Developing a real-time proteogenomics pipeline

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To maximize treatment efficacy in complex diseases such as cancer, medical approaches need to be precisely tailored to an individual's condition. With precision medicine within reach, the focus is more than ever on technologies enabling us to better understand our bodies, their differences and their dysfunctions. Each cell in our body contains three essential biological entities: the genome (DNA) contains the genetic information including genes, which are transcribed into transcripts (RNA) if expressed, which themselves are then translated into proteins, which control much of the cells' behaviour. Throughout the last decades, innovative technologies have made reading one's genome, or transcriptome, reliable, affordable and quick. However, the challenge is still ongoing to accurately and efficiently capture the protein landscape of cells or their proteome.The proteome is commonly investigated by shotgun mass spectrometry. In this approach, proteins are digested into small peptides that are fragmented to be identified using a mass spectrometer. However, the proteome is a complex environment and biological samples contain more peptides than a mass spectrometer can possibly measure. Thus, mass spectrometers select only the most abundant for fragmentation and identification. This strategy prevents the fragmentation and detection of less abundant peptides that may carry important biological information. To address this problem, software controlling mass spectrometers' behavior and adjusting the selection of peptides in real-time are emerging.Yet, these are still biased by the suboptimal theoretical framework used in proteomics analyses. The mass spectrometer yields footprints (called mass spectra) of proteins. The analysis of proteomics data consists in linking each footprint to the correct protein. Current methods rely on a pre-established list of all possible proteins in the analyzed sample, generally derived from the known genome and genes of the species from which the sample originates. However, recent methods and serendipitous discoveries have highlighted the existence of proteins, which have eluded gene annotations. In human, tens of thousands of proteins are lacking from known genome annotations. Therefore, these proteins can never be identified by current methods of analysis of proteomic data.In this NOVA-FRQNT-NSERC Program, we propose to develop a software pipeline for real-time proteogenomic analyses of biological samples. We will develop an artificial intelligence method to identify proteins by mass spectrometry in real-time; and we will control the mass spectrometer behavior using genomic data (DNA and/or RNA sequencing data) to maximize protein identifications in biological samples. We will evaluate our pipeline in mammalian cells and yeasts subjected or not to oxidative stress. Oxidative stress is a known regulator of the proteome and multiple non-annotated proteins are expressed under stress. Furthermore, we will use single-cell sequencing of a complex tissue to increase the sensibility of the identification of proteins based on this in-depth transcriptomic data. This program will develop a proteomics method that has a great potential for research and use in clinics. Furthermore, it will be a unique opportunity for students in our groups to gain interdisciplinary experience in cutting-edge algorithms and laboratory techniques concomitantly, training them as hybrid computational and experimental researchers of the future.