Multidimensional cell recording with single-cell genomics

抽象的。受精卵--单个细胞--连续分裂，最终形成一个高度有组织的40个细胞团。 万亿个细胞构成了一个成年人。这棵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定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格式塔(基因组 用于血统追踪的合成靶点阵列的编辑)，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展。在我们迄今为止的实验中，我们 已经成功地捕捉到了单个有机体中数十万个细胞相互关联的命运，一个 朝着我们在全球绘制关键模式生物细胞谱系的最终目标迈出了关键的一步。在本K99/R01中 提案中，我描述了我的实验室将如何将我们最初的格式塔概念验证扩展到一个丰富、灵活的平台 对于生物记录，包括结合转录本的分子信号和细胞谱系历史， 监管环境，以及单细胞状态的其他测量。在我获奖的K99阶段，我将加强 格式塔系统的信息能力，并进一步发展必要的计算方法(目标1)， 并将谱系记录与单细胞转录图谱相结合(目标2)。在我的R00阶段 获奖后，我将把格式塔扩展为一个成熟的信息记录平台，能够记录密钥 生物发育过程中的信号事件(目标3)，然后将该集成平台应用于 制作一棵带注释的果蝇大脑发育树。我在这里开发的方法将使 我的实验室，以及整个领域，回答关于正常发展以及关于 具有复杂病因的疾病起源于细胞谱系(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).