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Decoding mechanisms of phenotypic memory in single cells

单细胞表型记忆的解码机制

基本信息

批准号：
10238987
负责人：
Sydney Shaffer
金额：
$ 40.63万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-16 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10238987
关键词：
Address Antibodies Binding Sites Biochemical Biological Biological Assay Biological Process Biomedical Engineering CRISPR screen CRISPR/Cas technology Cancer cell line Catalogs Cell Fraction Cell Separation Cell division Cell surface Cells Clinical Clustered Regularly Interspaced Short Palindromic Repeats Communities DNA Development Diagnostic Disease Disseminated Malignant Neoplasm Drug resistance Engineering Epigenetic Process Event Experimental Designs Faculty Financial Support Foundations Frequencies Future Gene Expression Gene Expression Profile Genes Genetic Screening Genome Heritability High-Throughput Nucleotide Sequencing In Vitro Individual Intuition Investigation Laboratories Libraries Maintenance Malignant Neoplasms Measurement Measures Medicine Memory Mentorship Methods Modernization Molecular Mutate Mutation Neoplasm Metastasis Pathology Patients Pattern Pennsylvania Personnel Management Pharmaceutical Preparations Phenotype Phosphotransferases Population Positioning Attribute Postdoctoral Fellow Predisposition Process Property Protein Tyrosine Kinase Proteins RNA Research Research Personnel Resistance Resources Rest Sampling Site Students Techniques Testing Time Training Universities Work XCL1 gene anticancer research base cancer cell cancer therapy cancer type cellular imaging design experience high dimensionality in vivo innovation laboratory experience live cell imaging medical schools melanoma multidisciplinary new technology new therapeutic target prevent professor programs receptor refractory cancer response single cell analysis single cell sequencing single-cell RNA sequencing targeted treatment tool transcription factor transcriptome sequencing tumor wound healing

项目摘要

PROJECT SUMMARY Drug resistance and metastasis are both deadly processes in cancer that remain poorly understood. In many instances, resistance or metastasis arise from a small subset of the cells within an individual's tumor that behave differently from the rest. Because these cells are only a small fraction of the cells in a patient's tumor, they cannot be sequenced or profiled using traditional methods. Thus, single-cell analysis provides a window into the variability between cells that underlie these harmful processes; however, methods such as single-cell sequencing are performed after fixing or lysing of the cells, which prevents researchers from being able to perform further downstream analysis such as testing the cells for resistant or invasive phenotypes. These efforts catalogue the molecular variability at the single-cell level, but fail to determine how these variable features relate to the phenotypes present in single cells. My research addresses this hurdle via development of a unbiased, high-throughput sequencing method for identifying variability that is sufficiently ingrained in single cells to generate phenotypes. This approach is based upon Luria and Delbrück's 1943 fluctuation analysis. It combines their clever experimental design with the modern twist of high-throughput sequencing assays. When combined with RNA sequencing, our method (MemorySeq) allows us to quantify gene expression dynamics in order to find single-cell gene expression states that are slowly fluctuating and heritable through multiple cell divisions. We hypothesize that these slowly fluctuating gene expression states allow for significant and ingrained changes in single-cells, which are necessary to generate the detrimental phenotypes of resistance and metastasis in cancer. We aim to use our new MemorySeq method to 1) test the hypothesis that long-lived fluctuations in gene expression underly important phenotypes in cancer, specifically drug resistance and invasion, and to 2) identify transcription factors, kinases, and epigenetic regulator proteins responsible for generating and maintaining these long-lived fluctuations in gene expression. These aims will be accomplished using a highly innovative and complementary approach that combines high-throughput sequencing, CRISPR/Cas9 genetic screening, and single-cell imaging. This line of research will determine the single-cell gene expression signatures of rare resistant and invasive populations in multiple cancer types, and will enumerate the transcription factors, kinases, and epigenetic regulator proteins that govern these expression states. The results of this work will be significant to the cancer research community as they will yield new therapeutic targets to specifically inhibit or destroy these undesirable rare cell populations. Furthermore, this conceptual framework is generalizable and broadly accessible to the scientific research community. In the future, these fluctuation analysis methods can be applied to unravel the contribution of slowly fluctuating gene expression states in other biological processes such as development, wound healing, and cell fate decisions.

项目概要耐药性和转移都是癌症的致命过程，但人们对此仍知之甚少。在在许多情况下，耐药性或转移源于个体肿瘤内的一小部分细胞，这些细胞行为与其他人不同。因为这些细胞只占患者肿瘤细胞的一小部分，无法使用传统方法对它们进行测序或分析。因此，单细胞分析提供了一个窗口研究这些有害过程背后的细胞之间的变异性；然而，单细胞等方法测序是在细胞固定或裂解后进行的，这使得研究人员无法进行进一步的下游分析，例如测试细胞的耐药或侵袭表型。这些努力对单细胞水平的分子变异进行分类，但未能确定这些变量如何特征与单细胞中存在的表型有关。我的研究通过开发公正的高通量测序方法解决了这个障碍用于识别单细胞中充分根深蒂固的变异性以产生表型。这种方法是基于 Luria 和 Delbrück 1943 年的波动分析。它将他们巧妙的实验设计与高通量测序分析的现代转折。当与 RNA 测序相结合时，我们的方法 (MemorySeq) 使我们能够量化基因表达动态，以发现单细胞基因表达通过多次细胞分裂缓慢波动且可遗传的状态。我们假设这些慢慢地波动的基因表达状态允许单细胞发生显着且根深蒂固的变化，这些变化是是产生癌症耐药和转移的有害表型所必需的。我们的目标是利用我们的新的 MemorySeq 方法 1) 测试基因表达长期波动的假设癌症中的重要表型，特别是耐药性和侵袭，以及 2) 识别转录负责产生和维持这些长寿的因子、激酶和表观遗传调节蛋白基因表达的波动。这些目标将通过高度创新和结合高通量测序、CRISPR/Cas9 基因筛选和单细胞成像。这一系列研究将确定稀有物种的单细胞基因表达特征多种癌症类型中的耐药性和侵袭性人群，并将列举转录因子，激酶和控制这些表达状态的表观遗传调节蛋白。这项工作的结果对于癌症研究界具有重要意义，因为它们将产生新的成果特异性抑制或破坏这些不良稀有细胞群的治疗靶点。此外，这概念框架具有普遍性，可供科学研究界广泛使用。在未来，这些波动分析方法可以用于揭示缓慢波动基因的贡献其他生物过程中的表达状态，例如发育、伤口愈合和细胞命运决定。