QE Orbitrap质谱DIA测试隔离窗口设置为5.1相关文献

The use of high-resolution mass spectrometry (HRMS) for the simultaneous target and non-target analysis of pesticide residues in food control is a subject that has been studied over the last decade. However, proving its efficacy compared to the more established triple quadrupole mass spectrometers (QQQ-MS2) is challenging. Various HRMS platforms have been evaluated, seemingly showing this approach not to be as effective as QQQ-MS2 for quantitative analysis, especially in routine food testing laboratories. The two main reasons are (i) the lower sensitivity especially in the case of the fragment ions produced and (ii) the lack of familiarity and an understanding of the most appropriate combination of HRMS acquisition modes to use. In fact, the number of different acquisition modes can appear as a puzzle to inexperienced users. This work was therefore focused on obtaining experimental data to gain a better understanding of the extended acquisition capabilities of a new Q-Orbitrap platform. Experimental data were obtained for 244 pesticides and their degradation products in commodities of varying matrix complexity (tomato, onion, avocado, and orange) using various combinations of acquisition modes. The best results for targeted analysis were obtained with a combination of full scan (FS), all-ions fragmentation (AIF) and target MS2 (tMS2) modes, and for non-target analysis using full scan (FS) and data-dependent MS2 (ddMS2) modes. All these acquisition modes (FS, AIF, tMS2, and ddMS2) could be applied simultaneously with cycle times ≤ 1 s. The tMS2 especially, proved to be a very powerful approach to increase sensitivity for MS2 fragments and identification rates. Overall, the results for the various pesticide-commodity combinations were fully satisfactory in terms of limit of quantitation (LOQ) repeatability and identification when considered against the SANTE EU Guideline criteria. In addition, the screening capabilities were evaluated for a non-target survey with the use of spectral libraries, the presence of non-target compounds was detected, thus proving the efficacy of the proposed approach. Another issue often overlooked is the optimization of use of spectral libraries, but in our experiments the compounds present in these libraries were not blindly sought in the screening analyses. To minimize the potential for false positives detects in our study, the extractability of the compounds present in the libraries, was also taken into account. The extractability of compounds using a QuEChERS acetonitrile procedure was estimated based on the physicochemical properties of target compounds. By removing compounds that will not be extracted, reduces the occurrences of false detects, reducing the time required for data processing and thus improving the efficiency of the overall screening workflow.

… ) feature of Q-Orbitrap (Q Exactive) and developed MSX-DIA, … DIA method using quadruple and Orbitrap (Fig.1A): an isolation window width of 20 amu was used; entire mass range (m/z …

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.

The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ~8%; DDA = ~16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (~1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

… DDA or fragment ion screening based on DIA is better for LC-… will involve componentization, where m/z-RT features are … The risk of detector saturation in orbitrap-systems should be …

… (DIA) modes, allowing for both quantitative analysis and qualitative screening using Orbitrap's dd-MS/MS mode (Q Exactive) and Q-… of 0, 20, and 40 eV across an m/z range of 50–1000. …

Steady improvement in Orbitrap-based mass spectrometry (MS) technologies has greatly advanced the peptide sequencing speed and depth. In-depth analysis of the performance of state-of-the-art MS and optimization of key parameters can improve sequencing efficiency. In this study, we first systematically compared the performance of two popular data-dependent acquisition approaches, with Orbitrap as the first-stage (MS1) mass analyzer and the same Orbitrap (high-high approach) or ion trap (high-low approach) as the second-stage (MS2) mass analyzer, on the Orbitrap Fusion mass spectrometer. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. However, regardless of the acquisition method, there are still more than 60% of peptide features untargeted for MS2 scan. We then systematically optimized the MS parameters using the high-high approach. Increasing the isolation window in the high-high approach could facilitate faster scan speed, but decreased MS2 identification rate. On the contrary, increasing the injection time of MS2 scan could increase identification rate but decrease scan speed and the number of identified MS2 spectra. Dynamic exclusion time should be set properly according to the chromatography peak width. Furthermore, we found that the Orbitrap analyzer, rather than the analytical column, was easily saturated with higher loading amount, thus limited the dynamic range of MS1-based quantification. By using optimized parameters, 10 000 proteins and 110 000 unique peptides were identified by using 20 h of effective liquid chromatography (LC) gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor ion targeting, fragment ion detection, and chromatographic separation for high efficient data-dependent proteomics.

