We would like to thank Dr Christian Lanct&#244;t at BIOCEV (Prague, Czech Republic) for teaching us the mouth micropipette technique to mount fixed worms and Dr Fatima Santos and Dr Debbie Drage for sharing safety setup on mouth micropipette. We also thank Francesca Hodge for editing the manuscript, Sharlene Murdoch and Babraham Institute Facilities for their support. OC is supported by ERC 638426 and BBSRC [BBS/E/B000C0426].

1. Fixation of worms for fat-content imaging using a fluorescent dye for lipid droplets (BODIPY)10
<ol>
	<li>Prepare a synchronized population of C. elegans by bleaching according to standard procedures2. Plate about 1,000 L1-stage worms onto a 9 cm nematode growth media (NGM) plate per condition. 
	NOTE: More worms are prepared than actually quantified at the end, due to loss of worms when handling.</li>
	<li>To image animals at young adult stage (around 50 h post L1 plating at 20 &#176;C), wash each 9 cm plate with 15 mL of M9 in a conical&#160;tube, centrifuge at 252 x g for 1 min, and remove the supernatant.&#160;</li>
	<li>Repeat the wash and leave 1 mL of M9 over the pellet of worms.</li>
	<li>Transfer 1 mL of M9 containing the worms in a 2 mL low protein binding microcentrifuge tube, using low retention tips.</li>
	<li>Spin down at 252 x g in a microcentrifuge for 1 min and remove the supernatant, being careful not to touch the worm pellet at the bottom of the tube.</li>
	<li>For monitoring fat content using 4,4-difluoro-1,3,5,7,8-Pentamethyl-4-bora-3a,4a-diaza-s-indacene (BODIPY) staining10 (Table of Materials), fix worms either by adding 0.5 mL of 60% isopropanol to the worm pellet for 3 min11, or by adding 2 mL of ice-cold methanol for 10 min. For both procedures, invert the tube every 30 s.&#160; 
	CAUTION: If methanol is the chosen fixation reagent, this step must be undertaken in a fume hood due to its toxicity.</li>
	<li>Remove as much fixative as possible without touching the worm pellet and add 1 mL of M9. Let the worms settle at the bottom of the tube for 3&#8722;5 min.</li>
	<li>Wash again with 1 mL of M9, let the worms settle and remove the supernatant, leaving about 50 &#181;L in the tube. Secure the tube using a clamp.&#160;</li>
	<li>Freeze-crack the worms by plunging the securely closed tube in liquid nitrogen and then into a warm water bath at ~40 &#176;C.</li>
	<li>Repeat step 1.9 one more time.</li>
	<li>Add 0.5 mL of BODIPY diluted in M9 at a final concentration of 1 &#181;g/&#181;L and place on a rotator at room temperature for 1 h.</li>
	<li>After 1 h, let the worms settle at the bottom of the tube for 3&#8722;5 min.&#160;</li>
	<li>Remove supernatant and add 1 mL of M9.</li>
	<li>Let the worms settle at the bottom and remove supernatant.&#160; 
	NOTE: Alternatively, it is possible to spin down at 252 x g for 1 min, as this does not seem to affect the morphology.</li>
	<li>Add 0.5 mL of M9.&#160; 
	NOTE: If the fixative is 60% isopropanol, the worms can only be utilized within 48 h. If the fixative is methanol, the worms should be utilized within 24h.</li>
</ol>
2. Preparing agarose pads to mount the worms
NOTE: The critical step while preparing an agarose pad is to obtain a pad of regular thickness. Otherwise, worms across the pad will be in different focal planes, making it tricky to get a focused image of a wider field of view.&#160;
<ol>
	<li>Prepare 2% agarose pads by adding 0.2 g of agarose to 10 mL of sterilized H2O in a beaker or a conical flask. Heat in the microwave for 30 s until the agarose starts boiling.&#160; 
	NOTE: Do not overheat and stop as soon as bubbles appear; otherwise it increases the viscosity of the agarose making it harder to achieve the desired pad thickness.</li>
	<li>Once the agarose is melted, dispense a drop of melted agarose in the center of a microscope glass slide using a 1000 &#181;L pipette tip with its very end cut. Immediately, but delicately, hold another glass slide very close to the agarose drop and release it onto the agarose to form a pad of regular thickness for best microscopy results. 
	NOTE: Releasing the top slide from a height will not only form bubbles but will result in an uneven pad. Alternatively, it is also possible to use two glass slides flanking the slide with the pad, with a thin layer of tape on each slide.&#160;</li>
	<li>After a minimum of 2&#8722;3 min, gently remove the top slide. Using the side of the top slide, trim the pad to make it square shaped. Mount the worms within 5 min, to prevent the pad from drying before use.</li>
</ol>
3. Mounting fixed worms for imaging
<ol>
	<li>Create a mouth micropipette by extending a thin glass capillary in the flame of a Bunsen burner (Figure 1A). After extension in the flame, break the capillary into two pieces (Figure 1B). Choose the most adequate, usually the longest, and&#160;test whether the end of the capillary is open by trying to aspirate water.&#160; 
	NOTE: If the liquid does not come into the capillary, it is too thin or blocked. Gently cut the end of the capillary with a pair of scissors, avoiding cutting too much as worms will be aspirated if the hole is too big.&#160;</li>
	<li>Plug the glass capillary from step 3.1 into the capillary adaptor (Figure 1C), linked to the 6 mm silicone tube and plug the other end of the 6 mm silicone tube into a 0.2 &#181;m filter, attached to a 3 mm silicone tube on its other end (Figure 1C).&#160;Plug a 1 mL filter tip (Figure 1C) into the free end of the 3 mm silicone tube to aspirate liquid. 
	NOTE: The 0.2 &#181;m filter is added between the mouth of the experimenter and the capillary, to ensure maximum safety of the experimenter. A similar solution is used for mouth micropipettes used to handle mouse embryos12. Change the filter (usually every few months) if the mouth micropipette is not aspirating liquid anymore.</li>
	<li>Using the mouth micropipette under a dissection microscope, remove as much liquid as possible around the worm pellet of fixed worms (Figure 2). 
