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Author: search.filters.author.Alfredo J. IbáñezSubject: search.filters.subject.Mass spectrometry

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    How is mass spectrometry tackling the COVID-19 pandemic?
    (Frontiers Media SA, 2022-01-01)
    Most of us have never faced a pandemic before. The World Health Organization declared the 2019 novel coronavirus infectious disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), a pandemic by March 11th, 2020. Today, this illness has reported more than 5′331,019 fatalities worldwide (December 17th, 2021). The COVID-19 pandemic has posed an unprecedented global challenge and put the academic community on “the spot.” The following mini-review reports how the MS community improved the understanding of the SARS-CoV-2 virus pathophysiology while developing diagnostic procedures to complement the PCR-based approaches. For example, MS researchers identified the interaction sites between the SARS-CoV-2 virus and their hosts; this new knowledge is critical for developing antiviral drugs. MS researchers also realized that COVID-19 should be considered a systemic disease and not just a respiratory illness since its metabolic, lipidomic, and proteomic profile reflects four different clinical disorders: 1) acute inflammatory response, 2) a cardiovascular disease, 3) a prediabetic/diabetes and 4) liver dysfunction. Furthermore, MS researchers put forth the knowledge that the metabolic and lipidomic profile of several patients remained altered after being discharged, thus hinting at the scientific basis for the long COVID syndrome.
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    Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings
    (Nature Research, 2024-12-01)
    Mass spectrometry (MS)-based metabolomics analysis is a powerful tool, but it comes with its own set of challenges. The MS workflow involves multiple steps before its interpretation in what is denominate data mining. Data mining consists of a two-step process. First, the MS data is ordered, arranged, and presented for filtering before being analyzed. Second, the filtered and reduced data are analyzed using statistics to remove further variability. This holds true particularly for MS-based untargeted metabolomics studies, which focused on understanding fold changes in metabolic networks. Since the task of filtering and identifying changes from a large dataset is challenging, automated techniques for mining untargeted MS-based metabolomic data are needed. The traditional statistics-based approach tends to overfilter raw data, which may result in the removal of relevant data and lead to the identification of fewer metabolomic changes. This limitation of the traditional approach underscores the need for a new method. In this work, we present a novel deep learning approach using node embeddings (powered by GNNs), edge embeddings, and anomaly detection algorithm to analyze the data generated by mass spectrometry (MS)-based metabolomics called GEMNA (Graph Embedding-based Metabolomics Network Analysis), for example for an untargeted volatile study on Mentos candy, the data clusters produced by GEMNA were better than the ones used traditional tools, i.e., GEMNA has, vs. the traditional approach has.