Protein post‐translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data‐independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, that is, detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA‐based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in‐depth and accurate PTMs measurements.

Proteomics has become an increasingly important tool in medical and medicinal applications. It is necessary to improve the analytical throughput for these applications, particularly in large-scale drug screening to enable measurement of a large number of samples. In this study, we aimed to establish an ultrafast proteomic method based on 5-min gradient LC and quadrupole-Orbitrap mass spectrometer (Q-Orbitrap MS). We precisely optimized data-independent acquisition (DIA) parameters for 5-min gradient LC and reached a depth of >5000 and 4200 proteins from 1000 and 31.25 ng of HEK293T cell digest in a single-shot run, respectively. The throughput of our method enabled the measurement of approximately 80 samples/day, including sample loading, column equilibration, and wash running time. We demonstrated that our method is applicable for the screening of chemical responsivity via a cell stimulation assay. These data show that our method enables the capture of biological alterations in proteomic profiles with high sensitivity, suggesting the possibility of large-scale screening of chemical responsivity.

Mass\nspectrometry (MS) enables specific and accurate quantification\nof proteins with ever-increasing throughput and sensitivity. Maximizing\nthis potential of MS requires optimizing data acquisition parameters\nand performing efficient quality control for large datasets. To facilitate\nthese objectives for data-independent acquisition (DIA), we developed\na second version of our framework for data-driven optimization of\nMS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net)\nallows one to optimize and evaluate results from both label-free and\nmultiplexed DIA (plexDIA) and supports optimizations particularly\nrelevant to single-cell proteomics. We demonstrate multiple use cases,\nincluding optimization of duty cycle methods, peptide separation,\nnumber of survey scans per duty cycle, and quality control of single-cell\nplexDIA data. DO-MS allows for interactive data display and generation\nof extensive reports, including publication of quality figures that\ncan be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

The RNA-interacting proteome is commonly characterized by UV-crosslinking followed by RNA purification, with protein recovery quantified using SILAC labeling followed by data-dependent acquisition (DDA) of proteomic data. However, the low efficiency of UV-crosslinking, combined with limited sensitivity of the DDA approach often restricts detection to relatively abundant proteins, necessitating multiple mass spec injections of fractionated peptides for each biological sample. Here we report an application of data-independent acquisition (DIA) with SILAC in a total RNA-associated protein purification (TRAPP) UV-crosslinking experiment. This gave 15% greater protein detection and lower inter-replicate variation relative to the same biological materials analyzed using DDA, while allowing single-shot analysis of the sample. As proof of concept, we determined the effects of arsenite treatment on the RNA-bound proteome of HEK293T cells. The DIA dataset yielded similar GO term enrichment for RNA-binding proteins involved in cellular stress responses to the DDA dataset while detecting extra proteins unseen by DDA. Overall, the DIA SILAC approach improved detection of proteins over conventional DDA SILAC for generating RNA-interactome datasets, at a lower cost due to reduced machine time.

… Orbitrap-XL equipped with HCD collision cell. Precursors were selected in the linear trap with an isolation window … Resulting fragment ions were detected in the Orbitrap mass analyzer …

… ) fixed DIA methods on the recently introduced Orbitrap Astral … contrast to the vertical isolation windows used in dia-PASEF (… dia-PASEF scans to prevent a multitude of isolation windows …

… RF; (2) DIA-based MS2 acquisition with 15 Th isolation windows for precursors (z=2-7), applying 35% normalized HCD energy and 1×10 5 AGC target, with profile mode data recording. …

… -DIA method can be used on other instruments besides the Orbitrap Fusion/ Lumos instruments and with other DIA … scans with different isolation windows to generate DIA segments from …

Data-Independent Acquisition (DIA) LC–MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.

… [14] used TOF-MS screening in positive ionization mode with DIA with ChromaLynx XS data … The present study showed that Orbitrap technology was suitable for qualitative plasma …

This review traces the first 20 years of Orbitrap mass spectrometry as a mainstream high‑resolution and accurate‑mass (HR/AM) technology. It outlines the historical development of the Orbitrap analyzer, the evolution of major instrument families, and the key technological innovations that enabled its widespread adoption. Particular emphasis is placed on hybrid and Tribrid architectures, democratization of HR/AM through benchtop platforms, extension to high‑mass and native analysis, and integration with ion mobility, advanced fragmentation methods, and emerging applications such as structural biology, isotope ratio measurements, and space research. The review concludes with a perspective on future directions and the anticipated role of Orbitrap instrumentation as the core workhorse in analytical laboratories worldwide.

