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.

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			<td>Sigma-Aldrich</td>
			<td style="width:344px;">A5177</td>
			<td>to create mouth micro-pipette (see protocol step)</td>
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			<td height="34" style="height:35px;">Beaker</td>
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			<td>stock solution prepared in DMSO at 1mg/mL</td>
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			<td>631-0120</td>
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			<td>Filter, to create mouth micro-pipette (see protocol step)</td>
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			<td height="40" style="height:40px;">Isopropanol</td>
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			<td>3mM solution prepared by dissolving levamisole in M9</td>
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			<td>Liquid Nitrogen facility</td>
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			<td height="40" style="height:40px;">Low Retention Tip 1000&#181;L</td>
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			<td>S1182-1830</td>
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			<td>VWR chemicals</td>
			<td>20847.307</td>
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			<td height="40" style="height:40px;width:216px;">Microscope Slides, MENZEL GLASSER</td>
			<td>Thermo Scientific</td>
			<td>BS7011/2</td>
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			<td>Eclipse Ti</td>
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			<td>Delongi</td>
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			<td>prepared in the lab according to15</td>
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			<td height="35" style="height:35px;">9cm NGM Plates</td>
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			<td>prepared in the lab according to15</td>
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			<td height="38" style="height:39px;">PBS</td>
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			<td>prepared in the lab</td>
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			<td height="40" style="height:40px;">Protein LoBind Tube 2ml</td>
			<td>Eppendorf</td>
			<td>22431102</td>
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			<td height="40" style="height:40px;">Triton</td>
			<td>SIGMA</td>
			<td>T9284-500ML</td>
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			<td height="40" style="height:40px;">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>
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			<td height="40" style="height:40px;">Rotator</td>
			<td>Stuart Scientific</td>
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			<td height="40" style="height:40px;">Silicone tubine translucent</td>
			<td>Scientific Laboratory Suppliers</td>
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			<td height="41" style="height:41px;">Sterilized H2O</td>
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			<td>Starlab</td>
			<td>S1120-3810</td>
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			<td height="40" style="height:40px;">200&#181;L Tips</td>
			<td>Starlab</td>
			<td>S1120-8810</td>
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			<td height="40" style="height:40px;">1000&#181;L Tips</td>
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			<td>S1122-1830</td>
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			<td height="40" style="height:40px;">15mL Centrifuge Tube</td>
			<td style="width:221px;">CORNING</td>
			<td>430791</td>
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			<td>VECTOR</td>
			<td>94010</td>
			<td>antifade mounting medium (H-1000) without DAPI</td>
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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

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Biology

5.9K Views.  Babraham Institute. 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 heat-shock induced expression in live animals or lipid droplets in fixed samples.

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

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 ...

Vampiric Isolation of Extracellular Fluid from Caenorhabditis elegans

The genetically tractable model organism C. elegans has provided insights into a myriad of biological questions, enabled by its short generation time, ease of growth and small size. This small size, though, has disallowed a number of technical approaches found in other model systems. For example, blood transfusions in mammalian systems and grafting techniques in plants enable asking questions of circulatory system composition and signaling. The circulatory system of the worm, the pseudocoelom, has until recently been impossible to assay directly. To answer questions of intercellular signaling and circulatory system composition C. elegans researchers have traditionally turned to genetic analysis, cell/tissue specific rescue, and mosaic analysis. These techniques provide a means to infer what is happening between cells, but are not universally applicable in identification and characterization of extracellular molecules. Here we present a newly developed technique to directly assay the pseudocoelomic fluid of C. elegans. The technique begins with either genetic or physical manipulation to increase the volume of extracellular fluid. Afterward the animals are subjected to a vampiric reverse microinjection technique using a microinjection rig that allows fine balance pressure control. After isolation of extracellular fluid, the collected fluid can be assayed by transfer into other animals or by molecular means. To demonstrate the effectiveness of this technique we present a detailed approach to assay a specific example of extracellular signaling molecules, long dsRNA during a systemic RNAi response. Although characterization of systemic RNAi is a proof of principle example, we see this technique as being adaptable to answer a variety of questions of circulatory system composition and signaling.

Vampiric Isolation of Extracellular Fluid from Caenorhabditis elegans

The model organism C. elegans uses pseudocoelomic fluid as a passive circulatory system. Direct assay of this fluid has not been previously possible. Here we present a novel technique to directly assay the extracellular space, and use systemic silencing signals during an RNAi response as a proof of principle example.

The genetically tractable model organism C. elegans has provided insights into a myriad of biological questions, enabled by its short generation time, ...

Basic Caenorhabditis elegans Methods: Synchronization and Observation

Research into the molecular and developmental biology of the nematode Caenorhabditis elegans was begun in the early seventies by Sydney Brenner and it has since been used extensively as a model organism 1. C. elegans possesses key attributes such as simplicity, transparency and short life cycle that have made it a suitable experimental system for fundamental biological studies for many years 2. Discoveries in this nematode have broad implications because many cellular and molecular processes that control animal development are evolutionary conserved 3.
C. elegans life cycle goes through an embryonic stage and four larval stages before animals reach adulthood. Development can take 2 to 4 days depending on the temperature. In each of the stages several characteristic traits can be observed. The knowledge of its complete cell lineage 4,5 together with the deep annotation of its genome turn this nematode into a great model in fields as diverse as the neurobiology 6, aging 7,8, stem cell biology 9 and germ line biology 10. 
An additional feature that makes C. elegans an attractive model to work with is the possibility of obtaining populations of worms synchronized at a specific stage through a relatively easy protocol. The ease of maintaining and propagating this nematode added to the possibility of synchronization provide a powerful tool to obtain large amounts of worms, which can be used for a wide variety of small or high-throughput experiments such as RNAi screens, microarrays, massive sequencing, immunoblot or in situ hybridization, among others. 
Because of its transparency, C. elegans structures can be distinguished under the microscope using Differential Interference Contrast microscopy, also known as Nomarski microscopy. The use of a fluorescent DNA binder, DAPI (4',6-diamidino-2-phenylindole), for instance, can lead to the specific identification and localization of individual cells, as well as subcellular structures/defects associated to them.

