Data-independent Acquisition Data Research Articles

DIA-MS2pep: a library-free framework for comprehensive peptide identification from data-independent acquisition data.

Identifying peptides directly from data-independent acquisition (DIA) data remains challenging due to the highly multiplexed MS/MS spectra. Spectral library-based peptide detection is sensitive, but it is limited to the depth of the library and mutes the discovery potential of DIA data. We present here, DIA-MS2pep, a library-free framework for comprehensive peptide identification from DIA data. DIA-MS2pep uses a data-driven algorithm for MS/MS spectrum demultiplexing using the fragments data without the need of a precursor. With a large precursor mass tolerance database search, DIA-MS2pep can identify the peptides and their modified forms. We demonstrate the performance of DIA-MS2pep by comparing it to conventional library-free tools in accuracy and sensitivity of peptide identifications using publicly available DIA datasets of varying samples, including HeLa cell lysates, phosphopeptides, plasma, etc. Compared with data-dependent acquisition-based spectral libraries, spectral libraries built directly from DIA data with DIA-MS2pep improve the accuracy and reproducibility of the quantitative proteome.

Investigation of Effects of the Spectral Library on Analysis of diaPASEF Data

Targeted analysis of data-independent acquisition (DIA) data needs a spectral library, which is generated by data-dependent acquisition (DDA) experiments or directly from DIA data. A comparison of the DDA library and DIA library in analyzing DIA data has been reported. However, the effects of different spectral libraries on the analysis of diaPASEF data have not been investigated. Here, we generate different spectral libraries with varying proteome coverage to analyze parallel accumulation-serial fragmentation (diaPASEF) data. Besides, we also employ the library-free strategy. The library, constructed by extensive fractionation DDA experiments, produces the highest numbers of precursors and proteins but with a high percentage of missing values. The library-free strategy identifies 10-20% fewer proteins than the library-based method but with a high degree of data completeness. A further study shows that the library-free strategy, although it identifies fewer proteins than the library-based method, leads to similar biological conclusions as the library-based method.

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues.

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR=1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.

Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease.

One of the potential benefits of using data-independent acquisition (DIA) proteomics protocols is that information not originally targeted by the study may be present and discovered by subsequent analysis. Herein, we reanalyzed DIA data originally recorded for global proteomic analysis to look for isomerized peptides, which occur as a result of spontaneous chemical modifications to long-lived proteins. Examination of a large set of human brain samples revealed a striking relationship between Alzheimer's disease (AD) status and isomerization of aspartic acid in a peptide from tau. Relative to controls, a surprising increase in isomer abundance was found in both autosomal dominant and sporadic AD samples. To explore potential mechanisms that might account for these observations, quantitative analysis of proteins related to isomerization repair and autophagy was performed. Differences consistent with reduced autophagic flux in AD-related samples relative to controls were found for numerous proteins, including most notably p62, a recognized indicator of autophagic inhibition. These results suggest, but do not conclusively demonstrate, that lower autophagic flux may be strongly associated with loss of function in AD brains. This study illustrates that DIA data may contain unforeseen results of interest and may be particularly useful for pilot studies investigating new research directions. In this case, a promising target for future investigations into the therapy and prevention of AD has been identified.

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Intact N-Linked Glycopeptides.

N-linked protein glycosylation is a key regulator in various biological functions. Previous studies have shown that aberrant glycosylation is associated with many diseases. Therefore, it is essential to elucidate protein modifications of glycosylation by quantitatively profiling intact N-linked glycopeptides. Data-independent acquisition (DIA) mass spectrometry (MS) is a cost-effective, flexible, and high-throughput method for global proteomics. However, substantial challenges are still present in the quantitative analysis of intact glycopeptides with high accuracy at high throughput. In this study, we have established a novel integrated platform for the DIA analysis of intact glycopeptides isolated from complex samples. The established analysis platform utilizes a well-designed DIA-MS method for raw data collection, a spectral library constructed specifically for intact glycopeptide quantification providing accurate results by the inclusion of Y ions for quantification and filtering of quantified intact glycopeptides with low-quality MS2 spectra automatically using a set of criteria. Intact glycopeptides isolated from human serum were used to evaluate the performance of the integrated platform. By utilizing 100 isolation windows for DIA data acquisition, a well-constructed human serum spectral library containing 1123 nonredundant intact glycopeptides with Y ions, and automated data inspection, 620 intact glycopeptides were quantified with high confidence from DIA-MS. In summary, our integrated platform can serve as a reliable quantitative tool for characterizing intact glycopeptides isolated from complex biological samples to assist our understanding of biological functions of N-linked glycosylation.

