We thank the Morimoto lab for C. elegans strains and Esmeralda Bosman for assistance on the high-throughput confocal microscope. This work was funded by a start-up grant from Utrecht University to T.S.

1. Growth of an age-synchronized population of C. elegans
<ol>
	<li>Maintain the C. elegans strains on nematode growth medium (NGM) plates seeded with Escherichia coli OP50 at 20 &#176;C according to standard procedures18.</li>
	<li>Perform a synchronized egg-lay by placing 10 adult nematodes onto a 6 cm seeded NGM plate with a platinum worm pick. Leave the adults to lay eggs for ~2 h at 20 &#176;C before removing them. Prepare 1-4 plates per strain, depending on the fertility of the strain and the number of time points to be taken.</li>
	<li>Place the plates with eggs in the incubator at 20 &#176;C. Monitor the development of the animals until they reach adulthood. 
	NOTE: The day on which the animals have reached adulthood is defined here as day 1. Typically, this is three days after the egg-lay.</li>
	<li>Starting from day 1, transfer the animals to new seeded NGM plates daily to separate them from their offspring. To compensate for animals that die or are lost during transfer, transfer ~40 animals per strain times the number of points to be imaged (see step 2). Proceed until the animals have ceased to lay fertilized eggs (~day 6 of adulthood). 
	&#8203;NOTE: Exclude animals with bagging or other developmental phenotypes. Bagging is commonly observed in strains expressing aggregation-prone proteins.</li>
</ol>
2. Sample preparation of C. elegans in a multiwell plate
NOTE: As the imaging procedure requires anesthetics that will eventually kill the animals, the same animals cannot be reused for subsequent time points. Instead, different animals from the same age-synchronized batch are imaged on different days. Even though most strains will have few inclusions at day 1, it is recommended to include this time point as a baseline.
<ol>
	<li>Prepare the 384-well plate by filling the required number of wells with 100 &#181;L of M9 buffer supplemented with 25 mM NaN3 as an anesthetic. Fill one well per strain to be imaged. 
	NOTE: Sodium azide (NaN3) is toxic and should be handled with care.</li>
	<li>For each strain, transfer 20 animals into one well using a platinum worm pick. 
	&#8203;NOTE: The worms must be placed outside the bacterial lawn before placing them in the well. Bacteria make the animals adhere to the worm pick, which can prevent their release and will cloud the well contents. Generally, 20 is the optimal number of animals per well to prevent overlap between the worms while limiting unnecessary imaging of empty well space.</li>
	<li>Cover the plate with the lid to prevent evaporation, and image the plate within 1 h after preparation.</li>
	<li>Repeat steps 2.1-2.3 daily until a steady plateau in the inclusion numbers is reached or until most animals have died. Perform the sample preparation and imaging at the same time every day to ensure intervals of 24 h.</li>
</ol>
3. Image acquisition on the high-throughput confocal microscope
NOTE: This experiment can also be set up on a regular spinning disk confocal microscope with a multiwell plate holder. A camera with a large field-of-view is beneficial to limit the number of tiles needed to be imaged to span the entire well. See the Table of Materials for details about the microscope and software used in this protocol.
<ol>
	<li>Switch on the instrument and open the software.</li>
	<li>Start a new protocol by going to Measurement Settings | New. Select the correct multiwell plate type and click Create a New Measurement Setting.</li>
	<li>Set up the channel for fluorescence by going to Ch 1. Set the objective to 10x. Select 488 nm as the light source and BP525/25 as the emission filter to image YFP. Set binning to 2x2 to reduce file size.</li>
	<li>Click Add Channel and select Brightfield as the method.</li>
	<li>To add a z-stack confocal fluorescence image to the measurement, choose 3D Fluorescence acquisition under Action List. Go to Select and choose Ch 1. To minimize file sizes, set Image Processing to Maximum so that the maximum projection image is saved rather than the full z-stack.</li>
	<li>Click BF/Ph Acquisition | Select | Ch 2 for the brightfield channel.</li>
	<li>Click on the play button (look for the right-pointing triangle symbol) next to Unload Well Plates and place the 384-well plate in the microscope.</li>
	<li>Under 3D Fluorescence Acquisition, click Test and select a well containing worms to determine the optimal shifting distance at which the worms are centered correctly. Set Ascending Distance to 50 &#181;m, Descending Distance to -50 &#181;m, and Slicing Interval to 2 &#181;m to capture the entire thickness of the animals in the z-stack.</li>
	<li>Optimize the exposure time to get a good signal intensity for all four strains while avoiding saturation. Use the same exposure time for all strains and time points.</li>
	<li>Select the wells to be imaged under Well Plate Scan Setting. Select Tile and Acquire Whole Well.</li>
	<li>Save the Measurement Setting and start the experiment by clicking Start Measurement. For subsequent time points, open the same Measurement Setting and adjust the shifting distance and the wells to be imaged.</li>
</ol>
4. Stitching tiled images in ImageJ
NOTE: This step is only required when using an objective larger than 4x, for which the image of each well is acquired as multiple tiles. In this analysis workflow, stitching of the tiles is performed using the free software FIJI/ImageJ19 (Figure 2). Depending on the instrument used in step 3, it may also be possible to perform stitching directly in the accompanying software.
<ol>
	<li>Download FIJI20 and open it.</li>
	<li>Go to Plugins | Stitching | Grid/Collection Stitching21.</li>
	<li>In the popup window, Grid/Collection Stitching, select the type and order by which the tiles were collected. Choose Grid: row-by-row and Right &#38; Down.</li>
	<li>In the next window, Grid stitching: Grid: row-by-row, Right &#38; Down, insert the number of tiles in x and y directions. For the 10x objective used here, choose 2 as the Grid size x, 3 as the Grid size y, and 0 as the Tile overlap.</li>
	<li>Click Browse and select the folder containing the TIFF images to be stitched.</li>
	<li>Insert the common file name under File names for tiles, using {i} at the position of the tile number in each file name.</li>
	<li>Untick all boxes below.</li>
	<li>Run the plugin.</li>
	<li>Save the resulting images as TIFF files for analysis in the next step.</li>
</ol>
5. Automated inclusion counting using CellProfiler22
<ol>
	<li>Download and install the open-source image analysis software, CellProfiler23. Download the InclusionCounting.cpproj pipeline from github.com/sinnigelab/aggregate-quantification.</li>
	<li>Open CellProfiler and drag the pipeline into the Drop a pipeline file here window. Click Yes to load the project.</li>
	<li>Click on the Images input module and drag the stitched images into the window Drop files and folder here.</li>
	<li>Click on the Metadata input module. Adjust Regular expression to extract from file name according to the names of the stitched images.</li>
	<li>Click on the NamesandTypes input module and adjust Select the rule criteria to match the channels in the file names. 
	NOTE: In the default settings of the pipeline, file names containing BF are recognized as brightfield images and are named Worms. File names containing YFP are recognized as fluorescence images and are named Fluorescence.</li>
	<li>Click on View output settings to select a default folder to save the output from CellProfiler.</li>
	<li>Click on Start Test Mode to check the settings of the pipeline using the first imaging dataset. Click on Run to run through all modules in the pipeline or Step to run through the pipeline one module at a time. To adjust the worm outlines in the EditObjectsManually module, click on Help to see the instructions and click on Done to continue running the pipeline. 
	NOTE: The extracted measurements will not be exported while in&#160;test mode. The thresholding parameters to detect worms and inclusions may need to be adjusted based on the strains and magnification used.</li>
	<li>Click on Exit Test Mode and Analyze Images.</li>
	<li>Open the output folder to view the output files. Open the images with the original file name followed by outlines to check whether the worms and inclusions were correctly overlaid. 
	NOTE: The number of inclusions per worm can be found in the file named ExpandedWormObjects. More information about the input images can be found in the file named Image. Additional output can be selected in the ExportToSpreadsheet module in the pipeline.</li>
</ol>
6. Global fitting of inclusion count data using AmyloFit5
NOTE: This step can only be performed when data for multiple protein concentrations are available. For Q40-YFP, a set of four strains with different levels of overexpression in the body wall muscle cells has been created previously17. In other cases, novel strains should be generated using plasmid microinjection and genomic integration24.
<ol>
	<li>Go to the free online fitting platform for aggregation kinetics AmyloFit25. Either register or log in with an existing account. 
	NOTE: An extensive manual on how to use AmyloFit can be accessed for additional help. See the link on the top-left of the webpage (after login) for more information.</li>
	<li>To start using AmyloFit, name the project and click on Create project. Open the project by clicking on Open and create a session by giving it a name and clicking on Create &#38; load session.</li>
	<li>Click Add Data and upload the file containing the average numbers of inclusions per animal, following the data format requirements shown in the left panel. Click Load New Data.</li>
	<li>Skip the preprocessing steps, which are not required for inclusion count data, by setting number of points to average over for zero-point offset and number of points to average over for plateau to 0. Click Submit. Repeat this step for each protein concentration (i.e., each column in the uploaded file).</li>
	<li>Select Custom in the model panel, enter Ncells*(1-exp(-Kcell*m**(n)*(t-1))) in the equation box and click Load model. 
	NOTE: As AmyloFit was originally designed for the analysis of kinetic data from in vitro assays, a custom-made model must be loaded to analyze the inclusion count data of C. elegans. In the equation used here, Ncells is the number of cells in which inclusion formation takes place, Kcell is the nucleation rate constant, m the intracellular protein concentration, and n the reaction order of nucleation.</li>
	<li>Set the parameter types to Global Const for Ncells, Global fit for Kcell and n, and Const for m. Set Value of Ncells to 95 for body wall muscle cells and Initial guess for Kcell&#160;and&#160;n to 1. Enter the values of m for the different strains in the left panel. 
	NOTE: Initial guesses are not relevant for the relatively simple model used here. For more complex models, it is beneficial to enter an estimate of the expected values to shorten the calculation time.</li>
	<li>Leave the number of basin hops unchanged and click Fit in the fitting panel.</li>
	<li>Extract the fit by clicking Download Data and Fit. 
	NOTE: The parameters extracted by the global fit of the model will be listed in the bottom-right corner. A plot of the data and fit will be automatically generated in the top-right panel. This plot can be extracted by clicking Download pdf and customized by going to Display&#160;Plot&#160;Options. Kcell has units of molecules&#160;concentration-n&#160;time-1&#160;cell-1. To compare values with different n, Kcell can be converted to the nucleation rate at a given protein concentration by multiplying it with mn.</li>
</ol>

