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

用于基于空间分辨质谱的蛋白质组学的集成微技术平台

• 批准号: 10564117
• 负责人: Sindy Kam-Yan Tang
• 金额: $ 63.98万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-06 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10564117
• 关键词: Acceleration; Address; Antibodies; Biological; Biomedical Research; Cancer Biology; Cell physiology; Cells; Couples; Data; Development; Devices; Diagnosis; Disease; Dissection; Early Diagnosis; Ethanol; Extracellular Protein; Functional disorder; Goals; Heterogeneity; Human; Immunosuppression; Intervention; Label; Liquid Chromatography; Manuals; Maps; Mass Spectrum Analysis; Measures; Methods; Microdissection; Microfluidic Microchips; Microfluidics; Molecular; Neoplasm Metastasis; Outcome; Pathology; Pathway interactions; Pharmaceutical Preparations; Play; Post-Translational Protein Processing; Preparation; Process; Proteins; Proteome; Proteomics; Publishing; RNA; Resolution; Role; Running; Sampling; Site; Slice; Spatial Distribution; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Technology; Tissue Preservation; Tissues; Tumor Tissue; Work; biomarker identification; cancer initiation; clinical diagnostics; design; fabrication; imaging approach; innovation; innovative technologies; laser capture microdissection; mass spectrometric imaging; meter; nano; nanoDroplet; nanofabrication; new technology; new therapeutic target; novel; novel marker; novel therapeutic intervention; preservation; protein biomarkers; response; skin squamous cell carcinoma; spatial integration; tandem mass spectrometry; technology platform; therapeutic target; tissue fixing; tissue mapping; transcriptomics; tumor; tumor heterogeneity; tumor initiation; tumor microenvironment; tumor progression

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.

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关键作用。为 例如，肿瘤微环境的空间异质性决定了肿瘤的发生、转移和药物治疗。 反应尽管空间转录组学在绘制组织中的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。由于蛋白质丰度无法推断 精确地从转录组学数据中，重要的是测量蛋白质丰度及其空间分布， 更好地预测病理生理现象，以及确定生物标志物和治疗靶点。 以前的空间蛋白质组学工作是基于抗体识别，质谱成像，或 对组织进行物理解剖，然后进行液相色谱-串联质谱法（LC-MS/MS）。 基于抗体的和质谱成像方法具有低蛋白质组覆盖率（<100个蛋白质）。的 具有深度覆盖（>3000个蛋白质）的唯一方法是组织解剖，然后进行LC-MS/MS。 利用最先进的LC-MS/MS的功能，实现数千种蛋白质的深入定量 沿着它们的翻译后修饰。然而，这种方法受到当前解剖方法的限制。 手动解剖的通量低，空间分辨率差。激光捕获显微切割（LCM）具有高 分辨率，但是组织映射所需的许多像素的隔离是乏味的并且遭受样本损失。 该项目的目标是开发一种高通量和可扩展的技术， 保留组织空间信息并直接耦合到已建立的LC-MS/MS工作流程的显微切割 用于蛋白质组的深度和无偏空间映射。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型的组织微切割设备（“µDicer”）， 用于具有单细胞灵敏度的LC-MS/MS分析的纳米液滴样品制备平台（“nanoPOTS”），以及 微流控装置（“µMapper”），用于将切片的组织像素从µDicer转移到nanoPOTS阵列，同时 保持空间秩序。我们的方法是创新的，因为目前没有技术可以执行 组织显微切割并将其平行转移到宏观威尔斯孔中进行LC-MS/MS，同时保留空间 信息.具体目标是优化µDicer，以便将固定的组织切片切成10-100 µm的微组织 像素，开发并验证µMapper，以将组织像素从µDicer传输到nanoPOTS芯片上，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的空间蛋白质组学， 推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，并将能够鉴定新的蛋白质生物标志物， 治疗靶点，以促进疾病的早期检测、诊断和干预。