Thibault Courtellemont (Lausanne / CH), Romain Hamelin (Lausanne / CH), Florence Armand (Lausanne / CH), Jessica Sordet-Dessimoz (Lausanne / CH), Maria Pavlou (Lausanne / CH)
Over the past decade, the field of spatial biology has experienced remarkable growth, leading to a paradigm shift in our understanding of cellular organization and function. Initially dominated by spatial genomics and transcriptomics, spatial proteomics is now emerging as a pivotal discipline within this domain, providing unprecedented insights into the spatial distribution of proteins within cells and tissues. Despite significant technological advancements, spatial proteomics remains challenging due to limited cell quantities, diverse contaminants, and significant sample loss throughout the protocol steps, all of which constrain the dynamic range of proteins and result in a reduced number of quantified proteins. Overcoming these obstacles requires innovative strategies and resourcefulness.
Recently, the combination of methods involving artificial intelligence-driven image analysis and segmentation, single-cell proteomics, and high-throughput techniques has paved a new road in spatial proteomics (1). This pioneering workflow can utilize formalin fixation and paraffin-embedding (FFPE) samples, providing access to billions of samples from patient biopsies stored in biobank archives, thus offering new hope for our understanding of some pathologies (2).
In this study, we aim to generalize spatial proteomics by capitalizing on the competencies present at the EPFL-SV Research Core Facilities (Proteomic , Histology , Bioimaging and Optics and Flow Cytometry Core Facility). We have developed an accessible and user-friendly approach for manual tissue microdissection coupled with bottom-up MS-based proteomic analysis to investigate the spatial variability of the proteome in different samples.
Leveraging creative solutions and do-it-yourself techniques, we have developed a streamlined workflow that can be easily implemented on most proteomic platforms without requiring substantial investments. Our approach combines laser-cut microscopy (LCM) with deep proteome profiling using a sensitive data-independent acquisition (DIA) on an Orbitrap Exploris 480 mass spectrometer. Inspired by a recent study (3), we adopted a high-resolution small mass range and large window DIA approach. This enabled us to quantify up to 7000 proteins from regions containing fewer than 100 mouse hepatocytes. Notably, our method delivers rapid results, with a turnaround time of less than a day from slide preparation to final data analysis. This method can also be easily adapted to cell sorting for FACS downstream analysis.
We are now collaborating with research groups at EPFL to validate our assay in diverse biological studies, spanning various models from tissue organisms to organoid models. This collaborative effort aims to address fundamental questions related to organism development and pathology, thus advancing our understanding of complex biological systems.
1.https://doi.org/10.1038/s41587-022-01302-5
2.DOI: 10.1002/path.5420
3.doi: 10.1021/acs.jproteome.3c00736