Microenvironmental Confinement Drives Density-Dependent Neuron-Like Cell Proliferation and Contact-Dependent Neurite Outgrowth

This README file was generated on 2025-06-16 by Stephen Casale.Last updated: 2025-07-18. -------------------GENERAL INFORMATION------------------- // Title of Dataset: Dataset to accompany manuscript entitled "Microenvironmental Confinement Drives Density-Dependent Neuron-Like Cell Proliferation and Contact-Dependent Neurite Outgrowth"// DOI: [none]// Contact Information // Name: Prof. Dr. Christine Selhuber-Unkel // Institution: Ruprecht Karls Universität Heidelberg: Heidelberg, Baden-Württemberg, DE // Email: [e-mail] // ORCID: https://orcid.org/0000-0002-5051-4822 <Whenever applicable, the following information should be registered in the metadata schema of heiDATA. In the text below, remove fields/lines that are not applicable, and leave the rest unchanged. >// Contributors: Stephen Casale// Kind of data: *.csv, *.py, *.swc, *.tiff, *.traces, *.txt// Date of data collection/generation: 2023, 2024// Geographic location: Heidelberg, Baden-Württemberg, DE// Funding sources: (excerpt from "Funding" section of manuscript) "This research was conducted within the Max Planck School Matter to Life supported by the German Federal Ministry of Education and Research (BMBF) in collaboration with the Max Planck Society and the Institute for Molecular Systems Engineering and Advanced Materials (IMSEAM). For support with PC12 cell culture and cell seeding in 3D-printed structures, the authors thank Dr. Sadaf Pashapour and the Microfabrication and Microfluidics Core Facility at the Institute for Molecular Systems Engineering and Advanced Materials (IMSEAM), which is partially funded by the Health + Life Science Alliance Heidelberg Mannheim. The Health + Life Science Alliance provided state funds approved by the state parliament of Baden-Württemberg. We also thank the Flagship Initiative "Engineering Molecular Systems" funded by the German Federal Ministry of Education and Research (BMBF) and the Ministry of Science Baden-Württemberg within the framework of the Excellence Strategy of Federal and State Governments of Germany. The authors also acknowledge funding by the DFG under Germany's Excellence Strategy 2082/1-390761711 (3D Matter Made to Order) and the Carl Zeiss Foundation, as well as the Volkswagen Foundation through the initiative “Life?” (Az. 96733). F.T. acknowledges support by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) through SPP SE 1801/5-1, (2 303 731). M.S. acknowledges the European Research Council through the Proof of Concept Grant VASCUGRAFT, no. 899701. 3D printing was accomplished using the Nanoscribe PP GT2 located in the Institute for Molecular Sciences Engineering and Advanced Materials at Universität Heidelberg, purchased via the following funding number: INST 35/1643-1 FUGG." // Description of dataset: There are a few datatypes in this dataset. First, there are *.tiff images that were analyzed using Fiji's SNT ("Simple Neurite Tracer") Plug-in. This analysis generated the following filetypes: *.csv, *.swc, *.traces, *.txt. The *.py files were used to analyze data in the *.csv files. The *.swc and *.traces files were retained to regenerate images acquired via SNT, if necessary for quality control purposes. *.txt files were generated periodically to intermittently test the efficacy of the python scripts. --------------------------METHODOLOGICAL INFORMATION-------------------------- // Description of sources and methods used for collection/generation of data: Phase-contrast microscopy images (*.tiff) were acquired using an Olympus IX-81 inverted microscope. To prepare samples, 3D-printed confinement structures were fabricated on glass substrates, after which PC12 cells were seeded directly into these confinement structures. Images obtained were subsequently analyzed using Fiji software with the Simple Neurite Tracer (SNT) plugin. Data analysis via SNT resulted in additional files (*.csv, *.swc, and *.traces), which document neurite outgrowth and cellular proliferation patterns within confined environments. All experimental procedures and data collection were performed by Stephen Casale. // Methods for processing the data: The provided .csv, .swc, and .traces files are raw data outputs directly generated from the manual tracing of cellular features within the phase-contrast microscopy images (*.tiff) using Fiji’s Simple Neurite Tracer (SNT) plug-in. No additional post-processing or manual edits were performed on these raw output files. Python scripts (*.py) included in this dataset were used to perform all further analyses described in the manuscript, including data filtering, normalization, aggregation, statistical analyses, annotation of .tiff files, and generation of manuscript figures. These scripts reproduce all results and visualizations presented in the manuscript. Each Python script contains basic commenting to guide usage. File and directory paths in the accompanying Python scripts have been intentionally left blank or replaced with placeholders. Users should replace these with their own desired paths or variables based on their system setup. This ensures flexibility across different environments and file structures. For further detailed explanations of the scripts' functions, users may consider utilizing AI-driven code analysis tools. No additional data cleaning, quality assurance, or outlier removal was performed prior to analysis. --------------------DATA & FILE OVERVIEW-------------------- // File List: The dataset is structured into several directories organized according to the corresponding