A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens.

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

Automated Sample Preparation for Mass Spectrometry-Based Clinical Proteomics.

Mass spectrometry (MS)-based proteomics is a rapidly maturing discipline, thus gaining momentum for routine molecular profiling of clinical specimens to improve disease classification, diagnostics, and therapy development. Yet, hurdles need to be overcome to enhance reproducibility in preanalytical sample processing, especially in large, quantity-limited sample cohorts. Therefore, automated sonication and single-pot solid-phase-enhanced sample preparation (autoSP3) was developed as a streamlined workflow that integrates all tasks from tissue lysis and protein extraction, protein cleanup, and proteolysis. It enables the concurrent processing of 96 clinical samples of any type (fresh-frozen or FFPE tissue, liquid biopsies, or cells) on an automated liquid handling platform, which can be directly interfaced to LC-MS for proteome analysis of clinical specimens with high sensitivity, high reproducibility, and short turn-around times.

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis.

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Immersion by rotation-based application of the matrix for fast and reproducible sample preparations and robust results in mass spectrometry imaging.

Automated matrix deposition for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is crucial for producing reproducible analyte ion signals. Here we report an innovative method employing an automated immersion apparatus, which enables a robust matrix deposition within 5 minutes and with scalable throughput by using MAPS matrix and non-polar solvents. MSI results received from mouse heart and rat brain tissues were qualitatively similar to those from nozzle sprayed samples with respect to peak number and quality of the ion images. Overall, the immersion-method enables a fast and careful matrix deposition and has the future potential for implementation in clinical tissue diagnostics.