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

在完整蛋白质组水平上了解蛋白质多定位和反式定位

• 批准号: BB/N023129/1
• 负责人: Kathryn Lilley
• 金额: $ 20.7万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN023129%2F1
• 关键词: Understanding; protein; multi; trans; localisation

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.

在生物学中，定位就是功能。细胞显示出复杂的亚细胞结构，其中每个小生境的特征在于特定的生化条件和履行专门的功能。蛋白质必须定位于其预期的亚细胞生态位，以满足其相互作用伙伴并具有功能活性。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，一个领域创造的空间蛋白质组学，是细胞生物学的主要兴趣。为了进一步描绘蛋白质的空间亚细胞景观的准确视图，已知它们显示多个亚细胞位置，并在不同的这样的小生境之间进行交通。前一种现象被称为多定位，第二种现象，无论是由正常的生物触发，病理细胞状态，还是外部刺激，如细胞营养素的变化或药物的作用，都被称为trans-localisation。最后，蛋白质的错误定位与细胞功能障碍和癌症等疾病有关。蛋白质组空间蛋白质组学中信息最丰富的数据集是使用高精度质谱技术生成的，该技术可以识别和量化复杂生物样品中的蛋白质组含量。这些数据集是高质量的丰富数据源，使用各种强大的监督统计机器学习方法进行挖掘，这些方法已被证明可以产生有价值的蛋白质-细胞器预测。特别地，申请人最近公布了hyperLOPIT，这是一种能够获得精细空间分辨率的技术进步。他们在小鼠胚胎干细胞上使用这项突破性的技术，以前所未有的空间分辨率鉴定了7000种蛋白质的定位，揭示了细胞器的组织，亚细胞器区室，蛋白质复合物，功能网络以及蛋白质的稳态动力学，包括意想不到的亚细胞位置。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，以可靠地识别全蛋白质组水平的多定位和跨定位事件。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，以稳健和统计学合理的方式识别微妙但具有生物学重要性的模式，例如混合定位的蛋白质和在扰动时改变定位的蛋白质。我们还将开发专用的可视化平台，以突出我们分析管道的输出，并实现多维空间数据的交互式探索。我们将把这些工具应用于来自各种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的曝光，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。

项目成果

Subcellular Transcriptomics and Proteomics: A Comparative Methods Review.

亚细胞转录组学和蛋白质组学：比较方法综述。

• DOI: 10.17863/cam.80101
• 发表时间: 2022
• 作者: Christopher J

A Comprehensive Subcellular Atlas of the Toxoplasma Proteome via hyperLOPIT Provides Spatial Context for Protein Functions.

• DOI: 10.1016/j.chom.2020.09.011
• 发表时间: 2020-11-11
• 期刊: Cell host & microbe
• 影响因子: 30.3
• 作者: Barylyuk K;Koreny L;Ke H;Butterworth S;Crook OM;Lassadi I;Gupta V;Tromer E;Mourier T;Stevens TJ;Breckels LM;Pain A;Lilley KS;Waller RF
• 通讯作者: Waller RF

A Bioconductor workflow for processing and analysing spatial proteomics data.

• DOI: 10.12688/f1000research.10411.2
• 发表时间: 2016
• 期刊: F1000Research
• 作者: Breckels LM;Mulvey CM;Lilley KS;Gatto L
• 通讯作者: Gatto L