Here, a method is presented for the analysis of protein aggregation kinetics in the nematode Caenorhabditis elegans. Animals from an age-synchronized population are imaged at different time points, followed by semiautomated inclusion counting in CellProfiler and fitting to a mathematical model in AmyloFit.
The mechanisms of protein aggregation have so far been investigated mostly in vitro. With our protocol we can do similar quantitative studies in the model organism C.elegans. The main advantage of our method is the unbiased quantification of inclusion numbers.
And by fitting these high quality data, to mathematical models, we can get insights into the aggregation mechanism. Protein aggregation occurs in many diseases, including Alzheimer's and Huntington's. Understanding the molecular mechanism of protein aggregation in vivo is crucial to develop new therapies.
Begin by placing 10 adult nematodes onto a 6 cm seeded NGM plate with a platinum worm pick to perform a synchronized egg-lay. Leave the adults to lay eggs for approximately two hours at 20 C before removing them from the plate. Place the plates with eggs in the incubator at 20 C.Monitor the development of the animals until they reach adulthood on approximately the third day after the egg-lay.
Prepare the 384-well plate by filling the required number of wells with 100 L of M9 buffer supplemented with 25 mM sodium azide as an anesthetic. Fill one well per strain to be imaged. For each strain, transfer 20 animals into one well using a platinum worm pick.
Cover the plate with the lid to preventive evaporation, and image the plate within one hour after preparation. Switch on the instrument, and open the software. Click on the Play button next to Unload Well Plates, and place the 384-well plate in the microscope.
Under Action List and 3D Fluorescence Acquisition, click Test, and select the well containing worms. Set Image Processing to None. And click the Play button to determine the optimal shifting distance at which the worms are centered correctly.
Set Ascending Distance to 50 m, Descending Distance to 50 m, and Slicing Interval to 2 m to capture the entire thickness of the animals in the z-stack. Set Image Processing back to Maximum. Select the wells to be imaged under Well Plate Scan Setting.
And then select Tile and Acquire Whole Well. Save the measurement setting. And click Start Measurement to start the experiment.
Open CellProfiler and drag the pipeline into the Drop a pipeline file here window. Click Yes to load the project. Next, drag the stitched images into the Drop files and folder here window.
Click on the Metadata input module. Adjust Regular expression to extract from file name according to the names of the stitched images. Click on the NamesAndTypes input module, and adjust Select the rule criteria to match the channels in the file names.
Then, click on Output Settings to select a folder to save the output from CellProfiler. Click on Start Test Mode to check the settings of the pipeline using the first imaging data set. Click on Step to run through the pipeline, one module at a time.
To adjust the worm outlines, click on Show Help in the EditObjectsManually module to see the instructions. Correct the outlines. And then click on Done to continue running the pipeline.
Click on Exit Test Mode and Analyze Images. Open the output folder to view the output files. And open the images with the original file name followed by outlines to check whether the worms and inclusions were correctly overlaid.
To start using AmyloFit, name the project and click on Create project. Click on Open to open it. And create a session by giving it a name and clicking on Create and load session.
Click on Add data, and upload the file containing the average numbers of inclusions per animal, following the data format requirements shown in the left panel. Then, click on Load new data. Set Number of points to average over for zero-point offset to 0, and set Number of points to average over for plateau to 0 to skip the pre-processing steps, which are not required for inclusion count data.
Then, click on Submit. Repeat this step for each protein concentration. Select Custom in the Model panel.
Enter the equation for independent nucleation in the Equation box. And click Load model. For Ncells, set the parameter type to Global Constant, and set Value to 95, corresponding to the number of body wall muscle cells.
Set the parameter types to Global fit for Kcell and n. And to Constant for m. Enter the values of m for the different strains in the left panel.
Leave the number of basin hops unchanged, and click Fit in the Fitting panel. Finally, extract the fit by clicking Download Data and Fit. The inclusion numbers were monitored in four C.elegans strains expressing different Q40 concentrations.
The independent nucleation model describes the aggregation kinetics well. The fit yielded a reaction order of 2.1, and a nucleation rate of 0.38 molecules per day per cell at an intracellular protein concentration of 1 mM. Two independent biological replicates in a previous study showed closely corresponding values for the reaction order and nucleation rate.
One thing that should be kept in mind is that you need high quality data sets for multiple protein concentrations to obtain reliable fits. This method can be used to find ways to interfere with protein aggregation in a living organism, such as genetic knockdowns or small molecule treatments.
