MICA: Delivering a production platform and atlas for next-generation biomarker discovery, validation and assay development in clinical proteomics

MICA：为临床蛋白质组学中的下一代生物标志物发现、验证和检测开发提供生产平台和图谱

• 批准号: MR/N028457/1
• 负责人: Andrew Dowsey
• 金额: $ 76.98万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN028457%2F1
• 关键词: MICA; Delivering; production; platform; atlas

The genomic revolution has advanced medical science to an important tipping point. We can now attempt to understand the complex interactions between the molecular building blocks of life that control human function and how they are perturbed and break down under disease. These perturbations and dysfunctions can lead to tell-tale biomolecular signals in our cells and tissues, often linked to changes in the underlying genetic code and other physiological characteristics. Since each individual case can be different, a single, common treatment option may not be effective or safe for all patients. This has led to the concept of stratified medicine, where different treatments are associated with the different molecule signatures of the individual patients. Critical to the success of such an approach is a diagnostic programme that can reliably characterise these molecules, ideally the proteins, for early disease detection and subsequent stratification based on drug safety or efficacy. The push to systematically discover these so called 'biomarkers' has been enhanced through the establishment of a number of large-scale facilities worldwide, including the MRC-funded Stoller Biomarker Discovery Centre (SBDC) in Manchester. The SBDC is a £25M facility that combines the latest instrumentation and techniques for high-throughput profiling of proteins, validation of candidate biomarker sets on thousands of samples, through to the development of clinical tests ('assays') for the routine measurement of individual biomarkers in clinic. The SBDC uses mass spectrometry (MS) to do this, a pervasive technique for gaining a snapshot of a sample, which measures each constituent compound's mass and quantity e.g. for profiling proteins - 'proteomics'. The SBDC and other recently launched centres employ a new strategy for MS called Data-Independent Acquisition (DIA). DIA produces a comprehensive digital record of the sample, unlike previous approaches potentially enabling the identification and quantification of all detectable proteins. Nevertheless, due to biological variations, it is necessary to analyse multiple samples to get a reliable understanding of patient populations. The DIA-based SWATH-MS approach from SCIEX Ltd. has generated considerable clinical interest as it enables reliable and reproducible monitoring of potential biomarkers over thousands of samples. SWATH-MS, like all clinical MS approaches, must digest proteins to smaller peptides for analysis. However, this leads to challenges for both SWATH-MS analysis and the development of clinical assays with MS, when selecting reproducible peptide(s) to base the test upon. We have recently developed a new statistical (Bayesian) modelling approach to assess peptide reproducibility, and a fundamentally novel workflow for biomarker discovery that for the first time performs statistical modelling on the unprocessed data delivering a significant performance increase. The purpose of this project is to exploit the sensitivity of our workflow to deliver a robust, production quality biomarker discovery and validation software platform for routine use by the SBDC and beyond. Moreover, since the SBDC will analyse up to 12,000 samples per annum and is underpinned by rigorous standard operating procedures controlling sample collection, preparation and analysis, it also provides a unique opportunity to collate and understand the biological and experimental variation in protein levels across vast patient populations, in health and disease. We will build an 'atlas' of this variation, stratified across genetic, physiological and other clinical data. To achieve this, we will combine 'big data' computing approaches and web infrastructure. The atlas will enable biomarker verification and peptide characterisation for assay development much earlier in the pipeline than is currently possible, and realise further step-change improvements in the sensitivity and specificity of our discovery platform.

