Fully automated and ultra-high-throughput platform for in-depth single-cell proteomics

项目摘要/摘要 发展有效的治疗方法以增进人类健康需要深入的分子水平 了解细胞过程和单个细胞之间的动态相互作用。传统人口- 基于生物化学的测量提供的效用有限，因为单个细胞的贡献被平均和 关键信息丢失了。因此，需要对单个细胞的生化组成进行直接测量 描述细胞转变、调节机制和微环境的贡献。单人- 细胞RNA测序正在对生物学研究产生巨大影响，但蛋白质介导了大部分 细胞功能以及RNA和蛋白质丰度之间的相关性往往很差。此外，RNA 测量无法告知重要的翻译后修改，而这些修改很容易通过 质谱学。目前在单细胞中直接定量靶蛋白的努力，如细胞周期蛋白和 免疫组织化学有一个共同的缺点，那就是只能分析有限数量的蛋白质。 因此，迫切需要能够直接产生无偏见和深入的单因素分析的技术 细胞蛋白质图谱，以提供更完整的细胞过程图景。我们最近开发了一种证明- 名为纳米POTS(一锅处理痕量样品的纳米微滴处理)的概念平台 将样品处理量缩小到纳升量级，以减少样品损失。结合使用 超灵敏的液质联用(LC-MS)，纳米POTS使全球蛋白质组图谱成为可能 通过细胞分选或组织切片的小区域分离的单个分离细胞中的~1000个蛋白质组 通过显微解剖分离。在这个概念验证平台的基础上，我们的总体目标是开发 完全自动化的原型，产生比目前可以实现的更大的蛋白质组覆盖率和吞吐量， 提供类似于单细胞的直接、深入和大规模的蛋白质定量能力 RNA序列目标1的研究将侧重于完全自动化样品制备和减少样品处理。 体积至少增加10倍，以进一步减少样本损失和增加蛋白质组覆盖率。AIM 2将实现自动化 将样品转移到分析平台，并开发全自动、超灵敏的LC-MS工作流程 100%的MS利用率。AIM 3将把这些在灵敏度、吞吐量和自动化方面的进步扩展到 基于具有唯一等压标记的条形码的单个细胞的多路分析。我们将结合两个不同的 多路复用方法，可在一次运行中同时分析多达32个样品。已完成的 平台将完全自动化，能够进行高定量、无标记和多重的单细胞蛋白质组 分析深度为每细胞3000个蛋白质，并将实现前所未有的测量吞吐量 &gt；每天300个单细胞，用于多路分析。这将构成一种独特和广泛的使能技术 用于获取基本的生物医学知识。

PROJECT SUMMARY/ABSTRACT The development of effective therapies to advance human health requires an in-depth molecular-level understanding of cellular processes and dynamic interactions between individual cells. Conventional population- based biochemical measurements provide limited utility, as contributions from individual cells are averaged and crucial information is lost. Direct measurements of the biochemical makeup of single cells are thus needed to characterize cellular transitions, regulatory mechanisms and the contribution of the microenvironment. Single- cell RNA sequencing is making a tremendous impact on biological research, but proteins mediate the bulk of cellular function and the correlation between RNA and protein abundance is often poor. In addition, RNA measurements are unable to inform on important posttranslational modifications that are readily measured by mass spectrometry. Current efforts to directly quantify targeted proteins in single cells such as CyTOF and immunohistochemistry share common shortcomings in that only a limited number of proteins can be analyzed. There is thus an urgent unmet need for technologies capable of directly generating unbiased and in-depth single- cell protein profiles to provide a more complete picture of cellular processes. We recently developed a proof-of- concept platform termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples) that effectively downscales sample processing volumes to the nanoliter scale to reduce sample losses. In combination with ultrasensitive liquid chromatography-mass spectrometry (LC-MS), nanoPOTS enables global proteome profiling of ~1000 protein groups in individual dissociated cells isolated by cell sorting or small regions of tissue sections isolated by microdissection. Building upon this proof-of-concept platform, our overall objective is to develop a fully automated prototype that yields far greater proteome coverage and throughput than is currently achievable, 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 fully automating sample preparation and decreasing sample processing volumes at least tenfold to further reduce sample losses and increase proteome coverage. Aim 2 will automate sample transfer to the analytical platform and develop a fully automated and ultrasensitive LC-MS workflow with 100% MS utilization efficiency. Aim 3 will extend these advances in sensitivity, throughput and automation to the multiplexed analysis of single cells based on barcoding with unique isobaric labels. We will combine two distinct multiplexing approaches to enable simultaneous analysis of up to 32 samples in a single run. The completed platform will be fully automated, capable of highly quantitative label-free and multiplexed single cell proteome profiling to a depth of >3000 proteins per cell, and will achieve an unprecedented measurement throughput of >300 single cells per day for multiplexed analyses. This will constitute a unique and broadly enabling technology for the acquisition of basic biomedical knowledge.