BRONK: An Open-Source Multi-Channel Cell Segmentation Software for Single-Cell Tracking and Analysis
The BRONK Segmentation Pipeline is a powerful software package for automated cell segmentation and analysis in microscopy images. This software is designed to handle images generated using various microscopy techniques, such as bright field, phase contrast, fluorescence microscopy (including wide field, confocal, and light sheet), and more. The BRONK Segmentation Pipeline supports both 2D and 3D image analysis, making it a versatile tool for a wide range of applications in cell biology and histology.
The primary aim of the BRONK Segmentation Pipeline is to provide an efficient and user-friendly solution for cell segmentation, feature extraction, and quantification in large microscopy datasets. The software is implemented in MATLAB and incorporates advanced image processing algorithms for accurate and reliable cell segmentation, with additional support for customizable parameters to suit specific experimental conditions and image properties.
- Automated cell segmentation: The software is capable of segmenting cells in microscopy images using various user-defined parameters, allowing for accurate and consistent cell identification across different experimental setups and imaging modalities.
- Feature extraction and quantification: Once cells are segmented, the BRONK Segmentation Pipeline extracts and quantifies a comprehensive set of cellular features, such as area, intensity, shape, and more. These features can be easily exported for further analysis in external software packages.
- Customizable analysis workflows: The BRONK Segmentation Pipeline is designed with flexibility in mind, allowing users to create and modify their analysis workflows to fit their specific needs. This includes options for analyzing select wells or timepoints, as well as support for parallel processing to expedite analysis of large datasets.
- Integration with Baxter Algorithms: The pipeline is compatible with the Baxter Algorithms software package, enabling users to leverage the powerful cell tracking and analysis capabilities of Baxter Algorithms in conjunction with the segmentation provided by the BRONK Segmentation Pipeline.
- Open-Source and Customizable: The BRONK Segmentation Pipeline is developed with a strong focus on openness and adaptability, enabling users to not only utilize the software but also actively contribute to its improvement. The code is extensively documented, ensuring that researchers can easily understand its structure, functionality, and underlying algorithms.
Intracellular drug delivery remains a significant challenge due to the difficulty of overcoming endolysosome entrapment, which hinders the therapeutic use of biologic drugs that act intracellularly. Current methods for screening prospective nanoscale endosome-disrupting delivery technologies are indirect and cumbersome, which has led to a demand for more direct, quantitative, and predictive approaches. Galectin-8 (Gal8) intracellular tracking has emerged as a promising solution that addresses these limitations and enables high-throughput screening and in vivo validation. Our work at the Duvall Lab builds off the “Galectin 8 Recruitment Analysis MATLAB Software” software and prospectives of Kameron Kilchrist [1].
The motivations behind this project, which focuses on the development and optimization of a Gal8-based cell segmentation and tracking platform, are threefold:
- Enhancing endosomal disruption measurement: The direct measurement of endosomal disruption is challenging due to its rarity and difficulty in high-throughput capacities. Traditional approaches rely on tracking pH-sensitive fluorophores or imaging colocalization, which can be limited in sensitivity and may require supra-therapeutic dosing. By developing a live-cell, high-throughput-amenable endosomal escape screening assay based on Gal8 recruitment, we aim to overcome these limitations and provide a more direct, sensitive, and accurate method for assessing endosomal disruption.
- Improving intracellular bioavailability prediction: Establishing quantitative correlations between carrier-mediated Gal8 recruitment levels and intracellular biologic cargo bioavailability and activity is crucial for validating the use of Gal8 recruitment imaging as a predictive tool. Our project aims to rigorously investigate these correlations, paving the way for the adoption of Gal8-based assays in the high-throughput screening pipeline for evaluating the endosome disruptive potency of candidate formulations.
- Facilitating large-scale studies and drug delivery optimization: The ability to efficiently process and analyze large datasets of microscopy images is essential for advancing our understanding of complex cellular processes and optimizing drug delivery systems. Our project aims to develop a user-friendly software solution that enables researchers to effectively handle large-scale studies and leverage the power of Gal8-based assays for the rapid optimization of endosome-disrupting intracellular delivery technologies.
