Leveraging natural and engineered genetic barcodes from single cell RNA sequencing to investigate cellular evolution, clonal expansion, and associations between cellular genotypes and phenotypes

利用单细胞 RNA 测序中的天然和工程遗传条形码来研究细胞进化、克隆扩增以及细胞基因型和表型之间的关联

• 批准号: 10679186
• 负责人: Jideofor Ezike
• 金额: $ 4.92万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-05 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10679186
• 关键词: Algorithms; Alzheimer' s Disease; Bar Codes; Benign; Biological; Biology; Cancer cell line; Cancerous; Cell Fate Control; Cell Line; Cell Lineage; Cells; Chronic; Clonal Expansion; Clustered Regularly Interspaced Short Palindromic Repeats; Communication; Computer Analysis; Coupled; DNA; DNA Sequence Alteration; DNA amplification; Data; Deamination; Detection; Development; Developmental Biology; Dimensions; Disease; Disease Progression; Dropout; Environment; Epigenetic Process; Evolution; Genetic Engineering; Genetic Induction; Genetic Transcription; Genome; Genomics; Genotype; Goals; Grant; Health; Hematopoietic; Heterogeneity; Home; Human; Individual; Institution; Length; Lesion; Link; Liver; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Manuscripts; Maps; Mediating; Methods; Mitochondria; Modality; Mutagenesis; Mutation; Neurodevelopmental Disorder; Noise; Normal Cell; Normal tissue morphology; Pathogenesis; Pathogenicity; Patients; Persons; Phenotype; Phylogenetic Analysis; Phylogeny; Physiological; Probability; Process; RNA; Research; Resolution; Resources; Risk; Role; Sampling; Schizophrenia; Scientist; Somatic Mutation; Stress; Structure; Testing; Therapeutic; Time; Tissue Sample; Tissues; Training; Transcript; Trees; Ultraviolet Rays; Variant; Writing; autism spectrum disorder; autoencoder; cancer genomics; career; experimental study; genetic association; genetic manipulation; genome sequencing; genomic biomarker; genomic tools; improved; insight; interest; novel; premalignant; reconstruction; single cell analysis; single-cell RNA sequencing; skills; trait; transcriptomics; tumor; tumor microenvironment; tumor progression; tumorigenesis; whole genome

PROJECT SUMMARY Cells are constantly altering their states, whether due to physiological stress or exogenous forces. Clonal expansion is a well-defined process that contributes to this alteration and indiscriminately occurs in all types of tissue throughout the body, irrespective of the malignant or disease potential of that tissue. Any mutations or epigenetic changes that one sustains over the course of a lifetime are thus at risk of being clonally expanded and ultimately propagated within cell lineages15,16. However, questions still remain as to why some of these expansions result in cancer while others remain benign and as to how the specific steps that individual cells take genetically and transcriptionally to become pathogenic and ultimately evolve and embody different phenotypic states. These phenotypes include expression cell state, activity of mutational processes (e.g., endogenous APOBEC DNA/RNA deamination mutagenesis), and propensity to persist under treatment. Understanding how cells change their states provides insight into how to control cell fate, which can have ramifications on our understanding of cell plasticity, development, evolution, and disease progression. Computational analysis of single-cell genomes offers an opportunity to provide insight into these questions in biology, but there is a gap in the current ability of existing methods to extract confident variant calls from single- cell RNA sequencing data. Research to date has relied on laborious, inefficient methods limited to mostly cell lines or inherently noisy single-cell DNA data to attempt to understand this interplay between cell lineages, acquired mutations and genomic features (i.e., creating artificially-induced genetic barcodes or using natural DNA mutations)17-20. This project focuses on the development of a more robust genomic tool for building these single cell phylogenies and associating them with cellular phenotypes by leveraging the cell’s transcriptional machinery with full length scRNA-seq. The specific aims of this project can be summarized as follows: 1. Utilize scRNA-seq and CRISPR-based lineage tracing data to reconstruct phylogenies and identify specific genomic associations at the single cell level. 2. Investigate the role mutational processes have on clonal expansion and disease progression across tissues at single-cell resolution. To achieve these project goals as well as my own career objectives to becoming a successful independent genomic scientist, my training plan includes training in machine learning, phylogenetics, and mechanistic biology, as well as further training in scientific communication skills such as manuscript writing and grant writing. My excellent research environment includes the Broad Institute of MIT and Harvard, where my home lab of Dr. Gad Getz is located. This is a world-class institution for genomics research rich in people resources and all other necessary resources needed to perform my proposed research.

项目概要 无论是由于生理压力还是外源性力量，细胞都在不断改变其状态。 扩张是一个明确的过程，它有助于这种改变，并且不加区别地发生在所有类型的事物中。 整个身体的组织，无论该组织的恶性或疾病潜力如何。 因此，一个人一生中所经历的表观遗传变化面临着克隆扩展的风险 并最终在细胞谱系中繁殖15,16 然而，为什么其中一些仍然存在疑问。 扩张会导致癌症，而另一些则保持良性，以及单个细胞如何具体步骤 通过遗传和转录来致病并最终进化并体现出不同的特征 这些表型包括表达细胞状态、突变过程的活性（例如， 内源性 APOBEC DNA/RNA 脱氨突变），以及在治疗下持续存在的倾向。 了解细胞如何改变其状态可以深入了解如何控制细胞命运，这可以 对我们对细胞可塑性、发育、进化和疾病进展的理解的影响。 单细胞基因组的计算分析提供了深入了解这些问题的机会 生物学，但现有方法目前从单一样本中提取可信变异调用的能力存在差距。 迄今为止的研究主要依赖于费力、低效的细胞 RNA 测序数据。 线或固有的嘈杂的单细胞 DNA 数据来尝试理解细胞谱系之间的相互作用， 获得性突变和基因组特征（即，创建人工诱导的遗传条形码或使用自然 DNA 突变）17-20 该项目专注于开发更强大的基因组工具来构建这些突变。 单细胞系统发育并通过利用细胞的转录将它们与细胞表型相关联 全长 scRNA-seq 的机器 该项目的具体目标可概括如下： 1. 利用 scRNA-seq 和基于 CRISPR 的谱系追踪数据重建系统发育并鉴定 单细胞水平上的特定基因组关联。 2. 研究突变过程对克隆扩增和疾病进展的作用 单细胞分辨率的组织。 为了实现这些项目目标以及我自己的职业目标，成为一名成功的独立人士 基因组科学家，我的培训计划包括机器学习、系统发育学和机制方面的培训 生物学，以及科学沟通技巧的进一步培训，例如手稿写作和资助 我优秀的研究环境包括麻省理工学院和哈佛大学的博德研究所，我的家。 Gad Getz博士的实验室就位于这里，是一个人力资源丰富的世界级基因组学研究机构。 以及进行我提议的研究所需的所有其他必要资源。