High-resolution spatial transcriptomics through light patterning

通过光图案化进行高分辨率空间转录组学

基本信息

批准号：
10112854
负责人：
Georg Seelig
金额：
$ 18.17万
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/10112854
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 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 detection sensitivity 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.

项目概要肿瘤的细胞组成以及肿瘤内细胞的空间排列是主要因素对治疗的反应和耐药性出现的决定因素。为了提高我们对肿瘤异质性，加速新药靶点的发现或实现更好的患者分层有必要开发能够解析肿瘤内分子定义的细胞类型并捕获其空间的工具关系。在单细胞 RNA 测序 (scRNA-seq) 技术进步的推动下，全面的普查肿瘤内分子定义的细胞类型现在已经可以实现。然而，由于 scRNA-seq 需要分离的细胞并且无法保留有关细胞在其原始环境中的空间排列的信息，它对基因表达、细胞类型识别和肿瘤之间的关系给出了不完整的描述建筑学。需要在保持位置的同时测量单细胞基因表达的技术信息早已被认可，但现有解决方案的蜂窝吞吐量、空间分辨率，或基因检测灵敏度。我们建议开发组合光激活空间测序 (CLASSeq) 是一种空间转录组学的变革性方法，克服了这些限制。 CLASSeq 使用图案光照明将编码位置信息的 DNA 条形码附加到所有细胞上对组织切片感兴趣，空间分辨率仅受光波长的限制。空间条形码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，并通过计算重新创建组织范围的基因表达模式。因为测序是在之后进行的解离时，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。到实现高通量和可重复性，并促进广泛采用，我们将努力实现自动化通过构建一个原型仪器来构建标签工作流程，该仪器将条形码传输的流体学与图案照明。为了验证我们的方法并展示其在癌症研究中的实用性，CLASSeq 将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的 B 细胞淋巴瘤，其中肿瘤微环境生态位对于肿瘤成功逃避宿主免疫至关重要，从而控制着对临床免疫调节疗法的反应或缺乏。