<ol><li>Knowles, T. P. J., Vendruscolo, M., Dobson, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+amyloid+state+and+its+association+with+protein+misfolding+diseases%5BTitle%5D)+AND+%22Nature+Reviews.+Molecular+Cell+Biology%22%5BJournal%5D)">The amyloid state and its association with protein misfolding diseases.</a> Nature Reviews. Molecular Cell Biology. 15 (6), 384-396 (2014).</li>
<li>Chiti, F., Dobson, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+misfolding%2C+amyloid+formation%2C+and+human+disease%3A+a+summary+of+progress+over+the+last+decade%5BTitle%5D)+AND+%22Annual+Review+of+Biochemistry%22%5BJournal%5D)">Protein misfolding, amyloid formation, and human disease: a summary of progress over the last decade.</a> Annual Review of Biochemistry. 86 (1), 27-68 (2017).</li>
<li>Knowles, T. P. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+analytical+solution+to+the+kinetics+of+breakable+filament+assembly%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">An analytical solution to the kinetics of breakable filament assembly.</a> Science. 326 (5959), 1533-1537 (2009).</li>
<li>Cohen, S. I. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Proliferation+of+amyloid-%CE%B242+aggregates+occurs+through+a+secondary+nucleation+mechanism%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Proliferation of amyloid-β42 aggregates occurs through a secondary nucleation mechanism.</a> Proceedings of the National Academy of Sciences of the United States of America. 110 (24), 9758-9763 (2013).</li>
<li>Meisl, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+mechanisms+of+protein+aggregation+from+global+fitting+of+kinetic+models%5BTitle%5D)+AND+%22Nature+Protocols%22%5BJournal%5D)">Molecular mechanisms of protein aggregation from global fitting of kinetic models.</a> Nature Protocols. 11 (2), 252-272 (2016).</li>
<li>Satyal, S. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Polyglutamine+aggregates+alter+protein+folding+homeostasis+in+Caenorhabditis+elegans%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Polyglutamine aggregates alter protein folding homeostasis in Caenorhabditis elegans.</a> Proceedings of the National Academy of Sciences of the United States of America. 97 (11), 5750-5755 (2000).</li>
<li>Morley, J. F., Brignull, H. R., Weyers, J. J., Morimoto, R. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+threshold+for+polyglutamine-expansion+protein+aggregation+and+cellular+toxicity+is+dynamic+and+influenced+by+aging+in+Caenorhabditis+elegans%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">The threshold for polyglutamine-expansion protein aggregation and cellular toxicity is dynamic and influenced by aging in Caenorhabditis elegans.</a> Proceedings of the National Academy of Sciences of the United States of America. 99 (16), 10417-10422 (2002).</li>
<li>Brignull, H. R., Moore, F. E., Tang, S. J., Morimoto, R. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Polyglutamine+proteins+at+the+pathogenic+threshold+display+neuron-specific+aggregation+in+a+pan-neuronal+Caenorhabditis+elegans+model%5BTitle%5D)+AND+%22Journal+of+Neuroscience%22%5BJournal%5D)">Polyglutamine proteins at the pathogenic threshold display neuron-specific aggregation in a pan-neuronal Caenorhabditis elegans model.</a> Journal of Neuroscience. 26 (29), 7597-7606 (2006).</li>
<li>Moronetti Mazzeo, L. E., Dersh, D., Boccitto, M., Kalb, R. G., Lamitina, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stress+and+aging+induce+distinct+polyQ+protein+aggregation+states%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Stress and aging induce distinct polyQ protein aggregation states.</a> Proceedings of the National Academy of Sciences of the United States of America. 109 (26), 10587-10592 (2012).</li>
<li>Prahlad, V., Morimoto, R. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neuronal+circuitry+regulates+the+response+of+Caenorhabditis+elegans+to+misfolded+proteins%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Neuronal circuitry regulates the response of Caenorhabditis elegans to misfolded proteins.</a> Proceedings of the National Academy of Sciences of the United States of America. 108 (34), 14204-14209 (2011).</li>
<li>Nollen, E. A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genome-wide+RNA+interference+screen+identifies+previously+undescribed+regulators+of+polyglutamine+aggregation%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Genome-wide RNA interference screen identifies previously undescribed regulators of polyglutamine aggregation.</a> Proceedings of the National Academy of Sciences of the United States of America. 101 (17), 6403-6408 (2004).</li>
<li>Silva, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+genetic+screening+strategy+identifies+novel+regulators+of+the+proteostasis+network%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">A genetic screening strategy identifies novel regulators of the proteostasis network.</a> PLoS Genetics. 7 (12), 1002438(2011).</li>
<li>Brehme, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+chaperome+subnetwork+safeguards+proteostasis+in+aging+and+neurodegenerative+disease%5BTitle%5D)+AND+%22Cell+Reports%22%5BJournal%5D)">A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease.</a> Cell Reports. 9, 1-16 (2014).</li>
<li>Calamini, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Small-molecule+proteostasis+regulators+for+protein+conformational+diseases%5BTitle%5D)+AND+%22Nature+Chemical+Biology%22%5BJournal%5D)">Small-molecule proteostasis regulators for protein conformational diseases.</a> Nature Chemical Biology. 8 (2), 185-196 (2012).</li>
<li>Sinnige, T., Stroobants, K., Dobson, C. M., Vendruscolo, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biophysical+studies+of+protein+misfolding+and+aggregation+in+in+vivo+models+of+Alzheimer%27s+and+Parkinson%27s+diseases%5BTitle%5D)+AND+%22Quarterly+Reviews+of+Biophysics%22%5BJournal%5D)">Biophysical studies of protein misfolding and aggregation in in vivo models of Alzheimer's and Parkinson's diseases.</a> Quarterly Reviews of Biophysics. 49, 22(2020).</li>
