DOI: 10.5061/dryad.8gtht771s
title: Data from: High-content phenotyping reveals Golgi dynamics and their role in cell cycle regulation

description: Recent advances in quantitative bioimage analysis have enabled detailed
 analyses of cellular and subcellular morphological features, enhancing our
 understanding of cellular functions. Here, we introduce an image-based
 phenotyping pipeline designed for the comprehensive analysis of dynamic
 organelle morphology, particularly the Golgi apparatus and cilia, during
 cell cycle progression. Our approach emphasizes interpretable feature
 extraction, enabling detection of both prominent and subtle morphological
 changes. By using well-characterized morphological dynamics of
 intracellular structures as benchmarks, we demonstrated that our method
 can reliably detect established phenotypic changes and serves as a valid
 tool for quantitative profiling. Further investigation of the G0/G1
 transition revealed an unexplored link between Golgi dynamics and ciliary
 disassembly. Specifically, inhibition of the G0/G1 transition correlated
 with ciliary persistence and unique Golgi dispersion, involving Aurora
 kinase A (AURKA). Our results thus indicate an association of Golgi
 morphology with cell cycle re-entry and ciliary dynamics, underscoring the
 value of our profiling method in studying cellular regulation in health
 and disease.
description: # Data from: High-content phenotyping reveals Golgi dynamics and their
 role in cell cycle regulation Dataset DOI:
 [10.5061/dryad.8gtht771s](https://doi.org/10.5061/dryad.8gtht771s) ####
 Description of the data and file structure This dataset accompanies our
 study investigating the dynamic morphological changes of cellular
 structures, including the Golgi apparatus and cilia, during the cell
 cycle, using image-based high-content phenotyping. These files include the
 uncropped immunofluorescence (IF) image and Western blot (WB) data, cell
 profile data, and Python code, used for analysis of cell morphological
 features and figure generation, as described in **Cao, Peng, Yang et al.,
 J Cell Biol, 2025**. The files also include the cell phenotypic profile
 datasets generated in this article. For detailed instructions on running
 the code, reproducing analyses, and understanding the file structure with
 schematic diagrams, please refer to `Readme.pdf`. The image data and code
 are organized according to the corresponding figures (e.g.,
 `Figure_1_S1.zip`). Data and code include: image data (max intensity
 projection `.tif` files), segmentation results (`.npy` files), cell
 phenotype profiles (`.csv` files), and code (`.ipynb` Jupyter notebooks in
 the “Code” folder. The code was executed in **Miniconda** using **VS
 Code**. * To run code that includes raincloud plots, the
 `ptitprince_full.yaml` An environment is recommended. * Otherwise, all
 code should run with the `cellphenotype_full.yaml` environment. The cell
 phenotype features included in the cell profile CSV files are summarized
 in Table S1 of the article, and the source data file
 (`Variables_TableS1.xlsx`) for this table is also provided in this dataset
 for reference. #### Files and variables **File: Figure_1_S1.zip**
 **Description:** Data and code for Figure 1 and Figure S1. HeLa cells were
 arrested at mitosis and treated with DMSO (control), taxol, or monastrol,
 followed by IF labeling for DNA, α-tubulin, and GM130 (Golgi).
 Segmentation results (`.npy` files) and cell phenotype profiles (`.csv`
 files) are included. **File: Figure_2BC_S2.zip** **Description:** Data and
 code for Figure 2B, C, and Figure S2. HeLa cells were arrested at G2 and
 treated with DMSO (control), SP, CytD, taxol, or BFA, followed by IF
 labeling for DNA, actin, Ki67 (proliferation marker), and GM130 (Golgi).
 **File: Figure_2FGH_S3AB.zip** **Description:** Data and code for Figure
 2F–H and Figure S3A, B. ARPE-19 cells were serum-starved and stimulated,
 followed by IF labeling for DNA, actin, acetylated tubulin (cilia), and
 GRASP65 (Golgi). **File: Figure_3B_S3D.zip** **Description:**  Data and
 code for Figure 3B and Figure S3D. Analysis of cell cycle phase (nuclear
 intensity of cell cycle markers) and ciliation rates in ARPE-19 cells
 transfected with siJNK1, siJNK2, or siAURKA, or treated with SP. **File:
 Figure_3CDE_S3CE.zip** **Description:** Data and code for Figure 3C–E and
 Figure S3, E. Similar experiments as Figure_2FGH_S3AB.zip, with siRNA
 transfection or drug treatment. **File: Figure_4A_S5A.zip**
 **Description:** Data and code for Figure 4A and Figure S5A.
 Quantification of centrosomal AURKA fluorescence signal in ARPE-19 cells
 under various conditions and time points. **File: Figure_4B.zip**
 **Description:** Data and code for Figure 4B. Measurement of spatial
 distances between the Golgi, nucleus, and centrosome in ARPE-19 cells.
