scholarly journals Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

2018 ◽  
Vol 15 (5) ◽  
pp. 371-378 ◽  
Author(s):  
Ryan Peckner ◽  
Samuel A Myers ◽  
Alvaro Sebastian Vaca Jacome ◽  
Jarrett D Egertson ◽  
Jennifer G Abelin ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Independent Acquisition ◽  
Targeted Analysis

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

2017 ◽  
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.

scholarly journals A repository of assays to quantify 10,000 human proteins by SWATH-MS

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
George Rosenberger ◽  
Ching Chiek Koh ◽  
Tiannan Guo ◽  
Hannes L. Röst ◽  
Petri Kouvonen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
A Priori ◽  
Selected Reaction Monitoring ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Data Independent Acquisition ◽  
Targeted Analysis ◽  
Human Proteins ◽  
The One ◽  
Multiple Samples

Abstract Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely on the quantitatively accurate and reproducible measurement of proteins across multiple samples. It requires the one-time, a priori generation of a specific measurement assay for each targeted protein. SWATH-MS is a mass spectrometric method that combines data-independent acquisition (DIA) and targeted data analysis and vastly extends the throughput of proteins that can be targeted in a sample compared to selected reaction monitoring (SRM). Here we present a compendium of highly specific assays covering more than 10,000 human proteins and enabling their targeted analysis in SWATH-MS datasets acquired from research or clinical specimens. This resource supports the confident detection and quantification of 50.9% of all human proteins annotated by UniProtKB/Swiss-Prot and is therefore expected to find wide application in basic and clinical research. Data are available via ProteomeXchange (PXD000953-954) and SWATHAtlas (SAL00016-35).

scholarly journals Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 160
Author(s):  
Evelyn Rampler ◽  
Gerrit Hermann ◽  
Gerlinde Grabmann ◽  
Yasin El Abiead ◽  
Harald Schoeny ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Protein Identification ◽  
Regulatory Elements ◽  
Yeast Fermentation ◽  
Compound Identification ◽  
Targeted Metabolomics ◽  
Targeted Analysis ◽  
Orthogonal State ◽  
Starting Point

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is an essential discovery tool in metabolomics. To date, standardization and validation remain a challenge. Community-wide accepted cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of >200 identified metabolites based on compound identification by accurate mass, matching retention times, and MS/MS, as well as a comprehensive literature search. The library includes metabolites from the classes of (1) organic acids and derivatives (2) nucleosides, nucleotides, and analogs, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds, and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the human metabolome database (HMDB). Orthogonal state-of-the-art reversed-phase (RP-) and hydrophilic interaction chromatography mass spectrometry (HILIC-MS) non-targeted analysis and authentic standards revealed that 104 out of the 206 confirmed metabolites were reproducibly recovered and stable over the course of three years when stored at −80 °C. Overall, 67 out of these 104 metabolites were identified with comparably stable areas over all three yeast fermentation and are the ideal starting point for benchmarking experiments. The provided yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments but also allowed in-house routines for instrumental performance tests. Transferring the quality control strategy of proteomics workflows based on the number of protein identification in HeLa extracts, metabolite IDs in the yeast benchmarking material can be used as metabolomics quality control. Finally, the benchmark material opens new avenues for batch-to-batch corrections in large-scale non-targeted metabolomics studies.

scholarly journals Data-Independent Acquisition for the Quantification and Identification of Metabolites in Plasma

Metabolites ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 514
Author(s):  
Tom van der Laan ◽  
Isabelle Boom ◽  
Joshua Maliepaard ◽  
Anne-Charlotte Dubbelman ◽  
Amy C. Harms ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Mass Range ◽  
Correct Identification ◽  
Hydrophilic Interaction ◽  
Specific Mass ◽  
Structural Isomers ◽  
Data Independent Acquisition ◽  
Targeted Analysis ◽  
Theoretical Mass ◽  
Accurate Quantification

A popular fragmentation technique for non-targeted analysis is called data-independent acquisition (DIA), because it provides fragmentation data for all analytes in a specific mass range. In this work, we demonstrated the strengths and weaknesses of DIA. Two types of chromatography (fractionation/3 min and hydrophilic interaction liquid chromatography (HILIC)/18 min) and three DIA protocols (variable sequential window acquisition of all theoretical mass spectra (SWATH), fixed SWATH and MSALL) were used to evaluate the performance of DIA. Our results show that fast chromatography and MSALL often results in product ion overlap and complex MS/MS spectra, which reduces the quantitative and qualitative power of these DIA protocols. The combination of SWATH and HILIC allowed for the correct identification of 20 metabolites using the NIST library. After SWATH window customization (i.e., variable SWATH), we were able to quantify ten structural isomers with a mean accuracy of 103% (91–113%). The robustness of the variable SWATH and HILIC method was demonstrated by the accurate quantification of these structural isomers in 10 highly diverse blood samples. Since the combination of variable SWATH and HILIC results in good quantitative and qualitative fragmentation data, it is promising for both targeted and untargeted platforms. This should decrease the number of platforms needed in metabolomics and increase the value of a single analysis.

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