An enhanced strategy integrating offline two-dimensional separation with data independent acquisition mode and deconvolution: Characterization of metabolites of Uncaria rhynchophylla in rat plasma as a case

The importance to clarify the drug metabolites is beyond doubt in view of their potential efficacy and safety. However, due to the complex matrix interference, relatively low content and the co-eluting effect, it is of a great challenge to comprehensively and systematically characterize the metabolites in vivo, especially for the traditional Chinese medicines (TCMs) due to the numerous types of components. In the present study, a comprehensive off-line two-dimensional separation system combining with data independent acquisition (DIA) mode and multi-dimensional data deconvolution method was established for chromatographic separation, data acquisition and data procession of indole alkaloids in rat plasma after intragastrically administrated with the extract of Uncaria rhynchophylla at the dose of 1 g/kg. The orthogonality of the off-line 2D separation system consisting of HILIC for first-dimensional separation and the PRLC for second-dimensional separation was valuated with the “asterisk” equations, and the results showed that off-line 2D separation system had passable orthogonality (A0 = 53.3%). Furthermore, the DIA mode was applied to capture MS/MS spectra in view of its advantage in acquiring MS data, and an effective multi-dimensional deconvolution method integrating the calculation of chemical formula, the extraction of diagnostic ion, the filter of ring double bond (RDB) and the judgement of neutral loss was established to parse the spectra for the complicated DIA data for comprehensive analysis of metabolites in rat plasma. Ultimately, a total of 127 indole alkaloids were tentatively characterized, and the main metabolic pathways were inferred as demethylation, dehydrogenation, hydroxylation and deglycosylation. The off-line two-dimensional separation system was applied for the comprehensive characterization of metabolites in vivo for the first time. This study suggested a new approach to enable the enrichment, separation and analysis of the low content components in vivo.

Alpha-XIC: a deep neural network for scoring the coelution of peak groups improves peptide identification by data-independent acquisition mass spectrometry.

The peptide-centric identification methodologies of data-independent acquisition (DIA) data mainly rely on scores for the mass spectrometric signals of targeted peptides. Among these scores, the coelution scores of peak groups constructed by the chromatograms of peptide fragment ions have a significant influence on the identification. Most of the existing coelution scores are achieved by artificially designing some functions in terms of the shape similarity, retention time shift of peak groups. However, these scores cannot characterize the coelution robustly when the peak group is in the circumstance of interference. On the basis that the neural network is more powerful to learn the implicit features of data robustly from a large number of samples, and thus minimizing the influence of data noise, in this work, we propose Alpha-XIC, a neural network-based model to score the coelution. By learning the characteristics of the coelution of peak groups derived from the being analyzed DIA data, Alpha-XIC is capable of yielding robust coelution scores even for peak groups with interference. With this score appending to initial scores generated by the accompanying identification engine DIA-NN, the ensuing statistical validation can report the identification result and recover the misidentified peptides. In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5 to 2 h, Alpha-XIC delivered 9.4-16.2% improvements in the number of identified precursors at 1% false discovery rate. Furthermore, Alpha-XIC was tested on LFQbench, a mixed-species dataset with known ratios, and increased the number of peptides and proteins fell within valid ratios by up to 16.4% and 17.8%, respectively, compared to the initial identification by DIA-NN. Source code is available at https://github.com/YuAirLab/Alpha-XIC.

DIAmeter: matching peptides to data-independent acquisition mass spectrometry data.

MotivationTandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories.ResultsHere, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool.Availability and implementationThe open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms).Supplementary informationSupplementary data are available at Bioinformatics online.

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/.

Proteomic analyses on the browning of shade-dried Thompson seedless grape

China is one of the main producers in the worldwide raisin market. Most China’s raisins are produced in Xinjiang where the Thompson seedless grape (Vitis vinifera L.cv.Thompson seedless) is the main variety of green raisin. However, the browning of Thompson seedless grape during drying has been well-acknowledged as the primary factor affecting the development of the raisin industry. Data independent acquisition (DIA)-based protein profiling was performed on fresh and shade-dried Thompson seedless grapes. As a result, 5431 proteins were identified, among which the amounts of 739 proteins in fresh grape were found to be significantly different with those in dried grape. The functional annotation based on the Blast2GO showed that the ‘organic substance metabolic process’, ‘regulation of molecular function’, ‘enzyme regulator activity’, and ‘isomerase activity’ related proteins became very active during browning. Further analyses revealed that the browning-related proteins, which with significant different amounts in fresh and in dried grapes, are primarily involved in the phenylpropanoid biosynthesis, tyrosine metabolism, phenylalanine metabolism, oxidative phosphorylation metabolism, plutathione metabolism, peroxisome pathway, and fatty acid degradation. And five random differential proteins were verified with parallel reaction monitoring (PRM). The PRM results were in agreement with the DIA data. The main browning-related proteins of Thompson seedless grape were identified in this study. Their properties were tested, and their roles in the browning mechanism were indicated. This will lay base to a better understanding on the enzymatic browning of Thompson seedless grape, and it will also provide guidance for controlling the quality of Thompson seedless grapes in industry.

A data-independent acquisition-based global phosphoproteomics system enables deep profiling

Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.