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation, and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micropillar array columns, as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4% were obtained even at only 1.5 data points per peak (full width at half-maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology, and molecular biology.

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2–4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

Data‐independent acquisition (DIA) is an emerging technology for quantitative proteomics. Current DIA focusses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z. An alternative approach is WiSIM‐DIA, which combines conventional DIA with wide‐SIM (wide selected‐ion monitoring) windows to partition the precursor m/z space to produce high‐quality precursor ion chromatograms. However, WiSIM‐DIA has been underexplored; it remains unclear if it is a viable alternative to DIA. We demonstrate that WiSIM‐DIA quantified more than 24 000 unique peptides over five orders of magnitude in a single 2 h analysis of a neuronal synapse‐enriched fraction, compared to 31 000 in DIA. There is a strong correlation between abundance values of peptides quantified in both the DIA and WiSIM‐DIA datasets. Interestingly, the S/N ratio of these peptides is not correlated. We further show that peptide identification directly from DIA spectra identified >2000 proteins, which included unique peptides not found in spectral libraries generated by DDA.

… Thus, a narrower isolation window is required to reduce the … a narrower isolation window requires more windows to cover … , we present a narrow isolation window (0.6 Th) DIA method to …

… DIA all ions within a certain m/z window (25 Da) are fragmented in the MS/MS analysis and multiple windows … Isolated ACL injury was found via clinical exam (þLachman test with intra-…

The combination of short liquid chromatography (LC) gradients and data-independent acquisition (DIA) by mass spectrometry (MS) has proven its huge potential for high-throughput proteomics. However, the optimization of isolation window schemes resulting in a certain number of data points per peak (DPPP) is understudied, although it is one of the most important parameters for the outcome of this methodology. In this study, we show that substantially reducing the number of DPPP for short-gradient DIA massively increases protein identifications while maintaining quantitative precision. This is due to a large increase in the number of precursors identified, which keeps the number of data points per protein almost constant even at long cycle times. When proteins are inferred from its precursors, quantitative precision is maintained at low DPPP while greatly increasing proteomic depth. This strategy enabled us to quantify 6018 HeLa proteins (>80 000 precursor identifications) with coefficients of variation below 20% in 30 min using a Q Exactive HF, which corresponds to a throughput of 29 samples per day. This indicates that the potential of high-throughput DIA-MS has not been fully exploited yet. Data are available via ProteomeXchange with identifier PXD036451.

The characterization of peptides presented by human leukocyte antigen (HLA) class I molecules is crucial for understanding immune processes, biomarker discovery, and the development of novel immunotherapies or vaccines. Mass spectrometry allows the direct identification of thousands of HLA‐bound peptides from cell lines, blood, or tissue. In recent years, data‐independent acquisition (DIA) mass spectrometry methods have evolved, promising to increase reproducibility and sensitivity over classical data‐dependent acquisition (DDA) workflows. Here, we describe a DIA setup on the Q Exactive mass spectrometer, optimized regarding the unique properties of HLA class I peptides. The methodology enables sensitive and highly reproducible characterization of HLA peptidomes from individual cell lines. From up to 16 DDA analyses of 100 million human cells, more than 10 000 peptides could be confidently identified, serving as basis for the generation of spectral libraries. This knowledge enabled the subsequent interrogation of DIA data, leading to the identification of peptide sets with >90% overlap between replicate samples, a prerequisite for the comparative study of closely related specimens. Furthermore, >3000 peptides could be identified from just one million cells after DIA analysis using a library generated from 300 million cells. The reduction in sample quantity and the high reproducibility of DIA‐based HLA peptidome analysis should facilitate personalized medicine applications.

