Computational tools for estimating cell-type-specific effects in bulk RNA-seq and spatial transcriptomics data, using reference single-cell RNA-seq datasets

使用参考单细胞 RNA-seq 数据集估计批量 RNA-seq 和空间转录组数据中细胞类型特异性效应的计算工具

• 批准号: 10407563
• 负责人: Paul Geeleher
• 金额: $ 43.68万
• 依托单位: ST. JUDE CHILDREN'S RESEARCH HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10407563
• 关键词: Award; Bar Codes; Biological; Blood Cells; Breast Cancer Risk Factor; Cell Separation; Cells; Computerized Medical Record; Computing Methodologies; Coupled; Data; Data Set; Disease; Dissociation; Emerging Technologies; Ethnic Origin; Gene Expression; Gene set enrichment analysis; Genetic; Genetic Variation; Genotype; Genotype-Tissue Expression Project; Human; Immune response; In Situ; Inherited; Mammary Neoplasms; Mathematics; Measures; Methods; Molecular; Molecular Biology; Outcome; Pharmacotherapy; Phenotype; Protocols documentation; Research; Resolution; Sample Size; Sampling; Sex Differences; Spottings; Techniques; The Cancer Genome Atlas; Tissue Sample; Tissue-Specific Gene Expression; Tissues; United States National Institutes of Health; Variant; Whole Blood; cell type; clinical phenotype; computerized tools; cost; data tools; differential expression; experimental study; insight; miniaturize; novel; phenotypic data; programs; risk variant; sex; single-cell RNA sequencing; tool; transcriptome sequencing; transcriptomics

PROJECT SUMMARY / ABSTRACT RNA-seq is a powerful tool for studying molecular biology. However, without cell sorting (or related techniques), conventional RNA-seq applied to tissue samples cannot determine gene expression in underlying cell-types. This is problematic because differential gene expression observed at the tissue level is not necessarily reflected in underling cell-types, which obscures biological insight. For example, Schmiedel et al. recently applied RNA- seq to 13 purified blood cell-types from 106 individuals1, which uncovered the molecular basis of sex-specific differences in immune response. However, this was obscured when they applied RNA-seq to only whole-blood. Single-cell RNA-seq is the obvious candidate to probe cell-type-specific effects more broadly. However, for most tissues, single-cell RNA-seq has been restricted to small sample sizes, due to specialized dissociation protocols and cost. Thus, only bulk-tissue RNA-seq data are available for large sample sizes. Crucially, much of these bulk data are paired to enormous stores of informative clinical phenotypic data and additional -omics data. These datasets include large NIH initiatives such as GTEx, TCGA, and All of Us, which have collected data on genetics, disease status, outcome, drug treatments, ethnicity, sex, and much more. The critical gap is that we cannot currently study the relationship between cell-type level gene expression and any of these phenotypes. To overcome this limitation, we will develop computational tools for estimating cell-type-specific differential expression from bulk RNA-seq data, when a small reference single-cell RNA-seq dataset is available from the same tissue-type. This will allow us to study the cell-type-specific differences in expression that drive human phenotypes and diseases, unlocking the tens-of-thousands of bulk RNA-seq samples paired to phenotypic data. The basis for this research program is a previous study where we developed a method to recover the cell-type- specific effects of inherited genetic variation on gene expression in bulk breast-tumor RNA-seq data. This method allowed us to discover a novel breast cancer risk gene—which was obscured using conventional methods. Here, we posit that a similar mathematical framework can be adapted to recover any cell-type-specific effect from bulk-tissue RNA-seq. Hence, we can develop specific tools to perform multiple commonly applied analyses at cell-type-specific resolution from bulk-tissue RNA-seq by leveraging matched single-cell data, including differential expression, correlative and gene set enrichment analysis. Finally, new spatial transcriptomics technologies are emerging that enable spatially resolved gene expression to be measured directly in tissue sections. These platforms quantify gene expression in situ in ~100μm barcoded spots. Each spot captures a small cluster of cells—akin to a miniaturized bulk-tissue RNA-seq experiment. Hence, the same abstract mathematical framework can be used to identify effects such as cell-type-specific spatial variation in gene expression. Computational tools for these data are evolving quickly; thus, this award will also allow us to develop methods that meet the changing needs of these new gene expression platforms.

项目摘要 /摘要 RNA-Seq是研究分子生物学的强大工具。但是，没有细胞分类（或相关技术）， 应用于组织样品的常规RNA-SEQ无法确定潜在细胞类型中的基因表达。 这是有问题的，因为在组织水平上观察到的差异基因表达不一定反映 在掩盖细胞类型的过程中，它掩盖了生物学洞察力。例如，Schmiedel等人。最近应用的RNA- 从106个个体中的Seq至13个纯化的血细胞类型1，发现了性别特异性的分子基础 免疫响应的差异。但是，当他们仅将RNA-Seq应用于全血时，这被掩盖了。 单细胞RNA-seq是更广泛探测细胞类型效应的明显候选者。但是，对于大多数人来说 由于专门的解离方案 和成本。这，只有大量的RNA-seq数据可用于大型样本量。至关重要的是，其中大部分 批量数据与信息丰富的临床表型数据和其他摩体数据的巨大存储配对。这些 数据集包括大型NIH计划，例如GTEX，TCGA和我们所有人，它们都收集了有关遗传学的数据， 疾病状况，结果，药物治疗，种族，性别等等。关键的差距是我们不能 当前研究细胞类型水平基因表达与这些表型中的任何一种之间的关系。 为了克服这一限制，我们将开发用于估计细胞类型特异性差异的计算工具 从散装RNA-seq数据中的表达式，当一个小参考单细胞RNA-seq数据集可从 相同的组织类型。这将使我们能够研究驱动人类表达的细胞类型特异性差异 表型和疾病，解锁了成千上万的大量RNA-Seq样品与表型数据配对。 该研究计划的基础是先前的研究，我们开发了一种恢复细胞类型的方法 遗传差异对散装乳腺肿瘤RNA-Seq数据中基因表达的特定影响。此方法 使我们能够发现一种新型的乳腺癌风险基因，该基因使用常规方法掩盖了。 在这里，我们肯定可以适应类似的数学框架以恢复任何细胞类型的效果 来自散装组织RNA-Seq。因此，我们可以开发特定的工具来执行多个常用分析 通过利用匹配的单细胞数据（包括） 差异表达，相关和基因集富集分析。 最后，正在出现新的空间转录组技术，使空间分辨的基因表达能够 直接在组织切片中进行测量。这些平台在〜100μm条形码中量化基因表达的原位 斑点。每个斑点都捕获了一小群细胞，即Akin到一个微型散装组织RNA-Seq实验。 因此，可以使用相同的抽象数学框架来识别诸如细胞类型的效果 基因表达的空间变异。这些数据的计算工具正在迅速发展；因此，这个奖项将 还允许我们开发满足这些新基因表达平台不断变化的需求的方法。