Generation of single cell multi-omics computational methods for biological insights and novel deconvolution techniques for bulk omics data

生成用于生物学见解的单细胞多组学计算方法和用于批量组学数据的新型反卷积技术

• 批准号: 2764861
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2764861
• 关键词: Generation; single; cell; multi; omics

Studentship strategic priority area:Bioenergy and Industrial BiotechnologyKeywords: Machine learning; Single cell biology; deconvolution; irritation; SkinIn recent years, many bulk RNA-Seq and epigenetic data has been generated, but we lack the resolution of cell type and tissue positional information to draw strong conclusions.Advances in single-cell transcriptomics sequencing (scRNA-Seq) have been rapid and is revolutionizing the ability to understand, for example, cellular heterogeneity and cell-cell communication . To date, however, there are no efficient methods to combine scRNA-Seq, single-cell open chromatin capture (scATAC) and spatial transcriptomics (histological location of gene expression) and use them to better understand existing bulk 'omics data. For instance, an increase in chromatin opening in specific cells should link to changes in methylation and expression in the same genomic region found in bulk data. We propose to generate a multi-omics single-cell dataset from skin "cutaneous irritation", as an example biological domain, to build and test models for integration and interpretation of these multi-omic single-cell technologies that can be used to better interpret existing bulk omics data.Objectives:1) Generate novel computational models to integrate gene expression (scRNA-Seq), epigenetic (scATACseq) and mRNA tissue location data (spatial transcriptomics) to generate cell-specific tissue-localised signatures and multi-omic networks. 2) In-silico 'bulkify' cell signatures (2a) and validate with the actual bulk RNAseq data (2b).3) Deconvolute existing RNAseq and methylation data using models from 1 and signatures from 2, to map differential signatures onto cell signatures and multi-omic networks.4) Identify irritation signatures at a single cell level in skin and its relationship with skin conditions.

学生战略重点领域：生物能源与工业生物技术单细胞生物学；反褶积;刺激;近年来，大量的RNA-Seq和表观遗传学数据已经产生，但我们缺乏细胞类型和组织位置信息的分辨率来得出强有力的结论。单细胞转录组测序技术（scRNA-Seq）的进展非常迅速，正在彻底改变理解细胞异质性和细胞间通讯等方面的能力。然而，到目前为止，还没有有效的方法将scRNA-Seq、单细胞开放染色质捕获（scATAC）和空间转录组学（基因表达的组织学位置）结合起来，并利用它们更好地理解现有的大量组学数据。例如，在大量数据中发现，特定细胞中染色质开放的增加应该与同一基因组区域的甲基化和表达的变化有关。我们建议从皮肤“皮肤刺激”中生成一个多组学单细胞数据集，作为一个生物领域的例子，来构建和测试这些多组学单细胞技术的集成和解释模型，这些模型可以用来更好地解释现有的大量组学数据。1)生成新的计算模型，整合基因表达（scRNA-Seq）、表观遗传（scATACseq）和mRNA组织定位数据（空间转录组学），生成细胞特异性的组织定位特征和多组学网络。2)在计算机上“体积化”细胞签名（2a），并使用实际的批量RNAseq数据进行验证（2b）。3)利用1中的模型和2中的特征对现有的RNAseq和甲基化数据进行反卷积，将差异特征映射到细胞特征和多组学网络上。4)识别皮肤单细胞水平的刺激信号及其与皮肤状况的关系。