We describe an improved version of the data‐independent acquisition (DIA) computational analysis tool DIA‐Umpire, and show that it enables highly sensitive, untargeted, and direct (spectral library‐free) analysis of DIA data obtained using the Orbitrap family of mass spectrometers. DIA‐Umpire v2 implements an improved feature detection algorithm with two additional filters based on the isotope pattern and fractional peptide mass analysis. The targeted re‐extraction step of DIA‐Umpire is updated with an improved scoring function and a more robust, semiparametric mixture modeling of the resulting scores for computing posterior probabilities of correct peptide identification in a targeted setting. Using two publicly available Q Exactive DIA datasets generated using HEK‐293 cells and human liver microtissues, we demonstrate that DIA‐Umpire can identify similar number of peptide ions, but with better identification reproducibility between replicates and samples, as with conventional data‐dependent acquisition. We further demonstrate the utility of DIA‐Umpire using a series of Orbitrap Fusion DIA experiments with HeLa cell lysates profiled using conventional data‐dependent acquisition and using DIA with different isolation window widths.

… system was coupled to Q-Exactive MS (Thermo … the DIA strategy with 25 Da isolation window. (C) MS/MS spectra of arginine generated by the DIA strategy with 50 Da isolation window. (…

… window settings in each DIA methods. We further included a routine DDA method with 1.2-m/z isolation … directly used to identify peptides in 5 m/z window DIA, we applied MaxQuant [15] …

In this article, we provide a proteomic reference dataset that has been initially generated for a benchmarking of software tools for Data-Independent Acquisition (DIA) analysis. This large dataset includes 96 DIA .raw files acquired from a complex proteomic standard composed of an E.coli protein background spiked-in with 8 different concentrations of 48 human proteins (UPS1 Sigma). These 8 samples were analyzed in triplicates on an Orbitrap mass spectrometer with 4 different DIA window schemes. We also provide the spectral libraries and FASTA file used for their analysis and the software outputs of the six tools used in this study: DIA-NN, Spectronaut, ScaffoldDIA, DIA-Umpire, Skyline and OpenSWATH. This dataset also contains post-processed quantification tables where the peptides and proteins have been validated, their intensities normalized and the missing values imputed with a noise value. All the files are available on ProteomeXchange. Altogether, these files represent the most comprehensive DIA reference dataset acquired on an Orbitrap instrument ever published. It will be a very useful resource to the proteomic scientists in order to assess the performance of DIA software tools or to test their processing pipelines, to the software developers to improve their tools or develop new ones and to the students for their training on proteomics data analysis.

RATIONALE Multiplexed quantitative proteomics enables simultaneous analysis of multiple biological samples, increasing throughput while reducing instrument time and sample requirements. However, integrating sample multiplexing with data-independent acquisition (DIA) remains challenging. We present a TMTpro plexDIA strategy leveraging MS1-level mass differences between nonisobaric TMTpro reagent variants to enable multiplexed quantification without compromising DIA sensitivity. METHODS Three Saccharomyces cerevisiae deletion strains (Δmet6, Δpfk2, and Δura2) were labeled with TMTproZero (light), TMTpro16 (standard), and super-heavy TMTpro (heavy), mixed in three permutations, and analyzed by narrow-window DIA (2 m/z isolation, 300 scan events) on an Orbitrap Astral mass spectrometer. Database searching was performed using FragPipe/MSFragger with plex-DIA quantification enabled. RESULTS Over 2000 protein groups and over 20 000 precursors were identified per channel per mixture, with closely matched identification rates across all three channels. All nine expected deletion patterns were correctly identified, with channel-specific depletion reproduced consistently across precursor charge states (2+, 3+, and 4+). CONCLUSIONS TMTpro plex-DIA enables accurate, multiplexed quantitative proteomics through MS1-level mass separation of the nonisobaric TMTpro isotopologs. The characteristic deletion patterns observed for each knockout strain serve as intrinsic molecular barcodes, validating sample identity and demonstrating the broad utility of plex-DIA for high-throughput, multiplexed proteomics applications.

Targeted analysis of data-independent\nacquisition (DIA) mass spectrometry\ndata requires elegant software tools and strict statistical control.\nOpenSWATH-PyProphet-TRIC is a widely used DIA data analysis workflow.\nThe OpenSWATH-PyProphet-TRIC workflow is typically executed by running\ncommand lines. Here, we present QuantPipe, which is a graphic interface\nsoftware tool based on the OpenSWATH-PyProphet-TRIC workflow. In addition\nto OpenSWATH-PyProphet-TRIC functions, QuantPipe can convert the spectral\nlibrary to the assay library and output peptides and protein intensities.\nWe demonstrated that QuantPipe can be used to analyze SWATH-MS data\nfrom TripleTOF 5600 and TripleTOF 6600, phospho-SWATH-MS data, DIA\ndata from Orbitrap instrument, and diaPASEF data from TimsTOF Pro\ninstrument. The executable files, user manual, and source code of\nQuantPipe are freely available at https://github.com/tachengxmu/QuantPipe/releases.

