Multidimensional cell recording with single-cell genomics

抽象的。一个受精卵--一个单细胞--连续分裂，最终产生一个高度组织化的40 一万亿个细胞组成了一个成年人这种复杂的细胞谱系树是由遗传、分子和 环境线索尽管在过去的150年里取得了巨大的进步--如何、何时和 决定发育谱系树的决策在哪里仍然知之甚少， 只有人类，但几乎所有的多细胞生物。我和同事们一起开创了GESTALT（基因组 用于谱系追踪的合成靶阵列的编辑），这是一种基于体内基因组编辑的新技术， 能够在整个动物的规模上追踪细胞谱系的发育。在迄今为止的实验中，我们 已经成功地捕捉到了一个生物体中数十万个细胞的相互关联的命运， 这是我们朝着在关键模式生物中绘制全球细胞谱系的最终目标迈出的关键一步。在此K99/R 01 在提案中，我描述了我的实验室将如何将我们最初的GESTALT概念验证扩展为一个丰富、灵活的平台 用于生物记录，包括分子信号和细胞谱系历史与转录组结合， 调控景观和单细胞状态的其他测量。在K99阶段，我将提高 GESTALT系统的信息容量，并进一步开发必要的计算方法（目标1）， 并将谱系记录与单细胞转录谱分析相结合（Aim 2）。在R 00阶段， 获得该奖项后，我将把GESTALT扩展为一个成熟的信息记录平台，能够记录密钥 在整个生物体发育过程中的信号事件（目标3），然后将这个集成平台应用于 制作了一个果蝇大脑发育的注释树。我在这里开发的方法将使 我的实验室，以及整个领域，来回答关于正常发展以及 起源于细胞谱系的复杂病因的疾病（例如癌症、发育障碍）。

Abstract. A zygote - a single cell - successively divides to ultimately give rise to a highly organized mass of 40 trillion cells that constitutes an adult human. This complex cell lineage tree is shaped by genetic, molecular and environmental cues. Despite enormous progress over the past one hundred and fifty years – how, when and where the decisions are made that determine the developmental lineage tree remain poorly understood for not only humans but nearly all multicellular organisms. Together with colleagues, I pioneered GESTALT (genome editing of synthetic target array for lineage tracing), a new technology based on in vivo genome editing during development that is capable of tracing cell lineage at the scale of whole animals. In our experiments to date, we have successfully captured the interrelated fates of hundreds of thousands of cells within a single organism, a critical step towards our eventual goal of globally mapping cell lineage in key model organisms. In this K99/R01 proposal, I describe how my lab will expand our initial proof-of-concept of GESTALT into a rich, flexible platform for biological recording, including molecular signals and cell lineage history in conjunction with transcriptomes, regulatory landscapes, and other measurements of single cell state. In the K99 phase of my award, I will enhance the information capacity of GESTALT system and further develop the requisite computational methods (Aim 1), and also integrate lineage recording with single cell transcriptional profiling (Aim 2). In the R00 phase of my award, I will expand GESTALT into a fully-fledged information recording platform, capable of recording key signaling events over the span of organismal development (Aim 3), and then apply this integrated platform to produce an annotated tree of brain development in Drosophila. The methods that I develop here will empower my lab, and the field at large, to answer long-standing questions about normal development as well as about the origins of diseases with complex etiologies rooted in cell lineage (e.g. cancer, developmental disorders).