<li>Mondal, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Large-scale+microfluidics+providing+high-resolution+and+high-throughput+screening+of+Caenorhabditis+elegans+poly-glutamine+aggregation+model%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Large-scale microfluidics providing high-resolution and high-throughput screening of Caenorhabditis elegans poly-glutamine aggregation model.</a> Nature Communications. 7, 13023(2016).</li>
<li>Sinnige, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Kinetic+analysis+reveals+that+independent+nucleation+events+determine+the+progression+of+polyglutamine+aggregation+in+C.+elegans%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Kinetic analysis reveals that independent nucleation events determine the progression of polyglutamine aggregation in C. elegans.</a> Proceedings of the National Academy of Sciences of the United States of America. 118 (11), 202188118(2021).</li>
<li>Brenner, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Caenorhabditis+elegans%5BTitle%5D)+AND+%22Methods%22%5BJournal%5D)">Caenorhabditis elegans.</a> Methods. 77 (1), 71-94 (1974).</li>
<li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fiji%3A+an+open-source+platform+for+biological-image+analysis%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li>
<li>FIJI/ImageJ. , Available from: 
 <a target="_blank" href="https://imagej.net/downloads">https://imagej.net/downloads</a> (2012).</li>
<li>Preibisch, S., Saalfeld, S., Tomancak, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Globally+optimal+stitching+of+tiled+3D+microscopic+image+acquisitions%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Globally optimal stitching of tiled 3D microscopic image acquisitions.</a> Bioinformatics. 25 (11), 1463-1465 (2009).</li>
<li>Lamprecht, M. R., Sabatini, D. M., Carpenter, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CellProfiler%3A+free%2C+versatile+software+for+automated+biological+image+analysis%5BTitle%5D)+AND+%22BioTechniques%22%5BJournal%5D)">CellProfiler: free, versatile software for automated biological image analysis.</a> BioTechniques. 42 (1), 71-75 (2007).</li>
<li>CellProfiler. Broad Institute. , Available from: 
 <a target="_blank" href="https://cellprofiler.org/releases">https://cellprofiler.org/releases</a> (2021).</li>
<li>Mello, C. C., Kramer, J. M., Stinchcomb, D., Ambros, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Efficient+gene+transfer+in+C.+elegans%3A+extrachromosomal+maintenance+and+integration+of+transforming+sequences%5BTitle%5D)+AND+%22EMBO+Journal%22%5BJournal%5D)">Efficient gene transfer in C. elegans: extrachromosomal maintenance and integration of transforming sequences.</a> EMBO Journal. 10 (12), 3959-3970 (1991).</li>
<li>Knowles group, University of Cambridge. , Available from: 
 <a target="_blank" href="https://amylofit.com/amylofitmain/login/">https://amylofit.com/amylofitmain/login/</a> (2021).</li>
<li>Lazaro-Pena, M. I., Cornwell, A. B., Samuelson, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantifying+tissue-specific+proteostatic+decline+in+Caenorhabditis+elegans%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%3A+JoVE%22%5BJournal%5D)">Quantifying tissue-specific proteostatic decline in Caenorhabditis elegans.</a> Journal of Visualized Experiments: JoVE. (175), (2021).</li>
<li>Feldman, N., Kosolapov, L., Ben-Zvi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fluorodeoxyuridine+improves+Caenorhabditis+elegans+proteostasis+independent+of+reproduction+onset%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Fluorodeoxyuridine improves Caenorhabditis elegans proteostasis independent of reproduction onset.</a> PLoS One. 9 (1), 85964(2014).</li>
<li>Brunquell, J., Bowers, P., Westerheide, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fluorodeoxyuridine+enhances+the+heat+shock+response+and+decreases+polyglutamine+aggregation+in+an+HSF-1-dependent+manner+in+Caenorhabditis+elegans%5BTitle%5D)+AND+%22Mechanisms+of+Ageing+and+Development%22%5BJournal%5D)">Fluorodeoxyuridine enhances the heat shock response and decreases polyglutamine aggregation in an HSF-1-dependent manner in Caenorhabditis elegans.</a> Mechanisms of Ageing and Development. 141-142, 1-4 (2014).</li>
<li>David, D. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Widespread+protein+aggregation+as+an+inherent+part+of+aging+in+C.+elegans%5BTitle%5D)+AND+%22PLoS+Biology%22%5BJournal%5D)">Widespread protein aggregation as an inherent part of aging in C. elegans.</a> PLoS Biology. 8 (8), 47-48 (2010).</li>
<li>Hunter, S., Maulik, M., Scerbak, C., Vayndorf, E., Taylor, B. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Caenorhabditis+sieve%3A+A+low-tech+instrument+and+methodology+for+sorting+small+multicellular+organisms%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%3A+JoVE%22%5BJournal%5D)">Caenorhabditis sieve: A low-tech instrument and methodology for sorting small multicellular organisms.</a> Journal of Visualized Experiments: JoVE. (137), (2018).</li>
<li>Burnett, K., Edsinger, E., Albrecht, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Rapid+and+gentle+hydrogel+encapsulation+of+living+organisms+enables+long-term+microscopy+over+multiple+hours%5BTitle%5D)+AND+%22Communications+Biology%22%5BJournal%5D)">Rapid and gentle hydrogel encapsulation of living organisms enables long-term microscopy over multiple hours.</a> Communications Biology. 1, 73(2018).</li>
<li>Dong, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Reversible+and+long-term+immobilization+in+a+hydrogel-microbead+matrix+for+high-resolution+imaging+of+Caenorhabditis+elegans+and+other+small+organisms%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Reversible and long-term immobilization in a hydrogel-microbead matrix for high-resolution imaging of Caenorhabditis elegans and other small organisms.</a> PLoS One. 13 (3), 0193989(2018).</li>
</ol>

