An integrated microtechnology platform for spatially resolved mass spectrometry-based proteomics

项目摘要 生物组织中细胞和分子的空间组织在病理生理学中起着至关重要的作用。为 例如，肿瘤微环境中的空间异质性决定了肿瘤的起始、转移和药物 回应。尽管空间转录学在绘制组织中的RNA方面取得了进展，但它是蛋白质，而不是RNA， 驱动大多数细胞过程并决定疾病状态的物质。因为无法推断蛋白质的丰度 准确地说，根据转录数据，测量蛋白质丰度及其空间分布对于 更好地预测病理生理现象，以及识别生物标志物和治疗靶点。 空间蛋白质组学之前的工作是基于抗体识别、质谱学成像或 对组织进行物理解剖，然后进行LC-MS/MS。 基于抗体和质谱学的成像方法蛋白质组覆盖率很低(&lt；100个蛋白质)。这个 唯一有深度覆盖(&gt；3000蛋白质)的方法是组织解剖，然后是LC-MS/MS。 利用最先进的LC-MS/MS的能力实现对数千种蛋白质的深入定量 以及它们的翻译后修改。然而，这种方法受到当前解剖方法的限制。 手工解剖吞吐量低，空间分辨率差。激光捕获显微切割技术(LCM)具有很高的应用价值 分辨率很高，但组织测绘所需的许多像素的分离是繁琐的，而且还会造成样本丢失。 该项目的目标是开发一种高通量和可扩展的技术来执行组织 保留组织空间信息并直接连接到已建立的LC-MS/MS工作流程的显微解剖 用于蛋白质组的深度和无偏见的空间映射。我们将在肿瘤切片上展示我们的技术 皮肤鳞状细胞癌。我们的方法集成了一种新的组织微切割设备(µDier)， 用于单电池灵敏度LC-MS/MS分析的纳米液滴样品制备平台(“NanPOTS”)，以及 微流控装置(微映射器)，用于将切碎的组织像素从微切割器转移到纳米POTS阵列，同时 维持它们的空间秩序。我们的方法是创新的，因为目前还没有技术可以执行 保留空间的LC-MS/MS组织显微解剖及其向宏观孔道的并行转移 信息。具体目标是优化用于将固定组织切片切成10-100微米微组织的µ切割机 像素，开发并验证µ映射器以将组织像素从微切割器传输到纳米POTS芯片，以及 开发高通量和集成的空间蛋白质组学工作流程，并将其应用于绘制人类肿瘤切片。 该项目意义重大，因为它将加速基于质谱学的空间蛋白质组学，从而 促进我们对组织异质性在病理生理学中的作用的理解，例如肿瘤的作用 微环境对癌症进展的影响，并将有助于识别新的蛋白质生物标记物和 治疗目标，以促进疾病的早期发现、诊断和干预。

Project Summary The spatial organization of cells and molecules in biological tissues plays a critical role in pathophysiology. For example, spatial heterogeneity in the tumor microenvironment determines tumor initiation, metastasis, and drug response. Despite advances in spatial transcriptomics to map RNA in tissues, it is proteins, rather than RNA, that drive most cellular processes and determine disease state. As protein abundance cannot be inferred precisely from transcriptomic data, it is important to measure protein abundance and their spatial distribution to better predict pathophysiological phenomena, as well as to identify biomarkers and therapeutic targets. Previous work on spatial proteomics is based on antibody recognition, mass spectrometry imaging, or physical dissection of the tissue followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Antibody-based and mass spectrometry imaging approaches have low proteome coverage (<100 proteins). The only approach with deep coverage (>3000 proteins) is tissue dissection followed by LC-MS/MS. This method leverages the power of state-of-the-art LC-MS/MS to achieve in-depth quantification of thousands of proteins along with their post-translational modifications. However, this approach is limited by current dissection methods. Manual dissection has low throughput and poor spatial resolution. Laser capture microdissection (LCM) has high resolution, but the isolation of many pixels, required for tissue mapping, is tedious and suffers from sample loss. The goal of this project is to develop a high throughput and scalable technology to perform tissue microdissection that preserves tissue spatial information and couples directly to established LC-MS/MS workflow for deep and unbiased spatial mapping of the proteome. We will demonstrate our technology on tumor slices of cutaneous squamous cell carcinoma. Our approach integrates a novel tissue micro-dicing device (“µDicer”), a nanodroplet sample preparation platform (“nanoPOTS”) for LC-MS/MS analysis with single-cell sensitivity, and a microfluidic device (“µMapper”) to transfer the diced tissue pixels from the µDicer to the nanoPOTS array while preserving their spatial order. Our approach is innovative because no technology currently exists to perform tissue micro-dissection and their transfer to macroscopic wells in parallel for LC-MS/MS while preserving spatial information. The specific aims are to optimize the µDicer for dicing fixed tissue slices into 10-100 µm micro-tissue pixels, develop and validate the µMapper to transfer tissue pixels from the µDicer onto nanoPOTS chips, and develop a high throughput and integrated spatial proteomics workflow and apply it to map human tumor slices. The project is significant because it will accelerate mass spectrometry-based spatial proteomics, thereby advancing our understanding of the role of tissue heterogeneity in pathophysiology, such as the role of the tumor microenvironment on cancer progression, and will enable the identification of novel protein biomarkers and therapeutic targets to facilitate the early detection, diagnosis, and intervention of diseases.