High-resolution spatial transcriptomics through light patterning

项目摘要 肿瘤的细胞组成以及肿瘤内细胞的空间排列是主要的 治疗反应的决定因素和耐药性的出现。为了提高我们对 肿瘤异质性，加速发现新的药物靶点或使更好的患者分层，因此， 有必要开发可以解析肿瘤内分子定义的细胞类型并捕获其空间分布的工具。 关系。在单细胞RNA测序（scRNA-seq）技术进步的推动下， 在肿瘤内的分子定义的细胞类型现在是触手可及的。然而，由于scRNA-seq需要 分离的细胞并且不能保存关于细胞在其原始环境中的空间排列的信息， 它对基因表达、细胞类型和肿瘤之间的关系给出了不完整的描述。 架构需要在单细胞中测量基因表达的技术，同时保留位置 信息已经被长期认识到，但是现有的解决方案具有不足的蜂窝吞吐量、空间 分辨率或基因检测灵敏度。我们建议开发组合式光激活空间 测序（CLASSeq），一种克服这些限制的空间转录组学的变革性方法。 CLASSeq使用图案化的光照射将编码位置信息的DNA条形码附着到所有细胞， 组织切片内的感兴趣区域，空间分辨率仅受光波长的限制。空间条码 在将组织解离成单个细胞或细胞核后与细胞转录组一起测序， 并通过计算重建组织范围的基因表达模式。因为测序是在 在解离过程中，可以使用任何已建立的scRNA-seq工作流程，从而实现高灵敏度和细胞通量。到 实现高通量和再现性，并促进广泛采用，我们将致力于自动化， 通过构建原型仪器来实现标记工作流程， 图案照明。为了验证我们的方法并证明其对癌症研究的实用性，CLASSeq将在 用于表征霍奇金淋巴瘤的细胞多样性和组织，霍奇金淋巴瘤是一种成熟B细胞淋巴瘤， 肿瘤微环境生态位对于肿瘤成功逃避宿主免疫至关重要， 对临床免疫调节疗法的应答或缺乏应答。

PROJECT SUMMARY The cellular composition of a tumor as well as the spatial arrangement of cells within the tumor are major determinants of the response to therapy and the emergence of resistance. To improve our understanding of tumor heterogeneity, accelerate the discovery of new drug targets or enable better patient stratification it is thus necessary to develop tools that can resolve molecularly defined cell types within a tumor and capture their spatial relationships. Driven by progress in single-cell RNA sequencing (scRNA-seq) technologies, a complete census of molecularly defined cell types within a tumor is now within reach. However, because scRNA-seq requires dissociated cells and cannot preserve information about the spatial arrangement of cells in their original context, it gives an incomplete picture of the relationship between gene expression, cell type identity and tumor architecture. The need for technologies that measure gene expression in single cells while retaining position information has long been recognized, but existing solutions have insufficient cellular throughput, spatial resolution, or gene detection sensitivity. We propose to develop Combinatorial Light-Activated Spatial Sequencing (CLASSeq), a transformative approach to spatial transcriptomics that overcomes these limitations. CLASSeq uses patterned light illumination to attach DNA barcodes encoding location information to all cells of interest within a tissue section, with spatial resolution limited only by the wavelength of light. Spatial barcodes are sequenced together with cellular transcriptomes after dissociating the tissue into individual cells or nuclei, and tissue-wide gene expression patterns are computationally recreated. Because sequencing is performed after dissociation, any established scRNA-seq workflow can be used, enabling high sensitivity and cell throughput. To achieve high throughput and reproducibility, and facilitate wide adoption, we will work toward automating the labeling workflow by constructing a prototype instrument that integrates fluidics for barcode delivery with patterned illumination. To validate our approach and demonstrate its utility to cancer research, CLASSeq will be used to characterize cellular diversity and organization in Hodgkin lymphoma, a mature B-cell lymphoma in which the tumor microenvironment niche is critical to the tumor's success for host immune evasion and thus governs the response or lack thereof to clinical immune modulatory therapies.