 **File: Figure_S4A.zip** **Description:** Data and code for Figure S4A.
 siAURKA + siJNK2 double knockdown experiment. **File: Figure_S4B.zip**
 **Description:** Uncropped IF images for Figure S4B. Double labeling for
 IFT88 and acetylated tubulin (AcTub) to validate cilia detection. **File:
 Figure_S4C.zip** **Description:** Data and code for Figure S4C. Double
 labeling for GRASP65 and GM130 to validate Golgi detection and feature
 correlation. The analysis code and output data are included, which show a
 high correlation between the morphological features of the Golgi obtained
 from images of each marker. **File: Figure_S5B.zip** **Description:** WB
 images for Figure S5B, showing knockdown efficiency. **File:
 Datasets.pdf** **Description:** Datasets 1–7 in a single PDF. This file
 provides the phenotypic profiles obtained in this study. It is included
 here for reference and corresponds directly to the dataset descriptions
 and analyses presented in the article. **Dataset 1**. Profiling results of
 metaphase-arrested cells for all analyzed features. (A) Representative
 original images overlaid with color-coded labels assigned by DBSCAN. Label
 information is derived from Fig. 1F; note that the DMSO-treated cell image
 is identical to that in Fig. 1G. (B) UMAP plots displaying the projection
 of values for all features used in the analysis. These UMAP plots are the
 same as those shown in Fig. 1E. (C) Heatmap of the mean value of each
 feature for each cluster and experimental condition. **Dataset 2**.
 Distribution profiles of analyzed features in metaphase-arrested cells via
 violin plots. Violin plots for all analyzed features are presented for the
 overall cell population as well as for each cluster. **Dataset 3**. Cell
 phenotype profiles reflecting drug treatment effects on late G2 cells. (A,
 B) All features used in the analysis are mapped onto the UMAP plot from
 Fig. 2B (A) and shown as violin plots by experimental condition (B). The
 complete dataset, including the features presented in.g S2E, F, is shown.
 **Dataset 4**. Subcluster analysis of SP treatment effects on Golgi
 morphology in late G2 cells. Violin plots show the distributions of
 morphological features for two subclusters defined by Golgi morphology
 (see Fig. 2C) in both control and SP-treated groups. The complete dataset,
 including the features presented in Fig. 2C, is provided. **Dataset 5.**
 Analysis of changes in cell phenotype profiles upon serum re-addition. (A,
 B) All features used in the analysis are mapped onto the UMAP plot (A) and
 shown as violin plots grouped by clusters (B). Data from all time points
 are combined. Several key features highlighted in the text are marked with
 green boxes. **Dataset 6.** Cell phenotype profiles reflecting the effects
 of kinase gene knockdown and SP treatment during the G0/G1 transition. (A,
 B) All features used in the analysis are mapped onto the UMAP plot from
 Fig. 3D (A) and shown as violin plots grouped by experimental condition
 (B). The complete dataset, including the features presented in.g 3E, is
 provided. **Dataset 7**. Cell phenotype profiles reflecting the effects of
 kinase gene knockdown and SP treatment during the G0/G1 transition,
 including siAURKA/siJNK2 double knockdown. (A, B) All features used in the
 analysis are mapped onto the UMAP plot from Fig. S4A (A) and shown as
 violin plots grouped by experimental condition (B). **File: Readme.pdf**
 **Description:** Detailed instructions on data structure, analysis steps,
 and code execution. **File: cellphenotype_full.yaml** **Description:** A
 virtual environment to run all the code except the code containing
 raincloud plots. **File: ptitprince_full.yaml** **Description:** To run
 code that includes raincloud plots, this environment is recommended.
 **Variables: Variables_TableS1.xlsx** **Description:** This file provides
 the source data for Table S1 in the article. It lists and explains all
 variables (cell phenotypic features) that appear as column headers in the
 cell profile CSV files included in this dataset. Each variable, such as
 *mask_area* and *mean_nuc*, is accompanied by a brief description of its
 meaning, units and measurement.