed us to reduce the number of candidate compositional formulas. The improved reliability and precision of the averaged spectra will contribute to a more efficient annotation of product ion spectral data.

Evaluating Peptide Fragment Ion Detection Using Traveling Wave Ion Mobility Spectrometry with Signal-Enhanced MSE (SEMSE).

The inherent poor sampling of fragment ions in time-of-flight mass analyzers was recently improved for data-dependent acquisition (DDA) by considering their drift times in traveling wave ion mobility spectrometry (TWIMS). Here, we extend this TWIMS-DDA approach to the data-independent acquisition (DIA) mode MSE to improve the signal intensities of fragment ions by providing improved ion beam sampling efficiency, which we termed therefore signal-enhanced MSE (SEMSE). The theoretical expectation that SEMSE improves the number of identified peptides, the number of quantifiable peptides, and the lower limit of quantitation in wideband DIA was evaluated on an electrospray ionisation-ion mobility spectrometry-quadrupole-time-of-flight-MS (ESI-IMS-Q-TOF-MS) (Synapt G2-Si) in comparison to five established TWIMS-DDA and TWIMS-MSE methods with respect to the number of peptide identifications, the spectral quality of supporting peptide spectra matches, and (most importantly) fragment ion signal sensitivity. A comparison of the fragment signals clearly indicated that SEMSE provides 6.8- to 11.5-fold larger peak areas than established MSE techniques. While this clearly shows the advantages of SEMSE, the inherent limitations of the current software tools do not allow using all benefits in routine analyses. As the simultaneous fragmentation of co-eluting peptides limited peptide identification, DDA and MSE data sets were integrated using Skyline.

Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry

Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics.

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples

Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Background ions into exclusion list: A new strategy to enhance the efficiency of DDA data collection for high-throughput screening of chemical contaminations in food

Foods contaminated with hazardous compounds, could pose potential risks for human health. To date, there is still a big challenge in accurate identification. In this study, a novel data-dependent acquisition (DDA) approach, based on a combination of inclusion list and exclusion list, was proposed to acquire more effective MS/MS spectra. This strategy was successfully applied in a large-scale screening survey to detect 50 mycotoxins in oats, 155 veterinary drugs in dairy milk, and 200 pesticides in tomatoes. Compared with traditional acquisition modes, this new strategy has higher detection rate, particularly at ultra-low concentration by eliminating background influence, thereby generating the MS/MS spectra for more potential hazardous materials instead of matrix interference. Additionally, the obtained MS/MS spectra are simpler and more likely to be traced back than DIA. Moreover, this new strategy would be more comprehensively applied in food safety monitoring with the improvement of HRMS and post-acquisition techniques.

BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics.

Data-dependent acquisition (DDA) methods are the current standard for quantitative proteomics in many biological systems. However, DDA preferentially measures highly abundant proteins and generates data that is plagued with missing values, requiring extensive imputation. Here, we demonstrate that library-free BoxCarDIA acquisition, combining MS1-level BoxCar acquisition with MS2-level data-independent acquisition (DIA) analysis, outperforms conventional DDA and other library-free DIA (directDIA) approaches. Using a combination of low- (HeLa cells) and high- (Arabidopsis thaliana cell culture) dynamic range sample types, we demonstrate that BoxCarDIA can achieve a 40% increase in protein quantification over DDA without offline fractionation or an increase in mass-spectrometer acquisition time. Further, we provide empirical evidence for substantial gains in dynamic range sampling that translates to deeper quantification of low-abundance protein classes under-represented in DDA and directDIA data. Unlike both DDA and directDIA, our new BoxCarDIA method does not require full MS1 scans while offering reproducible protein quantification between replicate injections and providing more robust biological inferences. Overall, our results advance the BoxCarDIA technique and establish it as the new method of choice for label-free quantitative proteomics across diverse sample types.

DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution.

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied upon specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.

DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

Holistic quality evaluation of Saposhnikoviae Radix (Saposhnikovia divaricata) by reversed-phase ultra-high performance liquid chromatography and hydrophilic interaction chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry-based untargeted metabolomics

Untargeted metabolomics more suits the quality evaluation of TCM because of its holistic property. To assess the holistic quality difference of Saposhnikoviae Radix (the roots of Saposhnikovia divaricata), we integrate ultra-high-performance liquid chromatography coupled with ion mobility/quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS)-based untargeted metabolomics and quantitative assay. A BEH C18 column in the reversed-phase mode and a BEH Amide column in Hydrophilic Interaction Chromatography (HILIC) mode were utilized for metabolites profiling, which enabled high coverage of the non-polar to polar components in Saposhnikoviae Radix. Moreover, the application of major components knockout strategy enlarged the exposure of those minor components. Integrated use of high-definition MSE (HDMSE) and data-dependent acquisition (DDA) could enhance the metabolites characterization by providing reliable fragmentation information and collision cross section values. Computational in-house library-driven automated peak annotation of the HDMSE and DDA data assisted to characterize 104 components from Saposhnikoviae Radix. Chemometric analyses of the commercial Saposhnikoviae Radix samples (64 batches collected from 11 cultivars aging from 1 to 4 years), based on the positive MSE data, in general could indicate large discrimination between Guan-Fang-Feng (from Heilongjiang) and the others, but negligible difference among Saposhnikoviae Radix from the other ten provinces of China and with different ages. Quantitative assays of prim-O-glucosylcimifugin and 4′-O-β-D-glucosyl-5-O-methylvisamminol, by a rapid and fully validated UHPLC-UV method, could primarily deduce that Guan-Fang-Feng aging 2 and 3 years exhibited better quality. The methods established can holistically assess the quality of TCM with wide spans of plant metabolites covered.

