Systematic and Comprehensive Sampling of Peptides in Mixtures by Tandem Mass Spectrometry

 描述(由申请人提供)：在过去的十年中，新的蛋白质组学技术的开发和应用有所增加。这些技术开始对我们理解癌症生物学中的基本和临床问题产生重大影响。然而，在我们能够获得多肽的串联质谱学数据并在序列数据库中进行搜索的20年里，总体策略基本上保持不变。基本上，蛋白质混合物被消化成多肽混合物，使用数据相关采集(DDA)用液相色谱-串联质谱仪分析混合物，并通过数据库搜索来解释这些光谱。随着时间的推移，我们的领域使用同样的通用方法改进了我们的方法，当结合质谱仪硬件的改进时，蛋白质组学实验的综合性和吞吐量得到了极大的提高。要继续产生影响，我们需要进一步提高我们实验的深度和吞吐量。最终和崇高的目标是在许多样本中检测和量化超过检测极限的所有多肽。目前，没有校准的实验室和平台之间的多肽强度测量是不可比较的，这使得每个实验都是孤立的事件，实验室几乎没有机会建立现有的数据。虽然我们作为一个领域已经取得了很大的进步，但我们需要继续改进我们的方法，我们现在需要改变我们的战略，以便我们能够在未来进一步前进。我们建议开发新的数据采集和分析策略，以改进大规模分子表型鉴定所需的大量样本的系统分析。具体地说，我们计划1)提高数据独立采集(DIA)的选择性，2)提高DDA的重现性和采样深度，3)将串联质谱学数据的分析从以光谱为中心改为以肽为中心。有必要继续并行开发DDA和DIA两种数据采集策略，再经过几个周期的硬件改进，这两种方法最终可能会趋同。然后，我们将演示这些技术的使用，以表明我们可以快速表现出细胞状态，以响应癌症治疗。

 DESCRIPTION (provided by applicant): The last decade has seen an increase in the development and application of new proteomics technologies. These technologies are beginning to make a significant impact on our understanding of basic and clinical questions in cancer biology. However, in the 20 years that we have been able to take tandem mass spectrometry data of peptides and search it against a sequence database, the general strategy has remained largely the same. Basically, protein mixtures are digested to peptide mixtures, the mixture is analyzed by liquid chromatography-tandem mass spectrometry using data dependent acquisition (DDA), and these spectra are interpreted using database searching. Overtime, our field has refined our methods using this same general approach and when combined with improvements in mass spectrometry instrument hardware, the comprehensiveness and throughput of proteomics experiments has improved greatly. To continue to make an impact, we need to further improve the depth and throughput of our experiments. The ultimate and lofty goal being the detection and quantification of all peptides above the limit of detection across many samples. Peptide intensity measurements are currently not comparable between laboratories and platforms without calibration making each experiment an isolated event with little opportunity for labs to build on existing data. While we have made a lot of progress as a field, we need to continue to improve our methods and we need to alter our strategy now so that we can move further forward in the future. We are proposing the development of new data acquisition and analysis strategies that will improve the systematic analysis of large numbers of samples for large-scale molecular phenotyping. Specifically we plan to 1) improve the selectivity of data independent acquisition (DIA), 2) improve the reproducibility and sampling depth of DDA, and 3) change the analysis of the tandem mass spectrometry data from spectrum-centric to peptide-centric. It is necessary to continue to develop both DDA and DIA data acquisition strategies parallel and that with another few cycles of hardware improvements the two methods may ultimately converge. We will then demonstrate the use of these technologies to show that we can rapidly phenotype the cellular state in response to cancer therapeutics.