The authors have no conflicts of interest to disclose.

The method presented herein facilitates an unbiased and quantitative analysis of protein aggregation kinetics in the model organism C. elegans. It depends on four key elements (Figure 1): 1) maintaining an age-synchronized population of nematodes; 2) fluorescence microscopy in multiwell plates; 3) automated inclusion counting in CellProfiler; 4) data fitting in AmyloFit. Compared to manual counting of inclusions in freely moving animals or from saved images26, quantification in CellProfiler is both faster and more unbiased. The other key advancement of the protocol is the acquisition of kinetic data, rather than single timepoints, which provides quantitative insights into the aggregation mechanism upon fitting the data to a mathematical model.
The four elements of the protocol can be used as independent modules that can be modified depending on the application. Age-synchronized populations can also be maintained using 5-fluoro-2&#8242;-deoxyuridine (FUDR) to sterilize the animals. This compound affects lifespan and proteostasis24,25 and is highly carcinogenic to the experimenter; however, it precludes manual transfer of the worms, which can be labor-intensive when large numbers are handled. Other alternatives are the use of sterile mutants29 or filtration devices to separate offspring30.
The fluorescence microscopy step can also be adjusted, for example, using higher magnifications to monitor protein aggregation in neurons. Widefield microscopy may be sufficient to monitor polyQ aggregation in muscle cells when the relative difference between conditions is more important than the absolute numbers of inclusions. The CellProfiler pipeline can still be used in these cases, although the settings to recognize worms and inclusions&#160;will need to be adjusted by the user. The throughput of the technique is currently limited by the need for manual picking of the animals into the 384-well plate. This can potentially be remedied by the use of microfluidic devices16. Sodium azide is a relatively harsh anesthetic, which could be replaced by physical immobilization with hydrogels or beads28,29.
The analysis in AmyloFit presented here is based on an aggregation mechanism consisting of independent nucleation events in individual cells. In cases where this model does not fit, the user should consider an alternative such as the cooperative aggregation model developed previously17. A limitation of this approach is that strains expressing the protein of interest at different concentrations need to be available, although these can be generated using routine C. elegans methods24.
Altogether, this protocol provides the means to obtain high-quality data for protein aggregation kinetics in an in vivo model system, allowing for detailed analysis of aggregation mechanisms17. Although the method was demonstrated for polyQ aggregation in the C. elegans muscle tissue, future applications of the protocol may include other proteins and tissues and the effects of proteostasis factors and small molecules.

The accumulation of misfolded proteins into insoluble deposits occurs in a wide range of diseases. Well-known examples are the aggregation of amyloid-&#946; and tau in Alzheimer&#39;s disease, &#945;-synuclein in Parkinson&#39;s disease, and huntingtin with expanded polyQ in Huntington&#39;s disease1,2. The misfolding of these polypeptides into amyloid fibrils is associated with toxicity and cell death by mechanisms that are still largely unclear. Elucidating the mechanisms of amyloid formation will be crucial to developing effective therapies, which&#160;are currently unavailable.
Detailed investigations of amyloid formation have been performed in vitro based on thioflavin T fluorescence measurements, leading to a mechanistic understanding of the aggregation process and the effect of inhibitory molecules3,4,5. However, it is not clear whether the same aggregation mechanisms hold true in the complex environment of living cells and organisms. The nematode worm Caenorhabditis elegans is a suitable model organism to study protein aggregation in vivo. It has a relatively simple anatomy but consists of multiple tissues, including muscle, intestine, and a nervous system. It is genetically well-characterized, and tools for genetic modification are readily available. Furthermore, it has a short generation time of ~3 days and a total lifespan of 2-3 weeks. As such, protein aggregation can be examined across the lifespan of the animal on an experimentally convenient timescale. Finally, the nematode is optically transparent, enabling the tracking of the aggregation of fluorescently labeled proteins in live animals.
These features of C. elegans have been previously exploited to investigate the aggregation of polyQ proteins as a model for Huntington&#39;s and other polyQ expansion diseases. Above the pathogenic threshold of 35-40 glutamine residues, the polyQ proteins labeled with yellow fluorescent protein (YFP) can be observed to form insoluble inclusions in the muscle tissue6,7, neurons8, and the intestine9,10. These features have been widely used to screen for genes11,12,13 and small-molecule modifiers14 of protein aggregation and toxicity.
C. elegans has the potential to play an important role in bridging the gap between in vitro studies of protein aggregation and more complex disease models such as mice15. C. elegans is amenable to drug screening16 but can also be exploited to obtain a fundamental understanding of the molecular mechanisms of protein aggregation in vivo, as demonstrated recently17. However, for both applications, it is of prime importance to extract a quantitative and reproducible measure of protein aggregation. Here, this is achieved with the use of a high-throughput confocal microscope combined with a dedicated image analysis pipeline (Figure 1).

