Factorial design applied to LC-ESI-QTOF mass spectrometer parameters for untargeted metabolomics.

Investigations of untargeted metabolomics are based on high-quality data acquisition usually from multiplatform systems that include high-resolution mass spectrometry equipment. The comprehensive set of results is used as data entry for bioinformatics and machine learning sciences to access reliable metabolic and biochemical information for clinical, forensic, environmental, and endless applications. In this context, design of experiments is a powerful tool for optimizing data acquisition procedures, using a multivariate approach, which enables the maximization of a high-quality amount of information with reduced number of tests. In this study, we applied a 33 Box-Behnken factorial design with central point triplicate for optimizing the ionization of an HPLC-ESI-QTOF method used for screening urine samples. Nozzle voltage (V), fragmentor voltage (V) and nebulizer pressure (psig) were the factors selected for variation. The response surface methodology was applied in the molecular features extracted at each level, resulting in a statistical model that helps evaluating the synergic interaction between these factors. Together with the qualitative analysis of the resulting total ion chromatograms, we came across a reproducible (6.14% RSD) and highly efficient method for untargeted metabolomics of human urine samples. The proposed method can be useful for applications in several urine-based metabolomics-driven studies, as the factorial design can be applied in the development of any analytical protocol considering different LC-MS setups.

Mass spectrometry applied to diagnosis, prognosis, and therapeutic targets identification for the novel coronavirus SARS-CoV-2: A review.

Mass spectrometry (MS) has found numerous applications in medicine and has been widely used in the detection and characterization of biomolecules associated with viral infections such as COVID-19. COVID-19 is a multisystem disease and, therefore, the need arises to carry out a careful and conclusive assessment of the pathophysiological parameters involved in the infection, to develop an effective therapeutic approach, assess the prognosis of the disease, and especially the early diagnosis of the infected population. Thus, the urgent need for highly accurate methods of diagnosis and prognosis of this infection presents new challenges for the development of laboratory medicine, whose methods require sensitivity, speed, and accuracy of the techniques for analyzing the biological markers involved in the infection. In this context, MS stands out as a robust analytical tool, with high sensitivity and selectivity, accuracy, low turnaround time, and versatility for the analysis of biological samples. However, it has not yet been adopted as a frontline clinical laboratory technique. Therefore, this review explores the potential and trends of current MS methods and their contribution to the development of new strategies to COVID-19 diagnosis and prognosis and how this tool can assist in the discovery of new therapeutic targets, in addition, to comment what could be the future of MS in medicine.