… standard 480 Orbitrap Exploris 480 DDA and DIA protocols, … windows on both Orbitrap Astral and timsTOF Ultra, DIA on … 725% more than Orbitrap Exploris 480 in DIA acquisition mode. …

… coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode … Variable isolation windows were applied, and the detailed information is provided in …

… In Orbitrap-based mass spectrometers, separation efficiency of … injections to achieve narrower DIA windows for better gas-… of windows is allowed, and fixed or variable window schemes …

… for a Thermo Scientific Orbitrap mass analyzer to record … DIA, we investigated this strategy in more detail. Figure 5A shows the advantage of using variable rather than fixed windows…

… (Var-DIA) confirmed IsoPS-DIA’s superior accuracy and … IsoPS-DIA is compatible with both Orbitrap and quadrupole time-of-flight … DIA with fixed scanning window (Fix-DIA), variable Q1 …

Quadrupole Orbitrap instruments (Q Orbitrap) permit high-resolution mass spectrometry-based full scan acquisitions and have a number of acquisition modes where the quadrupole isolates a particular mass range prior to a possible fragmentation and high-resolution mass spectrometry-based acquisition. Selecting the proper acquisition mode(s) is essential if trace analytes are to be quantified in complex matrix extracts. Depending on the particular requirements, such as sensitivity, selectivity of detection, linear dynamic range, and speed of analysis, different acquisition modes may have to be chosen. This is particularly important in the field of multi-residue analysis (eg, pesticides or veterinary drugs in food samples) where a large number of analytes within a complex matrix have to be detected and reliably quantified. Meeting the specific detection and quantification performance criteria for every targeted compound may be challenging. It is the aim of this paper to describe the strengths and the limitations of the currently available Q Orbitrap acquisition modes. In addition, the incorporation of targeted acquisitions between full scan experiments is discussed. This approach is intended to integrate compounds that require an additional degree of sensitivity or selectivity into multi-residue methods.

… -Orbitrap offer unique applications for pesticide analysis using full MS scan with data independent acquisition (DIA) … using either DIA at 17,500 FWHM with 9 isolation windows or all ion …

… We developed a new variable window DIA method specific for serum samples. Without protein depletion and prefractionation, the single-run DIA method allows the quantification of over …

Orbitrap Exploris 480 MS with FAIMS Pro provides fast, sensitive and robust profiling of proteomes when combined with Evosep One. The combination of Data Independent Acquisition (DIA) and FAIMS with single compensation voltages enables analysis of up to 2000 peptides per LC gradient minute and more than 5000 protein groups in twenty minutes. DIA-FAIMS based label-free quantitation achieves similar depth and quantitative accuracy as the well-established isobaric labelling approaches, but with less LC-MS measurement time, making it a good choice for large cohort of samples. Graphical Abstract Highlights Increased proteome coverage with Orbitrap Exploris 480 MS and FAIMS using single compensation voltages and short LC gradients. Towards single-cell proteomics with high-sensitivity analysis of 5 ng HeLa with more than 1,000 proteins identified in 5 minutes using FAIMS and DIA. Deep proteome profiling across twelve rat organs tissues by label-free quantitation using DIA compared to TMT-multiplexing and turboTMT acquisition using phi-SDM. Rapid and sensitive phosphoproteomics with automated enrichment using Ti-IMAC magnetic beads and direct DIA analysis. State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 min LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument, we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430–670 <i>m</i>/<i>z</i> using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