Basic Caenorhabditis elegans Methods: Synchronization and Observation

The easiness of maintaining and propagating the nematode C. elegans make it a nice model organism to work with. The possibility of synchronizing worms allows the work with a significant amount of subjects at the same developmental stage, what facilitates the study of one particular process in many animals.

Research into the molecular and developmental biology of the nematode Caenorhabditis elegans was begun in the early seventies by Sydney Brenner and it has ...

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.
Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software1&#160;to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly ...

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 ...

Observation and Quantification of Telomere and Repetitive Sequences Using Fluorescence In Situ Hybridization (FISH) with PNA Probes in Caenorhabditis elegans

Telomere is a ribonucleoprotein structure that protects chromosomal ends from aberrant fusion and degradation. Telomere length is maintained by telomerase or an alternative pathway, known as alternative lengthening of telomeres (ALT)1. Recently, C. elegans has emerged as a multicellular model organism for the study of telomere and ALT2. Visualization of repetitive sequences in the genome is critical in understanding the biology of telomeres. While telomere length can be measured by telomere restriction fragment assay or quantitative PCR, these methods only provide the averaged telomere length. On the contrary, fluorescence in situ hybridization (FISH) can provide the information of the individual telomeres in cells. Here, we provide protocols and representative results of the method to determine telomere length of C. elegans by fluorescent in situ hybridization. This method provides a simple, but powerful, in situ procedure that does not cause noticeable damage to morphology. By using fluorescently labeled peptide nucleic acid (PNA) and digoxigenin-dUTP-labeled probe, we were able to visualize two different repetitive sequences: telomere repeats and template of ALT (TALT) in C. elegans embryos and gonads.

Observation and Quantification of Telomere and Repetitive Sequences Using Fluorescence In Situ Hybridization FISH with PNA Probes in Caenorhabditis elegans

We report a concise procedure of fluorescence in situ hybridization (FISH) in the gonad and embryos of Caenorhabditis elegans for observing and quantifying repetitive sequences. We successfully observed and quantified two different repetitive sequences, telomere repeats and template of alternative lengthening of telomeres (TALT).

Telomere is a ribonucleoprotein structure that protects chromosomal ends from aberrant fusion and degradation. Telomere length is maintained by telomerase ...

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in &#181;Manager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in &#181;Manager and data analysis is undertaken in FLIMfit.

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

Quantification of Lipid Abundance and Evaluation of Lipid Distribution in Caenorhabditis elegans by Nile Red and Oil Red O Staining

Caenorhabditis elegans is an exceptional model organism in which to study lipid metabolism and energy homeostasis. Many of its lipid genes are conserved in humans and are associated with metabolic syndrome or other diseases. Examination of lipid accumulation in this organism can be carried out by fixative dyes or label-free methods. Fixative stains like Nile red and oil red O are inexpensive, reliable ways to quantitatively measure lipid levels and to qualitatively observe lipid distribution across tissues, respectively. Moreover, these stains allow for high-throughput screening of various lipid metabolism genes and pathways. Additionally, their hydrophobic nature facilitates lipid solubility, reduces interaction with surrounding tissues, and prevents dissociation into the solvent. Though these methods are effective at examining general lipid content, they do not provide detailed information about the chemical composition and diversity of lipid deposits. For these purposes, label-free methods such as GC-MS and CARS microscopy are better suited, their costs notwithstanding.

Quantification of Lipid Abundance and Evaluation of Lipid Distribution in Caenorhabditis elegans by Nile Red and Oil Red O Staining

Nile red staining of fixed Caenorhabditis elegans is a method for quantitative measurement of neutral lipid deposits, while oil red O staining facilitates qualitative assessment of lipid distribution among tissues.

Caenorhabditis elegans is an exceptional model organism in which to study lipid metabolism and energy homeostasis. Many of its lipid genes are conserved ...

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts whose expression is associated with insecticide resistance phenotypes in Anopheles gambiae mosquitoes. The application can be used online or downloaded and used locally by anyone. The local application can be modified to add new insecticide resistance datasets generated from multiple -omics platforms. This guide demonstrates how to add new datasets and handle missing data. Furthermore, IR-TEx can be completely and easily recoded to use-omics datasets from any experimental data, making it a valuable resource to many researchers. The protocol illustrates the utility of IR-TEx in identifying new insecticide resistance candidates using the the microsomal glutathione transferase, GSTMS1, as an example. This transcript is upregulated in multiple pyrethroid resistant populations from C&#244;te D&#39;Ivoire and Burkina Faso. The identification of co-correlated transcripts provides further insight into the putative roles of this gene.

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any platform.

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts ...