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: 10028501
• 负责人: Paul Geeleher
• 金额: $ 43.67万
• 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/10028501
• 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; Genes; 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.

项目总结/摘要 RNA-seq是研究分子生物学的有力工具。然而，如果没有细胞分选（或相关技术）， 应用于组织样品的常规RNA-seq不能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 在底层细胞类型中，这模糊了生物学的见解。例如，Schmiedel等人最近将RNA- seq对来自106个个体的13种纯化血细胞类型进行了测序，揭示了性别特异性的分子基础。 免疫反应的差异。然而，当他们仅将RNA-seq应用于全血时，这一点被掩盖了。 单细胞RNA-seq是更广泛地探测细胞类型特异性效应的明显候选者。但对于大多数 组织，单细胞RNA-seq已被限制到小样本量，由于专门的解离协议， 和成本因此，对于大样本量，仅批量组织RNA-seq数据可用。重要的是， 大量数据与大量信息性临床表型数据和附加组学数据的存储配对。这些 数据集包括大型NIH计划，如GTEx，TCGA和All of Us，它们收集了遗传学数据， 疾病状态、结果、药物治疗、种族、性别等等。关键的差距是我们不能 目前正在研究细胞类型水平的基因表达与任何这些表型之间的关系。 为了克服这一限制，我们将开发计算工具，用于估计细胞类型特异性差异， 当小的参考单细胞RNA-seq数据集可从 相同的组织类型这将使我们能够研究细胞类型特异性的表达差异，这些差异驱动人类 表型和疾病，解锁数以万计的批量RNA-seq样本配对表型数据。 这项研究计划的基础是以前的一项研究，我们开发了一种方法来恢复细胞类型， 遗传变异对大量乳腺肿瘤RNA-seq数据中基因表达的特定影响。该方法 让我们发现了一个新的乳腺癌风险基因--用传统方法是模糊的。 在这里，我们认为，一个类似的数学框架可以适用于恢复任何细胞类型的具体影响， 从大体积组织RNA-seq.因此，我们可以开发特定的工具来执行多种常用的分析 通过利用匹配的单细胞数据，从大体积组织RNA-seq中获得细胞类型特异性分辨率，包括 差异表达分析、相关分析和基因集富集分析。 最后，新的空间转录组学技术正在出现，使空间分辨的基因表达， 直接在组织切片中测量。这些平台在~100μm条形码中原位定量基因表达 斑点。每个点捕获一小群细胞-类似于小型化的大块组织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特异性的效应。 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，该奖项将 也使我们能够开发出满足这些新基因表达平台不断变化的需求的方法。