Advanced Sample Preparation, Separation and Multiplexed Analysis for In-Depth Proteome Profiling of >1000 Single Cells Per Day

项目摘要/摘要 癌症组织表现出高度的表型异质性和可塑性，并含有许多 处于不同状态的细胞亚群。在单细胞水平和分子水平上量化这种异质性 大量单元格的深度提供了大量无法获得的信息，并将 最终带来更好的诊断和更有效的治疗。而单细胞核酸测序 方法正在对癌症研究产生重大影响，蛋白质介导了大部分细胞功能 是大多数治疗学的靶子。因此，迫切需要为大型石油公司开发新技术。 在单细胞水平上进行直接蛋白质组谱分析。为了填补这一空白，基于质谱学(MS)的分析 最近，通过实施更有效的方法证明了单细胞中的蛋白质表达 样品处理工作流程、新颖的实验设计和改进的仪器灵敏度。无标签MS- 基于蛋白质组学现在可以在4个数量级的动态范围内量化每个细胞的2,000个蛋白质组 范围，但每天对几十个以上细胞进行分析的努力导致蛋白质组显著减少 覆盖范围。这种低吞吐量不足以满足大规模统计支持研究的需要 描述癌细胞群体的异质性。要增加测量吞吐量，多路传输 基于等压串联质量标签(TMT)的工作流程可以在LC中测量多达18个单细胞- MS分析，但这些仍然被限制在大约100个细胞/天，并且像通常实施的那样，遭受 大比例的缺失值和其他影响量化绩效的问题。我们的总体目标是 为了开发一种将简化的无吸管高通量样品制备与快速、 多柱液相色谱分离和“贪婪”数据采集用于Profile&gt；2000 每个细胞的蛋白质，每天的测量吞吐量为1000个单细胞。我们假设 先进的样品制备和分离，结合更高效的MS采集工作流程，将 实现深度SCP，吞吐量提升10倍，提供直接、深度、大规模的能力 类似于单细胞RNA序列的蛋白质定量。目标1的研究将集中在开发 大规模平行离心纳升点胶，每天制备10,000个单细胞，总试剂和 耗材成本为&lt；$0.40/cell。在目标2中，我们将开发快速、健壮和高峰容量的20分钟纳米LC 100%占空比的分离。在目标3中，我们将开发一种新颖的“贪婪”数据获取策略，在该策略中 只有蛋白型多肽被选择用于碎裂，并具有定制的自动增益控制设置和 每种多肽的碎裂能量，提供前所未有的灵敏度和吞吐量的组合。 有了这个下一代平台，我们将分析10,000个细胞，以研究获得的自噬抵抗力 抑制剂在自噬依赖三阴性乳腺癌的背景下，从而建立了一种创新的 促进生物医学研究和个体化治疗的平台。

PROJECT SUMMARY/ABSTRACT Cancer tissues exhibit a high degree of phenotypic heterogeneity and plasticity and contain numerous subpopulations of cells in various states. Quantifying this heterogeneity at the single-cell level and with molecular depth across large numbers of cells provides information that cannot be obtained at the bulk scale and will ultimately lead to improved diagnostics and more effective treatments. While single-cell nucleic acid sequencing approaches are having a significant impact on cancer research, proteins mediate the bulk of cellular function and are the targets of most therapeutics. There is thus an urgent need to develop new technologies for large- scale direct proteome profiling at the single-cell level. To fill this gap, mass spectrometry (MS)-based profiling of protein expression in single cells has recently been demonstrated through the implementation of more efficient sample processing workflows, novel experimental designs and improved instrument sensitivity. Label-free MS- based proteomics can now quantify >2,000 protein groups per cell across >4 orders of magnitude of dynamic range, but efforts to profile more than a few dozen cells per day have resulted in significantly reduced proteome coverage. This low throughput is insufficient for the large-scale statistically powered studies required to characterize heterogeneity in cancer cell populations. To increase measurement throughput, multiplexed workflows based on isobaric tandem mass tags (TMTs) enable up to 18 single cells to be measured in an LC- MS analysis, but these have still been limited to ~100 cells/day and, as generally implemented, suffer from a large proportion of missing values and other issues affecting quantitative performance. Our overall objective is to develop a platform that combines simplified pipette-free high-throughput sample preparation with rapid, multicolumn liquid chromatography separations and ‘greedy’ data-dependent acquisition to profile >2000 proteins per cell with a measurement throughput of >1000 single cells per day. We hypothesize that the advanced sample preparation and separation, combined with a far more efficient MS acquisition workflow, will achieve in-depth SCP with a 10× throughput gain, thus providing a capability for direct, in-depth and large-scale protein quantification that is analogous to single-cell RNA-seq. Studies in Aim 1 will focus on developing massively parallel centrifugal nanoliter dispensing to prepare >10,000 single-cells per day at a total reagent and consumables cost of <$0.40/cell. In Aim 2, we will develop rapid, robust and high-peak-capacity 20-min nanoLC separations with 100% duty cycle. In Aim 3, we will develop a novel ‘greedy’ data acquisition strategy in which only proteotypic peptides are selected for fragmentation, and with custom automatic gain control settings and fragmentation energy for each peptide, providing an unprecedented combination of sensitivity and throughput. With this next-generation platform, we will profile >10,000 cells to study acquired resistance to autophagy inhibitors in the context of autophagy-dependent triple negative breast cancer, thus establishing an innovative platform for advancing biomedical research and individualizing therapy.