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Publications

Dataset Journal Article Epublish

PubMed: 39266559 DOI: 10.1038/s41597-024-03822-y

A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study.

Sci Data 11(1):998

Authors: Ammar Tahir, Agnes Draxler, Tamara Stelzer, Amelie Blaschke, Brenda Laky, Marton Széll, Jessica Binar, Viktoria Bartak, Laura Bragagna, Lina Maqboul, Theresa Herzog, Rainer Thell, Karl-Heinz Wagner

Affiliations

  • Department of Pharmaceutical Sciences, Division of Pharmacognosy, University of Vienna, Vienna, Austria.
  • Section of Biomedical Sciences, Department of Health Sciences, FH Campus Wien, University of Applied Sciences, Vienna, Austria.
  • Department of Nutritional Sciences, University of Vienna, Vienna, Austria.
  • Vienna Doctoral School for Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria.
  • Department of Health Sciences, FH Campus Wien, University of Applied Sciences, Vienna, Austria.
  • Department of Nutritional Sciences, University of Vienna, Vienna, Austria. Vienna Doctoral School for Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria.
  • Klinik Donaustadt, Emergency Department, Vienna, Austria.
  • Medical University of Vienna, Vienna, Austria.
  • Austrian Society of Regenerative Medicine, Vienna, Austria.
  • Sigmund Freud University Vienna, Vienna, Austria.
  • Section of Biomedical Sciences, Department of Health Sciences, FH Campus Wien, University of Applied Sciences, Vienna, Austria. Department of Nutritional Sciences, University of Vienna, Vienna, Austria.
  • Research Platform Active Ageing, University of Vienna, Vienna, Austria.

Abstract

A significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.

MeSH

Sars-Cov-2, Case-Control Studies, Metabolomics, Severe Acute Respiratory Syndrome-Related Coronavirus, Lipidomics

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