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

AbstractMurine models are amongst the most widely used systems to study biology and pathology. Targeted quantitative proteomic analysis is a relatively new tool to interrogate such systems. Recently the need for relative quantification on hundreds to thousands of samples has driven the development of Data Independent Acquisition methods. One such technique is SWATH-MS, which in the main requires prior acquisition of mass spectra to generate an assay reference library. In stem cell research, it has been shown pluripotency can be induced starting with a fibroblast population. In so doing major changes in expressed proteins is inevitable. Here we have created a reference library to underpin such studies. This is inclusive of an extensively documented script to enable replication of library generation from the raw data. The documented script facilitates reuse of data and adaptation of the library to novel applications. The resulting library provides deep coverage of the mouse proteome. The library covers 29519 proteins (53% of the proteome) of which 7435 (13%) are supported by a proteotypic peptide.

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Arabidopsis proteome and the mass spectral assay library

Scientific Data ◽

10.1038/s41597-019-0294-0 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 2

Author(s):

Huoming Zhang ◽

Pei Liu ◽

Tiannan Guo ◽

Huayan Zhao ◽

Dalila Bensaddek ◽

...

Keyword(s):

Quantitative Proteomics ◽

Model Organism ◽

Massively Parallel ◽

Spectral Library ◽

Mass Spectral ◽

Novel Proteins ◽

Data Independent Acquisition ◽

Arabidopsis Proteome ◽

Expression Atlas ◽

Targeted Approach

AbstractArabidopsis is an important model organism and the first plant with its genome completely sequenced. Knowledge from studying this species has either direct or indirect applications for agriculture and human health. Quantitative proteomics by data-independent acquisition mass spectrometry (SWATH/DIA-MS) was recently developed and is considered as a high-throughput, massively parallel targeted approach for accurate proteome quantification. In this approach, a high-quality and comprehensive spectral library is a prerequisite. Here, we generated an expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We applied DIA-MS using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups of which 1,787 were differentially expressed. MS data are available via ProteomeXchange with identifier PXD012708 and PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.

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DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

10.1101/2021.04.22.440949 ◽

2021 ◽

Author(s):

Mingxuan Gao ◽

Wenxian Yang ◽

Chenxin Li ◽

Yuqing Chang ◽

Yachen Liu ◽

...

Keyword(s):

Data Analysis ◽

State Of The Art ◽

Data Driven ◽

Spectral Library ◽

Software Suite ◽

High Coverage ◽

Discriminative Models ◽

Data Independent Acquisition ◽

Comprehensive Information ◽

Negative Controls

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

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Global proteomic analyses of human cytotrophoblast differentiation/invasion

Development ◽

10.1242/dev.199561 ◽

2021 ◽

Author(s):

Hao Chen ◽

Katherine E. Williams ◽

Elaine Y. Kwan ◽

Mirhan Kapidzic ◽

Kenisha A. Puckett ◽

...

Keyword(s):

Growth And Development ◽

Second Trimester ◽

Spectral Library ◽

Time Points ◽

Culture Time ◽

Data Independent Acquisition ◽

Molecular Features ◽

New Information ◽

Acquisition Strategy ◽

In Pregnancy

During human pregnancy, cytotrophoblasts (CTBs) from the placenta differentiate into specialized subpopulations that play critical roles in proper fetal growth and development. A subset of these CTBs differentiate along an invasive pathway, penetrating the decidua and anchoring the placenta to the uterus. A critical hurdle in pregnancy is the ability of these cells to migrate, invade, and remodel spiral arteries, ensuring adequate blood flow to nourish the developing fetus. While advances continue in describing the molecular features regulating the differentiation of these cells, assessment of their global proteomic changes at midgestation remain undefined. Here, using sequential window acquisition of all theoretical fragment-ion spectra (SWATH), a data-independent acquisition strategy, we characterized the protein repertoire of second trimester human CTBs during their differentiation towards an invasive phenotype. This mass spectrometry-based approach allowed identification of 3,026 proteins across four culture time points corresponding to sequential stages of differentiation, confirming the expression dynamics of established molecules and offering new information into other pathways involved. The availability of a SWATH CTB global spectral library serves as a beneficial resource for hypothesis generation and a foundation for further understanding of CTB differentiation dynamics.

