Next-generation mass spectrometry for clinical and spatial proteomics

Steigerwald, Sophia Anna Victoria (2024): Next-generation mass spectrometry for clinical and spatial proteomics. Dissertation, LMU München: Fakultät für Chemie und Pharmazie

Abstract

While DNA provides the blueprint, proteins represent the functional and biologically active units of a cell. As such the proteome is our closest proxy to the phenotype, and can give important insights into cellular function and disease pathology. Although other approaches exist, mass spectrometry (MS) based proteomics remains the method of choice for fast, sensitive, quantitative, and high-throughput analysis of proteins. Over the years, MS-based proteomics has seen great advancements and now enables the routine analysis of thousands of clinical samples, near full proteomes and even single cells. A key factor in these advancements are innovations in MS technology that enable the instruments to push the boundaries of sensitivity, resolution, and acquisition speed. In this thesis I therefore first focus on evaluating MS technologies and optimizing MS acquisition strategies to expand the usability of MS instruments, and second to apply them to clinical and spatial proteomics. Across the MS workflow, one can greatly improve performance by implementing novel technology, optimizing acquisition strategies and improving data analysis. In a first project, I evaluated the full mass range application of ΦSDM, a computational alternative to standard MS signal processing. By providing a two-fold increase in resolution or acquisition speed, as well as greatly improving signal-to-noise ratio, I showed that ΦSDM could be a useful addition to extend the potential of existing Orbitrap mass spectrometers for a wide range of proteomics applications. I then optimized a high-throughput acquisition strategy for plasma proteomics on a state-of-the-art LC/MS setup, which we applied to studying the effects of muscle loss in individuals undergoing bedrest in a study funded by the Italian Space Agency. While follow up is needed, the study identifies a potential biomarker candidate associated with muscle maintenance. To fully make use of the data obtained with state-of-the-art MS instruments and ever more complex data acquisition strategies, I contributed to benchmarking AlphaDIA, a modular, open-source framework for data independent acquisition data analysis developed in our lab. I next contributed to applying novel MS technology for low input proteomics. The Orbitrap Astral, as well as other highly-sensitive TOF detector instruments have pushed the boundaries of sensitivity, acquisition speed and identification. This has shown to be particularly advantageous for low input applications such as Deep Visual Proteomics (DVP). Through a combination of these ultra-high sensitivity MS instruments, and tailored DIA acquisition strategies, we were able to decrease the required cell input amount and broaden the range of application for DVP. Focusing first on tissues from a single patient with signet ring cell carcinoma, we showcased the potential of DVP for personalized medicine and were able to propose a treatment option that effectively halted tumor progression. We next evaluated the phenotypic shifts after xenotransplantation of organoid models. In a human mucosa model, we could show that xenotransplanted tissue was closer to human physiology and regained its functional profile in comparison to in-vitro organoid cultures and could provide valuable insights into human disease. Lastly, we extended the previously described single cell DVP workflow to formalin-fixed paraffin-embedded tissue, and applied it to study proteotoxic stress in alpha-1-antitrypsin deficiency. Using a tailored MS method with optimized variable DIA isolation windows, we were able to identify up to 3800 protein groups from a single hepatocyte shape, which is the equivalent to ~half of a full cell. In summary, my thesis highlights how a combination of technical, methodological, and computational improvements can help to advance MS-based proteomics and bridge the gap to clinical applications and personalized medicine.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Keywords: Mass spectrometry, Biochemistry, Proteomics
• Themengebiete: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 540 Chemie
• Fakultäten: Fakultät für Chemie und Pharmazie
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 9. Dezember 2024
• 1. Bericht­erstatter:in: Mann, Matthias