Synthetic symmetry breaking and programmable multicellular structure formation.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-09-20 Epub Date: 2023-09-08 DOI:10.1016/j.cels.2023.08.001
Noreen Wauford, Akshay Patel, Jesse Tordoff, Casper Enghuus, Andrew Jin, Jack Toppen, Melissa L Kemp, Ron Weiss
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Abstract

During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.

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合成对称性破坏和可编程多细胞结构形成。
在发育过程中,细胞经历对称性断裂,分化为自组织成复杂结构的分化亚群。1,2,3,4,5然而,很少有工具能够以可控和耦合的方式概括这些行为。6,7,8,9在这里,我们设计了一种可由小分子调节的随机重组酶遗传开关,以诱导可编程的对称性断裂,对下游细胞命运的承诺以及形态自组织。诱导剂决定承诺概率,产生可调的亚群作为诱导剂剂量的函数。我们使用这种开关来控制每种命运的细胞的细胞间粘附特性。10,11我们从单克隆群体中生成了各种各样的3D形态,并开发了一个与实验结果高度一致的计算模型,对细胞间粘附强度和下游形态之间的关系产生了新的定量见解。我们预计,可编程的对称性破坏,产生精确和可调的亚群比率,并与结构形成相结合,将成为复杂组织和类器官工程工具箱的一个组成部分。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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