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 无论是由于身体压力还是外源力，细胞都在不断改变其状态。克隆 扩展是一个明确定义的过程，有助于这种改变，并且在所有类型的 整个身体的组织，无论该组织的恶性或疾病潜力如何。任何突变或 因此，在一生中自杀的表观遗传变化有被克隆扩展的风险 并最终在细胞谱系中传播15,16。但是，关于为什么其中一些的问题仍然存在 扩张会导致癌症，而其他人保持良性以及单个细胞的特定步骤 在遗传和转录上采取致病性，并最终进化和体现不同 表型状态。这些表型包括表达细胞态，突变过程的活性（例如， 内源性apobec DNA/RNA脱氨基诱变），并持续治疗倾向。 了解细胞如何改变其状态，可以洞悉如何控制细胞命运，这可以拥有 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一个机会，可以在 生物学，但现有方法目前的能力有一个差距，从单一提取自信变体调用 细胞RNA测序数据。迄今为止的研究一直依赖于实验室，效率低下的方法仅限于细胞 线或固有的噪声单细胞DNA数据，以尝试了解细胞系之间的这种相互作用， 获得的突变和基因组特征（即创建人为诱导的遗传条形码或使用自然 DNA突变）17-20。该项目着重于开发更强大的基因组工具来构建这些工具 单细胞系统发育，并通过利用细胞的转录来将它们与细胞表型相关联 具有全长Scrna-Seq的机械。该项目的具体目的可以总结如下： 1。利用SCRNA-SEQ和基于CRISPR的谱系跟踪数据来重建系统发育并识别 单细胞水平的特定基因组关联。 2。研究突变过程对克隆扩张和疾病进展的作用 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，以成为成功的独立 基因组科学家，我的培训计划包括机器学习，系统发育和机械培训 生物学以及在科学沟通技巧（例如手稿写作和授予）方面的进一步培训 写作。我出色的研究环境包括麻省理工学院和哈佛大学的广泛研究所 Gad Getz博士的实验室已找到。这是一家世界一流的基因组学研究机构 以及进行我提出的研究所需的所有其他必要资源。