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近乎完整的单分子分辨率图 天然组织中的转录组，解锁了理解自然组织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其自身的结构密切相关 因此，转录组规模的 RNA 成像将为了解如何进行功能组织提供宝贵的见解。 分子和细胞在健康和患病组织中相互作用并共同执行系统级功能。 在不同的技术中，MERFISH（多重抗错荧光原位杂交）占据了主导地位。 以其高空间分辨率、高检测效率、单分子灵敏度、高灵敏度等优势处于领先地位。 复用能力。然而，当前的技术还不够快，无法处理任何组织块。 有意义的尺寸，留下 3D 组织分析、跨组织比较和大规模等关键问题 阿特拉斯的努力遥不可及。在这里，我们建议通过结合以下方式将这一差距至少缩小一个数量级： 定制生物技术与现代统计学相结合，构建下一代基于成像的单细胞转录组学 平台。我们将开发以下方面的实验技术和分析程序：1）高光谱成像和 2) 光学拥挤RNA分子的计算反卷积。沿着这些方向所做的努力很少， 并且没有一种方法被证明是有效的。最大的障碍是缺乏基于真实实验的参考 具有已知地面实况信号的数据集，没有这些数据集就无法正确验证新方法。对于每个 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数据 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方法集成 其他以及其他提高吞吐量的方法。从长远来看，我们的目标是创建原位单细胞 该平台可以在一天内对 >100mm2 组织体积中的数百万个细胞进行分析，并进行大规模分析 厚组织/器官块的比较研究。这将使多组织分析、比较研究成为可能 相关组织体积，大规模图谱建立，从而解锁人类新维度 基因组研究。