The main objective of the project is to identify current limitations and discrepancies of metabolomics datasets by studying how variances in metabolomics workflows can change outcomes in terms of metabolite annotation and identification, metabolome coverage, etc. with further aim to propose solutions for cross-cohort data integration and facilitate quality control schemes.
Apart from this, a major scope of the project is to investigate the potentials of Blood Microsampling (BμS) in biomarker discovery and to consolidate, create and implement bioanalytical methods and data analysis methods and tools using BμS in metabolomics studies that can be valuable in health and wellness monitoring.
The specific objectives for the project are summarised in the following:
1) Build a multidisciplinary consortium with the critical mass to address the limitations in human LC-MS-based blood metabolomics, such as result reproducibility and research fragmentation.
2) Perform cross-laboratory comparisons to identify the sources of variability in all steps of metabolomics studies: preanalytical, sample preparation, LC-MS analysis, data quality assessment, normalization, statistical processing, pathway analysis, and biochemical interpretation.
3) Promote the field towards effective integration and co-evaluation of data collected by various labs, improving consistency of findings, data reporting, database implementation, reproducibility, and re-usability of data. Identify bottlenecks in the integration of metabolomics data (pan-cohorts).
4) Investigate the implementation of novel blood microsampling techniques in clinical metabolomics and establish workflows for meaningful blood micro sample analysis in biomarker discovery and health monitoring.
5) Provide proof of concept for the utility of the developed tools by implementing two case studies on human cohorts following exercise and nutritional intervention.