sections of the manuscript. Each manuscript section has associated Python analysis scripts and raw data organized by experiments, with individual experiments further subdivided into timepoints (DIV_1, DIV_3, DIV_5) containing raw_images (*.tiff files) and corresponding Fiji SNT outputs (snt_files including .csv, .swc, .traces files). General Structure: <Section of Paper>/├── code/│ ├── *.py (Python scripts performing analysis, visualization, and annotation)│├── <Experiment_ID>/│ ├── DIV_1/│ │ ├── raw_images/ (*.tiff images, phase-contrast microscopy)│ │ └── snt_files/ (*.csv, *.swc, *.traces files from Fiji/SNT)│ ││ ├── DIV_3/│ │ ├── raw_images/ (*.tiff images)│ │ └── snt_files/ (*.csv, *.swc, *.traces files)│ ││ └── DIV_5/│ ├── raw_images/ (*.tiff images)│ └── snt_files/ (*.csv, *.swc, *.traces files) Key Directories and Content: Section 2.1 Low Density Confinement Enhances Proliferation Python Script: 39.20_mean_soma_count_conditions_v_div1_3_5_v9a_seaborn.py Experiments Included: 39.14_Mar_24_hrglass, 39.15_Mar_24_hrglass, 39.16_Mar_24_hrglass 39.23_may_2024_hrglass, 39.24_may_2024_hrglass, 39.25_may_2024_hrglass 39.33_oct_24_dense_cont, 39.34_oct_24_dense_cont, 39.35_oct_24_dense_cont Section 2.2 Confinement Restricts Neurite Growth Python Script: 39.20_39.14_edge_free_other_vs_control_all_and_longest_v6_seaborn.py Experiments Included: 39.14_Mar_24_hrglass, 39.15_Mar_24_hrglass, 39.16_Mar_24_hrglass 39.20_may_2024_hrglass, 39.21_may_2024_hrglass, 39.22_may_2024_hrglass 39.23_may_2024_hrglass, 39.24_may_2024_hrglass, 39.25_may_2024_hrglass Section 2.3 Structure Contact Drives Neurite Growth, Independent of Cell Density Python Script: 39.20_39.14_edge_free_other_pathlengths_by_occ_status_w_data_file_v6_seaborn.py Experiments Included: Same experiments as Section 2.2 above. Section 2.4 Time-Dependent Neurite Growth Across Cell Types is Modulated by Cell Density Python Script: 39.20_39.14_neurite_length_by_occ_status_v7_seaborn.py Experiments Included: Same as Sections 2.3 above. Section 2.5 Neurite Lengths of All Cells are Governed by Contact Cues Above a Threshold Cell Density Python Script: 39.20_39.14_occ_status_by_neurite_type_v6_seaborn.py Experiments Included: Same as Sections 2.3 and 2.4 above. Supplemental Section Python Script: 39.20_39.14_edge_free_other_vs_external_all_v5_seaborn_paper.py Experiments Included: Subset of experiments: 39.14_Mar_24_hrglass, 39.15_Mar_24_hrglass, 39.16_Mar_24_hrglass 39.23_may_2024_hrglass, 39.24_may_2024_hrglass, 39.25_may_2024_hrglass File Naming Conventions: Python scripts are named using a convention indicating the experiment IDs involved, type of analysis conducted, version, and the visualization library (seaborn). Experiment directories follow the convention <Experiment_ID>/<DIV_number>/, clearly separating images (raw_images) from processed SNT tracing data (snt_files). System Requirements: Fiji (ImageJ) software with the SNT plug-in is required to open .traces and .swc files. Python with libraries including Pandas, NumPy, Matplotlib, and Seaborn is required to run analysis scripts (*.py). // Relationship between files, if important: See Methodological Information above for further information on relationships between files. // Is this a new version of a previously published dataset? no -----------------------------------------DATA-SPECIFIC INFORMATION FOR: [FILENAME]----------------------------------------- The dataset contains two general types of `.csv` tabular files: 1. SNT-generated `.csv` files:** * These files contain measurements generated directly from Fiji’s Simple Neurite Tracer (SNT) plugin without additional manual editing.* Typical columns include (but may not be limited to): * `PathID`: Numeric identifier of neurite path traced. * `PathName`: Name assigned within SNT tracing (e.g., "edge," "free," "other"). * `SWCType`: Numeric code (standard SWC format) indicating neuronal compartment types. * `PrimaryPath`: Indicates if path is primary (`True`/`False`). * `PathLength`: Length of neurite path (units: micrometers). * `PathLengthUnits`: Units used (e.g., "microns"). * `StartX`, `StartY`, `StartZ`: Coordinates of neurite start-point (units: pixels or micrometers, based on image calibration). * `EndX`, `EndY`, `EndZ`: Coordinates of neurite end-point (units as above). * `ApproximateFittedVolume`: Calculated volume from traced paths (units: cubic micrometers).* **Decimal separator**: Point (`.`).* **Missing data**: Missing values are represented as empty cells. 2. Python-generated `.csv` files:** * These files result from analyses performed via the provided Python scripts.* Typical columns include but are not limited to: * Aggregated statistics such as `MeanPathLength`, `MedianPathLength`, `Count`, `SEM`, `NormalizedCount`, etc., generated during filtering, normalization, and aggregation steps. * Statistical test results (e.g., `p-value`, `statistic`). * Categorization labels (e.g., `ExperimentID`, `DIV`, `CellDensity`).* **Decimal separator**: Point (`.`).* **Missing data**: Missing or non-applicable values are represented as `NaN` or empty cells. **Specialized formats or abbreviations**: * DIV: Days In Vitro* SEM: Standard Error of the Mean* SNT: Simple Neurite Tracer plugin for Fiji/ImageJ software* SWC: Standardized format for neuronal morphology tracing files **Notes on Units**: * Measurements of lengths and distances in all files are provided in micrometers unless otherwise indicated in the file. --------------------------SHARING/ACCESS INFORMATION--------------------------// Licenses/Restrictions: See Terms tab.// Recommended citation: See citation generated by repository.