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

项目摘要 理解和衡量异质性的能力是必要的，以便在战斗中取得进展。 老年痴呆症、糖尿病和癌症。阐明细胞类型之间差异的能力， 它们在组织中的位置将提供对细胞如何相互作用以及机制的深入了解 这会导致各种疾病状态，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积极导致治疗失败的现象， 癌症患者的复发。当前用于理解和测量异质性的工具的局限性抑制了 个性化医疗和先进疗法的发现。目前的方法要么1）无法 在提供“组学”数据的同时提供空间数据，或2）工作流程过于繁琐，难以广泛采用。 bioSyntagma开发了一种通过关联空间信息来解决异质性的方法 用多组学分析的细胞图像。此工作流程通过其 微流控平台，其对组织进行成像，识别感兴趣的细胞，然后分离成像的细胞用于分子识别。 分析.我们建议利用这项技术来实现单细胞应用。 SA 1：在激光提取单细胞后确定RNA质量，用于富集工作流程：我们将优化 组织制备方案以最大化从微流体装置产生的遗传物质的质量。 这包括组织保存、荧光靶染色和细胞激光操作的参数。 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精确操纵。我们将证明我们有能力 以实现单细胞特异性并优化单细胞分析的参数。 SA3：证明检测肿瘤样本中转录组异质性的能力：我们将 通过鉴定肿瘤免疫细胞内的异质性来证明该技术的临床实用性 根据它们的相对位置。这种新型分析将使药物发现和患者筛选成为可能 用于个性化药物。 该建议的一个成功结果将是一种空间分辨单细胞分子分析的方法， 在免疫肿瘤学生物标志物发现中显示出实用性。该技术的应用将超越 癌症到大脑研究和其他疾病。进一步自动化以增加吞吐量将是 未来的第二阶段提交，以便能够转化为临床环境。

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.