Ancestry and evolution in cell biology Advances in experimental approaches for single-cell analysis allow in situ sequencing, genomic barcoding, and mapping of cell lineages within tissues and organisms. Large amounts of data have thus accumulated and present an analytical challenge. Stadler et al. recognized the need for conceptual and computational approaches to fully exploit these technological advances for the understanding of normal and disease states. The authors review ideas taken from phylodynamics of infectious disease and show how similar tree-building techniques can be applied to monitoring changes in somatic cell lineages for applications ranging from development and differentiation to cancer biology. Science, this issue p. eaah6266 BACKGROUND The birth, death, and diversification of individuals are events that drive biological processes across all scales. This is true whether the individuals in question represent nucleic acids, cells, whole organisms, populations, or species. The ancestral relationships of individuals can be visualized as branching trees or phylogenies, which are long-established representations in the fields of evolution, ecology, and epidemiology. Molecular phylogenetics is the discipline concerned with the reconstruction of such trees from gene or genome sequence data. The shape and size of such phylogenies depend on the past birth and death processes that generated them, and in phylodynamics, mathematical models are used to infer and quantify the dynamical behavior of biological populations from ancestral relationships. New technological advances in genetics and cell biology have led to a growing body of data about the molecular state and ancestry of individual cells in multicellular organisms. Ideas from phylogenetics and phylodynamics are being applied to these data to investigate many questions in tissue formation and tumorigenesis. ADVANCES Trees offer a valuable framework for tracing cell division and change through time, beginning with individual ancestral stem cells or fertilized eggs and resulting in complex tissues, tumors, or whole organisms (see the figure). They also provide the basis for computational and statistical methods with which to analyze data from cell biology. Our Review explains how “tree-thinking” and phylodynamics can be beneficial to the interpretation of empirical data pertaining to the individual cells of multicellular organisms. We summarize some recent research questions in developmental and cancer biology and briefly introduce the new technologies that allow us to observe the spatiotemporal histories of cell division and change. We provide an overview of the various and sometimes confusing ways in which graphical models, based on or represented by trees, have been applied in cell biology. To provide conceptual clarity, we outline four distinct graphical representations of the history of cell division and differentiation in multicellular organisms. We highlight that cells from an organism cannot be always treated as statistically independent observations but instead are often correlated because of phylogenetic history, and we explain how this can cause difficulties when attempting to infer dynamical behavior from experimental single-cell data. We introduce simple ecological null models for cell populations and illustrate some potential pitfalls in hypothesis testing and the need for quantitative phylodynamic models that explicitly incorporate the dependencies caused by shared ancestry. OUTLOOK We expect the rapid growth in the number of cell-level phylogenies to continue, a trend enhanced by ongoing technological advances in cell lineage tracing, genomic barcoding, and in situ sequencing. In particular, we anticipate the generation of exciting datasets that combine phenotypic measurements for individual cells (such as through transcriptome sequencing) with high-resolution reconstructions of the ancestry of the sampled cells. These developments will offer new ways to study developmental, oncogenic, and immunological processes but will require new and appropriate conceptual and computational tools. We discuss how models from phylogenetics and phylodynamics will benefit the interpretation of the data sets generated in the foreseeable future and will aid the development of statistical tests that exploit, and are robust to, cell shared ancestry. We hope that our discussion will initiate the integration of cell-level phylodynamic approaches into experimental and theoretical studies of development, cancer, and immunology. We sketch out some of the theoretical advances that will be required to analyze complex spatiotemporal cell dynamics and encourage explorations of these new directions. Powerful new statistical and computational tools are essential if we are to exploit fully the wealth of new experimental data being generated in cell biology. Multicellular organisms develop from a single fertilized egg. The division, apoptosis, and differentiation of cells can be displayed in a development tree, with the fertilized egg being the root of the tree. The development of any particular tissue within an organism can be traced as a subtree of the full developmental tree. Subtrees that represent cancer tumors or B cell clones may exhibit rapid growth and genetic change. Here, we illustrate the developmental tree of a human and expand the subtree representing haematopoiesis (blood formation) in the bone marrow. Stem cells in the bone marrow differentiate, giving rise to the numerous blood cell types in humans. The structure of the tree that underlies haematopoiesis and the formation of all tissues is unclear. Phylogenetic and phylodynamic tools can help to describe and statistically explore questions about this cell differentiation process. Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how “tree thinking” is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

