DOI: 10.5061/dryad.ht76hdrwn
title: Data from: A semi-automated pipeline for morphological analysis of myonuclei along single muscle fibers

description: Manual quantitation of skeletal muscle myonuclear number, spatial
 orientation, and morphology is time consuming and subject to error and
 bias. To overcome these limitations, we developed and validated a
 semi-automated, quantitative, and reproducible image analysis pipeline.
 The workflow combines FIJI-based preprocessing with custom Python scripts
 to process immunohistological images of individual muscle fibers, enabling
 high resolution and scalable quantification of nuclei. Analyses
 incorporate morphometric parameters including nuclear position, shape, and
 three dimensional orientation, as well as centroid to skeleton distance
 and nearest neighbor relationships to capture spatial patterns of
 myonuclear organization along the fiber. Outputs include per fiber and
 biopsy level summaries integrated with Imaris metrics. This semi-automated
 approach provides a robust and efficient platform for high throughput
 analysis of myonuclear number and structural features across large single
 fiber datasets.
description: # Example Input Data for A semi-automated pipeline for morphological
 analysis of myonuclei along single muscle fibers
 [https://doi.org/10.5061/dryad.ht76hdrwn](https://doi.org/10.5061/dryad.ht76hdrwn) **Date of Data Collection**: 2025-12-01 **Related Repository**: [https://doi.org/10.5281/zenodo.18395537](https://doi.org/10.5281/zenodo.18395537) ## Contributors * **Esben Schroeder†**: Department of Human Physiology, University of Oregon, Eugene, OR 97403 * **Helia G. Megowan†**: Department of Human Physiology, University of Oregon, Eugene, OR 97403 * **Madeline Luu†**: Department of Computer Science & Department of Data Science, University of Oregon, Eugene, OR 97403 * **Adam Shuaib**: Department of Neuroscience, University of Oregon, Eugene, OR 97403 * **Adam Fries**: Genomics & Cell Characterization Core Facility, University of Oregon, Eugene, OR 97403 * **Jake Searcy**: Department of Data Science, University of Oregon, Eugene, OR 97403 * **Hans C. Dreyer** (Corresponding Author): Department of Human Physiology, University of Oregon, Eugene, OR 97403. Email: [hcdreyer@uoregon.edu](mailto:hcdreyer@uoregon.edu) † Co-first authors ## Overview This dataset serves as a comprehensive collection of example input data designed for the **Single-Fiber Myonuclear Analysis Pipeline**. It is intended to facilitate the reproduction of results, testing of pipeline functionality, and validation of the algorithms described in our associated research paper. The data provided here represents the two primary stages of data acquisition required before running the automated Python quantification: 1. **Raw Microscopy Data**: Compressed archive containing the original `.lif` project file from the Leica microscope. 2. **Processed Macro Output**: The intermediate image files (skeletons, projections, and stacks) generated by the FIJI/ImageJ segmentation macro, which serve as the direct input for the Python analysis script (`SF_analysis_pipeline.py`). By providing these files, we allow users to test the full workflow—from raw image extraction to final morphological quantification. **The custom ImageJ macro code required to process the raw** **`.lif`** **data, as well as the Python analysis script (`SF_analysis_pipeline.py`) and its associated documentation, are available in the linked GitHub repository.** ## Software & Compatibility To utilize this data effectively, the following software environment is recommended: * **Primary Python Pipeline**: Single-Fiber Myonuclear Analysis Pipeline (v1.0). * **Image Processing**: FIJI / ImageJ. (The `.ijm` macro script and instructions for running it are provided in the GitHub repository listed above). * **Python Dependencies**: Python 3.10+, numpy, pandas, scikit-image, scikit-learn, scipy, openpyxl, Pillow. Available at [![DOI](https://zenodo.org/badge/1103407096.svg)](https://doi.org/10.5281/zenodo.18395536). ## Dataset Structure The dataset consists of two primary compressed archives: **1. Raw Microscopy Data** (`example_LIF_microscopy_project.zip`) File Format: `.zip` (Compressed Leica Image