Neutral competition explains the clonal composition of neural organoids.
IF 4.3
2区 生物学
引用次数: 0
Abstract
Neural organoids model the development of the human brain and are an indispensable tool for studying neurodevelopment. Whole-organoid lineage tracing has revealed the number of progenies arising from each initial stem cell to be highly diverse, with lineage sizes ranging from one to more than 20,000 cells. This high variability exceeds what can be explained by existing stochastic models of corticogenesis and indicates the existence of an additional source of stochasticity. To explain this variability, we introduce the SAN model which distinguishes Symmetrically diving, Asymmetrically dividing, and Non-proliferating cells. In the SAN model, the additional source of stochasticity is the survival time of a lineage's pool of symmetrically dividing cells. These survival times result from neutral competition within the sub-population of all symmetrically dividing cells. We demonstrate that our model explains the experimentally observed variability of lineage sizes and derive the quantitative relationship between survival time and lineage size. We also show that our model implies the existence of a regulatory mechanism which keeps the size of the symmetrically dividing cell population constant. Our results provide quantitative insight into the clonal composition of neural organoids and how it arises. This is relevant for many applications of neural organoids, and similar processes may occur in other developing tissues both in vitro and in vivo.中性竞争解释了神经器官组织的克隆组成。
神经器官模拟人脑的发育，是研究神经发育不可或缺的工具。对整个器官组织进行的系谱追踪显示，每个初始干细胞产生的后代数量高度多样化，系谱大小从一个细胞到超过20,000个细胞不等。这种高变异性超出了现有的皮质发生随机模型所能解释的范围，表明还存在一个额外的随机性来源。为了解释这种变异性，我们引入了 SAN 模型，该模型区分了对称潜行细胞、非对称分裂细胞和非增殖细胞。在 SAN 模型中，随机性的额外来源是一个系的对称分裂细胞池的存活时间。这些存活时间来自所有对称分裂细胞亚群内部的中性竞争。我们证明，我们的模型可以解释实验观察到的品系大小的变化，并推导出存活时间与品系大小之间的定量关系。我们还证明，我们的模型意味着存在一种调节机制，它能使对称分裂细胞群的大小保持恒定。我们的研究结果提供了对神经器官组织克隆组成及其产生方式的定量洞察。这与神经器官组织的许多应用相关，类似的过程也可能发生在体外和体内的其他发育组织中。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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来源期刊
PLoS Computational Biology
生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍：
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.
Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines.
Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights.
Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology.
Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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