Data-independent Acquisition Data Research Articles

Development of a Gill Assay Library for Ecological Proteomics of Threespine Sticklebacks (Gasterosteus aculeatus)

A data-independent acquisition (DIA) assay library for quantitative analyses of proteome dynamics has been developed for gills of threespine sticklebacks (Gasterosteus aculeatus). A raw spectral library was generated by data-dependent acquisition (DDA) and annotation of tryptic peptides to MSMS spectra and protein database identifiers. The assay library was constructed from the raw spectral library by removal of low-quality, ambiguous, and low-signal peptides. Only unique proteins represented by at least two peptides are included in the assay library, which consists of 1506 proteins, 5074 peptides, 5104 precursors, and 25,322 transitions. This assay library was used with DIA data to identify biochemical differences in gill proteomes of four populations representing different eco- and morpho-types of threespine sticklebacks. The assay library revealed unique and reproducible proteome signatures. Warm-adapted, low-plated, brackish-water fish from Laguna de la Bocana del Rosario (Mexico) show elevated HSP47, extracellular matrix, and innate immunity proteins whereas several immunoglobulins, interferon-induced proteins, ubiquitins, proteolytic enzymes, and nucleic acid remodeling proteins are reduced. Fully-plated, brackish-water fish from Westchester Lagoon (Alaska) display elevated ion regulation, GTPase signaling, and contractile cytoskeleton proteins, altered abundances of many ribosomal, calcium signaling and immunity proteins, and depleted transcriptional regulators and metabolic enzymes. Low-plated freshwater fish from Lake Solano (California) have elevated inflammasomes and proteolytic proteins whereas several iron containing and ion regulatory proteins are reduced. Gills of fully-plated, marine fish from Bodega Harbor (California) have elevated oxidative metabolism enzymes and reduced transglutaminase 2, collagens, and clathrin heavy chains. These distinct proteome signatures represent targets for testing ecological and evolutionary influences on molecular mechanisms of gill function in threespine sticklebacks. Furthermore, the gill assay library represents a model for other tissues and paves the way for accurate and reproducible network analyses of environmental context-dependent proteome dynamics in complex organisms.

Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra.

In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.

Data-Independent Acquisition of HLA Class I Peptidomes on the Q Exactive Mass Spectrometer Platform.

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 10000 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.

PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data.

In mass spectrometry-based shogun proteomics, data-independent acquisition (DIA) is an emerging technique for unbiased and reproducible measurement of protein mixtures. Without targeting a specific precursor ion, DIA MS/MS spectra are often highly multiplexed, containing product ions from multiple co-fragmenting precursors. Thus, detecting peptides directly from DIA data is challenging; most DIA data analyses require spectral libraries. Here we present a new library-free, peptide-centric tool PECAN that detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, enabling detection of low abundance analytes with poor precursor ion signal. We benchmarked PECAN with chromatographic peak picking accuracy and peptide detection capability. We further validated PECAN detection with data-dependent acquisition and targeted analyses. Last, we used PECAN to build a library from DIA data and to query sequence variants. Together, these results show that PECAN detects peptides robustly and accurately from DIA data without using a library.

Enhanced differential expression statistics for data-independent acquisition proteomics

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

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

The consistent detection and quantification of protein post-translational modifications (PTMs) across sample cohorts is an essential prerequisite for the functional analysis of biological processes. Data-independent acquisition (DIA), a bottom-up mass spectrometry based proteomic strategy, exemplified by SWATH-MS, provides complete precursor and fragment ion information of a sample and thus, in principle, the information to identify peptidoforms, the modified variants of a peptide. However, due to the convoluted structure of DIA data sets the confident and systematic identification and quantification of peptidoforms has remained challenging. Here we present IPF (Inference of PeptidoForms), a fully automated algorithm that uses spectral libraries to query, validate and quantify peptidoforms in DIA data sets. The method was developed on data acquired by SWATH-MS and benchmarked using a synthetic phosphopeptide reference data set and phosphopeptide-enriched samples. The data indicate that IPF reduced false site-localization by more than 7-fold in comparison to previous approaches, while recovering 85.4% of the true signals. IPF was applied to detect and quantify peptidoforms carrying ten different types of PTMs in DIA data acquired from more than 200 samples of undepleted blood plasma of a human twin cohort. The data approportioned, for the first time, the contribution of heritable, environmental and longitudinal effects on the observed quantitative variability of specific modifications in blood plasma of a human population.

New targeted approaches for the quantification of data-independent acquisition mass spectrometry.

The use of data-independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult-to-obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high-quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data-dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state-of-the-art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.

MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition.

