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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+amyloid+state+and+its+association+with+protein+misfolding+diseases.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+misfolding,+amyloid+formation,+and+human+disease:+a+summary+of+progress+over+the+last+decade.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+analytical+solution+to+the+kinetics+of+breakable+filament+assembly.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proliferation+of+amyloid-&#946;42+aggregates+occurs+through+a+secondary+nucleation+mechanism.">Proliferation of amyloid-&#946;42 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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+mechanisms+of+protein+aggregation+from+global+fitting+of+kinetic+models.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Polyglutamine+aggregates+alter+protein+folding+homeostasis+in+Caenorhabditis+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+threshold+for+polyglutamine-expansion+protein+aggregation+and+cellular+toxicity+is+dynamic+and+influenced+by+aging+in+Caenorhabditis+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Polyglutamine+proteins+at+the+pathogenic+threshold+display+neuron-specific+aggregation+in+a+pan-neuronal+Caenorhabditis+elegans+model.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stress+and+aging+induce+distinct+polyQ+protein+aggregation+states.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuronal+circuitry+regulates+the+response+of+Caenorhabditis+elegans+to+misfolded+proteins.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+RNA+interference+screen+identifies+previously+undescribed+regulators+of+polyglutamine+aggregation.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+genetic+screening+strategy+identifies+novel+regulators+of+the+proteostasis+network.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+chaperome+subnetwork+safeguards+proteostasis+in+aging+and+neurodegenerative+disease.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small-molecule+proteostasis+regulators+for+protein+conformational+diseases.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biophysical+studies+of+protein+misfolding+and+aggregation+in+in+vivo+models+of+Alzheimer's+and+Parkinson's+diseases.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large-scale+microfluidics+providing+high-resolution+and+high-throughput+screening+of+Caenorhabditis+elegans+poly-glutamine+aggregation+model.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinetic+analysis+reveals+that+independent+nucleation+events+determine+the+progression+of+polyglutamine+aggregation+in+C.+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Caenorhabditis+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+an+open-source+platform+for+biological-image+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Globally+optimal+stitching+of+tiled+3D+microscopic+image+acquisitions.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CellProfiler:+free,+versatile+software+for+automated+biological+image+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Efficient+gene+transfer+in+C.+elegans:+extrachromosomal+maintenance+and+integration+of+transforming+sequences.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+tissue-specific+proteostatic+decline+in+Caenorhabditis+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorodeoxyuridine+improves+Caenorhabditis+elegans+proteostasis+independent+of+reproduction+onset.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorodeoxyuridine+enhances+the+heat+shock+response+and+decreases+polyglutamine+aggregation+in+an+HSF-1-dependent+manner+in+Caenorhabditis+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Widespread+protein+aggregation+as+an+inherent+part+of+aging+in+C.+elegans.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Caenorhabditis+sieve:+A+low-tech+instrument+and+methodology+for+sorting+small+multicellular+organisms.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+and+gentle+hydrogel+encapsulation+of+living+organisms+enables+long-term+microscopy+over+multiple+hours.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reversible+and+long-term+immobilization+in+a+hydrogel-microbead+matrix+for+high-resolution+imaging+of+Caenorhabditis+elegans+and+other+small+organisms.">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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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>C. elegans Q40 line A</td>
			<td>Morimoto lab</td>
			<td>AM1228</td>
			<td>Genotype rmIs404[unc-54p::Q40::YFP]</td>
		</tr>
		<tr>
			<td>C. elegans Q40 line B</td>
			<td>Morimoto lab</td>
			<td>AM1229</td>
			<td>Genotype rmIs404[unc-54p::Q40::YFP]</td>
		</tr>
		<tr>
			<td>C. elegans Q40 line C</td>
			<td>Morimoto lab</td>
			<td>AM1230</td>
			<td>Genotype rmIs404[unc-54p::Q40::YFP]</td>
		</tr>
		<tr>
			<td>C. elegans Q40 line D</td>
			<td>Morimoto lab</td>
			<td>AM1231</td>
			<td>Genotype rmIs404[unc-54p::Q40::YFP]</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>E. coli 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 CV8000</td>
			<td></td>
		</tr>
		<tr>
			<td>M9 buffer</td>
			<td>Home-made</td>
			<td></td>
			<td>3 g/L KH2PO4, 6 g/L Na2HPO4, 0.5 g/L NaCl, 1 mM MgSO4</td>
		</tr>
		<tr>
			<td>NGM plates</td>
			<td>Home-made</td>
			<td></td>
			<td>&#160;17 g/L agar, 2.5 g/L bacto-peptone, 3 g/L NaCl, 25 mM KPO4 buffer pH 6.0, 1 mM MgSO4, 1 mM CaCl2, 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

2.7K Views.  Utrecht University. 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.

