Novel method for spatial, multi-omic visualization of single-cell heterogeneity

单细胞异质性空间多组学可视化的新方法

• 批准号: 9988595
• 负责人: Dmitry N Derkach
• 金额: $ 0.2万
• 依托单位: BIOSYNTAGMA, LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9988595
• 关键词: Adoption; Alzheimer' s Disease; Antibodies; Antigen-Presenting Cells; Antineoplastic Agents; Automation; Biological Markers; Brain; Buffers; CD34 gene; Cancer Patient; Cardiac; Cell Separation; Cells; Clinical; DNA; Data; Development; Diabetes Mellitus; Disease; Environment; Freezing; Future; Genetic Materials; Genomics; Heterogeneity; Image; Imagery; Immune; Immune response; Immunooncology; In Situ; Label; Lasers; Location; Malignant Neoplasms; Maps; Measures; Methods; Microdissection; Microfluidic Microchips; Microfluidics; Molecular Analysis; Mus; Muscle; Muscle Cells; Nucleic Acids; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Population; Preparation; Process; Proteins; Proteomics; Protocols documentation; RNA; Relapse; Resistance; Sampling; Slice; Smooth Muscle; Smooth Muscle Myocytes; Specificity; Stains; Striated Muscles; System; T-Lymphocyte; Technology; Therapeutic; Tissue Fixation; Tissue Preservation; Tissue Sample; Tissue imaging; Tissues; Translating; Translations; Treatment Failure; Troponin C; Tumor-infiltrating immune cells; Variant; base; biomarker discovery; cancer drug resistance; cell type; cellular imaging; clinical application; cytotoxicity; differential expression; drug development; drug discovery; drug relapse; exhaustion; fighting; insight; interest; melanoma; mouse model; multiple omics; new technology; novel; patient screening; personalized medicine; population based; precision medicine; preservation; single cell analysis; skeletal; targeted treatment; therapy resistant; tissue preparation; tool; transcriptomics; treatment response; tumor; tumor heterogeneity; tumor microenvironment; two photon microscopy

Project Summary The ability to understand and measure heterogeneity is necessary in order to make advances in fighting diseases like Alzheimer's, diabetes, and cancer. The ability to elucidate the differences between cell types and their locations within a tissue would provide insight into how cells interact with each other and the mechanisms that give rise to various disease states, and would help identify therapeutic pathways for new treatments. Specifically in cancer, intratumor heterogeneity is a phenomenon that actively leads to treatment failure and relapse in cancer patients. The limitations of current tools for understanding and measuring heterogeneity inhibit personalized medicine and the discovery of advanced therapeutics. Current methods are either 1) incapable of providing spatial data alongside `omics data or 2) have workflows too cumbersome to facilitate broad adoption. bioSyntagma has developed a method for resolving heterogeneity by correlating the spatial information in cellular imagery with multi-omic analysis. This workflow is easily automated and scalable through its microfluidic platform which images tissue, identifies cells of interest, and then isolates imaged cells for molecular analysis. We propose to utilize this technology to enable single-cell applications. SA1: Establish RNA quality after laser extraction of single cells for enrichment workflow: We will optimize tissue preparation protocols to maximize the quality of genetic material produced from the microfluidic device. This includes parameters of tissue preservation, staining for fluorescent targets, and laser manipulation of cells. SA2: Establish single-cell specificity during laser enrichment workflow: Isolating single cells on a microfluidic device after imaging requires precise manipulation with laser beams. We will demonstrate the ability to achieve single-cell specificity and optimize parameters for single-cell analysis. SA3: Demonstrate ability to detect transcriptomic heterogeneity within tumor samples: We will demonstrate the clinical utility of this technology by identifying heterogeneity within immune cells of a tumor based on their relative locations. This type of novel analysis would enable drug discovery and patient screening for personalized medicines. A successful outcome this proposal will be a method for spatially resolving single-cell molecular analysis with demonstrated utility in immuno-oncology biomarker discovery. Applications of the technology will extend beyond cancer into brain studies and other diseases. Further automation to increase throughput will be the subject of a future phase II submission in order to enable translation into a clinical environment.

项目概要 为了在战斗中取得进步，理解和衡量异质性的能力是必要的 阿尔茨海默氏症、糖尿病和癌症等疾病。阐明细胞类型和细胞之间差异的能力 它们在组织内的位置将有助于了解细胞如何相互作用及其机制 引起各种疾病状态，并将有助于确定新疗法的治疗途径。 特别是在癌症中，肿瘤内异质性是一种主动导致治疗失败和 癌症患者复发。当前用于理解和测量异质性的工具的局限性抑制 个性化医疗和先进疗法的发现。当前的方法要么 1) 无法 与“组学数据”一起提供空间数据或 2) 工作流程过于繁琐，无法促进广泛采用。 BioSyntagma 开发了一种通过关联空间信息来解决异质性的方法 通过多组学分析进行细胞成像。该工作流程可以通过其轻松实现自动化和可扩展 微流体平台对组织进行成像，识别感兴趣的细胞，然后分离成像细胞以进行分子分析 分析。我们建议利用这项技术来实现单细胞应用。 SA1：激光提取单细胞后确定 RNA 质量以进行富集工作流程：我们将优化 组织制备方案可最大限度地提高微流体装置产生的遗传物质的质量。 这包括组织保存参数、荧光目标染色以及细胞激光操作。 SA2：在激光富集工作流程中建立单细胞特异性：在 成像后的微流体装置需要用激光束进行精确操纵。我们将展示我们的能力 实现单细胞特异性并优化单细胞分析参数。 SA3：展示检测肿瘤样本内转录组异质性的能力：我们将 通过识别肿瘤免疫细胞内的异质性来证明该技术的临床实用性 基于它们的相对位置。这种类型的新颖分析将使药物发现和患者筛选成为可能 用于个性化药物。 该提案的一个成功成果将是一种空间解析单细胞分子分析的方法 已证明在免疫肿瘤学生物标志物发现中的实用性。该技术的应用将超越 癌症进入大脑研究和其他疾病。进一步自动化以提高吞吐量将是一个主题 未来的第二阶段提交，以便能够转化为临床环境。