Protein extraction and database construction in tea rhizosphere soil

Soil protein extraction and database construction are the key points of soil proteomics research. In this paper, tea tree rhizosphere soil was used as material. The soil proteins were extracted three times by citrate, SDS, and mixture of citrate and SDS, respectively. The total proteins were separated by 2-DE electrophoresis and identified by Data Dependent Acquisition (DDA). The DDA data collection was further separated by High-Performance Liquid Chromatography (HPLC) and identified by LC–MS/MS, then to build the database of soil protein and microbial species using fungus and bacteria databases. The result showed soil protein was identified and reached 2741 points, and the molecular weight was mainly distributed in between 2.64 and 338.33 kDa, and Isoelectric point (pI) is mainly distributed in between 3.78 and 12.15. The soil protein was mainly from 138 families, 346 species of microorganisms. This optimization method could obtain more proteins than previous methods, with a wider range of molecular weight and pI. This study lays an important foundation for the research and development of soil metaproteomics.

Evaluation of significant features discovered from different data acquisition modes in mass spectrometry-based untargeted metabolomics

Despite the growing popularity of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, no study has yet to systematically compare the performance of different data acquisition modes in the discovery of significantly altered metabolic features, which is an important task of untargeted metabolomics for identifying clinical biomarkers and elucidating disease mechanism in comparative samples. In this work, we performed a comprehensive comparison of three most commonly used data acquisition modes, including full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA), using a metabolomics study of human plasma samples from leukemia patients before and after one-month chemotherapy. After optimization of data processing parameters, we extracted and compared statistically significant metabolic features from the results of each data acquisition mode. We found that most significant features can be consistently found in all three data acquisition modes with similar statistical performance as evaluated by Pearson correlation and receiver operating characteristic (ROC) analysis. Upon comparison, DDA mode consistently generated fewer uniquely found significant features than full-scan and DIA modes. We then manually inspected over 2000 uniquely discovered significant features in each data acquisition mode and showed that these features can be generally categorized into four major types. Many significant features were missed in DDA mode, primarily due to its low capability of detecting or extracting these features from raw LC-MS data. We thus proposed a bioinformatic solution to rescue these missing significant features from the raw DDA data with good reproducibility and accuracy. Overall, our work asserts that data acquisition modes can influence metabolomics results, suggesting room for improvement of data acquisition modes for untargeted metabolomics.

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

ISwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis.

The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files.

Comparison of serum protein profiles between major depressive disorder and bipolar disorder

BackgroundMajor depressive disorder and bipolar disorder are prevalent and debilitating psychiatric disorders that are difficult to distinguish, as their diagnosis is based on behavioural observations and subjective symptoms. Quantitative protein profile analysis might help to objectively distinguish between these disorders and increase our understanding of their pathophysiology. Thus, this study was conducted to compare the peripheral protein profiles between the two disorders.MethodsSerum samples were collected from 18 subjects with major depressive disorder and 15 subjects with bipolar disorder. After depleting abundant proteins, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and label-free quantification were performed. Data-dependent acquisition data were statistically analysed from the samples of 15 subjects with major depressive disorder and 10 subjects with bipolar disorder who were psychotropic drug-free. Two-sided t-tests were performed for pairwise comparisons of proteomes to detect differentially-expressed proteins (DEPs). Ingenuity Pathway Analysis of canonical pathways, disease and functions, and protein networks based on these DEPs was further conducted.ResultsFourteen DEPs were significant between subjects with major depressive disorder and those with bipolar disorder. Ras-related protein Rab-7a (t = 5.975, p = 4.3 × 10− 6) and Rho-associated protein kinase 2 (t = 4.782, p = 8.0 × 10− 5) were significantly overexpressed in subjects with major depressive disorder and Exportin-7 (t = -4.520, p = 1.5 × 10− 4) was significantly overexpressed in subjects with bipolar disorder after considering multiple comparisons. Bioinformatics analysis showed that cellular functions and inflammation/immune pathways were significantly different.ConclusionsRas-related protein Rab-7a, Rho-associated protein kinase 2, and Exportin-7 were identified as potential peripheral protein candidates to distinguish major depressive disorder and bipolar disorder. Further large sample studies with longitudinal designs and validation processes are warranted.

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition.

Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.