Spatially resolved proteomics of a human brain tumour

Biological tissues contain diverse cell subpopulations crucial to their function, and proteins are the macromolecules that carry out many of these functions. Robust mass spectrometry-based methods that can accurately identify and quantify thousands of proteins within a sample have transformed the investigation of protein abundance and function within tissues and cells. However, mass spectrometry-based proteomics of tissue is traditionally performed on bulk samples. Therefore, information on the spatial composition and protein abundance patterns of those tissues is lost. Currently, the spatial context of proteins within a tissue is investigated using antibody-based high-resolution techniques with little multiplexing capacity. There is a clear need for techniques that generate unbiased, quantitative, spatial expression profiles of many hundreds to thousands of proteins within a tissue. Presented within this thesis is the development of sample preparation, acquisition, and analysis workflows for spatially resolved proteomics using bottom-up mass spectrometry. This method was used to analyse an atypical teratoid/rhabdoid tumour (ATRT), a rare paediatric brain tumour. Firstly, an optimised workflow was developed and used to characterise the proteomes of two neurons: Betz and Purkinje cells. Both cell types show highly specific proteomes. Purkinje cells show an increased abundance of several known Purkinje cell marker proteins. In addition, several novel candidate marker proteins for Betz cells were identified. Secondly, the method was extended to allow for unbiased sampling of tissue in a spatially resolved manner. The method shows clear signals from a starting material of approximately 50 cells isolated from ATRT tissue. Analysis of the data within the tissue’s spatial context highlights proteins and pathways showing spatially variable abundance within the tumour and adjacent normal tissue. This is performed without prior knowledge of the tissue composition by using spatially aware statistical methods. Finally, orthogonal validation was performed using immunohistochemistry and mass spectrometry imaging which recapitulates the spatial variability observed in the bottom-up mass spectrometry approach. Overall, the work presented here informs on methods for spatially resolved proteomics of tissue, laying the groundwork for further spatially resolved tissue proteomics and highlighting the utility of such approaches to understanding tissue biology.