Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application.
Jakob WoessmannValdemaras PetrosiusNil ÜresinDavid KotolPedro Aragon-FernandezAndreas HoberUlrich Auf dem Auf dem KellerFredrik EdforsErwin M SchoofPublished in: Analytical chemistry (2023)
Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.
Keyphrases
- mass spectrometry
- label free
- liquid chromatography
- single cell
- high resolution
- capillary electrophoresis
- gas chromatography
- high performance liquid chromatography
- electronic health record
- big data
- induced apoptosis
- cell cycle arrest
- stem cells
- cell therapy
- bone marrow
- artificial intelligence
- oxidative stress
- high throughput
- mesenchymal stem cells
- endoplasmic reticulum stress
- small molecule
- protein protein
- pet ct
- deep learning