Developing a real-time proteogenomics pipeline

为了最大限度地提高癌症等复杂疾病的治疗效果，需要根据个人的病情精确调整医疗方法。随着精准医疗的触手可及，人们比以往任何时候都更加关注技术，使我们能够更好地了解我们的身体，它们的差异和功能障碍。我们身体中的每个细胞都包含三个基本的生物实体：基因组（DNA）包含遗传信息，包括基因，如果表达，则转录成转录本（RNA），然后将其翻译成蛋白质，控制大部分细胞的行为。在过去的几十年里，创新技术使阅读一个人的基因组或转录组变得可靠、廉价和快速。然而，如何准确有效地捕获细胞的蛋白质景观或蛋白质组仍然是一个挑战，蛋白质组的研究通常采用鸟枪质谱法。在这种方法中，蛋白质被消化成小肽，这些小肽被片段化以使用质谱仪进行鉴定。然而，蛋白质组是一个复杂的环境，生物样品含有比质谱仪可能测量的更多的肽。因此，质谱仪只选择最丰富的片段和鉴定。这种策略防止了可能携带重要生物信息的丰度较低的肽的片段化和检测。为了解决这一问题，软件控制质谱仪的行为和实时调整肽的选择正在出现。然而，这些仍然受到蛋白质组学分析中使用的次优理论框架的影响。质谱仪产生蛋白质的足迹（称为质谱）。蛋白质组学数据的分析包括将每个足迹与正确的蛋白质联系起来。目前的方法依赖于分析样品中所有可能的蛋白质的预先建立的列表，这些蛋白质通常来源于样品来源的物种的已知基因组和基因。然而，最近的方法和偶然的发现突出了蛋白质的存在，这已经逃避了基因注释。在人类中，已知的基因组注释中缺少数万种蛋白质。因此，这些蛋白质永远不能通过目前的蛋白质组学数据分析方法进行鉴定。在这个NOVA-FRQNT-NSERC程序中，我们建议开发一个软件管道，用于生物样品的实时蛋白质组学分析。我们将开发一种人工智能方法，通过质谱法实时识别蛋白质;我们将使用基因组数据（DNA和/或RNA测序数据）控制质谱仪的行为，以最大限度地识别生物样品中的蛋白质。我们将在哺乳动物细胞和酵母中评估我们的管道是否受到氧化应激。氧化应激是蛋白质组的已知调节因子，并且多种未注释的蛋白质在应激下表达。此外，我们将使用复杂组织的单细胞测序来增加基于这种深入转录组学数据的蛋白质鉴定的灵敏度。该计划将开发一种蛋白质组学方法，该方法在临床研究和使用中具有巨大潜力。此外，这将是一个独特的机会，为学生在我们的群体获得跨学科的经验，在尖端的算法和实验室技术伴随，培训他们作为混合计算和实验研究人员的未来。

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.