	NOTE: Pipetting supernatant out using a manual micropipette can be used as an alternative to the mouth pipettes in steps 3.1 to 3.3 to remove as much supernatant as possible. Add 6 &#181;L of mounting medium. We find, however, that this method does not work as efficiently because excessive liquid in the pads creates a sparse worm density, which makes imaging more time-consuming. Resume step 3.6. Look at the bottom of the tube and make sure that the pipette does not aspirate the worms.&#160;</li>
	<li>Quickly add 10 &#181;L of mounting medium (Table of Materials) to the bottom of the tube.</li>
	<li>Rinse a 10 &#181;L tip in PBS with traces of detergent (0.01% Triton), to prevent worms from sticking to the sides of the plastic pipette tip. Cut the very end of the tip with a pair of scissors.</li>
	<li>Transfer 8 &#181;L of mounting medium containing the worms onto the agarose pad prepared in step 2.3. Under the microscope, gently shake the slide,&#160;to avoid overlap of the worms.</li>
	<li>Cover the drop of mounting medium on the agarose pad with an 18 mm x 18 mm coverslip (Figure 3). 
	NOTE: Smaller coverslips are preferred as they exert less pressure onto the worms. Hold the coverslip with a pair of tweezers and apply one edge of the coverslip against the agarose pad, before gently depositing the coverslip with the tweezers.</li>
</ol>
4. Mounting live worms for imaging
NOTE: To image live worms, they have to be immobilized on the pad. One way to achieve this is to paralyze them with the nicotinic receptor agonist: levamisole (Table of Materials).
<ol>
	<li>Pipette 3&#8722;4 &#181;L of 3 mM levamisole dissolved in M9 onto the agarose pad created in step 2.3. 
	NOTE: There should be enough liquid volume that the worms are not on top of each other but not too much liquid that the worms are too sparse on the pad. Experienced worm pickers can use 3 &#181;L of levamisole. Less experienced pickers might need to add a larger volume of levamisole, so that the levamisole does not totally evaporate.</li>
	<li>Pick 30&#8722;50 worms per condition into the drop of levamisole.</li>
	<li>Cover the drop of levamisole on the agarose pad with an 18 mm x 18 mm coverslip. Image worms within 1 h, as the paralyzing agent will eventually lead to death of the worms.</li>
</ol>
5. Imaging slides with an epifluorescence microscope&#160;
<ol>
	<li>With an agarose pad of uniform thickness, image all worms on the slide at once using a 6 x 6 or 7 x 7 large image for instance with the 20x objective of a fluorescent microscope. If the thickness of the pad is not even, acquire several smaller 3 x 3 or 4 x 4 images of the same slide.&#160;</li>
	<li>Use the same settings for fluorescence intensity and exposure time for all conditions.&#160;Adjust each setting to match the slide that has the brightest intensity, ensuring that there is no pixel saturation. For specific instructions on image acquisition,&#160;follow the microscope manufacturer&#8217;s instructions.&#160;</li>
</ol>
6. Creating montage images of aligned single worms using the Worm-align FIJI pipeline
<ol>
	<li>Install the open-source image analysis software package FIJI13/ImageJ14 from https://imagej.net/Fiji. If a prior installation of FIJI is available, ensure it is updated to version 1.52a or later, as some of the functions used in the macro (e.g., Table functions) require this. Download the Worm-align macro from Github: https://github.com/hannekeo/Worm-align.</li>
	<li>Open FIJI and run the Worm-align macro. Execute Worm-align by clicking Plugins | Macros | Run in the FIJI main menu bar (Figure S1). Now locate the Worm-align.ijm script on the computer.</li>
	<li>The macro initializes by asking for the location of the images. The pipeline will work on both single-channel and multi-channel fluorescence images (Figure S2). Select an input folder and the macro will automatically generate an output folder where all results will be saved (Figure S3). 
	NOTE: It is important that the selected folder contains only image files as the presence of other file formats will cause the macro to crash. Ideally, only group together images that were captured with the same microscope settings.&#160;Worm-align will accept many different file formats, including nd2, czi, tif, rgb, jpg and png.&#160;The name of the output folder will be the same as the input folder, with &#8216;_output&#8217; added as a postfix, i.e. if the input folder is &#8216;images&#8217;, the output will be found in &#8216;images_output&#8217;. The output folder will contain four subfolders, in which the data will be saved.</li>
	<li>Allow the macro to proceed to open the first image in the input folder and use it as a representative image to extract a setting to generate the montage. 
	NOTE: Worm-align will automatically select the first image in the input folder to generate the settings that will be applied to all other images in the folder. If another image is deemed to be more representative of the dataset, ensure this image is selected by saving a copy of the image in the input folder, changing the name so that is now listed at the top of the list (e.g., &#8216;0_RepresentativeImage&#8217;).</li>
	<li>With the Straight-line drawing tool, draw a line across the width of a worm (Figure S4) and use the length of this line to determine the height of the cropped regions for single worms. For each channel, specify the name, lookup table (LUT), intensity settings (B&#38;C) and whether it should be included in the montage (Figure S4). 
	NOTE: These parameters are recorded&#160;and saved in a settings file, Settings.csv, located in the CellProfiler subfolder of the output folder.</li>
	<li>Once all settings have been captured, the user is shown what the images will look like after application of the settings applied (Figure S5). Tick the top box if the settings are sufficient. Select options for montage generation (remove ticks, if not required): 1. Generate a montage of selected worms for each single image, or 2. Generate a combined montage in which all worms selected on all images in the input folder will be combined into a single montage. Click OK to execute the rest of the macro.&#160; 