The data-independent acquisition\n(DIA) performed in the latest\nhigh-resolution, high-speed mass spectrometers offers a powerful analytical\ntool for biological investigations. The DIA mass spectrometry (DIA-MS)\ncombined with the isotopic labeling approach holds a particular promise\nfor increasing the multiplexity of DIA-MS analysis, which could assist\nthe relative protein quantification and the proteome-wide turnover\nprofiling. However, the wide MS1 isolation windows employed in conventional\nDIA methods lead to a limited efficiency in identifying and quantifying\nisotope-labeled peptide pairs through peptide fragment ions. Here,\nwe optimized a high-selectivity DIA-MS named BoxCarmax that supports\nthe analysis of complex samples, such as those generated from Stable\nisotope labeling by amino acids in cell culture (SILAC) and pulse\nSILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition\nat both MS1 and MS2 levels, through the integration of BoxCar and\nMSX features, as well as a gas-phase separation strategy. We found\nBoxCarmax significantly improved the quantitative accuracy in SILAC\nand pSILAC samples by mitigating the ratio suppression of isotope–peptide\npairs. We further applied BoxCarmax to measure protein degradation\nregulation during serum starvation stress in cultured cells, revealing\nvaluable biological insights. Our study offered an alternative and\naccurate approach for the MS analysis of protein turnover and complex\nsamples.

Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets.

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation–enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

… The CE for each window was determined based on the … Summarizes the measurement schemes compared in this … when comparing DIA on the TripleTOF with DIA on the Q Exactive HF …

Data independent acquisition (DIA) is an attractive method for quantitative proteomics. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. Studies of non-human organisms, splice junctions, sequence variants, or simply working with small sample yields can make developing spectrum libraries impractical. Here we illustrate a method to efficiently generate DIA-only chromatogram libraries and demonstrate best practices for how to acquire, queue, and validate DIA data without spectrum libraries. Graphical Abstract Highlights Rapid DIA-only library building with gas-phase fractionation. Recommended DIA acquisition strategies with staggered windows and forbidden zones. Optimized DIA instrument settings for several Thermo Orbitrap instruments. Data analysis tutorial using open source DIA software. Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.

The development of targeted assays that monitor biomedically relevant proteins is an important step in bridging discovery experiments to large scale clinical studies. Targeted assays are currently unable to scale to hundreds or thousands of targets. We demonstrate the generation of large-scale assays using a novel hybrid nominal mass instrument. The scale of these assays is achievable with the Stellar mass spectrometer through the accommodation of shifting retention times by real-time alignment, while being sensitive and fast enough to handle many concurrent targets. Assays were constructed using precursor information from gas-phase fractionation data-independent acquisition (DIA). We demonstrate the ability to schedule methods from orbitrap and linear ion trap acquired gas-phase fractionation DIA library, and compare the quantification of a matrix-matched calibration curve from orbitrap DIA and linear ion trap parallel reaction monitoring (PRM). Two applications of these proposed workflows are shown with a cerebrospinal fluid neurodegenerative disease protein PRM assay and with a Mag-Net enriched plasma extracellular vesicle protein survey PRM assay. In cerebrospinal fluid, our assay targets proteins discovered previously to be associated with Alzheimer’s disease in a small independent sample set. For the Mag-Net enriched plasma survey assay, we observe that proteins selected based on their measurement robustness are still able to capture differences in abundance across disease groups in a small sample set. These highlight the application of highly multiplex, targeted protein assays in clinical research.

Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co‐purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme‐linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC‐MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace‐level HCPs. Here, we investigated the benefits of adding high‐field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC‐MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub‐ng/mg of mAb level.

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever‐growing number of high‐throughput proteomics methods that are available. This review discusses how data‐independent acquisition (DIA) and co‐fractionation mass spectrometry (CF‐MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF‐DIA‐MS shows promise in expanding our knowledge of interactomes, notably for non‐model organisms (NMOs). CF‐MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in‐depth understanding into the dynamics of numerous biological processes.

