High-throughput Differential Expression Proteomics

2001年，随着人类基因组序列草图的公布，达到了一个重要的里程碑。现在已经很明显，人类基因组中编码蛋白质的基因比人类蛋白质组中的蛋白质少得多。虽然基因组相对稳定，但每个组织都表现出完全不同的蛋白质表达，这些蛋白质表达也在其生命周期和环境刺激中动态变化。因此，蛋白质组学在阐明许多新基因及其产物的功能作用，以及理解它们在正常细胞过程和疾病中参与生物学相关表型方面发挥着重要作用。差异蛋白质组学已成为一个重要的工具，在发展早期和更准确的筛查和诊断测试的检测和治疗疾病。蛋白质生物标志物是通过确定蛋白质表达而发现的，该蛋白质表达在疾病状态的早期进展中发生独特变化。然后，这些生物标志物可以在非侵入性诊断的开发中被靶向，或者在药物发现中用作新药疗效的指标。高通量发现蛋白质生物标志物和筛选所有人类蛋白质以确定其功能和相互作用是当今蛋白质组学的两大生物学挑战。这些大规模的挑战对于单个实验室的资源来说太大了，因此开放的国际合作至关重要，并且正在由人类蛋白质组组织（HUPO-http：//www.hupo.org/）倡导。HUPO是一个国际联盟，旨在促进蛋白质组学研究的发展和认识，并促进HUPO成员及其倡议之间的科学合作。其中一项倡议是脑蛋白质组计划（BPP/http：//www.hbpp.org/）。BPP的目标是：-分析健康，神经疾病和老年状态下人类和小鼠模型的大脑蛋白质组，重点是阿尔茨海默氏症和帕金森氏症。推进神经疾病和衰老的知识，以开发新的诊断方法和药物。为了使神经蛋白质组学研究及其结果在科学界和社会中可用，大脑是高等生物体中最复杂的组织，因此阐明大脑的蛋白质组成是当今蛋白质组学分析技术的重大挑战的上限。英国在HUPO中发挥着重要作用，主要是通过由剑桥希斯顿的欧洲生物信息学研究所主办的HUPO蛋白质组标准倡议（PSI-http：//psidev.sourceforge.net/）。然而，英国在BPP中的代表性不足，特别是在整个蛋白质组信息学研究中。大规模蛋白质组学分析的两个最大的技术障碍是：-在差异表达蛋白质组学中需要大量的专家手动交互。使用传统技术，错误沿着管道传播，因此还需要大量的专家手动验证，这增加了显着的主观性。实验室之间蛋白质组学工作流程的显著方案差异，导致结果异质性，因此具有挑战性的结果整合和交叉验证问题。为了消除这些障碍，拟议的奖学金旨在通过自动化蛋白质组信息学管道来支持蛋白质组学研究，该管道：-整合多个重复实验的统计能力，以阐明所有信息，从而使差异分析和表达定量的准确性提高到完全自动化的水平，并且主观上被删除。从蛋白质组学实验的历史出发，建立差异表达蛋白质组学的统计形成模型，比较和对比差异蛋白质组学样品制备、分离和鉴定方案的灵敏度，为后续的实验设计提供依据。

In 2001, a major milestone was reached with the publication of the draft sequence of the human genome. It has now become apparent that there are far fewer protein-coding genes in the human genome than proteins in the human proteome. Whilst the genome is relatively stable, each tissue exhibits radically different protein expression that also changes dynamically over its life cycle and with environmental stimulus. Proteomics is therefore playing a major role in elucidating the functional role of many novel genes and their products, as well as in understanding their involvement in biologically relevant phenotypes both in normal cellular processes and disease. Differential proteomics has become a vital tool in the development of earlier and more accurate screening and diagnostic tests for the detection and treatment of disease. Protein biomarkers are discovered through determination of protein expression that changes uniquely through early progression of a disease state. These biomarkers can then be targeted in the development of non-invasive diagnosis, or used as indicators of the efficacy of new medications in drug discovery. The high-throughput discovery of protein biomarkers and the screening of all human proteins to ascertain their functions and interactions are the two major biology driven challenges in proteomics today.These large-scale challenges are too great for the resources of a single laboratory, so open international collaborations are essential and are being championed by the Human Proteome Organisation (HUPO - http://www.hupo.org/). HUPO is an international consortium that promotes the development and awareness of proteomics research and facilitates scientific collaborations between HUPO members and its initiatives. One such initiative is the Brain Proteome Project (BPP / http://www.hbpp.org/). The aims of the BPP are:- To analyse the brain proteome of human and mouse models in healthy, neurodiseased and aged states with emphasis on Alzheimer's and Parkinson's diseases.- To advance knowledge of neurodiseases and aging for developing new diagnostic approaches and medications.- To make neuroproteomic research and its results available in the scientific community and society.The brain is the most complex tissue of higher organisms, and therefore elucidating the protein complement of the brain is the upper limit of a significant challenge to today's current technologies in proteome analysis. The UK is playing a major role in HUPO, significantly through the HUPO Proteomic Standards Initiative (PSI - http://psidev.sourceforge.net/) hosted by the European Bioinformatics Institute, Hixton, Cambridge. However, the UK is under-represented in the BPP and notably in proteome informatics research as a whole. The two greatest technical barriers to large-scale proteomic analyses are:- The need for considerable expert manual interaction in differential expression proteomics. With conventional techniques errors propagate down the pipeline and so considerable expert manual validation is also required, which adds significant subjectivity.- Marked protocol variation in proteomic workflows between laboratories, leading to heterogeneity of results and therefore challenging results integration and cross-validation issues. To lift these barriers, the proposed fellowship aims to underpin proteomics research with an automated proteome informatics pipeline that:- Integrates the statistical power of multiple replicated experiments in order to elucidate all information, so that the accuracy of differential analysis and expression quantification increases to a level where full automation is possible and subjectively is removed.- Build up a statistical formation model of differential expression proteomics from a history of proteomics experiments, to compare and contrast the sensitivity of subtly different proteomic sample preparation, separation and identification protocols for use in subsequent experiment design.