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.

摘要 基于成像的单细胞转录组学技术创建了一个接近完整的单分子分辨率图谱， 转录组在天然组织，解开了理解的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 因此，转录组规模的RNA成像将提供宝贵的见解，如何 分子和细胞相互作用，并在健康和患病组织中共同执行系统水平的功能。 在不同的技术中，MERFISH（多重错误鲁棒荧光原位杂交）占据了 以其高空间分辨率、高检测效率、单分子灵敏度和高灵敏度， 多路复用能力然而，目前的技术还不够快，不能处理任何组织块。 有意义的大小，留下关键的问题，如3D组织分析，跨组织比较，和大规模 阿特拉斯的努力遥不可及。在这里，我们建议通过结合至少一个数量级来缩小这一差距 定制生物技术与现代统计学，以建立下一代基于成像的单细胞转录组学 平台我们将开发实验技术和分析程序1）高光谱成像， 2)光学拥挤的RNA分子的计算解卷积。几乎没有沿着这些方向的努力， 并且没有方法被证明是有效的。最大的障碍是缺乏真实的基于实验的参考 具有已知地面实况信号的数据集，没有这些信号，就无法正确验证新方法。为每个 战略，我们建议生成一个高质量的MERFISH参考数据集，以及开发新的统计 模型和推理程序来恢复真实信号。我们提出的方法可以与每个 其它方法以及其它增加吞吐量的方法。从长远来看，我们的目标是创造一个原位单细胞 该平台可以在一天内对> 100 mm2组织体积中的数百万个细胞进行分析，并进行大规模 厚组织/器官块的比较研究。这将使多组织分析，比较研究， 相关的组织体积，和大规模的图谱建立，从而解锁新的层面的人类 基因组研究