Automating Aggregate Quantification in Caenorhabditis elegans
October 14th, 2021
The following protocol describes the development and optimization of a high-throughput workflow for worm culturing, fluorescence imaging, and automated image processing to quantify polyglutamine aggregates as an assessment of changes in proteostasis.
A simple approach was taken to monitor changes in proteostasis by assessing polyglutamine aggregation and providing a framework that can be used for high throughput applications. Scoring a phenotype such as the number of aggregates by I can become subjective. Automating aggregate counting allows us to eliminate such bias, increase throughput and reproducibility.
This method helps to identify bacterial genes contributing to host proteostasis disruption. Understanding individual bacterial genes contribution will help us understand its implication in the pathogenesis of diseases like Alzheimer's, Parkinson's, and Huntington's. Begin by removing the plates containing worms from the freezer and letting them thaw at room temperature.
Then wipe away excess condensation and remove the lid prior to imaging. Use the microscope settings with exposure time 500 milliseconds, magnification of 40 X with 0.63 X camera adapter, and GFP intensity set to 100%during image capture. Now adjust the transmitted light controls until the worms appear brightly illuminated in comparison to the dark background, avoiding overexposure.
Alter the positions of worms within the well to prevent excessive clumping by using a 10 microliter pipette tip to gently push worms into the desired position. Set the channel to GFP filter for establishing a focal plane for both images. Capture a bright field image.
Take a corresponding fluorescence image without disturbing the plate, or changing the focus of the microscope. Now to assess the images, first download the CellProfiler. Open the software, and drag and drop the images of interest into the images box.
Click on the images in the file list to open them. Select the magnifier glass to highlight a region of interest and the arrow icon to measure the length of both worms and aggregates. Hover over the desired object to determine its intensity value, which can be seen on the bottom portion of the screen.
To utilize the CellProfiler image analysis pipeline, name the image pairs properly as described in the text manuscript after downloading the CellProfiler from the official website. Download the pipeline and upload it into CellProfiler by selecting file, import, and pipeline from file. Select the untangled worms module to open its settings to load a training set used to identify worms in the untangled worms module.
Then identify training set file name. Select the upload file icon and upload the supplemental file too. Upload images by selecting the images module in the top left corner.
And then drag and drop properly named images. Prior to analyzing the images, select the desired output folder that will be used to store the results by clicking on the output settings button located near the bottom left hand corner of the program. Then select the folder icon to the right of the default output to choose the desired output location.
Select the analyze images icon to begin image analysis. If the analysis takes too long to complete and is stuck processing a single image, then abort the run and identify the unprocessed image by sorting through the output folder, and noting which image name is not found. Following complete analysis, the software will organize the results into an Excel spreadsheet containing individual worms in column N, and the respective number of aggregates in column K.Download the metadata organizer to conveniently organize data from the output CSV file from CellProfiler.
For Windows OS, locate the downloaded file and extract it to the desired location. Locate and open the extracted folder named gui_Windowsos_64x. And launch the application by clicking on the GUI application icon.
A prompt may open requesting permission to run. Select more info and then click on run anyway. The metadata organizer is now ready to drag and drop CellProfiler output CSV files.
For Mac OS, locate the downloaded file and extract the file metadata organizer application file. Open the extracted folder found in downloads. Right click on the GUI application and select open.
A prompt will appear asking for permission to open due to the lack of an official license. Select open. Once your metadata organizer application is open in the respective OS, click on upload your files here, or drag and drop the desired CellProfiler CSV files.
Click on the organize button, which will bring the user to a new screen with a download files button. Click on the button and select the desired location to save the output file. The output file will appear as the original file name with _organized extension added to the file name.
In the presence of FUDR, worms that were fed various bacteria had a more consistent body size which allowed more uniform and accurate worm detection, thus solving the problem of overcrowding. Freezing the worms improved polyQ aggregate detection by eliminating the background fluorescence, as well as the accuracy of automated counting comparable to manual counting. Inverted brightfield illumination was used to detect the whole C.elegans and GFP channel to image polyQ YFP aggregates.
Worm detection, untangling, and aggregate quantification for each worm were done by applying an optimized CellProfiler image processing pipeline, which allows obtaining the number of aggregates per individual worm. The difference between the efficacy of automated aggregate quantification and by using the CellProfiler pipeline was minimal, indicating that the automated method can be applied to large scale screens. Out of 90 bacterial strains tested, colonization of C.elegans intestine with one candidate showed a significant decrease in the number of aggregates.
The confirmation experiments by manual counting revealed that none of the mutants, including the one that significantly decreased the number of aggregates affected the polyQ aggregation. Researchers who decide to use this framework should understand that the steps outlined are not absolute. Modification and a fundamental understanding of how to manipulate CellProfiler are essential for success.
Additional biochemical approaches such as western blotting can be used to confirm polyQ aggregation.
Overview
This study develops a high-throughput workflow for culturing worms, fluorescence imaging, and automated image processing to quantify polyglutamine aggregates as a measure of changes in proteostasis. The method enhances the assessment of these aggregates, particularly in exploring their role in neurodegenerative diseases.
Key Study Components
Research Area
- Proteostasis
- Neurodegenerative diseases
- High-throughput screening
Background
- Polyglutamine aggregation is linked to diseases such as Alzheimer's, Parkinson's, and Huntington's.
- Understanding bacterial gene contributions to proteostasis disruption is essential.
- Traditional scoring methods for aggregates can be subjective.
Methods Used
- High-throughput imaging and analysis using CellProfiler
- C. elegans as the biological model
- Automated quantification of polyglutamine aggregates
Main Results
- Automated image processing allows for bias elimination and increased reproducibility in aggregate counting.
- Identified bacterial genes affecting host proteostasis with significant findings in worm aggregate numbers.
- Validated results indicate automation is comparable to manual counting, supporting large-scale screens.
Conclusions
- This study provides a reliable framework for studying proteostasis through polyglutamine aggregation.
- Insights gained from this method are relevant for understanding mechanisms in neurodegenerative diseases.