<table><tbody><tr><td>384-well plate</td><td>Greiner</td><td>781091</td><td>Black with flat clear bottom</td></tr><tr><td>AmyloFit</td><td>Knowles lab</td><td>v2.0</td><td>Access at www.amylofit.ch.cam.ac.uk</td></tr><tr><td>&lt;em&gt;C. elegans&lt;/em&gt; Q40 line A</td><td>Morimoto lab</td><td>AM1228</td><td>Genotype &lt;em&gt;rmIs404[unc-54p::Q40::YFP]&lt;/em&gt;</td></tr><tr><td>&lt;em&gt;C. elegans&lt;/em&gt; Q40 line B</td><td>Morimoto lab</td><td>AM1229</td><td>Genotype &lt;em&gt;rmIs404[unc-54p::Q40::YFP]&lt;/em&gt;</td></tr><tr><td>&lt;em&gt;C. elegans&lt;/em&gt; Q40 line C</td><td>Morimoto lab</td><td>AM1230</td><td>Genotype &lt;em&gt;rmIs404[unc-54p::Q40::YFP]&lt;/em&gt;</td></tr><tr><td>&lt;em&gt;C. elegans&lt;/em&gt; Q40 line D</td><td>Morimoto lab</td><td>AM1231</td><td>Genotype &lt;em&gt;rmIs404[unc-54p::Q40::YFP]&lt;/em&gt;</td></tr><tr><td>CellProfiler</td><td>Broad Institute</td><td>4.1.3</td><td>Downloaded from https://cellprofiler.org</td></tr><tr><td>&lt;em&gt;E. coli&lt;/em&gt; OP50</td><td>Caenorhabditis Genetics Center (CGC)</td><td>OP50</td><td></td></tr><tr><td>FIJI</td><td>Open-source</td><td>(Fiji Is Just) ImageJ v2.1/1.5.3j</td><td>Downloaded from https://imagej.net/software/fiji/</td></tr><tr><td>High-throughput confocal microscope</td><td>Yokogawa</td><td>CellVoyager CV7000S</td><td></td></tr><tr><td>M9 buffer</td><td>Home-made</td><td></td><td>3 g/L KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;, 6 g/L Na&lt;sub&gt;2&lt;/sub&gt;HPO&lt;sub&gt;4&lt;/sub&gt;, 0.5 g/L NaCl, 1 mM MgSO&lt;sub&gt;4&lt;/sub&gt;</td></tr><tr><td>NGM plates</td><td>Home-made</td><td></td><td>&amp;nbsp;17 g/L agar, 2.5 g/L bacto-peptone, 3 g/L NaCl, 25 mM KPO&lt;sub&gt;4&lt;/sub&gt; buffer pH 6.0, 1 mM MgSO&lt;sub&gt;4&lt;/sub&gt;, 1 mM CaCl&lt;sub&gt;2&lt;/sub&gt;, 5 mg/L cholesterol</td></tr><tr><td>Pasteur pipette</td><td>WU Mainz</td><td>250</td><td>To make worm pick, 150 mm length</td></tr><tr><td>Platinum iridium wire</td><td>Alfa Aesar</td><td>39383</td><td>To make worm pick, 0.25 mm diameter</td></tr><tr><td>Sodium azide</td><td>Sigma-Aldrich</td><td>S2002</td><td></td></tr><tr><td>Stereomicroscope</td><td>Leica</td><td>S9</td><td></td></tr></tbody></table>

monitoring protein aggregation kinetics in vivo using automated inclusion counting in caenorhabditis elegans

Protein aggregation into insoluble inclusions is a hallmark of a variety of human diseases, many of which are age-related. The nematode Caenorhabditis elegans is a well-established model organism that has been widely used in the field to study protein aggregation and toxicity. Its optical transparency enables the direct visualization of protein aggregation by fluorescence microscopy. Moreover, the fast reproductive cycle and short lifespan make the nematode a suitable model to screen for genes and molecules that modulate this process.
However, the quantification of aggregate load in living animals is poorly standardized, typically performed by manual inclusion counting under a fluorescence dissection microscope at a single time point. This approach can result in high variability between observers and limits the understanding of the aggregation process. In contrast, amyloid-like protein aggregation in vitro is routinely monitored by thioflavin T fluorescence in a highly quantitative and time-resolved fashion.
Here, an analogous method is presented for the unbiased analysis of aggregation kinetics in living C. elegans, using a high-throughput confocal microscope combined with custom-made image analysis and data fitting. The applicability of this method is demonstrated by monitoring inclusion formation of a fluorescently labeled polyglutamine (polyQ) protein in the body wall muscle cells. The image analysis workflow allows the determination of the numbers of inclusions at different timepoints, which are fitted to a mathematical model based on independent nucleation events in individual muscle cells. The method described here may prove useful to assess the effects of proteostasis factors and potential therapeutics for protein aggregation diseases in a living animal in a robust and quantitative manner.