Novel acquisition strategies for metabolomics using drift tube ion mobility-quadrupole resolved all ions time-of-flight mass spectrometry (IM-QRAI-TOFMS)

The focus of this work was the implementation of ion mobility (IM) and a prototype quadrupole driver within data independent acquisition (DIA) using a drift tube IM-QTOFMS aiming to improve the level of confidence in identity confirmation workflows for non-targeted metabolomics. In addition to conventional all ions (IM-AI) acquisition, quadrupole resolved all ions (IM-QRAI) acquisition allows a drift time-directed precursor ion isolation in DIA using sequential isolation of precursor ions using mass windows of up to 100 Da which can be rapidly ramped across single ion mobility transients (i.e., <100 ms) according to the arrival times of precursor ions. Both IM-AI and IM-QRAI approaches were used for identity confirmation and relative quantification of metabolites in cellular extracts of the cell factory host Pichia pastoris. Samples were spiked with a uniformly 13C-labeled (U13C) internal standard and LC with low-field drift tube IM separation was used in combination with IM-AI and IM-QRAI. Combining excellent hardware performance and correlation of IM arrival times of natural (natC) and U13C metabolites enabled alignment of signals in the arrival time domain (DTCCSN2 differences ≤0.3%), and, in the case of IM-QRAI operation, maintenance of quantitative signals in comparison to IM-AI. The combination of tailored IM-QRAI methods for precursor ion isolation and IM separation also minimized the occurrence of spectral interferences in complex DIA datasets. Combined use of the software tools MS-DIAL, MS-Finder and Skyline for peak picking, feature alignment, reconciliation of natC and U13C isotopologue pairs, deconvolution of fragment spectra from DIA data, identity confirmation (including DTCCSN2) and targeted re-extraction of datafiles were employed for the data processing workflow. Overall, the combined new acquisition and data processing approaches enabled 87 metabolites to be identified between Level 1 (identified by standard compound) and Level 3.2 (accurate mass spectrum and number of carbons confirmed). The developed methods constitute promising metabolomics discovery tools and can be used to elucidate the number of carbon atoms present in unknown metabolites in stable isotope-supported metabolomics.

Recognizing Contamination Fragment Ions in Liquid Chromatography-Tandem Mass Spectrometry Data.

Tandem mass spectral (MS/MS) data in liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis are often contaminated as the selection of precursor ions is based on a low-resolution quadrupole mass filter. In this work, we developed a strategy to differentiate contamination fragment ions (CFIs) from true fragment ions (TFIs) in an MS/MS spectrum. The rationale is that TFIs should coelute with their parent ions, but CFIs should not. To assess coelution, we performed a parallel LC-MS/MS analysis in data-independent acquisition (DIA) with all-ion-fragmentation (AIF) mode. Using the DIA (AIF) data, peak-peak correlation (PPC) score is calculated between the extracted ion chromatogram (EIC) of the fragment ion using the MS/MS scans and the EIC of the precursor ion using the MS1 scans. A high PPC score is an indication of TFIs, and a low PPC score is an indication of CFIs. Tested using metabolomics data generated by high resolution QTOF and Orbitrap MS from various vendors in different LC-MS configurations, we found that more than 70% of the fragment ions have PPC scores < 0.8 and identified three common sources of CFIs, including (1) solvent contamination, (2) adjacent chemical contamination, and (3) undetermined signals from artifacts and noise. Combining PPC scores with other precursor and fragment ion information, we further developed a machine learning model that can robustly and conservatively predict CFIs. Incorporating the machine learning model, we created an R program, MS2Purifier, to automatically recognize CFIs and clean MS/MS spectra of metabolic features in LC-MS/MS data with high sensitivity and specificity.

Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition

Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SignificanceThe organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.

DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data

Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS2 spectral coverage of the DIA data and the high MS2 spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS2 coverage and high MS2 spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.

Diamond: a multi-modal DIA mass spectrometry data processing pipeline.

Currently, various software tools are used to support two mainstream workflows for data-independent acquisition (DIA) mass spectrometry (MS) data processing, namely, spectrum-centric scoring (SCS) and peptide-centric scoring (PCS). However, a fully automatic, easily reproducible and freely accessible pipeline that simultaneously integrates SCS and PCS strategies and supports both library-free and library-based modes is absent. We developed Diamond, a Nextflow-based, containerized, multi-modal DIA-MS data processing pipeline for peptide identification and quantification. Diamond integrated two mainstream workflows for DIA data analysis, namely, SCS and PCS, for use cases both with and without assay libraries. This multi-modal pipeline serves as a versatile, easy-to-use and easily extendable toolbox for large-scale DIA data processing. Diamond is hosted on GitHub (https://github.com/xmuyulab/Diamond) and is released under the highly permissive MIT license to encourage further customization and modification. The Docker image for Diamond is freely accessible at https://hub.docker.com/r/zeroli/diamond.

Sensitive Immunopeptidomics by Leveraging Available Large-Scale Multi-HLA Spectral Libraries, Data-Independent Acquisition, and MS/MS Prediction

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity—from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available.