Development of a multi-omics sample preparation workflow for comprehensive metabolomics, lipidomics and proteomics datasets using a single tissue sample

Introduction: Multi-omics approaches have gained popularity in recent years aiming to wholistically investigate the biochemical processes in the human body. A frequent problem is the requirement for specialized sample preparation for the different compound classes. This leads to issues with the comparability between analyses (e.g. tissue homogeneity, additional freeze-thaw cycles) and difficulties combining results for biological interpretation. The aim of the current study was to develop a multi-omics sample preparation workflow using a postmortem tissue sample to produce comprehensive Metabolomics, Lipidomics and Proteomics datasets.

Methods: Human postmortem muscle and liver tissue samples were used to evaluate different tissue homogenizations- (solvents (n=5), pulsing frequencies (n=5), sample material-to-solvent ratios (n=4) and sample amounts (n=4)) and extraction parameters (extraction solvents/mixtures (n=10)). The general sample preparation workflow included bead homogenization, extraction and compound class specific analysis with LC-MS/MS-based analytical methods as well as 1D-SDS PAGE and a BCA protein assay. Suitability of homogenization parameters were assessed based on a targeted evaluation of the Metabolomics data (microflow-LC-ion mobility separated qTOF analysis, DIA) and based on the numbers/intensities of protein bands visible on the 1D SDS-PAGE (protein fraction). The suitability of the extraction solvents was evaluated and ranked based on the following parameters: targeted and untargeted evaluation of the Metabolomics (see above) and Lipidomics dataset (high flow-LC-orbitrap analysis, DDA), untargeted bottom-up Proteomics data (microflow-LC-ion mobility separated qTOF analysis, DIA), 1D SDS-PAGE (protein- and metabolite fraction) and BCA assay results.

Results & Discussion: A methanol-to-water mixture (2:1) added in a 1:10 sample material-to-homogenization solvent ratio was found to lead to the best Metabolomics and visual protein results. A 20 mg tissue sample, homogenized at 3x30 s with a 1 min pause on ice in between pulses, was found to be sufficient to result in comprehensive datasets. The use of a small sample amount is ideal for studies with access to only limited sample amounts. Commonly used 2-phase extraction solvent mixtures for Proteomics studies lead to significantly weaker or less reproducible Metabolomics and Lipidomics results compared to 1-phasic extraction solvents more commonly seen in small molecule studies. A simple protein precipitation of the homogenized tissue samples with a methanol:acetone mixture (9:1) showed the best compromise while achieving comprehensive results.

Conclusion: In summary, a multi-omics sample preparation workflow was established that can lead to comprehensive targeted and untargeted Metabolomics, Lipidomics and Proteomics datasets from a single postmortem tissue sample (20 mg). This method can serve as a basis for multi-omics studies in other matrices.