Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

Bittremieux, Wout; Adams, Charlotte; Laukens, Kris; Dorrestein, Pieter C.; Bandeira, Nuno

doi:10.1021/acs.jproteome.0c00929

Cited by 12 publications

(22 citation statements)

References 154 publications

(373 reference statements)

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“…For this review we chose Python as a programming language, because it is widely known for its readability and versatility, as well as a shallow learning curve for new developers and a very active, supportive and collaborative community. The latter is particularly useful considering that ‘‘open code’’ and community engagement can benefit researchers by saving time and funding resources [98]. As a primer for proteomics visualization in R, we recommend [33].…”

Section: Custom Programmatic Data Visualizationmentioning

confidence: 99%

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

2022

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Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub (https: //github.com/MannLabs/ProteomicsVisualization).

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Section: Custom Programmatic Data Visualizationmentioning

confidence: 99%

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

2022

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“…These results contain 164 PPIs that overlap with the previously reported results [2] and 211 novel PPIs (Supplementary Figure 1). Notably, a previous reanalysis of these AP-MS data using alternative bioinformatics tools reported a similar overlap with the original PPI results [46]. The difference in detected PPIs is partly due to the difference in spectrum identification and PPI filtering strategies.…”

Section: Resultsmentioning

confidence: 54%

“…Although the vast majority of original spectrum identifications (88%) could be replicated using open modification searching, the overlap in PPIs was smaller (49%). Notably, another recent reanalysis of these data using alternative bioinformatics software tools showed a similarly limited overlap with the original PPI results [46]. Besides using stringent FDR control and other PPI filtering settings during all data processing steps, we validated the detected protein interactions through comparison with alternative MS-based SARS-CoV-2 studies and general virus–host interactions in the VirHostNet database.…”

Section: Discussionmentioning

confidence: 89%

“…Mass spectrometry research can help to provide insights into the etiology of SARS-CoV-2 infection and identify potential therapeutic targets by investigating the roles of viral and host proteins during infection, their protein interactions, and post-translational modifications (PTMs) [46]. Additionally, it can be used to develop diagnostic strategies, such as through efforts of the CoV-MS consortium [81] - a partnership by multiple academic and industrial groups to increase applicability, accessibility, sensitivity, and robustness of a mass spectrometry-based diagnostic test that detects proteolytically digested SARS-CoV-2 proteins.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Open modification searching of SARS-CoV-2–human protein interaction data reveals novel viral modification sites

Adams

Boonen

Laukens

et al. 2022

Preprint

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The outbreak of the SARS-CoV-2 coronavirus, the causative agent of the COVID-19 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions (PPIs) through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications (PTMs). We have reanalyzed public affinity purification mass spectrometry data using open modification searching to investigate the presence of PTMs in the context of the SARS-CoV-2 virus-host PPI network. Based on an over two-fold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. Additionally, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

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“…These studies are very challenging as a large and stratified cohort population is required. Moreover, disease assessments to verify the presence of other comorbidities are mandatory in order to better elucidate MS profiling readouts and avoid confounding the results [91,92].…”

Section: Maldi-ms For Pathogen Detection: a General Overviewmentioning

confidence: 99%

MALDI MS-Based Investigations for SARS-CoV-2 Detection

Preianò

Correnti

Pelaia

et al. 2021

BioChem

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The urgent need to fight the COVID-19 pandemic has impressively stimulated the efforts of the international scientific community, providing an extraordinary wealth of studies. After the sequence of the virus became available in early January 2020, safe and effective vaccines were developed in a time frame much shorter than everybody expected. However, additional studies are required since viral mutations have the potential of facilitating viral transmission, thus reducing the efficacy of developed vaccines. Therefore, improving the current laboratory testing methods and developing new rapid and reliable diagnostic approaches might be useful in managing contact tracing in the fight against both the original SARS-CoV-2 strain and the new, potentially fast-spreading CoV-2 variants. Mass Spectrometry (MS)-based testing methods are being explored, with the challenging promise to overcome the many limitations arising from currently used laboratory testing assays. More specifically, MALDI-MS, since its advent in the mid 1980s, has demonstrated without any doubt the great potential to overcome many unresolved analytical challenges, becoming an effective proteomic tool in several applications, including pathogen identification. With the aim of highlighting the challenges and opportunities that derive from MALDI-based approaches for the detection of SARS-CoV-2 and its variants, we extensively examined the most promising proofs of concept for MALDI studies related to the COVID-19 outbreak.

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Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

Cited by 12 publications

References 154 publications

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

A practical guide to interpreting and generating bottom‐up proteomics data visualizations

Open modification searching of SARS-CoV-2–human protein interaction data reveals novel viral modification sites

MALDI MS-Based Investigations for SARS-CoV-2 Detection

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