This work was conducted in the Phenotyping Core Facility, Donald Danforth Plant Science Center (RRID: SCR_019049), which provided funding support to K.M.M. and J.G.D. This work was supported by the Donald Danforth Plant Science Center Enterprise Rent-A-Car Research Institute Grant. D.S. was funded by the Taylor Geospatial Institute Block Grant and a US Economic Development Administration Center for AgTech and Applied Location Science and Technology (CATALST) grant (ED21HDQ0240072). We thank Kevin Reilly and Kris Haines (Integrated Plant Growth Facility, Donald Danforth Plant Science Center, RRID: SCR_024902) for their expertise and assistance in plant growth. We thank Noah Fahlgren, Malia Gehan, and Haley Schuhl for their advice on PlantCV.

The details on the reagents and the equipment used in this protocol are listed in the Table of Materials.
1. Preparation of photo studio
<ol>
	<li>Hang black photography-grade fabric from a wall or ceiling for side-view imaging. Drape fabric at least 0.5 m onto the floor so that the plant is placed on top of the fabric (Figure 1). 
	NOTE: Fabric should be at least 100 cm taller and wider than the plant to be imaged.</li>
	<li>Place a color card on the backdrop, propped up against an object such as a box so that it is visible in the frame (Figure 1). For plants larger than 30 cm tall or wide, use a color card that is at least 20 cm wide. For plants smaller than 30 cm tall or wide, use a color card that is between 4-8 cm wide.</li>
	<li>Plug an SD card into a laptop or desktop computer. Download Raspberry Pi Imager from the software section on the website (https://www.raspberrypi.com/software/), and download the provided raspberrypi-image-capture.img.gz file (https://github.com/danforthcenter/photo-maize-paper) to the&#160;laptop or desktop computer.</li>
	<li>Launch the Imager by opening the downloaded imager (.exe) file. Click on the Choose OS button and select the Use Custom button at the bottom. Select the image-capture.img.gz file.
	<ol>
		<li>Next, click on the Choose Storage button and select the SD card plugged into the computer. Click on the Write button. 
		NOTE: Ensure the correct SD card has been selected, as this process will delete all data stored on the card.</li>
	</ol>
	</li>
	<li>Connect the latest version of SBC to a keyboard using a USB cord, to an external monitor using an HDMI cord, and to a power strip using a power cord with a switch. Connect the monitor to the power strip using the power cord provided with the monitor. Place the SD card into the SBC. See Figure 1 for a labeled schematic of the setup.</li>
	<li>Turn on the power strip by pushing the switch on the strip, turn on the monitor using the button on the monitor, then turn on the SBC by flipping the switch on the power cord to &#34;on&#34;.</li>
	<li>Connect the SBC to the local internet using WIFI or an ethernet cable.</li>
	<li>If photos are to be transferred directly to a server or cloud storage:
	<ol>
		<li>Edit the file &#34;CAPTURE-PHOTO.py&#34; on the SBC to contain the file path to complete the transfer.</li>
		<li>Edit the win-credentials file with the path and password for the server or cloud storage using the following command in the terminal: sudo nano /etc/win-credentials. 
		NOTE: Contact the internet system administrator to ensure the folder has permission to transfer files.</li>
	</ol>
	</li>
	<li>Connect a wireless barcode scanner to the SBC using manufacturer&#39;s instructions provided with the barcode scanner, either using a USB or Bluetooth.</li>
	<li>Turn off the SBC by clicking on the Raspberry Pi button on the upper left of the screen, clicking on Logout, and then clicking on Shutdown to safely shut off the SBC. After shutdown, switch the SBC power switch &#34;on&#34; the power cord to &#34;off&#34; and the power strip to &#34;off&#34;.</li>
</ol>
2. Plant growth and stress treatment
NOTE: Any plant is suitable for this method of phenotyping; these plant growth methods are applicable to the representative results presented here.
<ol>
	<li>Obtain seeds of B73 or another maize variety from a stock center.</li>
	<li>Prepare soil media and growth conditions:
	<ol>
		<li>Fill 3.5-inch pots (0.7 L) with a 1:1 by volume mix of Turface MVP and Berger BM7-35 with 3 lb/cubic yard of slow-release fertilizer 14-14-14 (N-P-K). Place pots in a tray with holes and water thoroughly with reverse osmosis (RO) water.</li>
		<li>Set a growth chamber to the following environmental conditions: 30 &#176;C day/20 &#176;C night, 16 h day length, 60% humidity, and 500 mmol.m-2s-1 of light at the level of the pot during the day. Set a separate growth chamber with the same conditions, except 38 &#176;C day/28 &#176;C night for heat stress.</li>
	</ol>
	</li>
	<li>Plant 20 maize seeds, each in a 3.5-inch pot, by placing ~2 cm deep in the soil and covering it with the displaced soil. Water thoroughly with RO water and place in the control growth chamber. Water daily with liquid fertilizer (mixed according to the manufacturer&#39;s instructions; see Table of Materials). 