	NOTE: If the top box is not ticked before clicking &#8216;OK&#8217; the macro will rerun the setup, so image settings can be optimised.</li>
	<li>The Worm-align pipeline now proceeds to open all images in the input folder. For each image, draw lines on the longitudinal axis of all worms to be included in the montage and/or quantify (Figure 4 and Figure S6) using the &#8216;segmented line&#8217; tool (on the FIJI main menu bar). To ensure proper alignment of worm, draw lines consistently from head to tail end (or vice versa), and along the full length of the worm (see examples of &#8220;good&#8221; and &#8220;bad&#8221; line drawing in Figure 4B).</li>
	<li>Add each line to the ROI manager, by clicking Ctrl+T. Worm-align uses the lines added to the ROI manager, in combination with the worm width parameter (set in step 6.4) to generate cropped images of single selected worms. The collection of line ROIs is saved in the &#8216;data&#8217; subfolder. This folder also contains a copy of the original image showing the lines, as well as the number of the worm. Lines are color-coded with the Glasbey_on_dark lookup table.&#160;The images of straightened worms are saved in the single_worms subfolder of the output folder. In addition, if selected in step 6.6 of this protocol, Worm-align produces montages of all worms selected per overview image and/or for all overview images in the input folder (see Figure 4C and Figure S7). These montages can be found in the &#8216;aligned&#8217; subfolder of the output folder.&#160;</li>
</ol>
7. Analyzing single-worm fluorescence intensity using the output from Worm-align in an automated CellProfiler pipeline
<ol>
	<li>For downstream data processing and analysis of the Worm-align output, download and install the open-source image analysis software package &#8216;CellProfiler15,16 from https://cellprofiler.org/. Download the Worm_CP.cpproj pipeline from GitHub: https://github.com/hannekeo/Worm-align 
	NOTE: The example pipeline described here was constructed using CellProfiler2.2.0 and might not run in later versions of CellProfiler.</li>
	<li>Before starting the Worms_CP.cpproj pipeline, ensure that all expected output images are present in the CellProfiler subfolder of the Worm-align output folder: a processed copy of the original image (named: Original_), a binary image mask of the worm population (named: Mask_), a line mask of the lines drawn on selected worms (named: Lines_), and one image representing each of the channels present in the original image (named by channel number).</li>
	<li>To quantify the fluorescence intensity in single worms, click on the Images input module and simply drag the CellProfiler output folder into the window that says Drop files and folders here. If a list of images is present from previous analysis, first remove these by right clicking in the window and selecting Clear File List. To exclude the &#8216;Settings.csv&#8217; file from the images list, select either &#8216;Images only&#8217; or &#8216;Custom&#8217; with &#8216;Extension is tif, tiff, ome.tif, or ome.tiff&#8217; for the filter settings (Figure S8).</li>
	<li>Click on the Metadata input module. In the second extraction method, click on the yellow folder, and locate the CellProfiler subfolder in the WormAlign output folder (Figure S9). Select the Settings.csv file and link the settings in the file to the CellProfiler output by matching the metadata in the CSV files to those in the images (Channel metadata).&#160;</li>
	<li>Click on the NamesAndTypes input module and insert a new image for each channel of the original image to be quantified. 
	NOTE: Already present in the example pipeline are the worm mask, two color images, the line mask and the original image, and three grey scale images (the green and the red channel). The list in this module needs to be adjusted if the original images have more or fewer channels than the example images.</li>
	<li>Check that the name of the images in every column label/mask/worms is correct. 
	NOTE: The names of the images on one line should all correspond to the same initial image to analyze. If a mistake is made during the image analysis process, some of the output images will be missing and the names under the three columns label/mask/worms will not correspond to the same initial image anymore for each line. If this is the case, find where the mistake is.</li>
	<li>Press View output setting to select the folder to save the output from Cellprofiler.&#160;</li>
	<li>Click Start Test Mode. Once in Test Mode, double-check the settings of the pipeline by applying them to the first image in the dataset, by clicking Run (which will run through all active modules in the pipeline), or Step (which will run through the pipeline one module at a time). 
	NOTE: While in the Test Mode, the extracted measurements will not be exported, even if an ExportToSpreadsheet module is present (and active) in the pipeline.</li>
	<li>Once the pipeline performs the way it should, click Exit Test Mode and then press Analyze Images. As currently configured, the Worms_CP will (1) use the Mask_ image to segment a rough worm mask and the Lines_ image (Figure S10A) to single out the selected worms and produce single worm masks (Figure S10C), (2) measure the fluorescence intensity of all channels present in the input images in those worms identified and masked. The pipeline can also measure background intensity (Figure S10B) and correct all images accordingly (indicated by the word threshold in the name of the parameter measured. e.g.: Intensity_MeanIntensity_green_threshold), (3) measure the size of the selected worms, and (4) export all measured parameters (Figure S10D).</li>
	<li>Open the Worm_CP output consisting of two csv files, worms.csv and lines.csv that are&#160;saved in the selected output folder (see Figure S11). These files can be opened in Excel or R (Studio) for post-processing and reporting. If additional output is required, e.g. per image data, this can be selected in the ExportToSpreadsheet module in the pipeline.&#160;</li>
</ol>