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Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics

10.1101/152744 ◽

2017 ◽

Cited By ~ 4

Author(s):

Ryan Peckner ◽

Samuel A Myers ◽

Jarrett D Egertson ◽

Richard S Johnson ◽

Jennifer G. Abelin ◽

...

Keyword(s):

Mass Spectrometry ◽

Open Source Software ◽

Software Tool ◽

Nucleotide Polymorphisms ◽

Spectral Library ◽

New Paradigm ◽

Single Nucleotide ◽

Data Independent Acquisition ◽

Targeted Analysis ◽

Peptide Precursors

AbstractMass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

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Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

10.1101/2020.11.24.395426 ◽

2020 ◽

Author(s):

Weigang Ge ◽

Xiao Liang ◽

Fangfei Zhang ◽

Luang Xu ◽

Nan Xiang ◽

...

Keyword(s):

Protein Identification ◽

A Priori ◽

Peptide Identification ◽

Colorectal Tumor ◽

Spectral Library ◽

Library Size ◽

Test Dataset ◽

Proteome Coverage ◽

Data Independent Acquisition ◽

Computational Strategy

AbstractEfficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

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DIA-Pipe: Identification and Quantification of Post-Translational Modifications using exclusively Data-Independent Acquisition

10.1101/141382 ◽

2017 ◽

Author(s):

Jesse G. Meyer ◽

Sushanth Mukkamalla ◽

Alexandria K. D’Souza ◽

Alexey I. Nesvizhskii ◽

Bradford W. Gibson ◽

...

Keyword(s):

Peptide Identification ◽

Software Tool ◽

Label Free ◽

Spectral Library ◽

Automated Identification ◽

Post Translational Modifications ◽

Data Independent Acquisition ◽

Sample Amount ◽

Using Data ◽

Identification And Quantification

Label-free quantification using data-independent acquisition (DIA) is a robust method for deep and accurate proteome quantification1,2. However, when lacking a pre-existing spectral library, as is often the case with studies of novel post-translational modifications (PTMs), samples are typically analyzed several times: one or more data dependent acquisitions (DDA) are used to generate a spectral library followed by DIA for quantification. This type of multi-injection analysis results in significant cost with regard to sample consumption and instrument time for each new PTM study, and may not be possible when sample amount is limiting and/or studies require a large number of biological replicates. Recently developed software (e.g. DIA-Umpire) has enabled combined peptide identification and quantification from a data-independent acquisition without any pre-existing spectral library3,4. Still, these tools are designed for protein level quantification. Here we demonstrate a software tool and workflow that extends DIA-Umpire to allow automated identification and quantification of PTM peptides from DIA. We accomplish this using a custom, open-source graphical user interface DIA-Pipe (https://github.com/jgmeyerucsd/PIQEDia/releases/tag/v0.1.2) (figure 1a).

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Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

10.1101/2020.12.21.423800 ◽

2020 ◽

Author(s):

Sami Pietilä ◽

Tomi Suomi ◽

Laura L. Elo

Keyword(s):

Mass Spectrometry ◽

Open Source Software ◽

Previous Method ◽

Mass Spectrometry Data ◽

Analysis Framework ◽

Spectral Library ◽

Microbial Composition ◽

Data Independent Acquisition ◽

Open Source Software Package ◽

First Time

AbstractMass spectrometry based metaproteomics is a relatively new field of research that provides the ability to characterize the functionality of microbiota. Recently, we were the first to demonstrate the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the conventionally used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the previous method still required additional DDA data on the samples to assist the DIA analysis. Here, we introduce, for the first time, a DIA metaproteomics approach that does not require any DDA data, but instead replaces a spectral library generated from DDA data with a pseudospectral library generated directly from the metaproteomics DIA samples. We demonstrate that using the new DIA-only approach, we can achieve higher peptide yields than with the DDA-assisted approach, while the amount of required mass spectrometry data is reduced to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as open-source software package DIAtools 2.0, which is freely available from DockerHub.

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