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 ...

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to precisely maneuver the nucleation and crystal growth towards desired sizes of protein crystals. The controlled crystallization is based on sample investigation with in situ Dynamic Light Scattering (DLS) while all visual changes in the droplet are monitored online with the help of a microscope coupled to a CCD camera, thus enabling a full investigation of the protein droplet during all stages of crystallization. The use of in situ DLS measurements throughout the entire experiment allows a precise identification of the highly supersaturated protein solution transitioning to a new phase &#8211; the formation of crystal nuclei. By identifying the protein nucleation stage, the crystallization can be optimized from large protein crystals to the production of protein microcrystals. The experimental protocol shows an interactive crystallization approach based on precise automated steps such as precipitant addition, water evaporation for inducing high supersaturation, and sample dilution for slowing induced homogeneous nucleation or reversing phase transitions.

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Here we present a protocol for controlled production of protein microcrystals. The process uses an automated device allowing controlled manipulation of several crystallization parameters. The protein crystallization is carried-out by controlled and automated addition of crystallization solutions while monitoring and investigating the radius distribution of particles in the crystallization droplet.

The automated crystallization device is a patented technique1 especially developed for monitoring protein crystallization experiments with the aim to ...

Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast

Protein misfolding and aggregation into amyloid conformations have been related to the onset and progression of several neurodegenerative diseases. However, there is still little information about how insoluble protein aggregates exert their toxic effects in vivo. Simple prokaryotic and eukaryotic model organisms, such as bacteria and yeast, have contributed significantly to our present understanding of the mechanisms behind the intracellular amyloid formation, aggregates propagation, and toxicity. In this protocol, the use of yeast is described as a model to dissect the relationship between the formation of protein aggregates and their impact on cellular oxidative stress. The method combines the detection of the intracellular soluble/aggregated state of an amyloidogenic protein with the quantification of the cellular oxidative damage resulting from its expression using flow cytometry (FC). This approach is simple, fast, and quantitative. The study illustrates the technique by correlating the cellular oxidative stress caused by a large set of amyloid-&#946; peptide variants with their respective intrinsic aggregation propensities.

Protein aggregation elicits cellular oxidative stress. This protocol describes a method for monitoring the intracellular states of amyloidogenic proteins and the oxidative stress associated with them, using flow cytometry. The approach is used to study the behavior of soluble and aggregation-prone variants of the amyloid-&#946; peptide.

Protein misfolding and aggregation into amyloid conformations have been related to the onset and progression of several neurodegenerative diseases. ...

Small-Scale Colorimetric Assays of Intracellular Lactate and Pyruvate in the Nematode Caenorhabditis elegans

Lactate and pyruvate are key intermediates of intracellular energy metabolic pathways. Monitoring the lactate/pyruvate ratio in cells helps to determine whether there is an imbalance in age-related energy metabolism between mitochondrial oxidative phosphorylation and aerobic glycolysis. Here, we show the utilization of commercial colorimetric assay kits for lactate and pyruvate in the model organism C. elegans. Recently, the sensitivity and accuracy of the colorimetric/fluorimetric assay kits have been improved greatly by the research and development conducted by reagent manufacturers. The improved reagents have enabled the use of small-scale assays with a 96-well plate in C. elegans. In general, a fluorimetric assay is superior in sensitivity to a colorimetric assay; however, the colorimetric approach is more suitable for the use in common laboratories. Another important issue in these assays for quantitative determination is protein precipitation of homogenized C. elegans samples. In our protein precipitation method, common precipitants (e.g., trichloroacetic acid, perchloric acid and metaphosphoric acid) are used for sample preparation. A protein-free assay sample is prepared by directly adding cold precipitant (final concentration of 5%) during homogenization.

Small-Scale Colorimetric Assays of Intracellular Lactate and Pyruvate in the Nematode Caenorhabditis elegans

We describe a modified small-scale extraction and colorimetric assays of lactate and pyruvate in the nematode C. elegans. When utilizing commercial assay kits, the technical development of their sensitivity and accuracy is important. Protein precipitation in extraction is the most critical step for the quantitative determination of intracellular metabolites.