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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=Microfluidics-enabled+phenotyping+of+a+whole+population+of+C.+elegans+worms+over+their+embryonic+and+post-embryonic+development+at+single-organism+resolution.">Microfluidics-enabled phenotyping of a whole population of C. elegans worms over their embryonic and post-embryonic development at single-organism resolution.</a> Microsystems &amp; Nanoengineering. 4 (6), (2018).</li><li>San-Miguel, A., Lu, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microfluidics+as+a+tool+for+C.+elegans+research.">Microfluidics as a tool for C. elegans research.</a> WormBook. , (2013).</li><li>Atakan, H. 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The authors have nothing to disclose.

Worm-align is a FIJI-based image processing pipeline that readily generates montages of user-selected worms, in which worms are straightened and aligned to aid visual comparison, classification and representation. Although this feature is also offered by some existing tools, notably the WormToolbox module in CellProfiler7, Worm-align requires comparatively little prior image analysis experience: Users need only to trace those worms they would like to select for the montage (and analysis). Although tracing the worms on the raw image data is an easy process -particularly when a touch-screen computer or tablet is available-, it is paramount, that lines are correctly drawn along the longitudinal axis of the worms. Incomplete lines, that follow only part of the worm, will result in partial worms in the montage (i.e., worms missing heads of tail ends) and partial segmentation masks during CellProfiler analysis. Also, if lines from two individual worms cross, the worms will not be correctly processed in the worm alignment montage as well as for fluorescence quantification. For quality control an overlay image of the line selections on the original image is saved in the data folder, along with a QC table. From these, problematic lines that will lead to incorrectly segmented worms can readily be identified and excluded from montage and/or subsequent analysis.
Although the direct input from the experimenter in the selection of worms perhaps seems a little time-consuming, it presents a clear advantage of the workflow over others in experiments where worms from different developmental stages are present in the same image: Worms can be selected during the &#8220;tracing step&#8221;, by outlining only those worms that are in the right developmental stage. Alternatively, worms can be filtered using the output from Worm_CP based on either the length of the tracing line, or the area of the segmentation mask, both reliable indicators of the length/size of the worms. Arguably, machine-learning algorithms may struggle to recognize worms from different developmental stages, as their size and appearance in the DIC images is so different.
The output of Worm-align can be used for subsequent quantification of fluorescence intensity in single worms, either in FIJI directly, or in other image analysis software platforms. We demonstrated this by importing the Worm-align output into a simple CellProfiler pipeline (Worm_CP), which allows the quantification of multi-channel fluorescence intensity in those individual worms that were selected while running the Worm-align pipeline. We chose this approach because of the flexibility of the CellProfiler software: It is straightforward to incorporate additional modules into the pipeline to analyse additional features in individual worms (e.g. measuring the size of lipid droplets, or stress granules, nuclei, mitochondria). In addition, the single worm masks could potentially be used to train a new model for WormToolbox7.
The main advantages of this method are&#160;that it is fast and requires a simple worm mounting set-up. This method is faster as it does not require spending time learning software operation nor running training sets through a machine algorithm7. Furthermore, this method works with either live or fixed worms simply mounted on regular agarose pads. There is no need to use complex microfluidic chambers, as developed in other methods5,6.

A relatively simple organism, the nematode C. elegans, is an extremely useful model system for studying human diseases. About 38% of the genes in the C. elegans genome have functional counterparts in humans1,2. One of the unique characteristics of C. elegans is that it is optically transparent, enabling easy access to in vivo information regarding (sub) cellular expression of fluorescent reporters across tissues. This makes C. elegans&#160;a prime model organism for high-content screens using image-based platforms3. However, one issue that often complicates these studies is that when imaging dense populations of worms, they tend to cross and to cluster, making comparisons across individual worms challenging, clouding downstream image analysis and quantitation.&#160;
Existing solutions that overcome this issue typically rely on the optimization of the culturing and imaging protocol, such as through the use of micro-fluidics setups4, allowing single worms to be captured in separate images5,6. Others have applied machine-learning algorithms allowing for recognition of single worms, even in a clumped population. An excellent example of the latter is the WormToolbox, which is a modular extension of the open-source image analysis platform, CellProfiler7. WormToolbox offers a high-throughput and high-content solution for analysis of C. elegans, and clearly benefits from its inclusion in CellProfiler, as additional analysis modules can easily be included. Although WormToolbox comes supplied with a pre-trained model (DefaultWormModel.xml), retraining of the machine-learning algorithm is usually required for each new application. Online tutorials on how to do this are available on Github (https://cp-website.github.io/Worm-Toolbox/). Despite this, installing and using WormToolbox requires a significant time-investment for&#160;novice users.&#160;
Here,&#160;we describe a simple and cost- and time-effective protocol to culture, and image populations of C. elegans. To allow the assessment of individual worms in the acquired images we have developed a simple open-source FIJI-based workflow, named Worm-align. Worm-align can be used to generate single- or multi-channel montages of straightened and aligned worms. Firstly, the user must manually select individual worms for analysis by drawing a line from the head to the tail. Worm-align will use this selection to crop selected worms from the overview image, and generate a montage in which selected worms are straightened and aligned to facilitate visual comparison and presentation.&#160;
In addition, the output of Worm-align can be used for subsequent quantification of fluorescence intensity in single worms, either in FIJI directly, or in other image analysis software platforms. We demonstrate this by importing the Worm-align output into Worm_CP, a pipeline that uses the open-source CellProfiler software. CellProfiler&#8217;s flexibility enables the incorporation of additional modules for high-content screening. We have used the Worm_CP pipeline to quantify the heat shock response, a well conserved protective mechanism that refolds proteins that are misfolded due to stressors such as high temperature8. Specifically, we applied the pipeline to worms carrying an integrated multi-copy transgene, where the promoter of a heat shock inducible gene, hsp-70(C12C8.1), drives green fluorescent protein (GFP)9. We have also used the Worm_CP pipeline on fixed animals that have been labelled with a fluorescent dye that visualizes lipid droplets (LDs), the main fat storage organelle in C. elegans10.&#160;While this workflow does not have the throughput offered by WormToolBox, it is a user-friendly and simple alternative for visual presentation and analysis of image-based C. elegans&#160;experiments.

