Dataset Information

Synthetic cell division system: controlling equal vs. unequal divisions by design.

ABSTRACT: Cell division is one of the most fundamental and evolutionarily conserved biological processes. Here, we report a synthetic system where we can control by design equal vs. unequal divisions. We synthesized a micro-scale inverse amphipathic droplet of which division is triggered by the increase of surface to volume ratio. Using this system, we succeeded in selectively inducing equal vs. unequal divisions of the droplet cells by adjusting the temperature or the viscosity of the solvent outside the droplet cell accordingly. Our synthetic division system may provide a platform for further development to a system where intracellular contents of the parent droplet cell could be divided into various ratios between the two daughter droplet cells to control their functions and fates.

SUBMITTER: Sato Y

PROVIDER: S-EPMC3858794 | biostudies-other | 2013

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Synthetic cell division system: controlling equal vs. unequal divisions by design.

Sato Yoichi Y Yasuhara Kazuma K Kikuchi Jun-ichi J Sato Thomas N TN

Scientific reports 20131211

Cell division is one of the most fundamental and evolutionarily conserved biological processes. Here, we report a synthetic system where we can control by design equal vs. unequal divisions. We synthesized a micro-scale inverse amphipathic droplet of which division is triggered by the increase of surface to volume ratio. Using this system, we succeeded in selectively inducing equal vs. unequal divisions of the droplet cells by adjusting the temperature or the viscosity of the solvent outside the ...[more]

PMID: 24327069

Similar Datasets

Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Dataset Information

Synthetic cell division system: controlling equal vs. unequal divisions by design.

Publications

Synthetic cell division system: controlling equal vs. unequal divisions by design.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets