Data-dependent Acquisition Experiments Research Articles

Interrogating data-independent acquisition LC–MS/MS for affinity proteomics

AbstractData-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.

Cyanopeptolins and Anabaenopeptins Are the Dominant Cyanopeptides from Planktothrix Strains Collected in Canadian Lakes.

Common bloom-forming cyanobacteria produce complex strain-specific mixtures of secondary metabolites. The beneficial and toxic properties of these metabolite mixtures have attracted both research and public health interest. The advancement of mass spectrometry-based platforms and metabolomics data processing has accelerated the identification of new metabolites and feature dereplication from microbial sources. The objective of this study was to use metabolomics data processing to decipher the intracellular cyanopeptide diversity of six Planktothrix strains collected from Canadian lakes. Data-dependent acquisition experiments were used to collect a non-targeted high-resolution mass spectrometry dataset. Principal component analysis and factor loadings were used to visualize cyanopeptide variation between strains and identified features contributing to the observed variation. GNPS molecular networking was subsequently used to show the diversity of cyanopeptides produced by the Planktothrix strains. Each strain produced a unique mixture of cyanopeptides, and a total of 225 cyanopeptides were detected. Planktothrix sp. CPCC 735 produced the most (n = 68) cyanopeptides, and P. rubescens CPCC 732 produced the fewest (n = 27). Microcystins and anabaenopeptins were detected from all strains. Cyanopeptolins, microviridins and aeruginosins were detected from five, four and two strains, respectively. Cyanopeptolin (n = 80) and anabaenopeptin (n = 61) diversity was the greatest, whereas microcystins (n = 21) were the least diverse. Interestingly, three of the P. rubescens strains had different cyanopeptide profiles, despite being collected from the same lake at the same time. This study highlights the diversity of cyanopeptides produced by Planktothrix and further hints at the underestimated cyanopeptide diversity from subpopulations of chemotypic cyanobacteria in freshwater lakes.

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.

Eight key rules for successful data-dependent acquisition in mass spectrometry-based metabolomics.

In recent years, metabolomics has emerged as a pivotal approach for the holistic analysis of metabolites in biological systems. The rapid progress in analytical equipment, coupled to the rise of powerful data processing tools, now provides unprecedented opportunities to deepen our understanding of the relationships between biochemical processes and physiological or phenotypic conditions in living organisms. However, to obtain unbiased data coverage of hundreds or thousands of metabolites remains a challenging task. Among the panel of available analytical methods, targeted and untargeted mass spectrometry approaches are among the most commonly used. While targeted metabolomics usually relies on multiple-reaction monitoring acquisition, untargeted metabolomics use either data-independent acquisition (DIA) or data-dependent acquisition (DDA) methods. Unlike DIA, DDA offers the possibility to get real, selective MS/MS spectra and thus to improve metabolite assignment when performing untargeted metabolomics. Yet, DDA settings are more complex to establish than DIA settings, and as a result, DDA is more prone to errors in method development and application. Here, we present a tutorial which provides guidelines on how to optimize the technical parameters essential for proper DDA experiments in metabolomics applications. This tutorial is organized as a series of rules describing the impact of the different parameters on data acquisition and data quality. It is primarily intended to metabolomics users and mass spectrometrists that wish to acquire both theoretical background and practical tips for developing effective DDA methods.

Research progress and application of retention time prediction method based on deep learning