<table><tbody><tr><td>Agarose</td><td>MeLford Biolaboratories Ltd</td><td>MB1200</td><td></td></tr><tr><td>Aspirator tube</td><td>Sigma-Aldrich</td><td>A5177</td><td>to create mouth micro-pipette (see protocol step)</td></tr><tr><td>Beaker</td><td></td><td></td><td></td></tr><tr><td>BODIPY 493/593</td><td>Invitrogen</td><td>D3922</td><td>stock solution prepared in DMSO at 1mg/mL</td></tr><tr><td>Centrifuge</td><td>MSE MISTRAL 1000</td><td></td><td></td></tr><tr><td>Conical flask</td><td></td><td></td><td></td></tr><tr><td>Cover Slip</td><td>VWR</td><td>631-0120</td><td></td></tr><tr><td>Filter 0.2 &amp;micro;m for Syringe</td><td>Sartrius</td><td>16534-K</td><td>Filter, to create mouth micro-pipette (see protocol step)</td></tr><tr><td>Isopropanol</td><td></td><td></td><td></td></tr><tr><td>Levamisole hydrochloride</td><td>Sigma-Aldrich</td><td>BP212</td><td>3mM solution prepared by dissolving levamisole in M9</td></tr><tr><td>Liquid Nitrogen</td><td></td><td></td><td>Liquid Nitrogen facility</td></tr><tr><td>Low Retention Tip 1000&amp;micro;L</td><td>Starlab</td><td>S1182-1830</td><td></td></tr><tr><td>Methanol</td><td>VWR chemicals</td><td>20847.307</td><td></td></tr><tr><td>Microscope Slides, MENZEL GLASSER</td><td>Thermo Scientific</td><td>BS7011/2</td><td></td></tr><tr><td>Microscope</td><td>Nikon</td><td></td><td>Eclipse Ti</td></tr><tr><td>Microwave Oven</td><td>Delongi</td><td></td><td></td></tr><tr><td>M9</td><td></td><td></td><td>prepared in the lab according to&lt;sup&gt;15&lt;/sup&gt;</td></tr><tr><td>9cm NGM Plates</td><td></td><td></td><td>prepared in the lab according to&lt;sup&gt;15&lt;/sup&gt;</td></tr><tr><td>PBS</td><td></td><td></td><td>prepared in the lab</td></tr><tr><td>Protein LoBind Tube 2ml</td><td>Eppendorf</td><td>22431102</td><td></td></tr><tr><td>Triton</td><td>SIGMA</td><td>T9284-500ML</td><td></td></tr><tr><td>Ring Caps</td><td>SIGMA-ALDRICH</td><td>Z611247-250EA</td><td>glass micro-capillary tubes to create mouth micro-pipette (see protocol step)</td></tr><tr><td>Rotator</td><td>Stuart Scientific</td><td></td><td></td></tr><tr><td>Silicone tubine translucent</td><td>Scientific Laboratory Suppliers</td><td>TSR0600200P</td><td>6.0 mm x 2.0 mm wall - to create mouth micro-pipette (see protocol step)</td></tr><tr><td>Sterilized H2O</td><td></td><td></td><td>MilliQ water autoclaved in the lab</td></tr><tr><td>10&amp;micro;L Tips</td><td>Starlab</td><td>S1120-3810</td><td></td></tr><tr><td>200&amp;micro;L Tips</td><td>Starlab</td><td>S1120-8810</td><td></td></tr><tr><td>1000&amp;micro;L Tips</td><td>Starlab</td><td>S1122-1830</td><td></td></tr><tr><td>15mL Centrifuge Tube</td><td>CORNING</td><td>430791</td><td></td></tr><tr><td>Vecta Shield</td><td>VECTOR</td><td>94010</td><td>antifade mounting medium (H-1000) without DAPI</td></tr></tbody></table>

worm-align and worm_cp, two open-source pipelines for straightening and quantification of fluorescence image data obtained from caenorhabditis elegans

An issue often encountered when acquiring image data from fixed or anesthetized C. elegans is that worms cross and cluster with their neighbors. This problem is aggravated with increasing density of worms and creates challenges for imaging and quantification. We developed a FIJI-based workflow, Worm-align, that can be used to generate single- or multi-channel montages of user-selected, straightened and aligned worms from raw image data of C. elegans. Worm-align is a simple and user-friendly workflow that does not require prior training of either the user or the analysis algorithm. Montages generated with Worm-align can aid the visual inspection of worms, their classification and representation. In addition, the output of Worm-align can be used for subsequent quantification of fluorescence intensity in single worms, either in FIJI directly, or in other image analysis software platforms. We demonstrate this by importing the Worm-align output into Worm_CP, a pipeline that uses the open-source CellProfiler software. CellProfiler&#8217;s flexibility enables the incorporation of additional modules for high-content screening.&#160;As a practical example, we have used the pipeline on two datasets: the first dataset are images of heat shock reporter worms that express green fluorescent protein (GFP) under the control of the promoter of a heat shock inducible gene hsp-70, and the second dataset are images obtained from fixed worms, stained for fat-stores with a fluorescent dye.