	NOTE: Planting extra plants that are needed for the experiment will account for poor germination and lack of uniformity prior to the start of a stress application. Germination rate will vary by variety.</li>
	<li>After 21 days of growth, transplant 16 of the most uniform plants (n = 4 plants per treatment) to 3.79 L pots filled with the same potting soil mix, pre-wet with RO water. Return plants to the control growth chamber.</li>
	<li>On day 25 (4 days after transplanting) move half of the plants (n = 8) to the heat stress chamber at the time lights turn on in the chamber. Stop watering the drought-stressed plants in both chambers (n = 4 per chamber). 
	NOTE: Drought stress plants may need to be watered so that the plants survive until the end of the experiment; here, drought plants were watered on day 31 and day 37 with fertilizer water.</li>
	<li>On day 38 (13 days after the start of the stress), remove plants from the growth chambers for imaging.</li>
</ol>
3. Plant imaging
<ol>
	<li>Start up the SBC (see Figure 2):
	<ol>
		<li>Turn on the power strip using the switch on the power strip.</li>
		<li>Turn on the monitor using the button on the monitor. 
		NOTE: If the SBC is turned on before the monitor is turned on, it will not start correctly.</li>
		<li>Turn the switch on the SBC power cord to &#34;on&#34;. 
		NOTE: This will turn on the SBC, and it will boot up on the monitor; there is no &#34;on&#34; switch on the SBC itself, only on the power cord.</li>
	</ol>
	</li>
	<li>Place the camera on a tripod or stable table. 
	NOTE: The entire plant and color card should be in view on the camera screen. The camera should be perpendicular to the ground and have a direct view (no angle) of the center of the plant. The camera must be compatible with the gphoto2 package, and a list can be found here:&#160;http://www.gphoto.org/proj/libgphoto2/support.php.&#160;</li>
	<li>Provide power to the camera via a power cord with the camera or batteries. Turn the camera on using the button on the camera.</li>
	<li>Connect the camera to the USB port on the SBC using the data transfer cable provided with the camera.</li>
	<li>Arrange plant in photography setup:
	<ol>
		<li>Place a piece of dark-colored tape on the photography-grade fabric where the pot will be placed to ensure the placement is the same for each plant. Ensure the color card is in line with the piece of tape. 
		NOTE: Place the color card at least 10 cm from the edge of the plant. During quality control in the PlantCV workflow (below), the image should be able to be cropped to include only the plant and pot but not the color card to ensure that the color chips are not included in the ultimate trait extraction. See Figure 2 for examples of high and low-quality images.</li>
		<li>Place the pot on the piece of tape. For plants that grow in a plane, such as maize (Zea mays) and sorghum (Sorghum bicolor), face the widest angle of the plant towards the camera. For plants that do not grow in a plane, capture 0- and 90-degree angles of the plant, and average the resulting quantitative measurements after image analysis is complete.</li>
	</ol>
	</li>
	<li>On the SBC desktop screen shown on the monitor, open the terminal by clicking on the terminal button on the upper left menu bar.</li>
	<li>If saving directly to a server or cloud storage, mount the storage using the following command by editing to the necessary cloud storage path (cloud-storage-path in the below example) and password, typing the line of code into the open terminal window, and hitting enter on the keyboard. If unchanged from factory settings, the user ID (UID) will be &#34;pi&#34;. 
	NOTE: 
	sudo mount -t cifs -o credentials=/etc/win-credentials,uid=pi //cloud-storage-path /mnt/cloud-storage</li>
	<li>Type the following code into the terminal and hit enter on the keyboard. A new window will appear. 
