Integrated experimental and statistical tools for ultra-high-throughput spatial transcriptomics

用于超高通量空间转录组学的集成实验和统计工具

• 批准号: 10727130
• 负责人: Hee-Sun Han
• 金额: $ 43.62万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-22 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10727130
• 关键词: 3-Dimensional; Atlases; Biology; Biotechnology; Cell Communication; Cell Line; Cells; Characteristics; Collaborations; Color; Communities; Comparative Study; Complex; Computer software; Computing Methodologies; Crowding; Custom; DNA; Data; Data Set; Detection; Dimensions; Disease; Dreams; Dyes; Fluorescent in Situ Hybridization; Gene Expression; Genes; Human; Human Genome; Image; In Situ; Individual; Location; Maps; Methods; Modernization; Noise; Optics; Organ; Positioning Attribute; Procedures; Process; Protocols documentation; RNA; Research; Resolution; Signal Transduction; Speed; Spottings; Statistical Methods; Statistical Models; Structure; System; Techniques; Technology; Thick; Time; Tissue Expansion; Tissues; Training; Uncertainty; Work; density; experimental study; falls; high dimensionality; human disease; improved; innovation; insight; multiplexed imaging; near infrared dye; new technology; novel; prevent; single molecule; statistics; tool; transcriptome; transcriptomics

ABSTRACT Imaging-based single cell transcriptomics technologies create a single molecule resolution map of near complete transcriptome in native tissues, unlocking the long-standing dream of comprehending the spatial organization of molecules and cells in intact tissues. The structural organization of molecules and cells is closely tied to their functional organization, thus, transcriptome-scale RNA imaging would provide invaluable insights into how molecules and cells interact and collectively perform systems-level functions in healthy and diseased tissues. Among different technologies, MERFISH (multiplexed error-robust fluorescence in situ hybridization) occupies a leading position with its high spatial resolution, high detection efficiency, single molecule sensitivity, and high multiplexing capability. However, current technologies are not fast enough to process tissue blocks of any meaningful size, leaving critical questions like 3D tissue profiling, cross-tissue comparisons, and large-scale atlas efforts out of reach. Here, we propose to close this gap by at least an order of magnitude by combining custom biotechnology with modern statistics to build a next-generating imaging-based single cell transcriptomics platform. We will develop experimental techniques and analytical procedures for 1) hyperspectral imaging and 2) computational deconvolution of optically crowded RNA molecules. Few efforts along these directions exist, and no method has proven to be effective. The biggest hurdle is the absence of real experiment-based reference datasets with known ground-truth signals, without which no new methods can be properly validated. For each strategy, we propose to generate a high-quality MERFISH reference dataset as well as develop new statistical models and inference procedures to recover the true signals. Our proposed methods can be integrated with each other and with other approaches to increasing throughput. In long term, we aim to create an in situ single-cell platform that can profile millions of cells in >100mm2 tissue volumes within a day and perform large-scale comparative studies of thick tissue/organ blocks. This will enable multi-tissue analysis, comparative studies of relevant tissue volumes, and large-scale atlas establishment, thereby unlocking new dimensions of human genome research.

抽象的 基于成像的单细胞转录组学技术创建了一个单分子分辨率的近乎完整的分子映射 本地组织中的转录组，解锁了理解空间组织的长期梦想 完整组织中的分子和细胞。分子和细胞的结构组织与它们的结构组织紧密相关 因此，功能组织的转录组尺度RNA成像将为如何提供有关如何方式的宝贵见解 分子和细胞在健康和患病的组织中相互作用并集体执行系统级功能。 在不同的技术中，Merfish（多重误差荧光原位杂交）占据 领先位置具有高空间分辨率，高检测效率，单分子灵敏度和高 多路复用能力。但是，当前技术不够快，无法处理任何的组织块 有意义的规模，留下关键问题，例如3D组织分析，跨组织比较和大规模 阿特拉斯付出了巨大的努力。在这里，我们建议通过组合至少将此差距缩小至少一个数量级 具有现代统计数据的自定义生物技术，以构建基于成像的单细胞转录组学 平台。我们将针对1）高光谱成像和 2）光学拥挤的RNA分子的计算反卷积。这些方向很少努力， 而且没有证明没有方法是有效的。最大的障碍是缺乏基于实验的参考 具有已知地面真相信号的数据集，没有该数据集，无法正确验证新方法。每个 策略，我们建议生成高质量的Merfish参考数据集并开发新的统计 恢复真实信号的模型和推理程序。我们提出的方法可以与每种方法集成 其他以及其他方法可以增加吞吐量。从长远来看，我们旨在创建一个原位单细胞 平台可以在一天内介绍> 100mm2组织体积中的数百万个单元，并进行大规模进行 厚组织/器官块的比较研究。这将实现多组织分析，比较研究 相关的组织量和大规模的地图集，从而解锁了人类的新维度 基因组研究。