Untargeted UHPLC-ESI-QTOF-MS/MS analysis with targeted feature extraction at precursor and fragment level for profiling of the platelet lipidome with ex vivo thrombin-activation

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy)

“…Clearly, our results are based on a single Italian cohort of COVID-19 patients, thus future studies with larger cohorts including different racial, ethnic, and geographical cohorts will be necessary for extending our current understanding of lipid metabolic dysregulation. In addition, the number of healthy subjects is relatively small, but comparable in age difference with previous studies which employed as reference group healthy COVID(-) patients [16] , [17] , [18] , [19] , [20] , [21] . Before the translation to clinical settings, the prognostic value of the identified lipid panel on COVID-19 outcome should be applied to large and separate patients’ cohorts,…”

“…In other studies, with comparable numbers of patients, different proteomic and metabolomic profiles were demonstrated to be associated with clinical phenotypes of COVID-19, from mild to severe [ 32 ] . While previous lipidomics studies reported the observation of relative fold-changes of detected lipids between different study groups, the comparison of lipid concentration, is essential to support inter study comparison [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] . Nevertheless, only few studies have reported concentration data (as nmol/mL), and hence comparison is not possible.…”

“…Data preprocessing consisted of the following steps: all lipids missing in more than 50% in QCs and 75% in real samples were excluded, furthermore all lipids with a coefficient of variation (CV%) higher than 35% among QCs samples were discarded. In order to flatten the differences between samples and to avoid bias in the statistical analysis, we first normalized lipid intensities with the corresponding deuterated internal standard [ 19 ] and then the dataset was log transformed and auto scaled. A preliminary investigation was carried out using Principal Component Analysis (PCA) on pre-processed data.…”

Quantification of bulk lipid species in human platelets and their thrombin-induced release

“…Their data showed that less than 20% of the lipidome is altered by activation, mainly involving AA-containing lipids. Similarly, Cebo et al investigated the human platelet lipidome before and upon thrombin-activation 14 . These studies provide a comprehensive set of quantitative data constituting a basis for further understanding the role of platelet lipids.…”

Recent methodological developments in data-dependent analysis and data-independent analysis workflows for exhaustive lipidome coverage

“…SWATH experiments were performed to characterize 214 and 140 lipids from immortalized keratinocytes in both positive and negative ionization modes, respectively. As statistical treatment of the data is mainly based on retention times, authors strongly advise to add internal standards to correct any deviation (Calderón et al, 2020;Cebo et al, 2021). Interestingly, SWATH can be employed for compound annotation using MS/MS library search but also quantification at the MS/MS level because, unlike classic DDA, it offers continuous MS/MS spectra acquisition for particular fragment ions.…”