10.5061/DRYAD.5MKKWH76F
Li, Zhengda
0000-0002-9960-4394
Stanford University
Wang, Shiyuan
University of Michigan-Ann Arbor
Sun, Meng
University of Michigan–Ann Arbor
Jin, Minjun
University of Michigan–Ann Arbor
Khain, Daniel
University of Michigan–Ann Arbor
Yang, Qiong
0000-0002-2442-2094
University of Michigan–Ann Arbor
High-resolution mapping of the period landscape reveals polymorphism in
cell cycle frequency tuning
Dryad
dataset
2021
FOS: Biological sciences
National Science Foundation
https://ror.org/021nxhr62
MCB 1817909
National Institute of General Medical Sciences
https://ror.org/04q48ey07
R35GM119688
National Science Foundation
https://ror.org/021nxhr62
Early Career 1553031
2021-08-09T00:00:00Z
2021-08-09T00:00:00Z
en
https://doi.org/10.1101/2021.05.10.442602
https://doi.org/10.5281/zenodo.5165010
193380352 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Biological oscillators adapt to environmental changes with widely tunable
frequencies, a property theoretical studies attributed to positive
feedbacks. However, no experiments have tested this theory. Here, we
created synthetic cells to independently tune the frequency and feedback
strength of a cell-cycle oscillator, enabling continuous mapping of period
landscape in response to network perturbations. We found that although
inhibiting positive feedback of cyclin-dependent kinase (Cdk1) reduces the
tunability, the reduction is not as significant as theoretically
predicted, and the Cdk1-counteracting phosphatase, PP2A, provides
additional machinery to ensure frequency regulation. Additionally, cells
exhibit polymorphic responses to PP2A inhibition, showing a monomodal
distribution of oscillatory cells at low or high PP2A inhibition or a
bimodal distribution at both low and high inhibitions. We explained the
polymorphism by a model of two interlinked bistable switches of Cdk1 and
PP2A where cell-cycle oscillations exhibit two modes in the presence or
absence of PP2A bistability.
This dataset contains metadata after image segmentation and tracking.
Segmentation was achieved by a watershed algorithm with a seed generated
from the Hough circle detection. Tracking was performed by maximizing the
segmentation feature correlation between two consecutive time frames.
Average fluorescent intensity profiles of droplets were then extracted for
further analysis. All analysis above is performed on MatLab 2019a.
This dataset contains 3 different levels of simplification, 1. raw data
right after image processing. 2. cycle description after semi-automatic
peak detection and oscillatory feature calculation. 3. droplet level
statistics after peak detection. For each independent experiment, one or
more levels of simplification may be provided. See 'readMe.doc'
for detailed information about the simplification level of the dataset and
data structure annotation.
This dataset is mainly consisted of two types of files, MatLab metadata
(*.mat ) after image processing and MatLab scripts (*.m) to generate
figures. The dataset is categorized based on figures. Each script
controls metadata import, data analysis, and figure generation. The
scripts and corresponding figures are listed in readMe file.