Understanding protein multi- and trans-localisation at the full proteome level

在生物学中，本地化就是功能。细胞表现出复杂的亚细胞结构，其中每一个都以特定的生化条件为特征，并履行特定的功能。蛋白质必须定位于其预定的亚细胞生态位，才能与其相互作用的伙伴相遇并具有功能活性。因此，能够系统地测量蛋白质的位置，特别是完整的蛋白质组，这是一个被称为空间蛋白质组学的领域，是细胞生物学中的主要兴趣。为了进一步准确地描绘蛋白质的空间亚细胞景观，它们被认为显示了不止一个亚细胞位置，并在不同的这样的生态位之间进行交通。前一种现象被称为多定位现象，第二种现象被称为反式定位，无论是由正常的生物触发因素、病理细胞状态还是外部刺激(如细胞营养物质的变化或药物的作用)引发的。最后，蛋白质的错位与细胞功能失调和癌症等疾病有关。蛋白质组学空间蛋白质组学的最丰富信息数据集是使用高精度质谱学产生的，这是一种允许识别和定量复杂生物样本中蛋白质组含量的技术。这些数据集是使用各种稳健的监督统计机器学习方法挖掘的高质量丰富的数据来源，这些方法已显示出产生有价值的蛋白质细胞器预测。特别是，申请者最近发表了HyperLOPIT，这是一项能够获得精致空间分辨率的技术进步。在小鼠胚胎干细胞上使用这项突破性的技术，他们以前所未有的空间分辨率识别了7000种蛋白质的定位，揭示了细胞器、亚细胞器隔间、蛋白质复合体、功能网络的组织，以及蛋白质的稳态动态，包括意想不到的亚细胞定位。在这项提议中，我们的目标是用最先进的统计程序来补充当代空间蛋白质组学数据，以可靠地在整个蛋白质组水平上识别多定位和易位事件。这些新工具将补充我们现有的开源空间蛋白质组学软件套件，使蛋白质组学和细胞生物学社区能够将空间蛋白质组学数据挖掘到新的深度，以稳健和统计合理的方式识别微妙但具有生物重要性的模式，例如具有混合定位的蛋白质和在扰动时改变定位的蛋白质。我们还将开发专门的可视化平台，以突出我们分析管道的输出，并支持对多维空间数据的交互探索。我们将自己将这些工具应用于来自不同感兴趣的生物系统的广泛的空间蛋白质组学数据集。为了保证我们的工作得到广泛曝光，我们将分析的数据集和我们将推断的空间模式将通过社区数据库，特别是SpatialMap.org在线资源进一步传播。

In biology, localisation is function. Cells display a complex sub-cellular structure, where each of these niches are characterised by specific biochemical conditions and fulfil dedicated functions. A protein must be localised to its intended sub-cellular niche to meet its interaction partners and be functionally active. Hence, being able to systematically measure the locations of proteins, and in particular the full proteome, a field coined spatial proteomics, is of major interest in cell biology.To further depict an accurate view of the spatial sub-cellular landscape of proteins, they are known to display more than one sub-cellular location, and to traffic between different such niches. The former phenomenon is termed multi-localisation and the second one, whether initiated by normal biological triggers, pathological cellular states, or external stimuli such as changes in the cell nutrients or effect of a drug, is called trans-localisation. Finally, the mis-localisation of proteins have been associated with cellular dis-function and diseases such as cancer.The most information-rich datasets for proteome-wise spatial proteomics are generated using high accuracy mass-spectrometry, a technique that allows to identify and quantify the proteome content in complex biological samples. These datasets are high quality rich sources of data that have been mined using a variety of robust supervised statistical machine learning methods which have shown to yield valuable protein-organelle predictions. In particular, the applicants recently published hyperLOPIT, a technological advance enabling to obtain exquisite spatial resolution. Using this groundbreaking technology on mouse embryonic stem cells, they identified the localisation of 7000 proteins with unprecedented spatial resolution, uncovering the organisation of organelles, sub-organellar compartments, protein complexes, functional networks, and the steady-state dynamics of proteins including unexpected sub-cellular locations.In this proposal, we aim to complement contemporary spatial proteomics data with state-of-the-art statistical routines to reliably identify multi- and trans-localisation events at the full proteome level. These new tools, which will complement our existing open-source spatial proteomics suite of software, will enable the proteomics and cell biology community to mine spatial proteomics data to new depths, identifying subtle yet biologically important patterns such as proteins with mixed localisation and proteins that change localisations upon perturbation, in a robust and statistically sound way. We will also develop dedicated visualisation platforms to highlight the outputs of our analysis pipelines and enable interactive exploration of the multidimensional spatial data. We will apply these tools ourselves on a wide range of spatial proteomics datasets from various different biological systems of interest. To guarantee broad exposure of our work, the datasets we will analyse and the spatial patterns we will infer will further be disseminated through community databases, in particular the SpatialMap.org online resource.