By addressing these challenges and developing an optimized Gal8-based image analysis suite, we hope to significantly enhance the utility of microscopy images in biological research and drug delivery optimization. The efficacy and user-friendliness of the software will be validated through the analysis of both in vitro and in vivo nanoparticle drug delivery experiments, ultimately providing researchers with a powerful and accessible tool for unlocking the full potential of Gal8-based assays in understanding endosomal disruption and advancing scientific discovery.
BRONK is a cell segmentation pipeline written in MATLAB for analysis of large multi-channel experiments. Our pipeline is developed to handle different imaging and microscopy techniques.
- Multi Page tiff
- Different types of imaging
- Reasons for starting it
- Who we are, vanderbilt etc. → building off Kameron’s code
- best experiments
Many users are familiar with MATLAB and want to dive directly into the codebase. In this case, we have prepared an abridged tutorial outlining only the basics. Please refer to the Video Tutorial and the extensive information in the manual if additional help is needed along the way.
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Clone the repository from
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Navigate to BRONK.m
- Insert link here
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Clone the repository from https://github.com/VascoSingh/BRONK
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Open the folder containing you respository in the MATLAB app
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In MATLAB navigate to ‘Apps’ → ‘Get More Apps’
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Install the following packages:
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ND2 Reader 0.1.1
https://www.mathworks.com/matlabcentral/fileexchange/73271-nd2-reader
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writeFCS(fname, DATA, TEXT, OTHER)
https://www.mathworks.com/matlabcentral/fileexchange/42603-writefcs-fname-data-text-other
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Microscopy Image Browser 2 (MIB2)
https://www.mathworks.com/matlabcentral/fileexchange/63402-microscopy-image-browser-2-mib2
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Install and add the Bio-Formats library to path
- https://www.openmicroscopy.org/bio-formats/
- Download the MATLAB toolbox from the Bio-Formats downloads page
. Unzip
bfmatlab.zipand add the unzippedbfmatlabfolder to your MATLAB path. - Please see Using Bio-Formats in MATLAB for usage instructions. If you intend to extend the existing .m files, please also see the developer page for more information on how to use Bio-Formats in general.
Directly imports ‘.nd2’ files using nd2 reader
Before running the BRONK Segmentation Pipeline, users need to configure specific variables in the code to ensure proper analysis of their microscopy images. This section outlines the essential variables that must be set by the user, providing a brief description and an example for each variable. By carefully setting these variables, users can tailor the analysis to their specific experimental setup and requirements.
Basic Aspects: The user will need to define the location of the image file to be analyzed and the directory to which the segmented images should be exported. The following variables are used to set these locations:
ImgFile - This variable should be set to the location of the image file to be analyzed. ImgFile should be a string representing the file path to the given nd2/tiff file. Do not add trailing slashes.
ImgFile=char('C:\Users\John\Downloads\ND2File.nd2');exportdir - This variable should be set to the directory where segmented images will be exported. A new folder containing the data of your experiment will be created here.
exportdir=char('C:\Users\John\Experiments\ND2Data');Basic Information: The following variables will provide the basic information needed to analyze the image:
numPlanes - This variable should be set to the number of image planes to analyze. For example, if the image has three channels (Blue, Green, and Red), then numPlanes should be set to 3.
BaxExport - This variable should be set to 'true' if the user wishes to export images to a folder for analysis with BaxterAlgorithms. This option is recommended, but will take up more storage space.
MakeCompImage - This variable should be set to 'true' if the user wants to make a composite image of the various channels and segmentation. This option is useful for storing segments for quick checks, but will take up more storage space. It is recommended to leave this option on while checking parameters, but then turn it off for large files. The images can always be checked in BaxterAlgorithms.
Parallelize - This variable should be set to 'true' if the user wants to take advantage of multiple processing cores. This option is recommended, but may cause some computers to overheat. If troubleshooting is required, change the 'parfor' loop to a regular 'for' loop to stop parallel processing.
MiPerPix - This variable should be set to the resolution of the microscopy in microns per pixel. This is a very important parameter.
CellSize - This variable is used to scale as needed for different cells. It is not currently used, but may be incorporated later.
BitDepth - This variable should be set to the bit depth of the microscopy images. This is generally 8, 12, or 16 bits. For example, if the image is 12 bits, then BitDepth should be set to 12.
customrun - This variable should be set to ‘true’ if the user wants to run a custom set of user-defined wells and/or timepoints
wells - This variable should be set to a list of integer value corresponding to the wells the user wants to analyze. Only these wells will be analyzed if customrun is set to ‘true’.