Lactate and pyruvate are key intermediates of intracellular energy metabolic pathways. Monitoring the lactate/pyruvate ratio in cells helps to determine ...

Plate-based Large-scale Cultivation of Caenorhabditis elegans: Sample Preparation for the Study of Metabolic Alterations in Diabetes

Culturing Caenorhabditis elegans (C. elegans) in a large-scale manner on agar plates can be time-consuming and difficult. This protocol describes a simple and inexpensive method to obtain a large number of animals for the isolation of proteins to proceed with a western blot, mass spectrometry, or further proteomics analyses. Furthermore, an increase of nematode numbers for immunostainings and the integration of multiple analyses under the same culturing conditions can easily be achieved. Additionally, a transfer between plates with different experimental conditions is facilitated. Common techniques in plate culture involve the transfer of a single C. elegans using a platinum wire and the transfer of populated agar chunks using a scalpel. However, with increasing nematode numbers, these techniques become overly time-consuming. This protocol describes the large-scale culture of C. elegans including numerous steps to minimize the impact of the sample preparation on the physiology of the worm. Fluid and shear stress can alter the lifespan of and metabolic processes in C. elegans, thus requiring a detailed description of the critical steps in order to retrieve reliable and reproducible results. C. elegans is a model organism, consisting of neuronal cells for up to one-third, but lacking blood vessels, thus providing the possibility to investigate solely neuronal alterations independent of vascular control. Recently, early neurodegeneration in diabetic retinopathy was found prior to vascular alterations. Thus, C. elegans is of special interest for studying general mechanisms of diabetic complications. For example, an increased formation of advanced glycation end products (AGEs) and reactive oxygen species (ROS) is observed, which are reproducibly found in C. elegans. Protocols to handle samples of adequate size for a broader spectrum of investigations are presented here, exemplified by the study of diabetes-induced biochemical alterations. In general, this protocol can be useful for studies requiring large C. elegans numbers and in which liquid culture is not suitable.

Plate-based Large-scale Cultivation of Caenorhabditis elegans: Sample Preparation for the Study of Metabolic Alterations in Diabetes

This protocol describes a method for the large-scale cultivation of Caenorhabditis elegans on solid media. As an alternative to liquid culture, this protocol allows obtaining parameters of different scales under plate-based cultivation. This increases the comparability of results by omitting the morphological and metabolic differences between liquid and solid media culture.

Culturing Caenorhabditis elegans (C. elegans) in a large-scale manner on agar plates can be time-consuming and difficult. This protocol describes a simple ...

Analysis of Protein Folding, Transport, and Degradation in Living Cells by Radioactive Pulse Chase

Radioactive pulse-chase labeling is a powerful tool for studying the conformational maturation, the transport to their functional cellular location, and the degradation of target proteins in live cells. By using short (pulse) radiolabeling times (&lt;30 min) and tightly controlled chase times, it is possible to label only a small fraction of the total protein pool and follow its folding. When combined with nonreducing/reducing SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and immunoprecipitation with (conformation-specific) antibodies, folding processes can be examined in great detail. This system has been used to analyze the folding of proteins with a huge variation in properties such as soluble proteins, single and multi-pass transmembrane proteins, heavily N- and O-glycosylated proteins, and proteins with and without extensive disulfide bonding. Pulse-chase methods are the basis of kinetic studies into a range of additional features, including co- and posttranslational modifications, oligomerization, and polymerization, essentially allowing the analysis of a protein from birth to death. Pulse-chase studies on protein folding are complementary with other biochemical and biophysical methods for studying proteins in vitro by providing increased temporal resolution and physiological information. The methods as described within this paper are adapted easily to study the folding of almost any protein that can be expressed in mammalian or insect-cell systems.

Here we describe a protocol for a general pulse-chase method that allows the kinetic analysis of folding, transport, and degradation of proteins to be followed in live cells.

Radioactive pulse-chase labeling is a powerful tool for studying the conformational maturation, the transport to their functional cellular location, and ...