Watch this Scientific Journal Video about Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans at JoVE

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

Worm-align/Worm_CP is a simple FIJI/CellProfiler workflow that can be used to straighten and align Caenorhabditis elegans samples and to score whole-worm image-based assays without the need for prior training steps. We have applied Worm-align/Worm_CP to the quantification of&#160;heat-shock induced expression in live animals or lipid droplets&#160;in fixed samples.

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

An issue often encountered when acquiring image data from fixed or anesthetized C. elegans is that worms cross and cluster with their neighbors. This ...

worm-align-wormcp-two-open-source-pipelines-for-straightening

Research

JoVE Journal

Biology

6.0K Views.  Babraham Institute. We beschrijven hier een eenvoudige opensource Fiji-gebaseerde workflow genaamd Worm-align, die kan worden gebruikt om single of multi-channel montages van rechtgetrokken wormen van belang van ruwe microscopie beelden te genereren. Omdat deze workflow afhankelijk is van door de gebruiker geselecteerde dieren van belang, kan het vooral handig zijn bij het analyseren van afbeeldingen van C.elegans waar de wormen zich niet allemaal in de juiste fase of conditie bevinden. Opgemerkt moet worden dat...

Video: Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Sensory systems allow animals to detect, process, and respond to their environment. Food abundance is an environmental cue that has profound effects on animal physiology and behavior. Recently, we showed that modulation of longevity in the nematode Caenorhabditis elegans by food abundance is more complex than previously recognized. The responsiveness of the lifespan to changes in food level is determined by specific genes that act by controlling information processing within a neural circuit. Our framework combines genetic analysis, high-throughput quantitative imaging and information theory. Here, we describe how these techniques can be used to characterize any gene that has a physiological relevance to broad-range dietary restriction. Specifically, this workflow is designed to reveal how a gene of interest regulates lifespan under broad-range dietary restriction; then to establish how the expression of the gene varies with food level; and finally, to provide an unbiased quantification of the amount of information conveyed by gene expression about food abundance in the environment. When several genes are examined simultaneously under the context of a neural circuit, this workflow can uncover the coding strategy employed by the circuit.

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Here, we present a framework to relate broad-range dietary restriction to gene expression and lifespan. We describe protocols for broad-range dietary restriction and for quantitative imaging of gene expression under this paradigm. We further outline computational analyses to reveal underlying information processing features of the genetic circuits involved in food-sensing.

Sensory systems allow animals to detect, process, and respond to their environment. Food abundance is an environmental cue that has profound effects on ...

Genetics

Automated Quantification of Synaptic Fluorescence in C. elegans

Synapse strength refers to the amplitude of postsynaptic responses to presynaptic neurotransmitter release events, and has a major impact on overall neural circuit function. Synapse strength critically depends on the abundance of neurotransmitter receptors clustered at synaptic sites on the postsynaptic membrane. Receptor levels are established developmentally, and can be altered by receptor trafficking between surface-localized, subsynaptic, and intracellular pools, representing important mechanisms of synaptic plasticity and neuromodulation. Rigorous methods to quantify synaptically-localized neurotransmitter receptor abundance are essential to study synaptic development and plasticity. Fluorescence microscopy is an optimal approach because it preserves spatial information, distinguishing synaptic from non-synaptic pools, and discriminating among receptor populations localized to different types of synapses. The genetic model organism Caenorhabditis elegans is particularly well suited for these studies due to the small size and relative simplicity of its nervous system, its transparency, and the availability of powerful genetic techniques, allowing examination of native synapses in intact animals.
Here we present a method for quantifying fluorescently-labeled synaptic neurotransmitter receptors in C. elegans. Its key feature is the automated identification and analysis of individual synapses in three dimensions in multi-plane confocal microscope output files, tabulating position, volume, fluorescence intensity, and total fluorescence for each synapse. This approach has two principal advantages over manual analysis of z-plane projections of confocal data. First, because every plane of the confocal data set is included, no data are lost through z-plane projection, typically based on pixel intensity averages or maxima. Second, identification of synapses is automated, but can be inspected by the experimenter as the data analysis proceeds, allowing fast and accurate extraction of data from large numbers of synapses. Hundreds to thousands of synapses per sample can easily be obtained, producing large data sets to maximize statistical power. Considerations for preparing C. elegans for analysis, and performing confocal imaging to minimize variability between animals within treatment groups are also discussed. Although developed to analyze C. elegans postsynaptic receptors, this method is generally useful for any type of synaptically-localized protein, or indeed, any fluorescence signal that is localized to discrete clusters, puncta, or organelles.
The procedure is performed in three steps: 1) preparation of samples, 2) confocal imaging, and 3) image analysis. Steps 1 and 2 are specific to C. elegans, while step 3 is generally applicable to any punctate fluorescence signal in confocal micrographs.

