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础和临床问题产生重大影响。然而，在20年来，我们已经能够采取串联质谱数据的肽和搜索它对序列数据库，一般的策略基本上保持不变。基本上，将蛋白质混合物消化成肽混合物，通过液相色谱-串联质谱法使用数据依赖性采集（DDA）分析混合物，并使用数据库搜索解释这些光谱。随着时间的推移，我们的领域已经改进了我们的方法，使用相同的一般方法，当结合质谱仪器硬件的改进，蛋白质组学实验的全面性和吞吐量大大提高。为了继续产生影响，我们需要进一步提高实验的深度和吞吐量。最终和崇高的目标是在许多样品中检测和定量高于检测限的所有肽。肽强度测量目前在没有校准的实验室和平台之间不具有可比性 使得每个实验都是孤立的事件，实验室几乎没有机会建立在现有数据的基础上。虽然我们作为一个领域已经取得了很大的进展，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，这将改善大规模分子表型分析的大量样本的系统分析。具体而言，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数据采集策略，并且通过另外几个周期的硬件改进，这两种方法最终可能会收敛。然后，我们将展示这些技术的使用，以表明我们可以快速表型的细胞状态，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.