In Vivo Calcium Imaging in C. elegans Body Wall Muscles

The model organism C. elegans provides an excellent system to perform in vivo calcium imaging. Its transparent body and genetic manipulability allow for the targeted expression of genetically encoded calcium sensors. This protocol outlines the use of these sensors for the in vivo imaging of calcium dynamics in targeted cells, specifically the body wall muscles of the worms. By utilizing the co-expression of presynaptic channelrhodopsin, stimulation of acetylcholine release from excitatory motor neurons can be induced using blue light pulses, resulting in muscle depolarization and reproducible changes in cytoplasmic calcium levels. Two worm immobilization techniques are discussed with varying levels of difficulty. Comparison of these techniques demonstrates that both approaches preserve the physiology of the neuromuscular junction and allow for the reproducible quantification of calcium transients. By pairing optogenetics and functional calcium imaging, changes in postsynaptic calcium handling and homeostasis can be evaluated in a variety of mutant backgrounds. Data presented validates both immobilization techniques and specifically examines the roles of the C. elegans sarco(endo)plasmic reticular calcium ATPase and the calcium-activated BK potassium channel in the body wall muscle calcium regulation.

In Vivo Calcium Imaging in C. elegans Body Wall Muscles

This method provides a way to couple optogenetics and genetically encoded calcium sensors to image baseline cytosolic calcium levels and changes in evoked calcium transients in the body wall muscles of the model organism C. elegans.

The model organism C. elegans provides an excellent system to perform in vivo calcium imaging. Its transparent body and genetic manipulability allow for ...

Application of Biolayer Interferometry (BLI) for Studying Protein-Protein Interactions in Transcription

A transcription factor&#160;(TF) is a protein that regulates gene expression by interacting with the RNA polymerase, another TF, and/or template DNA. GrgA is a novel transcription activator found specifically in the obligate intracellular bacterial pathogen Chlamydia. Protein pulldown assays using affinity beads have revealed that GrgA binds two &#963; factors, namely &#963;66 and &#963;28, which recognize different sets of promoters for genes whose products are differentially required at developmental stages. We have used BLI to confirm and further characterize the interactions. BLI demonstrates several advantages over pulldown: 1) It reveals real-time association and dissociation between binding partners, 2) It generates quantitative kinetic parameters, and 3) It can detect bindings that pulldown assays often fail to detect. These characteristics have enabled us to deduce the physiological roles of GrgA in gene expression regulation in Chlamydia, and possible detailed interaction mechanisms. We envision that this relatively affordable technology can be extremely useful for studying transcription and other biological processes.

Application of Biolayer Interferometry BLI for Studying Protein-Protein Interactions in Transcription

Interactions of transcription factors (TFs) with the RNA polymerase are usually studied using pulldown assays. We apply a Biolayer Interferometry (BLI) technology to characterize the interaction of GrgA with the chlamydial RNA polymerase. Compared to pulldown assays, BLI detects real-time association and dissociation, offers higher sensitivity, and is highly quantitative.

A transcription factor&#160;(TF) is a protein that regulates gene expression by interacting with the RNA polymerase, another TF, and/or template DNA. GrgA ...

Synthesis of a Deuterated Standard for the Quantification of 2-Arachidonoylglycerol in Caenorhabditis elegans

This work presents a method to prepare an analytical standard to analyze 2-arachidonoyl glycerol (2-AG) qualitatively and quantitatively by liquid chromatography-electrospray Ionization-tandem mass spectrometry (LC-ESI-MS/MS). Endocannabinoids are conserved lipid mediators that regulate multiple biological processes in a variety of organisms. In C. elegans, 2-AG has been found to possess different roles, including modulation of dauer formation and cholesterol metabolism. This report describes a method to overcome the difficulties associated with the costs and stability of deuterated standards required for 2-AG quantification. The procedure for the synthesis of the standard is simple and can be performed in any laboratory, without the need for organic synthesis expertise or special equipment. In addition, a modification of Folch&#39;s method to extract the deuterated standard from C. elegans culture is described. Finally, a quantitative and analytic method to detect 2-AG using the stable isotopically labeled analog 1-AG-d5 is described, which provides reliable results in a fast-chromatographic run. The procedure is useful for studying the multiple roles of 2-AG in C. elegans while also being applicable to other studies of metabolites in different organisms.

Synthesis of a Deuterated Standard for the Quantification of 2-Arachidonoylglycerol in Caenorhabditis elegans

This work describes a robust and straightforward method to detect and quantify the endocannabinoid 2-arachidonoylglycerol (2-AG) in C. elegans. An analytical deuterated standard us prepared and used for the quantification of 2-AG by isotopic dilution and liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS).

This work presents a method to prepare an analytical standard to analyze 2-arachidonoyl glycerol (2-AG) qualitatively and quantitatively by liquid ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...