中文翻译：

---

细胞生物学家的系统动力学

细胞生物学的祖先和进化 单细胞分析实验方法的进步允许原位测序、基因组条形码以及组织和生物体内细胞谱系的映射。因此积累了大量数据并提出了分析挑战。斯塔德勒等人。认识到需要概念和计算方法来充分利用这些技术进步来理解正常和疾病状态。作者回顾了传染病系统动力学的观点，并展示了如何将类似的造树技术应用于监测体细胞谱系的变化，以实现从发育和分化到癌症生物学的应用。科学，这个问题 p。eaah6266 背景 出生、死亡、和个体多样化是推动所有尺度的生物过程的事件。无论所讨论的个体代表核酸、细胞、整个生物体、种群还是物种，都是如此。个体的祖先关系可以被形象化为分支树或系统发育，它们是进化、生态和流行病学领域长期建立的表征。分子系统发育学是与从基因或基因组序列数据重建此类树有关的学科。这种系统发育的形状和大小取决于产生它们的过去出生和死亡过程，在系统动力学中，数学模型用于从祖先关系推断和量化生物种群的动态行为。遗传学和细胞生物学方面的新技术进步导致关于多细胞生物中单个细胞的分子状态和祖先的数据越来越多。来自系统发育学和系统动力学的想法被应用于这些数据，以研究组织形成和肿瘤发生中的许多问题。ADVANCES Trees 提供了一个有价值的框架来追踪细胞分裂和随时间的变化，从单个祖先干细胞或受精卵开始，并导致复杂的组织、肿瘤或整个生物体（见图）。它们还为分析细胞生物学数据的计算和统计方法提供了基础。我们的评论解释了“树状思维”和系统动力学如何有助于解释与多细胞生物的单个细胞有关的经验数据。我们总结了发育和癌症生物学中的一些最新研究问题，并简要介绍了使我们能够观察细胞分裂和变化的时空历史的新技术。我们概述了基于树或由树表示的图形模型在细胞生物学中的应用方式，这些方式有时令人困惑。为了提供清晰的概念，我们概述了多细胞生物中细胞分裂和分化历史的四种不同的图形表示。我们强调，来自生物体的细胞不能总是被视为统计上独立的观察结果，而是由于系统发育历史而经常相关，并且我们解释了在尝试从实验单细胞数据推断动态行为时这如何导致困难。我们为细胞群引入了简单的生态零模型，并说明了假设检验中的一些潜在缺陷，以及明确包含由共享祖先引起的依赖性的定量系统动力学模型的必要性。展望我们预计细胞水平系统发育数量的快速增长将继续，这一趋势因细胞谱系追踪、基因组条形码和原位测序方面的持续技术进步而增强。特别是，我们预计会产生令人兴奋的数据集，这些数据集将单个细胞的表型测量（例如通过转录组测序）与采样细胞祖先的高分辨率重建相结合。这些发展将为研究发育、致癌、和免疫过程，但需要新的和适当的概念和计算工具。我们讨论了系统发育学和系统动力学模型将如何有利于在可预见的未来生成的数据集的解释，并将有助于开发利用细胞共享祖先并对其具有鲁棒性的统计测试。我们希望我们的讨论将启动将细胞水平的系统动力学方法整合到发育、癌症和免疫学的实验和理论研究中。我们勾勒出分析复杂时空细胞动力学所需的一些理论进展，并鼓励探索这些新方向。如果我们要充分利用细胞生物学中产生的大量新实验数据，强大的新统计和计算工具是必不可少的。多细胞生物从单个受精卵发育而来。细胞的分裂、凋亡和分化可以在发育树中展示，受精卵是树的根。生物体内任何特定组织的发育都可以作为完整发育树的子树进行追踪。代表癌症肿瘤或 B 细胞克隆的子树可能表现出快速生长和遗传变化。在这里，我们说明了人类的发育树，并扩展了代表骨髓中造血（血液形成）的子树。骨髓中的干细胞分化，在人类中产生多种血细胞类型。造血和所有组织形成基础的树的结构尚不清楚。系统发育和系统动力学工具可以帮助描述和统计探索有关这种细胞分化过程的问题。多细胞生物由通过祖先和祖细胞的后代连接的细胞组成。生物体内细胞出生、死亡和遗传的动力学引起了发育、分化和癌症的基本过程。分子生物学的技术进步现在使我们能够以生物体或组织内单个细胞的分辨率研究细胞组成、祖先和进化。在这里，我们对单细胞生物学采用系统发育和系统动力学方法。我们解释了“树状思维”对于解释不断增长的细胞级数据体的重要性，以及生态零模型如何有利于统计假设检验。