Data-independent acquisition (DIA) is a promising method for quantitative proteomics. Library-based DIA database searching against project-specific data-dependent acquisition (DDA) spectral libraries is the gold standard. These libraries are constructed using material-consuming pre-fractionation two dimensional DDA analysis. The alternative to this is library-free DIA analysis. Limited sample amounts restrict the use of fractionation to build spectral libraries for post-translational modifications (PTMs) DIA analysis. We present the use of gas-phase fractionation (GPF) DDA data to improve the depth of library-free DIA identification for the phosphoproteome, called GPF-DDA hybrid DIA. This method fully utilizes the remnants of samples post-DIA analysis and leverages both library-based and -free DIA database searching. GPF-DDA hybrid DIA analyzes phosphopeptides surplus sample after DIA analysis using a number of DDA injections with each scanning different mass-to-charge (m/z) windows, instead of preforming traditional off-line fractionation-based DDA. The GPF-DDA data is integrated into the library-free DIA database search to create a hybrid library, enhancing phosphopeptide identification. Two GPF-DDA injections proved to increase 18 % phosphopeptide and 13 % phosphosite identification in HEK293 cell lines, while five injections resulted in up to 28 % phosphopeptide and 21 % phosphosite increases compared to library-free DIA analysis alone. We used GPF-DDA hybrid DIA phosphoproteomics to characterize lung tissue upon direct (smoke induced) and indirect (sepsis induced) acute lung injury (ALI) in mice. The differentially expressed phosphosites (DEPsites) in direct ALI were found in proteins related to mRNA processing and RNA. DEPsites in indirect ALI were enriched in proteins related to microtubule polymerization, positive regulation of microtubule polymerization and fibroblast migration. This study demonstrates that GPF-DDA hybrid DIA analysis workflow can indeed promote depth of DIA analysis of phosphoproteome and could be extended to DIA analysis of other PTMs.

… Compared with data-independent acquisition (DIA), using … but effective approach, gasphase fractionation (GPF), dividing … Q-Tof and LTQorbitrap with different MS2 acquisition rates. …

Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition (DDA) and Sequential Window Acquisition of all THeoretical fragment ions (SWATH-type) Data Independent Acquisition (DIA) workflows on a high-field Orbitrap FusionTM TribridTM instrument for the robust identification and quantification of metabolites in human plasma. By using a set of 47 exogenous and 72 endogenous molecules, we compared the efficiency and complementarity of both approaches. We exploited the versatility of this mass spectrometer to collect meaningful MS/MS spectra at both high- and low-mass resolution and various low-energy collision-induced dissociation conditions under optimized DDA conditions. We also observed that complex and composite DIA-MS/MS spectra can be efficiently exploited to identify metabolites in plasma thanks to a reference tandem spectral library made from authentic standards while also providing a valuable data resource for further identification of unknown metabolites. Finally, we found that adding multi-event MS/MS acquisition did not degrade the ability to use survey MS scans from DDA and DIA workflows for the reliable absolute quantification of metabolites down to 0.05 ng/mL in human plasma.

Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.

… identify numerous peptides from a single tandem mass spectrum that are inherently recorded when using such relatively large DIA isolation widths. Simulations indicate that sufficient …

… or multiplexed fragment ion spectra generated by DIA require more elaborate processing … However, large isolation widths increase the number of precursors concurrently fragmented in …

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.

Proteomic studies using data-independent acquisition (DIA) have gained momentum in all fields of biology. Search engines are evolving to keep up with the latest developments in instrument technology. DIA-NN is the most popular software for DIA analysis under an academic use license. The QuantUMS algorithm in DIA-NN improves quantification quality control by calculating three scores (protein group MaxLFQ quality, empirical quality, and quantity quality) that assess the agreement between MS1 and MS2 features. Here, we show that applying specific cutoffs to these scores can significantly impact the results. To enable you to make a more informed decision about what represents a reasonable trade-off (identification and quantification), we evaluated the impact of different combinations of the scores on data acquired using different isolation windows and a mixture of two species with a known ratio. To test consistency and reproducibility across the six different versions of DIA-NN, we compared them and found high reproducibility except for version 1.9. We show that filtering by QuantUMS scores removes proteins with low abundances and high coefficients of variation. Finally, we developed the QC4DIANN Shiny application in the R language for interactive quality control automation.

Data-independent acquisition (DIA) in liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of molecular data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small molecules by mass spectral deconvolution. For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between the algal strains.

… in the product ion spectra. In these DIA workflows, a user-defined mass isolation width is either … Mass spectrometric analysis of tryptic peptides was performed using both a Synapt G2-Si …

Data-independent tandem mass spectrometry isolates and fragments all of the molecular species within a given mass-to-charge window, regardless of whether a precursor ion was detected within the window. For shotgun proteomics on complex protein mixtures, data-independent MS/MS offers certain advantages over the traditional data-dependent MS/MS: identification of low-abundance peptides with insignificant precursor peaks; more direct relative quantification, free of biases caused by competing precursors and dynamic exclusion; and faster throughput due to simultaneous fragmentation of multiple peptides. However, data-independent MS/MS, especially on low-resolution ion-trap instruments, strains standard peptide identification programs, because of less precise knowledge of the peptide precursor mass and large numbers of spectra composed of two or more peptides. Here we describe a computer program called DeMux that deconvolves mixture spectra and improves the peptide identification rate by ~25%. We compare the number of identifications made by data-independent and data-dependent MS/MS at the peptide and protein levels: conventional data-dependent MS/MS makes a greater number of identifications but is less reproducible from run to run.