在基于液相色谱-质谱联用的蛋白质组学研究中,肽段的保留时间作为有效区分不同肽段的特征参数,可以根据肽段自身的序列等信息对其进行预测。使用预测得到的保留时间辅助质谱数据鉴定肽段序列可以提高鉴定的准确性,因此对保留时间预测的工作一直受到领域内的广泛关注。传统的保留时间预测方法通常是根据氨基酸序列计算肽段的理化性质,进而计算肽段在特定色谱条件下的保留时间。近年来,深度学习方法取得了极大的进展,在蛋白质组学研究中发挥着越来越重要的作用。目前已发展出了多种基于深度学习的保留时间预测方法,与传统的保留时间预测方法相比有着更高的准确度,易于跨平台使用,并且能对修饰肽段的保留时间进行预测。但对某些复杂的修饰,如糖基化修饰等的预测结果还不够准确。如何进一步提高对修饰肽段预测的准确性是基于深度学习的保留时间预测方法的重要研究方向。这些预测的保留时间被应用于肽段鉴定的质量控制和方法评估,以及与预测的二级质谱谱图结合,建立模拟谱图库等方面。该文综述了深度学习方法在保留时间预测领域的最新研究进展以及应用成果,同时对其发展趋势和未来的应用方向进行了展望,以期为保留时间预测研究以及蛋白质组鉴定工作提供参考。

Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition.

Quantitative proteomics strategies – which are playing important roles in the expanding field of plant molecular systems biology – are traditionally designated as either hypothesis driven or non-hypothesis driven. Many of these strategies aim to select individual peptide ions for tandem mass spectrometry (MS/MS), and to do this mixed hypothesis driven and non-hypothesis driven approaches are theoretically simple to implement. In-depth investigations into the efficacies of such approaches have, however, yet to be described. In this study, using combined samples of unlabeled and metabolically 15N-labeled Arabidopsis thaliana proteins, we investigate the mixed use of targeted data acquisition (TDA) and data dependent acquisition (DDA) – referred to as TDA/DDA – to facilitate both hypothesis driven and non-hypothesis driven quantitative data collection in individual LC-MS/MS experiments. To investigate TDA/DDA for hypothesis driven data collection, 7 miRNA target proteins of differing size and abundance were targeted using inclusion lists comprised of 1558 m/z values, using 3 different TDA/DDA experimental designs. In samples in which targeted peptide ions were of particularly low abundance (i.e., predominantly only marginally above mass analyser detection limits), TDA/DDA produced statistically significant increases in the number of targeted peptides identified (230 ± 8 versus 80 ± 3 for DDA; p = 1.1 × 10-3) and quantified (35 ± 3 versus 21 ± 2 for DDA; p = 0.038) per experiment relative to the use of DDA only. These expected improvements in hypothesis driven data collection were observed alongside unexpected improvements in non-hypothesis driven data collection. Untargeted peptide ions with m/z values matching those in inclusion lists were repeatedly identified and quantified across technical replicate TDA/DDA experiments, resulting in significant increases in the percentages of proteins repeatedly quantified in TDA/DDA experiments only relative to DDA experiments only (33.0 ± 2.6% versus 8.0 ± 2.7%, respectively; p = 0.011). These results were observed together with uncompromised broad-scale MS/MS data collection in TDA/DDA experiments relative to DDA experiments. Using our observations we provide guidelines for TDA/DDA method design for quantitative plant proteomics studies, and suggest that TDA/DDA is a broadly underutilized proteomics data acquisition strategy.

Low Resolution Data-Independent Acquisition in an LTQ-Orbitrap Allows for Simplified and Fully Untargeted Analysis of Histone Modifications.