The method described here (Figure 1) was used to analyze the aggregation kinetics of a construct comprising 40 glutamines fused to YFP (Q40-YFP). The protein is expressed under the control of the unc-54 promoter, driving expression in the body wall muscle cells. As these are relatively large and easy to visualize, the use of a 10x objective is sufficient to resolve the inclusions formed by Q40-YFP in this tissue. Four strains (lines A-D) were previously developed expressing the protein to different extents to assess the concentration-dependence of polyQ aggregation in vivo17.
Age-synchronized populations of lines A-D were generated by a 2 h egg-lay, followed by daily transfer once the offspring reached adulthood. From day 1 to day 10 of adulthood, 20 animals from each of the four strains were imaged in a 384-well plate, using a high-throughput confocal microscope. The images of the wells were acquired as 6 tiles, which were stitched together using a plugin in ImageJ21 (Figure 2). The stitched images were subsequently analyzed using a custom-made CellProfiler22 pipeline (Figure 3) to quantify the average inclusion number per animal for each strain and time point.
The data were then fitted to a mathematical model in AmyloFit5 (Figure 4). The model is based on the assumption that each of the 95 body wall muscle cells independently acquires one inclusion by a rate-limiting nucleation event, followed by fast aggregate growth17. The fit yielded a nucleation rate constant of 9.9 &#215; 105&#160;molecules M-2.1&#160;d-1&#160;cell-1 and a reaction order of 2.1, corresponding to a nucleation rate of 0.38&#160;molecules d-1&#160;cell-1 at an intracellular protein concentration of 1 mM. Two independent biological replicates led to closely corresponding values for the nucleation rate and reaction order, which are in agreement with a previous study using a similar protocol17 (Table 1).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63365/63365fig01v2.jpg" /> 
Figure 1: Schematic overview of the method. (A) Age-synchronized C. elegans populations are generated by a timed egg-lay. (B) Animals from the same population are imaged in a 384-well plate at different time points. (C) The tiles are stitched together to form images of the entire wells, which are analyzed in CellProfiler to quantify the inclusion numbers per animal. (D) The data are fitted to a mathematical model using AmyloFit. <a href="https://www.jove.com/files/ftp_upload/63365/63365fig01largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63365/63365fig02.jpg" /> 
Figure 2: Screenshots of the stitching procedure in ImageJ using the plugin Grid/Collection stitching21. <a href="https://www.jove.com/files/ftp_upload/63365/63365fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63365/63365fig03.jpg" /> 
Figure 3: Schematic of the CellProfiler pipeline to quantify inclusions numbers. (A-C) The brightfield image (A) is used to identify the worms (B, close-up in C). (D-G) The fluorescence image (D, close-up in E) is used to identify the inclusions (F). The worms and inclusions are related to provide the number of inclusions for each worm in the well (G). The images shown are of Q40 line A animals at day 3 of adulthood. <a href="https://www.jove.com/files/ftp_upload/63365/63365fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63365/63365fig04.jpg" /> 
Figure 4: Fitting the data to a mathematical model in AmyloFit. (A) Data are uploaded to AmyloFit. (B) A custom equation is entered to model inclusion formation, assuming independent nucleation events in each cell. (C) Fitting of the aggregation kinetics for C. elegans lines A-D expressing different levels of Q40-YFP. The data are representative of two independent biological replicates. <a href="https://www.jove.com/files/ftp_upload/63365/63365fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Dataset 1</td>
			<td>Dataset 2</td>
			<td>Sinnige et al.17</td>
		</tr>
		<tr>
			<td>n</td>
			<td>2.1</td>
			<td>1.9</td>
			<td>1.6</td>
		</tr>
		<tr>
			<td>Kcell (molecules M-n d-1 cell-1)</td>
			<td>9.9 x 105</td>
			<td>1.4 x 105</td>
			<td>3.1 x 104</td>
		</tr>
		<tr>
			<td>Nucleation rate at 1 mM (molecules d-1 cell-1)</td>
			<td>0.38</td>
			<td>0.21</td>
			<td>0.35</td>
		</tr>
	</tbody>
</table>
Table 1: Values of the nucleation rate and reaction order of Q40-YFP aggregation. Data for two independent biological replicates of the protocol and comparison with previously published data17.

Watch this Scientific Journal Video about Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans at JoVE.com

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

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.

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Protein aggregation into insoluble inclusions is a hallmark of a variety of human diseases, many of which are age-related. The nematode Caenorhabditis ...

monitoring-protein-aggregation-kinetics-vivo-using-automated

Research

JoVE Journal

Biochemistry

2.8K Views.  Utrecht University. De mechanismen van eiwitaggregatie zijn tot nu toe voornamelijk in vitro onderzocht. Met ons protocol kunnen we vergelijkbare kwantitatieve studies doen in het modelorganisme C.elegans. Het grote voordeel van onze methode is de onbevooroordeelde kwantificering van inclusiegetallen.En door deze hoogwaardige gegevens aan te passen aan wiskundige modellen, kunnen we inzicht krijgen in het aggregatiemechanisme. Eiwitaggregatie komt voor bij veel ziekten, waaronder de ziekte van Alzheimer e...

Video: Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages for identifying biologically active chemical structures that can modify complex phenotypes such as lifespan. Described here is a simple protocol for producing hundreds of 96-well culture plates with fairly consistent numbers of C. elegans in each well. Next, we specified how to use these cultures to screen thousands of chemicals for effects on the lifespan of the nematode C. elegans. This protocol makes use of temperature sensitive sterile strains, agar plate conditions, and simple animal handling to facilitate the rapid and high throughput production of synchronized animal cultures for screening.

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Here we describe a simple protocol for rapidly producing hundreds of nematode growth media agar, 96-well culture plates with consistent numbers of Caenorhhabditis elegans per well. These cultures are useful for the phenotypic screening of whole organisms. We focus here on using these cultures to screen chemicals for pro-longevity effects.

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages ...

Characterization of Amyloid Structures in Aging C. Elegans Using Fluorescence Lifetime Imaging

Amyloid fibrils are associated with a number of neurodegenerative diseases such as Huntington&#39;s, Parkinson&#39;s, or Alzheimer&#39;s disease. These amyloid fibrils can sequester endogenous metastable proteins as well as components of the proteostasis network (PN) and thereby exacerbate protein misfolding in the cell. There are a limited number of tools available to assess the aggregation process of amyloid proteins within an animal. We present a protocol for fluorescence lifetime microscopy (FLIM) that allows monitoring as well as quantification of the amyloid fibrilization in specific cells, such as neurons, in a noninvasive manner and with the progression of aging and upon perturbation of the PN. FLIM is independent of the expression levels of the fluorophore and enables an analysis of the aggregation process without any further staining or bleaching. Fluorophores are quenched when they are in close vicinity of amyloid structures, which results in a decrease of the fluorescence lifetime. The quenching directly correlates with the aggregation of the amyloid protein. FLIM is a versatile technique that can be applied to compare the fibrilization process of different amyloid proteins, environmental stimuli, or genetic backgrounds in vivo in a non-invasive manner.