基因组革命将医学科学提高到了一个重要的转折点。现在，我们可以尝试了解控制人类功能的生命的分子构建块以及如何在疾病下扰动和分解之间的复杂相互作用。这些扰动和功能障碍可能导致我们的细胞和组织中的讲述的生物分子信号，通常与基本遗传密码和其他物理特征的变化有关。由于每个病例都可能不同，因此单一的常见治疗选择可能对所有患者都没有有效或安全。这导致了分层医学的概念，其中不同的治疗方法与单个患者的不同分子特征有关。这种方法的成功至关重要的是一个诊断程序，可以可靠地表征这些分子（理想情况下是蛋白质），以基于药物安全或功效的早期疾病检测和随后的分层。通过在全球建立了许多大规模的设施，包括在曼彻斯特的MRC资助的Stoller BioMark Discovery Center（SBDC），通过建立了许多大规模的设施来系统地发现这些所谓的“生物标志物”。 SBDC是一个耗资2500万英镑的设施，结合了蛋白质高通量分析的最新仪器和技术，验证候选生物标志物在数千个样本上的验证，从而开发临床测试（“分析”），以实现临床中个体生物标志物的常规测量。 SBDC使用质谱法（MS）来做到这一点，这是一种获得样品快照的普遍技术，该技术测量了每个组成化合物的质量和数量，例如用于分析蛋白质 - “蛋白质组学”。 SBDC和其他最近推出的中心采用了一种新的策略，用于MS，称为数据独立的收购（DIA）。 DIA产生样本的全面数字记录，与以前的方法不同，可以实现所有可检测蛋白的识别和量化。然而，由于生物学变化，有必要分析多个样本以获得对患者人群的可靠了解。 Sciex Ltd.的基于DIA的SWATH-MS方法已经产生了考虑临床兴趣，因为它可以可靠且可重复地监测数千个样本的潜在生物标志物。像所有临床MS方法一样，Swath-MS必须将蛋白质消化为较小的肽进行分析。但是，当选择可重复的肽以基于测试的基础时，这导致了Swath-MS分析和MS临床评估的发展挑战。我们最近开发了一种新的统计（贝叶斯）建模方法来评估肽的可重复性，并从根本上进行了新颖的生物标志物发现工作流，该发现首次对未经处理的数据进行了统计建模，从而提供了显着的性能提高。该项目的目的是利用我们的工作流程的敏感性，以提供强大的，生产质量的生物标志物发现和验证软件平台，用于SBDC及其他地区的日常使用。此外，由于SBDC每年将分析多达12,000个样本，并受到控制样本收集，准备和分析的严格标准操作程序的基础，因此它还提供了一个独特的机会，可以在健康和疾病中构成和了解蛋白质水平的生物学和实验性变化。我们将建立这种变异的“地图集”，并在遗传，物理和其他临床数据之间分层。为了实现这一目标，我们将结合“大数据”计算方法和Web基础架构。地图集将在管道中实现生物标志物验证和肽表征，以比目前可能更早，并实现我们发现平台的敏感性和特异性的进一步变化。

项目成果

The need for statistical contributions to bioinformatics at scale, with illustration to mass spectrometry

需要对大规模生物信息学做出统计贡献，并以质谱法为例

• DOI: 10.1177/1471082x17708519
• 发表时间: 2017
• 期刊: Statistical Modelling
• 影响因子: 1
• 作者: Dowsey A

Cognitive dysfunction in diabetic rats is prevented by pyridoxamine treatment. A multidisciplinary investigation

• DOI: 10.1016/j.molmet.2019.08.003
• 发表时间: 2019-10-01
• 期刊: MOLECULAR METABOLISM
• 影响因子: 8.1
• 作者: Kassab, Sarah;Begley, Paul;Gardiner, Natalie J.
• 通讯作者: Gardiner, Natalie J.

mzMLb: A Future-Proof Raw Mass Spectrometry Data Format Based on Standards-Compliant mzML and Optimized for Speed and Storage Requirements.

• DOI: 10.1021/acs.jproteome.0c00192
• 发表时间: 2021-01-01
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Bhamber RS;Jankevics A;Deutsch EW;Jones AR;Dowsey AW
• 通讯作者: Dowsey AW

Diagnostic MALDI-TOF MS can differentiate between high and low toxic Staphylococcus aureus bacteraemia isolates as a predictor of patient outcome.

• DOI: 10.1099/mic.0.001223
• 发表时间: 2022-08
• 期刊: MICROBIOLOGY-SGM
• 影响因子: 2.8
• 作者: Brignoli, Tarcisio;Recker, Mario;Lee, Winnie W. Y.;Dong, Tim;Bhamber, Ranjeet;Albur, Mahableshwar;Williams, Philip;Dowsey, Andrew W.;Massey, Ruth C.
• 通讯作者: Massey, Ruth C.

mzMLb: a future-proof raw mass spectrometry data format based on standards-compliant mzML and optimized for speed and storage requirements

mzMLb：一种面向未来的原始质谱数据格式，基于符合标准的 mzML，并针对速度和存储要求进行了优化

• DOI: 10.1101/2020.02.13.947218
• 发表时间: 2020
• 作者: Bhamber R