Automated Quantification of Synaptic Fluorescence in C. elegans

The abundance of neurotransmitter receptors clustered at synapses strongly influences synaptic strength. This method quantifies fluorescently-labeled neurotransmitter receptors in three dimensions with single-synapse resolution in C. elegans, allowing hundreds of synapses to be rapidly characterized within a single sample without distortions introduced by z-plane projection.

Synapse strength refers to the amplitude of postsynaptic responses to presynaptic neurotransmitter release events, and has a major impact on overall ...

Neuroscience

A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

C. elegans is the premier system for the systematic analysis of cell fate specification and morphogenetic events during embryonic development. One challenge is that embryogenesis dynamically unfolds over a period of about 13 h; this half day-long timescale has constrained the scope of experiments by limiting the number of embryos that can be imaged. Here, we describe a semi-high-throughput protocol that allows for the simultaneous 3D time-lapse imaging of development in 80&#8211;100 embryos at moderate time resolution, from up to 14 different conditions, in a single overnight run. The protocol is straightforward and can be implemented by any laboratory with access to a microscope with point visiting capacity. The utility of this protocol is demonstrated by using it to image two custom-built strains expressing fluorescent markers optimized to visualize key aspects of germ-layer specification and morphogenesis. To analyze the data, a custom program that crops individual embryos out of a broader field of view in all channels, z-steps, and timepoints and saves the sequences for each embryo into a separate tiff stack was built. The program, which includes a user-friendly graphical user interface (GUI), streamlines data processing by isolating, pre-processing, and uniformly orienting individual embryos in preparation for visualization or automated analysis. Also supplied is an ImageJ macro that compiles individual embryo data into a multi-panel file that displays maximum intensity fluorescence projection and brightfield images for each embryo at each time point. The protocols and tools described herein were validated by using them to characterize embryonic development following knock-down of 40 previously described developmental genes; this analysis visualized previously annotated developmental phenotypes and revealed new ones. In summary, this work details a semi-high-throughput imaging method coupled with a cropping program and ImageJ visualization tool that, when combined with strains expressing informative fluorescent markers, greatly accelerates experiments to analyze embryonic development.

A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

This work describes a semi-high-throughput protocol that allows simultaneous 3D time-lapse imaging of embryogenesis in 80&#8211;100 C. elegans embryos in a single overnight run. Additionally, image processing and visualization tools are included to streamline data analysis. The combination of these methods with custom reporter strains enables detailed monitoring of embryogenesis.

C. elegans is the premier system for the systematic analysis of cell fate specification and morphogenetic events during embryonic development. One ...

Developmental Biology

A Microfluidic Platform for Longitudinal Imaging in Caenorhabditis elegans

In the last decade, microfluidic techniques have been applied to study small animals, including the nematode Caenorhabditis elegans, and have proved useful as a convenient live imaging platform providing capabilities for precise control of experimental conditions in real time. In this article, we demonstrate live imaging of individual worms employing WormSpa, a previously-published custom microfluidic device. In the device, multiple worms are individually confined to separate chambers, allowing multiplexed longitudinal surveillance of various biological processes. To illustrate the capability, we performed proof-of-principle experiments in which worms were infected in the device with pathogenic bacteria, and the dynamics of expression of immune response genes and egg laying were monitored continuously in individual animals. The simple design and operation of this device make it suitable for users with no previous experience with microfluidic-based experiments. We propose that this approach will be useful for many researchers interested in longitudinal observations of biological processes under well-defined conditions.

A Microfluidic Platform for Longitudinal Imaging in Caenorhabditis elegans

In this article, we demonstrate live imaging of individual worms employing a custom microfluidic device. In the device, multiple worms are individually confined to separate chambers, allowing multiplexed longitudinal surveillance of various biological processes.

In the last decade, microfluidic techniques have been applied to study small animals, including the nematode Caenorhabditis elegans, and have proved ...

Bioengineering

Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software

Cellular processes, such as chromosome assembly, segregation and cytokinesis,are inherently dynamic. Time-lapse imaging of living cells, using fluorescent-labeled reporter proteins or differential interference contrast (DIC) microscopy, allows for the examination of the temporal progression of these dynamic events which is otherwise inferred from analysis of fixed samples1,2. Moreover, the study of the developmental regulations of cellular processes necessitates conducting time-lapse experiments on an intact organism during development. The Caenorhabiditis elegans embryo is light-transparent and has a rapid, invariant developmental program with a known cell lineage3, thus providing an ideal experiment model for studying questions in cell biology4,5and development6-9. C. elegans is amendable to genetic manipulation by forward genetics (based on random mutagenesis10,11) and reverse genetics to target specific genes (based on RNAi-mediated interference and targeted mutagenesis12-15). In addition, transgenic animals can be readily created to express fluorescently tagged proteins or reporters16,17. These traits combine to make it easy to identify the genetic pathways regulating fundamental cellular and developmental processes in vivo18-21. In this protocol we present methods for live imaging of C. elegans embryos using DIC optics or GFP fluorescence on a compound epifluorescent microscope. We demonstrate the ease with which readily available microscopes, typically used for fixed sample imaging, can also be applied for time-lapse analysis using open-source software to automate the imaging process.

Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software

The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.

Cellular processes, such as chromosome assembly, segregation and cytokinesis,are inherently dynamic. Time-lapse imaging of living cells, using ...

