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.

项目摘要 细胞不断地改变它们的状态，无论是由于生理压力还是外源性力量。克隆 扩张是一个明确的过程，有助于这种改变，并不加区别地发生在所有类型的 在整个身体的组织中，无论该组织的恶性或疾病潜力如何。任何突变或 因此，一个人一生中所维持的表观遗传变化有被克隆扩张的危险 并最终在细胞系内繁殖15，16。然而，问题仍然存在，为什么其中一些 扩张导致癌症，而其他人仍然是良性的，以及如何具体步骤，个别细胞 在基因和转录上变成致病性，最终进化并体现出不同的 表型状态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脱氨基诱变），以及在处理下持续存在的倾向。 了解细胞如何改变它们的状态，可以深入了解如何控制细胞的命运，这可以 对我们理解细胞可塑性，发育，进化和疾病进展的影响。 单细胞基因组的计算分析为深入了解这些问题提供了机会， 生物学，但现有方法的当前能力存在差距，无法从单个细胞中提取可靠的变异呼叫。 细胞RNA测序数据。迄今为止的研究一直依赖于费力，低效的方法，主要限于细胞 线或固有的噪声单细胞DNA数据，试图了解这种细胞谱系之间的相互作用， 获得的突变和基因组特征（即，创建人工诱导的遗传条形码或使用天然的 DNA突变）17-20。该项目的重点是开发一种更强大的基因组工具，用于构建这些 单细胞遗传学，并通过利用细胞的转录 机器与全长scRNA-seq.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遗传学并鉴定 在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，成为一个成功的独立 作为一名基因组科学家，我的培训计划包括机器学习、遗传学和机械学方面的培训。 生物学，以及进一步培训科学交流技能，如手稿写作和赠款 写作我优秀的研究环境包括麻省理工学院和哈佛的布罗德研究所，我的家 Gad Getz博士的实验室。这是一个世界一流的基因组学研究机构，人力资源丰富 以及其他所有必要的资源来完成我的研究计划。