High-resolution spatial transcriptomics through light patterning

通过光图案化的高分辨率空间转录组学

基本信息

批准号：
9886581
负责人：
Georg Seelig
金额：
$ 21.81万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9886581
关键词：
Adoption Antibodies Architecture B-Cell Lymphomas Back Bar Codes Biological Process Cell Nucleus Cell-Matrix Junction Cells Censuses Characteristics Chemistry Cleaved cell Clinical Complementary DNA Computing Methodologies Cultured Cells DNA Detection Disease Dissociation Drug resistance Gene Expression Gene Expression Profile Gene Expression Profiling Genes Heart Heterogeneity Hodgkin Disease Immune Evasion Immunohistochemistry Immunomodulators In Situ Hybridization Individual Knowledge Label Length Ligation Light Lighting Liquid substance Location Maps Mature B-Lymphocyte Measures Methods Molecular Nuclear Optics Pattern Pharmaceutical Preparations Phenotype Positioning Attribute Procedures Proteins Protocols documentation RNA Reaction Reproducibility Research Personnel Research Project Grants Resistance Resolution Sampling Series Shapes Split-Pool Ligation Transcriptome sequencing Techniques Technology Time Tissues Transcript Tumor Tissue Work anticancer research base cancer diagnosis cell type combinatorial cost experimental study flexibility genome-wide immunomodulatory therapies improved indexing innovation instrument interest new therapeutic target patient stratification predicting response preservation prototype response single-cell RNA sequencing spatial relationship success tool transcriptome transcriptomics tumor tumor heterogeneity tumor microenvironment

项目摘要

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.

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对治疗反应和抗药性出现的决定因素。提高我们对肿瘤异质性，加速发现新药物靶标或实现更好的患者分层开发可以解决肿瘤内分子定义的细胞类型并捕获其空间的工具所必需的关系。受单细胞RNA测序（SCRNA-SEQ）技术的进展驱动，完整的人口普查肿瘤内分子定义的细胞类型现在已接触到。但是，因为Scrna-Seq需要分离的细胞，无法保留有关细胞在原始情况下的空间排列的信息，它给出了基因表达，细胞类型身份和肿瘤之间关系的不完整图。建筑学。对在保持位置的同时测量基因表达的技术的需求长期以来已经识别出信息，但是现有的解决方案的细胞吞吐量不足分辨率或基因检测敏感性。我们建议开发组合光激活的空间测序（Classeq），一种用于克服这些局限性的空间转录组学的变革方法。 Classeq使用图案光照明将DNA条形码编码编码位置信息到所有单元格在组织部分中的兴趣，空间分辨率仅受光的波长限制。空间条形码将组织分离为单个细胞或核后，将与细胞转录组一起测序整个组织的基因表达模式是计算重现的。因为在解离，可以使用任何已建立的SCRNA-SEQ工作流程，从而可以使高灵敏度和细胞吞吐量。到实现高通量和可重复性，并促进广泛采用，我们将努力自动化通过构造将流体化的原型仪器与条形码交付的集成在一起的原型仪器来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将是用于表征霍奇金淋巴瘤中细胞多样性和组织，这是一种成熟的B细胞淋巴瘤肿瘤微环境利基市场对于肿瘤对宿主免疫的成功至关重要，因此控制对临床免疫调节疗法的反应或缺乏反应。