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

It has become increasingly clear that neural circuit activity in behaving animals differs substantially from that seen in anesthetized or immobilized animals. Highly sensitive, genetically encoded fluorescent reporters of Ca2+ have revolutionized the recording of cell and synaptic activity using non-invasive optical approaches in behaving animals. When combined with genetic and optogenetic techniques, the molecular mechanisms that modulate cell and circuit activity during different behavior states can be identified.
Here we describe methods for ratiometric Ca2+ imaging of single neurons in freely behaving Caenorhabditis elegans worms. We demonstrate a simple mounting technique that gently overlays worms growing on a standard Nematode Growth Media (NGM) agar block with a glass coverslip, permitting animals to be recorded at high-resolution during unrestricted movement and behavior. With this technique, we use the sensitive Ca2+ reporter GCaMP5 to record changes in intracellular Ca2+ in the serotonergic Hermaphrodite Specific Neurons (HSNs) as they drive egg-laying behavior. By co-expressing mCherry, a Ca2+-insensitive fluorescent protein, we can track the position of the HSN within ~ 1 &#181;m and correct for fluctuations in fluorescence caused by changes in focus or movement. Simultaneous, infrared brightfield imaging allows for behavior recording and animal tracking using a motorized stage. By integrating these microscopic techniques and data streams, we can record Ca2+ activity in the C. elegans egg-laying circuit as it progresses between inactive and active behavior states over tens of minutes.

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

This protocol describes the use of genetically encoded Ca2+ reporters to record changes in neural activity in behaving Caenorhabditis elegans worms. &#8195;

It has become increasingly clear that neural circuit activity in behaving animals differs substantially from that seen in anesthetized or immobilized ...

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here protocols are presented for preparing embryos and adult C. elegans animals for short- and long-term time-lapse microscopy and methods for tracking and quantification of developmental processes. The methods presented are all based on C. elegans strains available from the Caenorhabditis Genetics Center and on open-source software that can be easily implemented in any laboratory independently of the microscopy system used. A reconstruction of a 3D cell-shape model using the modelling software IMOD, manual tracking of fluorescently-labeled subcellular structures using the multi-purpose image analysis program Endrov, and an analysis of cortical contractile flow using PIVlab (Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB) are shown. It is discussed how these methods can also be deployed to quantitatively capture other developmental processes in different models, e.g., cell tracking and lineage tracing, tracking of vesicle flow.

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Here it is shown how to track and quantify developmental processes in C. elegans. The methods presented are based on open-source tools that can be easily implemented. It is demonstrated how to reconstruct 3D cell-shape models, how to manually track subcellular structures, and how to analyze cortical contractile flow.

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here ...

Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

In this protocol, a green fluorescence protein (GFP) fusion protein and 4',6-diamidino-2-phenylindole (DAPI) staining are used to track protein subcellular localization changes; in particular, a nuclear translocation under a heat stress condition. Proteins react correspondingly to external and internal signals. A common mechanism is to change its subcellular localization. This article describes a protocol to track protein localization that does not require an antibody, radioactive labeling, or a confocal microscope. In this article, GFP is used to tag the target protein EXL-1 in C. elegans, a member of the chloride intracellular channel proteins (CLICs) family, including mammalian CLIC4. An integrated translational exl-1::gfp transgenic line (with a promoter and a full gene sequence) was created by transformation and &#947;-radiation, and stably expresses the gene and gfp. Recent research showed that upon heat stress, not oxidative stress, EXL-1::GFP accumulates in the nucleus. Overlapping the GFP signal with both the nuclei structure and the DAPI signals confirms the EXL-1 subcellular localization changes under stress. This protocol presents two different fixation methods for DAPI staining: ethanol fixation and acetone fixation. The DAPI staining protocol presented in this article is fast and efficient and preserves both the GFP signal and the protein subcellular localization changes. This method only requires a fluorescence microscope with Nomarski, a FITC filter, and a DAPI filter. It is suitable for a small laboratory setting, undergraduate student research, high school student research, and biotechnology classrooms.

Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

This protocol demonstrates how to track a protein nuclear translocation under heat stress by using a green fluorescence protein (GFP) fusion protein as a marker and 4',6-diamidino-2-phenylindole (DAPI) staining. The DAPI staining protocol is fast and preserves the GFP and protein subcellular localization signals.

In this protocol, a green fluorescence protein (GFP) fusion protein and 4',6-diamidino-2-phenylindole (DAPI) staining are used to track protein ...

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Defining the cellular mechanisms underlying disease is essential for the development of novel therapeutics. A strategy frequently used to unravel these mechanisms is to introduce mutations in candidate genes and qualitatively describe changes in the morphology of tissues and cellular organelles. However, qualitative descriptions may not capture subtle phenotypic differences, might misrepresent phenotypic variations across individuals in a population, and are frequently assessed subjectively. Here, quantitative approaches are described to study the morphology of tissues and organelles in the nematode Caenorhabditis elegans using laser scanning confocal microscopy combined with commercially available bio-image processing software. A quantitative analysis of phenotypes affecting synapse integrity (size and integrated fluorescence levels), muscle development (muscle cell size and myosin filament length), and mitochondrial morphology (circularity and size) was performed to understand the effects of genetic mutations on these cellular structures. These quantitative approaches are not limited to the applications described here, as they could readily be used to quantitatively assess the morphology of other tissues and organelles in the nematode, as well as in other model organisms.

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

This study outlines quantitative measurements of synaptic size and localization, muscle morphology, and mitochondrial shape in C. elegans using freely available image processing tools. This approach allows future studies in&#160;C. elegans&#160;to quantitatively compare the extent of tissue and organelle structural changes as a result of genetic mutations.

Defining the cellular mechanisms underlying disease is essential for the development of novel therapeutics. A strategy frequently used to unravel these ...

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 ...

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

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