File) **Description**: This archive contains the original, multi-channel 3D confocal microscopy data (`23_Wu_20_D14_R_02.lif`) acquired directly from the instrument. It includes the Z-stacks of single muscle fibers before any segmentation or processing has occurred. **Usage**: Unzip this file to access the `.lif` project. This file is the starting point for the FIJI/ImageJ macro. Users wishing to test the segmentation portion of the workflow should download the macro from the GitHub repository and follow the instructions provided there to process this file. **2. Example Python Script Input** (`example_IJMacro_output.zip`) File Format: `.zip` (Compressed Directory) Description: This compressed archive contains the output of the FIJI/ImageJ macro. These files are structured specifically to serve as the input for the Python analysis pipeline. The directory structure mimics the exact hierarchy required by the `SF_analysis_pipeline.py script`. Directory Hierarchy: When unzipped, the folder structure is organized by Subject, Timepoint, Side, and Image Type: ``` example_IJmacro_output/ └── 20/ &lt;-- Subject ID ├── BL/ &lt;-- Timepoint (Baseline) │ └── L/ &lt;-- Side (Left Leg) │ ├── STDIP/ &lt;-- Standard Deviation Z-Projections │ ├── Skel/ &lt;-- Binary Skeletons of the Fiber │ └── TIFs/ &lt;-- Raw Z-Stacks (Converted from .lif) └── D14/ &lt;-- Timepoint (Day 14) └── R/ &lt;-- Side (Right Leg) ├── STDIP/ ├── Skel/ └── TIFs/ ... ``` **File Contents within Subfolders**: For each fiber analyzed, three corresponding files are present across the `STDIP`, `Skel`, and `TIFs` folders. The filenames share a common identifier to link them during processing. **1. STDIP Folder** (`.tif`): * Example: `STDIP_23_Wu_20_D14_R_02.lif_-_Fiber1-1_Merged.tif` * Content: A standard deviation intensity projection used for nuclear segmentation masks. **2. Skel Folder** (`.tif`): * Example: `Skel_23_Wu_20_D14_R_02.lif_-_Fiber1-1_Merged.tif` * Content: A binary skeleton image representing the centerline of the muscle fiber, used for orientation and distance calculations. **3. TIFs Folder** (`.tif`): * Example: `23_Wu_20_D14_R_02.lif_-_Fiber1-1_Merged.tif` * Content: The raw 3D Z-stack of the fiber, used for Z-position inference and intensity analysis. ## Usage and Accessibility These files are designed to be fully compatible with the open-source release of the **Single-Fiber Myonuclear Analysis Pipeline**. **Instructions for Use:** **Option A: Run the Python Pipeline Only (Quick Start)** *Use the pre-processed data to test the Python analysis script immediately.* 1. Download and unzip `example_IJMacro_output.zip`. 2. Clone the GitHub repository: `git clone https://github.com/DreyerLabUO/23Wu-SingleFiber`. 3. Install dependencies as listed in the repository `README.md`. 4. Run the Python pipeline pointing to the unzipped directory: ``` python SF_analysis_pipeline.py "path/to/example_IJmacro_output" \ --pixel_size_xy_um 0.329 \ --z_scale_um_per_index 2.7 ``` **Option B: Full Workflow Reproduction** *Start from the raw microscopy data, run the segmentation macro, and then analyze the results.* 1. Download and unzip `example_LIF_microscopy_project.zip` to retrieve the `.lif` file. 2. Download/Clone the GitHub repository to access the ImageJ macro file `23_Wu-SF_Analysis.ijm` and the Python script. 3. Run ImageJ Macro: Open FIJI, load the macro from the repository, and execute it on the extracted `.lif` file. This will generate a new output directory structured identically to `example_IJmacro_output`. 4. Run Python Pipeline: Execute the Python script, pointing it to the *newly generated* output folder from the previous step: ``` python SF_analysis_pipeline.py "path/to/YOUR_MACRO_OUTPUT_FOLDER" \ --pixel_size_xy_um 0.329 \ --z_scale_um_per_index 2.7 ``` For detailed troubleshooting, parameter adjustments, or macro installation guides, please refer to the `docs/` folder in the available repository. ## Human subjects data Our research group received explicit consent from participants to publish de-identified data publicly. This dataset has been de-identified in accordance with HIPAA standards by removing all 18 direct identifiers and specific quasi-identifiers, ensuring no reasonable basis exists to re-identify any individual.