Capillary zone electrophoresis (CE) combines high separation power, scalability, and speed to limited proteome analyses by mass spectrometry (MS). However, compressed separation in CE challenges the duty cycle of tandem MS, even during data-independent acquisition (DIA). To help remedy this limitation, we introduce the concept of <u>e</u>lectrophoresis-<u>co</u>rrelative (Eco) data acquisition for CE-MS. We recognize CE electrospray ionization (ESI) to sort peptide ions into reproducible mass-to-charge (<i>m</i>/<i>z</i>) vs migration time (MT) trends in the solution phase, before subsequent ionization and <i>m</i>/<i>z</i> analysis. We proposed that such a correlation can be leveraged to improve the economy of data acquisition. We test this hypothesis using DIA frames that are tailored to the observed <i>m</i>/<i>z</i>–MT trends. The resulting Eco-DIA method substantially improves the bandwidth utilization of tandem MS during CE-MS. In proof-of-principle studies, Eco-DIA identified and quantified ∼38% more proteins from 1 ng of the HeLa proteome digest compared to the classical DIA, without the assistance of a project-specific tandem MS spectral library. Eco-DIA was able to quantify ∼51% more proteins with <10% coefficient of variation vs the control DIA approach. Based on label-free quantification, the proteins that were exclusively measured by Eco-MS occupied the lower dynamic range of the detected proteome concentration, revealing sensitivity enhancement. In addition to marking the inception of Eco-MS, this work lays the foundation for the development of next-generation data acquisition strategies that leverage electrophoretic ion sorting for high-sensitivity proteomics.

Data dependent acquisition (DDA) and data independent acquisition (DIA) are traditionally separate experimental paradigms in bottom-up proteomics. In this work, we developed a strategy combining the two experimental methods into a single LC-MS/MS run. We call the novel strategy data dependent-independent acquisition proteomics, or DDIA for short. Peptides identified from DDA scans by a conventional and robust DDA identification workflow provide useful information for interrogation of DIA scans. Deep learning based LC-MS/MS property prediction tools, developed previously, can be used repeatedly to produce spectral libraries facilitating DIA scan extraction. A complete DDIA data processing pipeline, including the modules for iRT vs RT calibration curve generation, DIA extraction classifier training, and false discovery rate control, has been developed. Compared to another spectral library-free method, DIA-Umpire, the DDIA method produced a similar number of peptide identifications, but nearly twice as many protein group identifications. The primary advantage of the DDIA method is that it requires minimal information for processing its data.

Here we describe the use of data-independent acquisition (DIA) on a Q-Exactive mass spectrometer for the detection and quantification of peptides in complex mixtures using the Skyline Targeted Proteomics Environment (freely available online at http://skyline.maccosslab.org). The systematic acquisition of mass spectrometry (MS) or tandem MS (MS/MS) spectra by DIA is in contrast to DDA, in which the acquired MS/MS spectra are only suitable for the identification of a stochastically sampled set of peptides. Similarly to selected reaction monitoring (SRM), peptides can be quantified from DIA data using targeted chromatogram extraction. Unlike SRM, data acquisition is not constrained to a predetermined set of target peptides. In this protocol, a spectral library is generated using data-dependent acquisition (DDA), and chromatograms are extracted from the DIA data for all peptides in the library. As in SRM, quantification using DIA data is based on the area under the curve of extracted MS/MS chromatograms. In addition, a quality control (QC) method suitable for DIA based on targeted MS/MS acquisition is detailed. Not including time spent acquiring data, and time for database searching, the procedure takes ∼1–2 h to complete. Typically, data acquisition requires roughly 1–4 h per sample, and a database search will take 0.5–2 h to complete.

… Astral mass spectrometer. To enable a systematic comparison between DDA and DIA, we … The median for DIA decreased as isolation window width increased (Fig. 5E) from 0.92 (1.2 …

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECAN to build a plasma proteome library from DIA data and to query known sequence variants.