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

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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Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

BMC Bioinformatics ◽

10.1186/1471-2105-10-87 ◽

2009 ◽

Vol 10 (1) ◽

pp. 87 ◽

Cited By ~ 159

Author(s):

Navdeep Jaitly ◽

Anoop Mayampurath ◽

Kyle Littlefield ◽

Joshua N Adkins ◽

Gordon A Anderson ◽

...

Keyword(s):

Mass Spectrometry ◽

High Resolution ◽

Open Source ◽

Open Source Software ◽

Software Package ◽

High Resolution Mass Spectrometry ◽

Mass Spectrometry Data ◽

Open Source Software Package ◽

Automated Processing ◽

Resolution Mass

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Data-independent acquisition mass spectrometry enables reproducible characterization of microbiota function

10.1101/413021 ◽

2018 ◽

Author(s):

Juhani Aakko ◽

Sami Pietilä ◽

Tomi Suomi ◽

Mehrad Mahmoudian ◽

Raine Toivonen ◽

...

Keyword(s):

Mass Spectrometry ◽

Dynamic Range ◽

Functional Characterization ◽

Research Area ◽

Microbial Composition ◽

Complex Samples ◽

Data Independent Acquisition ◽

Novel Approach ◽

Open Source Software Package

AbstractMetaproteomics is an emerging research area which aims to reveal the functionality of microbial communities – unlike the increasingly popular metagenomics providing insights only on the functional potential. So far, the common approach in metaproteomics has been data-dependent acquisition mass spectrometry (DDA). However, DDA is known to have limited reproducibility and dynamic range with samples of complex microbial composition. To overcome these limitations, we introduce here a novel approach utilizing data-independent acquisition (DIA) mass spectrometry, which has not been applied in metaproteomics of complex samples before. For robust analysis of the data, we introduce an open-source software package diatools, which is freely available at Docker Hub and runs on various operating systems. Our highly reproducible results on laboratory-assembled microbial mixtures and human fecal samples support the utility of our approach for functional characterization of complex microbiota. Hence, the approach is expected to dramatically improve our understanding on the role of microbiota in health and disease.

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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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A flexible workflow for building spectral libraries from narrow window data independent acquisition mass spectrometry data

10.1101/2021.11.22.469568 ◽

2021 ◽

Author(s):

Lilian R. Heil ◽

William E. Fondrie ◽

Christopher D. McGann ◽

Alexander J. Federation ◽

William S. Noble ◽

...

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Data ◽

Spectral Library ◽

Post Translational Modifications ◽

Data Independent Acquisition ◽

Peptide Detection ◽

Phosphorylated Peptides ◽

Single Mass ◽

Fragmentation Patterns ◽

Spectral Libraries

Advances in library-based methods for peptide detection from data independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

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Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines

Protein Bioinformatics - Methods in Molecular Biology ◽

10.1007/978-1-4939-6783-4_19 ◽

2017 ◽

pp. 395-413 ◽

Cited By ~ 4

Author(s):

Erin L. Crowgey ◽

Andrea Matlock ◽

Vidya Venkatraman ◽

Justyna Fert-Bober ◽

Jennifer E. Van Eyk

Keyword(s):

Mass Spectrometry ◽

Biological Networks ◽

Quantitative Data ◽

Mass Spectrometry Data ◽

Data Independent Acquisition

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Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files

Nature Methods ◽

10.1038/nmeth.3593 ◽

2015 ◽

Vol 12 (12) ◽

pp. 1105-1106 ◽

Cited By ~ 51

Author(s):

Yuanyue Li ◽

Chuan-Qi Zhong ◽

Xiaozheng Xu ◽

Shaowei Cai ◽

Xiurong Wu ◽

...

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Data ◽

Data Independent Acquisition ◽

Multiple Data ◽

Data Files

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Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification

Analytical Chemistry ◽

10.1021/acs.analchem.6b04892 ◽

2017 ◽

Vol 89 (10) ◽

pp. 5294-5302 ◽

Cited By ~ 19

Author(s):

Simion Kreimer ◽

Yuanwei Gao ◽

Somak Ray ◽

Mi Jin ◽

Zhijun Tan ◽

...

Keyword(s):

Mass Spectrometry ◽

Host Cell ◽

Protein Profiling ◽

Mass Spectrometry Data ◽

Cell Protein ◽

Reaction Monitoring ◽

Parallel Reaction ◽

Host Cell Protein ◽

Data Independent Acquisition ◽

Parallel Reaction Monitoring

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Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines

10.1101/620468 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ngoc Hieu Tran ◽

Rui Qiao ◽

Lei Xin ◽

Xin Chen ◽

Baozhen Shan ◽

...

Keyword(s):

Mass Spectrometry ◽

Deep Learning ◽

De Novo ◽

Mass Spectrometry Data ◽

Main Role ◽

Accurate Identification ◽

Identification Rate ◽

Cell Responses ◽

First Time ◽

Deep Learning Model

AbstractTumor-specific neoantigens play the main role for developing personal vaccines in cancer immunotherapy. We propose, for the first time, a personalized de novo sequencing workflow to identify HLA-I and HLA-II neoantigens directly and solely from mass spectrometry data. Our workflow trains a personal deep learning model on the immunopeptidome of an individual patient and then uses it to predict mutated neoantigens of that patient. This personalized learning and mass spectrometry-based approach enables comprehensive and accurate identification of neoantigens. We applied the workflow to datasets of five melanoma patients and substantially improved the accuracy and identification rate of de novo HLA peptides by 14.3% and 38.9%, respectively. This subsequently led to the identification of 10,440 HLA-I and 1,585 HLA-II new peptides that were not presented in existing databases. Most importantly, our workflow successfully discovered 17 neoantigens of both HLA-I and HLA-II, including those with validated T cell responses and those novel neoantigens that had not been reported in previous studies.

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Arabidopsis Proteome and the Mass Spectral Assay Library

10.1101/665547 ◽

2019 ◽

Cited By ~ 1

Author(s):

Huoming Zhang ◽

Pei Liu ◽

Tiannan Guo ◽

Huayan Zhao ◽

Dalila Bensaddek ◽

...

Keyword(s):

Mass Spectrometry ◽

Abscisic Acid ◽

Acid Stress ◽

Model Organism ◽

Mass Spectrometry Data ◽

Mass Spectral ◽

Novel Proteins ◽

Data Independent Acquisition ◽

Arabidopsis Proteome ◽

Expression Atlas

AbstractArabidopsis is an important model organism and the first plant with its genome sequenced. Knowledge from studying this species has either direct or indirect applications to agriculture and human health. Quantitative proteomics by data-independent acquisition (SWATH/DIA-MS) was recently developed and considered as a high-throughput targetedlike approach for accurate proteome quantitation. In this approach, a high-quality and comprehensive library is a prerequisite. Here, we generated a protein 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 subsequently applied DIA-mass spectrometry using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups with 1,787 of them being differentially expressed which includes 65 proteins known to respond to abscisic acid stress. Mass spectrometry data are available via ProteomeXchange with identifier PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.

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