Spatially resolved structural proteomics at the proteoform level to study conformational changes in rat spinal cord injury model – STRUCTURAL

In recent years, MALDI Mass Spectrometry Imaging (MSI) has become a tool of choice to collect molecular information on tissues, in a spatially-resolved manner. MALDI-MSI can lead to the detection and localization of hundreds of biomolecules from a histological tissue section, in a label free manner and in a single experiment, avoiding tissue homogenization. However, despite all improvements, these approaches still show limitations for lower abundance proteins and the identification of Post Translational Modifications (PTMs). Moreover, Protein-Protein Interactions (PPIs), which are keys in biological signaling cannot be characterized. Regular bottom-up proteomics, guided by MALDI-MSI, have been used on tissues to bridge the spatial dimension of imaging and the strength in identification of large-scale proteomic approaches. However, even if these approaches have led to interesting results, they do not encompass the large-scale analysis of PTMs and PPIs. In the STRUCTURAL project, we therefore propose to develop specific structural proteomics approaches that will enable this important information to be obtained from tissues, where proteins are in their native microenvironment. PPIs will be addressed by developing appropriate cross-linking MS (XL-MS) strategy. For the characterization of protein PTMs, proteoforms will be characterized using dedicated Top-Down Proteomics (TDP) pipeline. These approaches will be optimized on rat brain, and further extended to the analysis of spinal cord injury (SCI). SCI is a devastating traumatic injury leading to severe sensory and motor deficits. The project relies on the complementary of two partners: PRISM Inserm U1192 lab which has lead expertise in MSI and Spatially-Resolved proteomics applied to clinical problematics including SCI and MSBio (Institut Pasteur Paris) which has strong expertise in structural proteomics and has already developed optimized pipelines in XL-MS and TDP.
To achieve this goal, STRUCTRUAL is organized into 4 different tasks. The first one aims at translating the conventional XL-MS workflow to the tissue context for limited areas of the sections. The main challenge here will be to increase the sensitivity of the XL-MS approach to a level compatible with the quantity of protein present in tissue section of biological interest (less than 1 mm², 1-5 µg of protein) while increasing spatial resolution. The eXL-MS workflow already developed by the MSBio Unit, which is based on an original trifunctional cross-linkers for efficient purification of cross-linked peptides by click-chemistry, will serve as basis for further optimization. In the second task, we will develop an optimized TDP approach allowing the identification of proteoforms from very localized area of tissue sections (again, less than 1 mm2). This optimization will cover both the sample preparation and the LC-MS/MS analysis of the intact proteins. The data obtained from the XL-MS and TDP (using high resolution Orbitrap mass spectrometers) will be merged to build maps of protein networks at the proteoform level in rat brains. Finally, in Task 3, the workflows developed within task 1 & 2 will be applied to identify PPIs and detect differentially expressed proteoforms in the SCI to demonstrate the validity of the method. Spatiotemporal studies will be performed directly from thin rat SCI tissue sections sampled at the lesion site, rostral and caudal regions of the spine and different time points (1h, 12h, 1 day, 7 days and 10 days post-lesion) and the generated data will be used to improve the knowledge on the regeneration mechanisms. Task 4 will be dedicated to management.
In a nutshell, the methodology developed will make it possible to go beyond the current limitations by bringing a completely novel dimension in Spatially-Resolved proteomics. This will provide better insights into physio or physio-pathological processes for a wide range of biological and clinical applications.