Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling Xenopus egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.

This dataset contains metadata after image processing, including segmentation and tracking of single droplets. 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 or Python 3.7.10.

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 'Data_structure_for_experimental_data.docx' for detailed information about the simplification level of the dataset and data structure annotation.

This dataset mainly consists of two types of files, MatLab metadata (*.mat ) and Osci data (*.txt) after image processing and MatLab scripts (*.m) to generate figures. 

The dataset is categorized based on figures. The data structures are described in the "Data_structure_for_experimental_data.docx" file.