With recent advances in mass spectrometry, there is an increased interest in data-independent acquisition (DIA) techniques for metabolomics. With DIA technique, all metabolite ions are sequentially selected and isolated using a wide window to generate multiplexed MS/MS spectra. Therefore, DIA strategy enables a continuous and unbiased acquisition of all metabolites and increases the data dimensionality, but presents a challenge to data analysis due to the loss of the direct link between precursor ion and fragment ions. However, very few DIA data processing methods are developed for metabolomics application. Here, we developed a new DIA data analysis approach, namely, MetDIA, for targeted extraction of metabolites from multiplexed MS/MS spectra generated using DIA technique. MetDIA approach considers each metabolite in the spectral library as an analysis target. Ion chromatograms for each metabolite (both precursor ion and fragment ions) and MS(2) spectra are readily detected, extracted, and scored for metabolite identification, referred as metabolite-centric identification. A minimum metabolite-centric identification score responsible for 1% false positive rate of identification is determined as 0.8 using fully (13)C labeled biological extracts. Finally, the comparisons of our MetDIA method with data-dependent acquisition (DDA) method demonstrated that MetDIA could significantly detect more metabolites in biological samples, and is more accurate and sensitive for metabolite identifications. The MetDIA program and the metabolite spectral library is freely available on the Internet.

Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers.

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.

High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation.

Targeted analysis of data‐independent acquisition (DIA) data is a powerful mass spectrometric approach for comprehensive, reproducible and precise proteome quantitation. It requires a spectral library, which contains for all considered peptide precursor ions empirically determined fragment ion intensities and their predicted retention time (RT). RTs, however, are not comparable on an absolute scale, especially if heterogeneous measurements are combined. Here, we present a method for high‐precision prediction of RT, which significantly improves the quality of targeted DIA analysis compared to in silico RT prediction and the state of the art indexed retention time (iRT) normalization approach. We describe a high‐precision normalized RT algorithm, which is implemented in the Spectronaut software. We, furthermore, investigate the influence of nine different experimental factors, such as chromatographic mobile and stationary phase, on iRT precision. In summary, we show that using targeted analysis of DIA data with high‐precision iRT significantly increases sensitivity and data quality. The iRT values are generally transferable across a wide range of experimental conditions. Best results, however, are achieved if library generation and analytical measurements are performed on the same system.

Multiplexed peptide analysis using data-independent acquisition and Skyline.

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.

Automated Validation of Results and Removal of Fragment Ion Interferences in Targeted Analysis of Data-independent Acquisition Mass Spectrometry (MS) using SWATHProphet

Proteomics by mass spectrometry technology is widely used for identifying and quantifying peptides and proteins. The breadth and sensitivity of peptide detection have been advanced by the advent of data-independent acquisition mass spectrometry. Analysis of such data, however, is challenging due to the complexity of fragment ion spectra that have contributions from multiple co-eluting precursor ions. We present SWATHProphet software that identifies and quantifies peptide fragment ion traces in data-independent acquisition data, provides accurate probabilities to ensure results are correct, and automatically detects and removes contributions to quantitation originating from interfering precursor ions. Integration in the widely used open source Trans-Proteomic Pipeline facilitates subsequent analyses such as combining results of multiple data sets together for improved discrimination using iProphet and inferring sample proteins using ProteinProphet. This novel development should greatly help make data-independent acquisition mass spectrometry accessible to large numbers of users.

DIA and Spectronaut: comprehensive and precise proteome profiling

Introduction In recent years the proteome coverage using shotgun proteomics has steadily increased. Low complex proteomes, such as the E. coli or yeast proteomes, can be measured almost completely in a single LC-MS measurement. For relative quantitation, the label free approach has recently gained in popularity mainly due to its simplicity. However, this approach has been limited by the semi-stochastic nature of shotgun proteomics which leads to a large number of missing values, especially if many conditions are measured. Even though MS1 alignment attenuates the missing value problem, it is difficult to control the reliability of identification with this approach. Further, low intensity signals in MS1 often show interferences which lowers the precision of relative quantitation. Data independent acquisition (DIA) has promised to solve the missing value problem. By using wide precursor windows, DIA consistently measures all precursors that are above the limit of detection. For DIA data analysis we have developed the Spectronaut software. Using Spectronaut, we get more identifications in a single LC-MS measurement as compared to shotgun proteomics. Further, this approach resulted in quasi gap-free quantitation matrices without alignment and higher precision of quantitation as compared to shotgun proteomics.

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.

As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.

Multiplexed MS/MS for improved data-independent acquisition

In mass spectrometry based proteomics, data-independent acquisition (DIA) strategies have the ability to acquire a single dataset useful for identification and quantification of detectable peptides in a complex mixture. Despite this, DIA is often overlooked due to noisier data resulting from a typical five to ten fold reduction in precursor selectivity compared to data dependent acquisition or selected reaction monitoring. We demonstrate a multiplexing technique which improves precursor selectivity five-fold.