To analyze only wells 2-5 → wells = [2 3 4 5];
timepoints - This variable should be set to a list of integer value corresponding to the time points the user wants to analyze. Only these time points will be analyzed if customrun is set to ‘true’.
To analyze only time points 2 and 4 → timepoints = [2 4];
OutputFCS - This variable should be set to ‘true’ if the user wants FCS files to be output on a per image level at the end of the program.
OutputPerWell - This variable should be set to ‘true’ if the user wants Well level data as an .xlsx file.
The ImageAnalyses variable is a cell array that contains the instructions for the different analysis passes that the program will perform. Each row in the ImageAnalyses array represents a pass of the computer analyzing the images. Each pass has seven cells, which contain different parameters and settings for the analysis. The following instructions will guide you through the process of creating and modifying the ImageAnalyses variable for your specific analysis needs.
- Determine the number of analysis passes you want to perform and create a row for each pass in the
ImageAnalysescell array. The array should have the following structure:
matlabCopy code
ImageAnalyses = {
{Pass 1},
{Pass 2},
{Pass 3},
...
};
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For each pass, fill in the seven cells with the appropriate parameters and settings, as described below:
a. Cell 1: Analysis Program - Enter the name of the analysis program you want to run for this pass, e.g., 'Cyt', 'NucPlus', etc.
b. Cell 2: Image Plane Number - Enter the channel number for the image you want to analyze during this pass.
c. Cell 3: Analysis Parameters - Provide the parameters required by the analysis program you selected in Cell 1. The required parameters vary depending on the program; consult the documentation for each program to determine the correct parameters.
d. Cell 4: Output Segmentation Image Color - Specify the color (1 = red, 2 = green, 3 = blue) for the segmentation output image generated by the analysis pass.
e. Cell 5: Color code for the overlay of the analysis mask perimeter on the output image - Specify the color code (3-digit) for the perimeter of the mask generated by the analysis pass.
f. Cell 6: Export a segmented image - Set this to true if you want to export the segmented image, or false if you do not.
g. Cell 7: Image brightness [min max] - Specify the minimum and maximum brightness values for the output image.
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Set the
CytosolicPassvariable to the pass number (not the channel number) that best represents the actual cell area. This is typically the pass that runs the 'CytWS' program after 'Cyt' and 'NucPlus' have been executed.
After you have created and configured the ImageAnalyses variable, the program will use the specified analysis passes to process your images. You can further customize the pipeline by modifying the main function and individual processing functions, as necessary, to accommodate your specific experimental requirements.
The software has been designed to work on Windows, Mac and Linux. Most of the testing has however been done on Windows. The software is written in MATLAB, but it also contains some algorithms written in C++, which are compiled into mex-files. The software has been tested with MATLAB 2019b. Later versions should work too, but it cannot be guaranteed. To run the software in MATLAB, you need the toolboxes for Image Processing, Optimization, Parallel Computing, and Statistics and Machine Learning.
The software is started by running the file BaxterAlgorithms.m in MATLAB. Further instructions on how to use the software can be found in UserGuide.pdf, which is located in the folder UserGuide. There are also video tutorials in the YouTube playlist https://tinyurl.com/ba-tutorials.
FCS stands for “Flow Cytometry Standard.” These files provide a standard for a uniform file format that allows files created by one type of acquisition hardware and software to be read by any other. If
To report any issues, suggest new features, or request enhancements for the BaxterAlgorithms software, please visit the following link: https://github.com/VascoSingh/BRONK/issues. When reporting a bug, please provide details such as the version of MATLAB, the operating system being used, the error message received, and a clear set of instructions on how to replicate the issue. Additionally, please specify if you are using a deployed version of the software. If you have any questions that are not addressed in the user guide, feel free to email them to brock.fletcher@vanderbilt.edu.
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Copyright (c) [2019] [Jacob Zuo]
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[1] Kilchrist, Kameron V et al. “Gal8 Visualization of Endosome Disruption Predicts Carrier
Mediated Biologic Drug Intracellular Bioavailability.” ACS nano vol. 13,2 (2019): 1136-1152. doi:10.1021/acsnano.8b05482