Characterization of Amyloid Structures in Aging C. Elegans Using Fluorescence Lifetime Imaging

Fluorescence lifetime imaging monitors, quantifies and distinguishes the aggregation tendencies of proteins in living, aging, and stressed C. elegans disease models.

Amyloid fibrils are associated with a number of neurodegenerative diseases such as Huntington&#39;s, Parkinson&#39;s, or Alzheimer&#39;s disease. These ...

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the health and fitness of the animals under study, one typically relies on motility readouts, such as the measurement of the number of body bends or the speed of movement. These measurements usually involve manual counting, making it challenging to obtain good statistical significance, as time and labor constraints often limit the number of animals in each experiment to 25 or less. Since high statistical power is necessary to obtain reproducible results and limit false positive and negative results when weak phenotypic effects are investigated, efforts have recently been made to develop automated protocols focused on increasing the sensitivity of motility detection and multi-parametric behavioral profiling. In order to extend the limit of detection to the level needed to capture the small phenotypic changes that are often crucial in genetic studies and drug discovery, we describe here a technological development that enables the study of up to 5,000 individual animals simultaneously, increasing the statistical power of the measurements by about 1,000-fold compared to manual assays and about 100-fold compared to other available automated methods.

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

We describe protocols for using the wide field-of-view nematode tracking platform (WF-NTP), which enables high-throughput phenotypic characterization of large populations of Caenorhabditis elegans. These protocols can be used to characterize subtle behavioral changes in mutant strains or in response to pharmacological treatment in a highly scalable fashion.

Caenorhabditis elegans is a well-established animal model in biomedical research, widely employed in functional genomics and ageing studies. To assess the ...

Immunology and Infection

Using Caenorhabditis elegans as a Model System to Study Protein Homeostasis in a Multicellular Organism

The folding and assembly of proteins is essential for protein function, the long-term health of the cell, and longevity of the organism. Historically, the function and regulation of protein folding was studied in vitro, in isolated tissue culture cells and in unicellular organisms. Recent studies have uncovered links between protein homeostasis (proteostasis), metabolism, development, aging, and temperature-sensing. These findings have led to the development of new tools for monitoring protein folding in the model metazoan organism Caenorhabditis elegans. In our laboratory, we combine behavioral assays, imaging and biochemical approaches using temperature-sensitive or naturally occurring metastable proteins as sensors of the folding environment to monitor protein misfolding. Behavioral assays that are associated with the misfolding of a specific protein provide a simple and powerful readout for protein folding, allowing for the fast screening of genes and conditions that modulate folding. Likewise, such misfolding can be associated with protein mislocalization in the cell. Monitoring protein localization can, therefore, highlight changes in cellular folding capacity occurring in different tissues, at various stages of development and in the face of changing conditions. Finally, using biochemical tools ex vivo, we can directly monitor protein stability and conformation. Thus, by combining behavioral assays, imaging and biochemical techniques, we are able to monitor protein misfolding at the resolution of the organism, the cell, and the protein, respectively.

Using Caenorhabditis elegans as a Model System to Study Protein Homeostasis in a Multicellular Organism

To study the relationship between protein homeostasis, stress and aging, we monitored changes in protein folding by following protein dysfunction, protein localization in the cell and protein stability at the organismal, cellular and protein levels, using the genetically tractable metazoan Caenorhabditis elegans as a model system.

The folding and assembly of proteins is essential for protein function, the long-term health of the cell, and longevity of the organism. Historically, the ...

Biology

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans

In the last decades, the prevalence of neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD), has grown. These age-associated disorders are characterized by the appearance of protein aggregates with fibrillary structure in the brains of these patients. Exactly why normally soluble proteins undergo an aggregation process remains poorly understood. The discovery that protein aggregation is not limited to disease processes and instead part of the normal aging process enables the study of the molecular and cellular mechanisms that regulate protein aggregation, without using ectopically expressed human disease-associated proteins. Here we describe methodologies to examine inherent protein aggregation in Caenorhabditis elegans through complementary approaches. First, we examine how to grow large numbers of age-synchronized C. elegans to obtain aged animals and we present the biochemical procedures to isolate highly-insoluble-large aggregates. In combination with a targeted genetic knockdown, it is possible to dissect the role of a gene of interest in promoting or preventing age-dependent protein aggregation by using either a comprehensive analysis with quantitative mass spectrometry or a candidate-based analysis with antibodies. These findings are then confirmed by in vivo analysis with transgenic animals expressing fluorescent-tagged aggregation-prone proteins. These methods should help clarify why certain proteins are prone to aggregate with age and ultimately how to keep these proteins fully functional.

Methods to Study Changes in Inherent Protein Aggregation with Age in Caenorhabditis elegans

The goal of the method presented here is to explore protein aggregation during normal aging in the model organism C. elegans. The protocol represents a powerful tool to study the highly insoluble large aggregates that form with age and to determine how changes in proteostasis impact protein aggregation.

In the last decades, the prevalence of neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD), has grown. These ...

Quantifying Tissue-Specific Proteostatic Decline in Caenorhabditis elegans

The ability to maintain proper function and folding of the proteome (protein homeostasis) declines during normal aging, facilitating the onset of a growing number of age-associated diseases. For instance, proteins with polyglutamine expansions are prone to aggregation, as exemplified with the huntingtin protein and concomitant onset of Huntington&#8217;s disease. The age-associated deterioration of the proteome has been widely studied through the use of transgenic Caenorhabditis elegans expressing polyQ repeats fused to a yellow fluorescent protein (YFP). This polyQ::YFP transgenic animal model facilitates the direct quantification of the age-associated decline of the proteome through imaging the progressive formation of fluorescent foci (i.e., protein aggregates) and subsequent onset of locomotion defects that develop as a result of the collapse of the proteome. Further, the expression of the polyQ::YFP transgene can be driven by tissue-specific promoters, allowing the assessment of proteostasis across tissues in the context of an intact multicellular organism. This model is highly amenable to genetic analysis, thus providing an approach to quantify aging that is complementary to lifespan assays. We describe how to accurately measure polyQ::YFP foci formation within either neurons or body wall muscle during aging, and the subsequent onset of behavioral defects. Next, we highlight how these approaches can be adapted for higher throughput, and potential future applications using other emerging strategies for C. elegans genetic analysis.

