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

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches.

A proof of concept study for the differentiation of SARS-CoV-2, hCoV-NL63, and IAV-H1N1 in vitro cultures using ion mobility spectrometry.

Rapid, high-throughput diagnostic tests are essential to decelerate the spread of the novel coronavirus disease 2019 (COVID-19) pandemic. While RT-PCR tests performed in centralized laboratories remain the gold standard, rapid point-of-care antigen tests might provide faster results. However, they are associated with markedly reduced sensitivity. Bedside breath gas analysis of volatile organic compounds detected by ion mobility spectrometry (IMS) may enable a quick and sensitive point-of-care testing alternative. In this proof-of-concept study, we investigated whether gas analysis by IMS can discriminate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from other respiratory viruses in an experimental set-up. Repeated gas analyses of air samples collected from the headspace of virus-infected in vitro cultures were performed for 5 days. A three-step decision tree using the intensities of four spectrometry peaks correlating to unidentified volatile organic compounds allowed the correct classification of SARS-CoV-2, human coronavirus-NL63, and influenza A virus H1N1 without misassignment when the calculation was performed with data 3 days post infection. The forward selection assignment model allowed the identification of SARS-CoV-2 with high sensitivity and specificity, with only one of 231 measurements (0.43%) being misclassified. Thus, volatile organic compound analysis by IMS allows highly accurate differentiation of SARS-CoV-2 from other respiratory viruses in an experimental set-up, supporting further research and evaluation in clinical studies.

Rapid detection of SARS-CoV-2 infection by multicapillary column coupled ion mobility spectrometry (MCC-IMS) of breath. A proof of concept study.

There is an urgent need for screening of patients with a communicable viral disease to cut infection chains. Recently, we demonstrated that ion mobility spectrometry coupled with a multicapillary column (MCC-IMS) is able to identify influenza-A infections in patients' breath. With a decreasing influenza epidemic and upcoming SARS-CoV-2 infections we proceeded further and analyzed patients with suspected SARS-CoV-2 infections. In this study, the nasal breath of 75 patients (34 male, 41 female, aged 64.4 ± 15.4 years) was investigated by MCC-IMS for viral infections. Fourteen were positively diagnosed with influenza-A infection and sixteen with SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) of nasopharyngeal swabs. In one patient RT-PCR was highly suspicious of SARS-CoV-2 but initially inconclusive. The remaining 44 patients served as controls. Breath fingerprints for specific infections were assessed by a combination of cluster analysis and multivariate statistics. There were no significant differences in gender or age according to the groups. In the cross validation of the discriminant analysis 72 of the 74 clearly defined patients could be correctly classified to the respective group. Even the inconclusive patient could be mapped to the SARS-CoV-2 group by applying the discrimination functions. Conclusion: SARS-CoV-2 infection and influenza-A infection can be detected with the help of MCC-IMS in breath in this pilot study. As this method provides a fast non-invasive diagnosis it should be further developed in a larger cohort for screening of communicable viral diseases. A validation study is ongoing during the second wave of COVID-19.Trial registration: ClinicalTrial.gov, NCT04282135 Registered 20 February 2020-Retrospectively registered,https://clinicaltrials.gov/ct2/show/NCT04282135?term=IMS&draw=2&rank=1.

Rapid non-invasive detection of Influenza-A-infection by multicapillary column coupled ion mobility spectrometry.

Infectious pathogens are a global issue. Global air travel offers an easy and fast opportunity not only for people but also for infectious diseases to spread around the world within a few days. Also, large public events facilitate increasing infection numbers. Therefore, rapid on-site screening for infected people is urgently needed. Due to the small size and easy handling, ion mobility spectrometry coupled with a multicapillary column (MCC-IMS) is a very promising, sensitive method for the on-site identification of infectious pathogens based on scents, representing volatile organic compounds (VOCs). The purpose of this study was to prospectively assess whether identification of Influenza-A-infection based on VOCs by MCC-IMS is possible in breath. Nasal breath was investigated in 24 consecutive persons with and without Influenza-A-infection by MCC-IMS. In 14 Influenza-A-infected patients, infection was proven by PCR of nasopharyngeal swabs. Four healthy staff members and six patients with negative PCR result served as controls. For picking up relevant VOCs in MCC-IMS spectra, software based on cluster analysis followed by multivariate statistical analysis was applied. With only four VOCs canonical discriminant analysis was able to distinguish Influenza-A-infected patients from those not infected with 100% sensitivity and 100% specificity. This present proof-of-concept-study yields encouraging results showing a rapid diagnosis of viral infections in nasal breath within 5 min by MCC-IMS. The next step is to validate the results with a greater number of patients with Influenza-A-infection as well as other viral diseases, especially COVID-19. Registration number at ClinicalTrials.gov NCT04282135.