Label-free peptide quantification in liquid chromatography-mass spectrometry (LC-MS) proteomics analyses is complicated by the presence of isobaric coeluting peptides, as they generate the same extracted ion chromatogram corresponding to the sum of their intensities. Histone proteins are especially prone to this, as they are heavily modified by post-translational modifications (PTMs). Their proteolytic digestion leads to a large number of peptides sharing the same mass, while carrying PTMs on different amino acid residues. We present an application of MS data-independent acquisition (DIA) to confidently determine and quantify modified histone peptides. By introducing the use of low-resolution MS/MS DIA, we demonstrate that the signals of 111 histone peptides could easily be extracted from LC-MS runs due to the relatively low sample complexity. By exploiting an LTQ-Orbitrap mass spectrometer, we parallelized MS and MS/MS scan events using the Orbitrap and the linear ion trap, respectively, decreasing the total scan time. This, in combination with large windows for MS/MS fragmentation (50 m/z) and multiple full scan events within a DIA duty cycle, led to a MS scan cycle speed of ∼45 full MS per minute, improving the definition of extracted LC-MS chromatogram profiles. By using such acquisition method, we achieved highly comparable results to our optimized acquisition method for histone peptide analysis (R(2) correlation > 0.98), which combines data-dependent acquisition (DDA) and targeted MS/MS scans, the latter targeting isobaric peptides. By using DIA, we could also remine our data set and quantify 16 additional isobaric peptides commonly not targeted during DDA experiments. Finally, we demonstrated that by performing the full MS scan in the linear ion trap, we achieve highly comparable results as when adopting high-resolution MS scans (R(2) correlation 0.97). Taken together, results confirmed that histone peptide analysis can be performed using DIA and low-resolution MS with high accuracy and precision of peptide quantification. Moreover, DIA intrinsically enables data remining to later identify and quantify isobaric peptides unknown at the time of the LC-MS experiment. These methods will open up epigenetics analyses to the proteomics community who do not have routine access to the newer generation high-resolution MS/MS generating instruments.

Whole cell, label free protein quantitation with data independent acquisition: quantitation at the MS2 level.

Label free quantitation by measurement of peptide fragment signal intensity (MS2 quantitation) is a technique that has seen limited use due to the stochastic nature of data dependent acquisition (DDA). However, data independent acquisition has the potential to make large scale MS2 quantitation a more viable technique. In this study we used an implementation of data independent acquisition--SWATH--to perform label free protein quantitation in a model bacterium Clostridium stercorarium. Four tryptic digests analyzed by SWATH were probed by an ion library containing information on peptide mass and retention time obtained from DDA experiments. Application of this ion library to SWATH data quantified 1030 proteins with at least two peptides quantified (∼ 40% of predicted proteins in the C. stercorarium genome) in each replicate. Quantitative results obtained were very consistent between biological replicates (R(2) ∼ 0.960). Protein quantitation by summation of peptide fragment signal intensities was also highly consistent between biological replicates (R(2) ∼ 0.930), indicating that this approach may have increased viability compared to recent applications in label free protein quantitation. SWATH based quantitation was able to consistently detect differences in relative protein quantity and it provided coverage for a number of proteins that were missed in some samples by DDA analysis.

Collision-Induced Dissociative Chemical Cross-Linking Reagents and Methodology: Applications to Protein Structural Characterization Using Tandem Mass Spectrometry Analysis

Chemical cross-linking combined with mass spectrometry is a viable approach to study the low-resolution structure of protein and protein complexes. However, unambiguous identification of the residues involved in a cross-link remains analytically challenging. To enable a more effective analysis across various MS platforms, we have developed a novel set of collision-induced dissociative cross-linking reagents and methodology for chemical cross-linking experiments using tandem mass spectrometry (CID-CXL-MS/MS). These reagents incorporate a single gas-phase cleavable bond within their linker region that can be selectively fragmented within the in-source region of the mass spectrometer, enabling independent MS/MS analysis for each peptide. Initial design concepts were characterized using a synthesized cross-linked peptide complex. Following verification and subsequent optimization of cross-linked peptide complex dissociation, our reagents were applied to homodimeric glutathione S-transferase and monomeric bovine serum albumin. Cross-linked residues identified by our CID-CXL-MS/MS method were in agreement with published crystal structures and previous cross-linking studies using conventional approaches. Common LC/MS/MS acquisition approaches such as data-dependent acquisition experiments using ion trap mass spectrometers and product ion spectral analysis using SEQUEST were shown to be compatible with our CID-CXL-MS/MS reagents, obviating the requirement for high resolution and high mass accuracy measurements to identify both intra- and interpeptide cross-links.