Quantifying Tissue-Specific Proteostatic Decline in Caenorhabditis elegans

Proteostatic decline is a hallmark of aging, facilitating the onset of neurodegenerative diseases. We outline a protocol to quantifiably measure proteostasis in two different Caenorhabditis elegans tissues through heterologous expression of polyglutamine repeats fused to a fluorescent reporter. This model allows rapid in vivo genetic analysis of proteostasis.

The ability to maintain proper function and folding of the proteome (protein homeostasis) declines during normal aging, facilitating the onset of a ...

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2

Proteins are synthesized and degraded constantly within a cell to maintain homeostasis. Being able to monitor the degradation of a protein of interest is key to understanding not only its life cycle, but also to uncover imbalances in the proteostasis network. This method shows how to track the degradation of the disease-causing protein huntingtin. Two versions of huntingtin fused to Dendra2 are expressed in the C. elegans nervous system: a physiological version or one with an expanded and pathogenic stretch of glutamines. Dendra2 is a photoconvertible fluorescent protein; upon a short ultraviolet (UV) irradiation pulse, Dendra2 switches its excitation/emission spectra from green to red. Similar to a pulse-chase experiment, the turnover of the converted red-Dendra2 can be monitored and quantified, regardless of the interference from newly synthesized green-Dendra2. Using confocal-based microscopy and due to the optical transparency of C. elegans, it is possible to monitor and quantify the degradation of huntingtin-Dendra2 in a living, aging organism. Neuronal huntingtin-Dendra2 is partially degraded soon after conversion and cleared further over time. The systems controlling degradation are deficient in the presence of mutant huntingtin and are further impaired with aging. Neuronal subtypes within the same nervous system exhibit different turnover capacities for huntingtin-Dendra2. Overall, monitoring any protein of interest fused to Dendra2 can provide important information not only on its degradation and the players of the proteostasis network involved, but also on its location, trafficking, and transport.

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2

Presented here is a protocol to monitor degradation of the protein huntingtin fused to the photoconvertible fluorophore Dendra2.

Proteins are synthesized and degraded constantly within a cell to maintain homeostasis. Being able to monitor the degradation of a protein of interest is ...

Quantification of Insoluble Protein Aggregation in Caenorhabditis elegans during Aging with a Novel Data-Independent Acquisition Workflow

We and others have shown that the aging process results in a proteome-wide accumulation of insoluble proteins. Knocking down genes encoding the insoluble proteins over 40% of the time results in an extension of the lifespan in C. elegans, suggesting that many of these proteins are key determinants of the aging process. Isolation and quantitative identification of these insoluble proteins are crucial to understand key biological processes that occur during aging. Here, we present a modified and improved protocol that details how to extract and isolate the SDS-insoluble proteins (insolublome) from C. elegans more efficiently to streamline mass spectrometric workflows via a novel label-free quantitative proteomics analysis. This improved protocol utilizes a highly efficient sonicator for worm lysis that greatly increases efficiency for protein extraction and allows us to use significantly less starting material (approximately 3,000 worms) than in previous protocols (typically using at least 40,000 worms). Subsequent quantitative proteomic analysis of the insolublome was performed using data-dependent acquisition (DDA) for protein discovery and identification and data-independent acquisition (DIA) for comprehensive and more accurate protein quantification. Bioinformatic analysis of quantified proteins provides potential candidates that can be easily followed up with other molecular methods in C. elegans. With this workflow, we routinely identify more than 1000 proteins and quantify more than 500 proteins. This new protocol enables efficient compound screening with C. elegans. Here, we validated and applied this improved protocol to wild-type C. elegans N2-Bristol strain and confirmed that aged day-10 N2 worms showed greater accumulation of the insolublome than day-2 young worms.

Quantification of Insoluble Protein Aggregation in Caenorhabditis elegans during Aging with a Novel Data-Independent Acquisition Workflow

This novel workflow efficiently extracts and isolates SDS-insoluble proteins (insolublome) from Caenorhabditis elegans with minimal starting material for quantitative differential proteomic analysis. The protocol uses a comprehensive data-independent acquisition mass spectrometry analysis to quantify the insolublome and bioinformatic analysis to gain biological insights into aging mechanisms and pathologies.

We and others have shown that the aging process results in a proteome-wide accumulation of insoluble proteins. Knocking down genes encoding the insoluble ...

Automating Aggregate Quantification in Caenorhabditis elegans

A rise in the prevalence of neurodegenerative protein conformational diseases (PCDs) has fostered a great interest in this subject over the years. This increased attention has called for the diversification and improvement of animal models capable of reproducing disease phenotypes observed in humans with PCDs. Though murine models have proven invaluable, they are expensive and are associated with laborious, low-throughput methods. Use of the Caenorhabditis elegans nematode model to study PCDs has been justified by its relative ease of maintenance, low cost, and rapid generation time, which allow for high-throughput applications. Additionally, high conservation between the C. elegans and human genomes makes this model an invaluable discovery tool. Nematodes that express fluorescently tagged tissue-specific polyglutamine (polyQ) tracts exhibit age- and polyQ length-dependent aggregation characterized by fluorescent foci. Such reporters are often employed as proxies to monitor changes in proteostasis across tissues. Manual aggregate quantification is time-consuming, limiting experimental throughput. Furthermore, manual foci quantification can introduce bias, as aggregate identification can be highly subjective. Herein, a protocol consisting of worm culturing, image acquisition, and data processing was standardized to support high-throughput aggregate quantification using C. elegans that express intestine-specific polyQ. By implementing a C. elegans-based image processing pipeline using CellProfiler, an image analysis software, this method has been optimized to separate and identify individual worms and enumerate their respective aggregates. Though the concept of automation is not entirely unique, the need to standardize such procedures for reproducibility, elimination of bias from manual counting, and increase throughput is high. It is anticipated that these methods can drastically simplify the screening process of large bacterial, genomic, or drug libraries using the C. elegans model.

Automating Aggregate Quantification in Caenorhabditis elegans

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 rise in the prevalence of neurodegenerative protein conformational diseases (PCDs) has fostered a great interest in this subject over the years. This ...

Fluorescence Lifetime Imaging of PolyQ Protein Aggregation in Caenorhabditis elegans Neurons

Source:&#160;Pigazzini, M. L.,&#160;et. al. Characterization of Amyloid Structures in Aging&#160;C. Elegans Using Fluorescence Lifetime Imaging.&#160;J. ...

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Samenvatting

Meer video's verkennen