A PROTEOMICS RESEARCH RESOURCE FOR INTEGRATIVE BIOLOGY

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 本年度报告的具体目标1侧重于与蛋白质组样品处理、自动化样品处理相关的发展，以及现场不对称波形离子迁移率光谱分析仪的发展。 这一领域的努力侧重于开发改进样品处理程序的一致性和吞吐量的方法，特别强调促进对非常小的生物样品进行蛋白质组学分析的方法。该中心的许多合作研究项目都面临着与极其有限的样本量有关的挑战，特别是那些寻求对人类活检样本进行蛋白质组学分析的项目。这一点在D.Smith的脑音素研究和M.Katze对人类肝脏活检的分析中最为明显。更多的新项目(Cuschieri、Moerman、Kulkarni和Warren)也将继续推动这一领域的发展需求。 改进的小样本存储和处理方法该中心开发了一种方法，将样本快速冷冻并保存在-80oC，直到直接在50%三氟乙醇(TFE)中均质处理，然后用5 mM三丁基膦(TBP)还原二硫键，并用胰酶消化蛋白质。该方案最大限度地减少了分析前所需的样品操作(例如，清理)，并有助于提高蛋白质回收率、样品质量以及最终的蛋白质组覆盖率。与OCT保存样品的结果相比，修改后的方法显示识别的蛋白质数量从每个OCT处理样品的~100-300个蛋白质大幅增加到快速冷冻样品的~700-800个蛋白质。这为改进人类肝脏活检标本的蛋白质组学分析(Katze合作项目)提供了基础，从而使小的非常规蛋白质组样本的蛋白质组覆盖率大大提高。 自动微量样品处理高效、重现性好的样品处理对于获得可靠的LC-MS结果非常重要。手工样品处理有几个变异性来源，这源于不同人员执行给定方案的情况，以及个人在不同日期执行方案的方式。自动化系统可以以较低的成本提供更高的产量，最重要的是，可以减少处理样本的变异性。研究中心的研究人员对Beckman Biomek FX自动液体处理机器人进行了胰酶消化和其他程序的评估。初步评估表明，它可以在同一时间段内处理100多个样本，而通常只允许手动处理大约15个样本。该自动化系统最初用于处理大量小鼠脑组织样本。由于单体素样本的自动化微尺度样本处理(D.Smith合作项目)被证明具有挑战性，该中心开发了一种用于组织裂解和蛋白质消化的简单方案。使用该方案，每1mm3体素通常可回收约20？g的多肽。使用LC-FTICR和AMT标签方法，最初可以为每个体素识别3000个肽和1000个蛋白质，预计该中心目前正在开发的改进的数据分析工具将进一步扩大这一覆盖范围。 在目标分析中应用FAIMS分离以提高灵敏度和选择性通过场不对称波形离子迁移光谱仪(FAIMS)分离气相中的多肽，与凝聚相(液)分离相比，分离速度显著提高，此外，由于FAIMS中的离子聚焦和由此导致的典型的化学噪声降低，还具有提高MS灵敏度和定量能力的额外好处。该中心开发了FAIMS过程的模拟，使研究人员能够确定通过FAIMS有效控制FAIMS分辨率和提高离子传输效率的途径。这些见解被用于随后的实验工作和FAIMS新技术的开发，导致FAIMS分离空间的显著扩大，从而导致分离峰容量(分辨率约为100)。FAIMS的发展预计将有利于中心为需要数据导向和目标分析的协作项目(ROSSIE协作项目)所做的努力，例如，通过显著降低背景信号水平和提高选择性，从而提供更好的S/N和检测限值。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Specific Aim 1 for this annual report focuses on developments associated with proteome sample processing, automated sample processing, and developments with field asymmetric waveform ion mobility spectrometry analyzers. The efforts in this area focused on the development of methods to improve the consistency and throughput of sample processing procedures, with a particular emphasis on methods that facilitate proteomic analyses of very small biological samples. Many of the Centers collaborative research projects present challenges related to extremely limited sample size, particularly those seeking proteomic analysis of human biopsy samples. This was most strikingly evident in the brain voxelation studies of D. Smith and the analyses of human liver biopsies done with M. Katze. More newer projects (Cuschieri, Moerman, Kulkarni, and Warren) will also continue to drive the need for developments in this area. Improved methods for small sample storage and processing  The Center has developed an approach in which samples are flash frozen and stored at -80o C until processed by homogenization directly in 50% trifluoroethanol (TFE) prior to reduction of disulfide bonds with 5 mM tributylphosphine (TBP) and proteolytic digestion with trypsin. This protocol minimizes the amount of sample manipulations (e.g. clean-up) necessary prior to analysis and facilitates improved protein recovery, sample quality and ultimately, proteome coverage. Compared to results with OCT stored samples, the revised protocol showed a substantial increase in the number of identified proteins from ~100-300 proteins per OCT-treated sample to ~700-800 proteins in flash frozen samples. This has provided a basis for refinements to the proteomic analysis of human liver biopsy specimens (Katze collaborative project), thus allowing significantly greater proteome coverage for small, non-conventional proteomic samples. Automated micro-scale sample processing  Efficient, reproducible sample processing is important for obtaining reliable LC-MS results. Manual sample processing has several sources of variability that stem from implementation of a given protocol by different personnel, as well as the way in which an individual implements a protocol on different days. An automated system can provide higher throughput at lower cost and, most importantly, can reduce variability in processed samples. A Beckman Biomek FX automated liquid-handling robot was evaluated by Center researchers for tryptic digestion and other procedures. The initial evaluation showed that it could process over a hundred samples in the same time period that typically allowed only ~15 samples to be processed manually. The automated system was initially applied to process large numbers of small mouse brain tissue samples. Because automated micro-scale sample processing of single voxel samples (D. Smith collaborative project) proved challenging, the Center developed a simple protocol for tissue lysis and protein digestion. With this protocol, ~20 ¿g of peptides are typically recovered per 1 mm3 voxel. Using LC-FTICR and the AMT tag approach ~3000 peptides and 1000 proteins could initially be identified for each voxel, and it is anticipated that improved data analysis tools currently under development in the Center will further expand this coverage. Applying FAIMS separations for increased sensitivity and selectivity in targeted analyses  Separating peptides in the gas phase by Field Asymmetric waveform Ion Mobility Spectrometry (FAIMS) provides a substantial improvement in separation speed compared to condensed phase (liquid) separations, with the added benefits of increased MS sensitivity and quantitative ability due to the ion focusing in FAIMS and the typical reduction of chemical noise that results. The Center has developed simulations of the FAIMS process that allowed researchers to identify the paths to effectively control FAIMS resolution and improve the ion transmission efficiency through the FAIMS. Those insights were employed in subsequent experimental work and new FAIMS technology development resulting in a major expansion of FAIMS separation space and thus separation peak capacity (resolving power of ~100). The developments in FAIMS are expected to benefit Center efforts for collaborative projects requiring data-directed and targeted analyses (Rossie collaborative project) by e.g. significantly decreasing background signal levels and improving selectivity so as to provide improved S/N and detection limits.