Decoding mechanisms of phenotypic memory in single cells

项目总结 耐药和转移都是癌症的致命过程，但人们对此知之甚少。在……里面 许多情况下，耐药或转移是由个体肿瘤内的一小部分细胞引起的 行为举止与其他人不同。因为这些细胞只占患者肿瘤细胞的一小部分， 它们不能使用传统方法进行测序或剖析。因此，单细胞分析提供了一个窗口 细胞之间的可变性是这些有害过程的基础；然而，诸如单细胞 测序是在细胞固定或裂解之后进行的，这阻止了研究人员能够 进行进一步的下游分析，如测试细胞的耐药或侵袭性表型。这些 努力在单细胞水平上对分子可变性进行分类，但未能确定这些变量是如何 这些特征与单个细胞中存在的表型有关。 我的研究通过开发一种无偏的、高通量的测序方法来解决这个障碍 用于识别在单个细胞中充分根深蒂固的可变性，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测序结合时，我们的方法 (记忆序列)使我们能够量化基因表达动态，以便发现单细胞基因表达 缓慢波动的状态，可通过多个细胞分裂而遗传。我们假设这些缓慢的 基因表达状态的波动允许单细胞发生显著和根深蒂固的变化，这些变化是 在癌症中产生有害的耐药和转移表型所必需的。我们的目标是用我们的 新的记忆序列方法1)检验基因表达长期波动不充分的假说 癌症中的重要表型，特别是耐药和侵袭，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基因筛查和 单细胞成像。这一系列研究将决定罕见的单细胞基因表达特征 多种癌症类型中的耐药和侵袭人群，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。 这项工作的结果将对癌症研究界具有重要意义，因为它们将产生新的 治疗靶点，以具体抑制或摧毁这些不受欢迎的稀有细胞群。此外，这一点 概念框架是可以概括的，科学研究界可以广泛使用。在 未来，这些波动分析方法可以用来揭示缓慢波动基因的贡献。 在其他生物过程中的表达状态，如发育、伤口愈合和细胞命运决定。

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.