= Emerging. More than 5 years before clinical availability. (24.37%, 2023)
= Expected to be clinically available in 1 to 4 years. (39.50%, 2023)
= Clinically available now. (36.13%, 2023)
MSACL 2023 : Luo

MSACL 2023 Abstract

Self-Classified Topic Area(s): Metabolomics > Multi-omics

Podium Presentation in Steinbeck 2 on Thursday at 10:20 (Chair: Daniel Globisch / Emma Guiberson)

Chemical Isotope Labeling LC-MS for Plasma Metabolomic Profiling of Different Stages of SARS-CoV-2 Infected Individuals

Xian Luo(1), Suguru Saito(2), Liang Li(1,3), Shokrollah Elahi(2)
(1)The Metabolomics Innovation Centre, University of Alberta, Edmonton, Alberta T6G 2G2, Canada; (2)School of Dentistry, Division of Foundational Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada; (3)Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2, Canada

Xian Luo, PhD (Presenter)
The Metabolomics Innovation Centre

Presenter Bio: I obtained my PhD degree from University of Alberta. I am currently appointed as a Research Scientist in The Metabolomics Innovation Centre, Canada.


Coronavirus disease 2019 (COVID-19) is mortality disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In the last two-years (2020-2011), COVID-19 caused over 5 million of death worldwide, and now this disease entries into next phase with huge social attention so called “Long-COVID-19”. Long-COVID-19 is characterized by multiple symptoms in the individual experienced SARS-CoV-2 infection. However, effective strategies for prediction and diagnosis haven’t been established yet. Therefore, we proposed a possibility that comprehensive metabolomic analysis is able to identify specific metabolome profile in Long-COVID-19. We analyzed plasma samples from healthy, acute COVID-19, post COVID-19 (non Long-COVID-19) and long-COVID-19 individuals using Chemical Isotope Labeling (CIL) LC-MS, which is a high-coverage and accurate quantification metabolomic profiling approach.

300 μL of cold methanol was added to each of vials containing 100 μL of plasma samples. After incubation at -20 °C freezer for 30 min, the precipitated protein was removed by centrifugation. The supernatants were dried down and re-dissolved in water. The pooled sample was generated by mixing aliquots from each individual samples. Each individual sample was labeled by 12C-DnsCl (dansyl chloride), and the pooled sample was labeled by 13C-DnsCl by the same protocol. The equal volume of 12C-labeled individual sample and 13C-labeled pool were mixed and injected onto LC-Q-TOF for analysis. The raw LC-MS data was exported as .csv files, and then processed by IsoMS Pro. IsoMS Pro was also used for metabolite identification and statistical analysis.

In total, 81 samples, including quality control (QC) samples, were analyzed. Among them, 15 samples were from healthy control group, 15 samples were from acute group, 15 samples were from recovered group, 30 samples were from “long-COVID-19” group, and 6 samples were from QC. On average, 2038 ± 25 peak pairs (putative metabolites) per sample were detected. Among them, 171 peak pairs were identified with very high confidence by searching against Tier 1 library, containing retention time and accurate mass information. 300 peak pairs were identified with high confidence by searching against Tier 2 library, containing predicted retention time and accurate mass information. 370, 701, and 294 peak pairs peak pairs were matched by searching against zero-, one-, two-reaction MCID (Tier 3) library. PCA and PLS-DA was performed to visualize the data. On the PCA plot, the tightly clustered QC demonstrates good performance of LC-MS. The acute group was clearly separated from other three groups, indicating the huge metabolome alternation in acute patients. On the PLS-DA plot, the four groups separated at different degrees. Besides acute group, long-COVID-19 group was also separated from healthy control and recovered group, which shows the impact of long-COVID-19 on the plasma metabolome. Interestingly, a minor separation is also observed between recovered and healthy control. This may reflect the mild alternation of patients’ metabolome after COVID recovery. The PLS-DA model passed the permutation test. The volcano plots were also generated to confirm the multivariate analysis results. Compared with healthy control, 772, 137, and 226 significantly changed (fold change>1.5, q-value<0.25) metabolites were detected from acute, long-COVID-19, and recovered group, respectively. Among them, 28 unique metabolites with significant change were detected from long-COVID-19 group. These metabolites may be used as biomarkers for prediction and diagnosis in Long-COVID-19.

Comprehensive plasma metabolomic profiling of different stages of SARS-CoV-2 infected individuals was performed. On average, 2038 ± 25 peak pairs (putative metabolites) per sample were detected. Acute group showed a clear separation with other groups on PCA plot, indicating the large blood metabolome alternation in acute patients. Several potential biomarkers for diagnosis of long-COVID patients were also identified.

Financial Disclosure

GrantsyesThe Metabolomics Innovation Centre
SalaryyesThe Metabolomics Innovation Centre
Board MemberyesTopic Advisory Panel of Metabolites
ExpensesyesThe Metabolomics Innovation Centre
IP Royaltyno

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