	./RUN-PHOTOSTUDIO-SHELL.sh 
	NOTE:&#160;The RUN-PHOTOSTUDIO-SHELL.sh file calls the CAPTURE-PHOTO.py file, which are pre-loaded onto the pi if it was loaded using the above method. Alternatively, these scripts have been provided on GitHub for putting onto an existing Raspberry Pi SD card.&#160;</li>
	<li>A graphical user interface window will open. Type in the name of the image, or scan the barcode or QR code of the plant using a barcode scanner.</li>
	<li>Select an option to save the image locally or to a mounted server/cloud storage.</li>
	<li>Click Click here to take a picture! button.</li>
	<li>If local storage was chosen, open the PHOTOS folder on the desktop to examine the image. If a server or cloud storage was chosen, open the image in that location.</li>
	<li>If necessary, adjust the settings on the camera by unplugging it from the SBC, adjusting using the manufacturer&#39;s instructions, and plugging it back into the SBC. 
	NOTE: Settings such as exposure, brightness contrast, etc., may need to be adjusted depending on the lighting in the room and the object being imaged. A common mistake is to over-expose the images. To check the exposure of a test image, proceed with the test image through the PlantCV image analysis workflow, which includes a quality control check for exposure. Adjust the camera settings as needed to reduce or increase the exposure until the histograms are appropriately distributed.</li>
	<li>After all images have been captured, transfer the images from the SBC to a local computer or cloud storage using the preferred method, such as USB storage, internet browser, or SSH transfer.</li>
</ol>
4. Trait extraction from images
<ol>
	<li>Download the following files to a local computer or server: &#34;photo-studio-SV-notebook.ipynb&#34;, &#34;workflow.py&#34;, and &#34;photo-studio-SV-config.json&#34;. All files are available on GitHub (https://github.com/danforthcenter/photo-maize-paper).</li>
	<li>Install PlantCV on the local computer or server using the steps described (https://plantcv.readthedocs.io/en/latest/installation/).&#160;</li>
	<li>Open the file photo-studio-SV-notebook.ipynb using the preferred code editor, such as JupyterLab or Visual Studio Code.</li>
	<li>Run each block of code, editing as necessary given the parameters outlined in the code block to obtain a clean mask of only the plant in the image.</li>
	<li>When satisfied with the analysis of the sample image, open the file workflow.py in the preferred code editor. Edit this file with any parameters changed in &#34;photo-studio-SV-notebook.ipynb&#34; and save it.</li>
	<li>Open the file photo-studio-SV-config.json and edit the file paths to direct to a folder of input images, a desired output folder, etc., as denoted in the file.</li>
	<li>Open a terminal and run the following code to activate PlantCV. To do so, type the code conda activate plantcv&#160;into the terminal and hit enter on the keyboard.</li>
	<li>Change the following line of code to the file path of the &#34;photo-studio-SV-config.json&#34; file (this is the only change to make in the below code).
	<ol>
		<li>In the terminal, type the edited code and hit enter on the keyboard. This will run the workflow over all of the images captured in the experiment. 
		NOTE: 
		plantcv-run-workflow --config /Users/USERNAME/photo-studio-SV-config.json</li>
	</ol>
	</li>
	<li>Inspect the output images to determine if the code was appropriate for all images. 
	NOTE: The blue and pink outline of the plant should be around the edges of the plant, and there should be no other objects in the image. If necessary, go back to the &#34;photo-studio-SV-analysis.ipynb&#34; and test problematic images and edit the code, specifically the masking steps and noise reduction, to obtain the desired results. See Figure 3 for examples of high, acceptable, and low-quality image segmentation.</li>
	<li>The output file will be in the form of a .json file. Go back to the terminal (ensure conda activate plantcv has already been run), type the following code, and hit enter on the keyboard to convert the file to .csv for downstream statistical analysis, changing the file path to that of the &#34;results-photo-studio.json&#34; file. 
	plantcv-utils json2csv -j /Users/USERNAME/results-photo-studio.json -c /Users/USERNAME/results-photo-studio.csv 
	NOTE: &#160;To convert the measurements from pixels to a standard unit of measure, such as cm, utilize the conversion provided from the find_color_card function in the PlantCV workflow.</li>
	<li>Perform statistical analysis of the output quantitative traits to determine differences in plant phenotypes due to different plant species, varieties, treatments, and timepoints. 
	NOTE: The pcvr package in R Studio has been formulated to analyze the outputs from PlantCV for statistical significance and plotting of results20. See a summary and visual representation of traits collected in Figure 4.</li>
</ol>

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Image-based phenotyping provides accurate, fast, quantitative measurements of plant phenotypes4,5,6,7. This method is significantly faster, more precise, more accurate, and provides a greater number of measurements than manually using a ruler to record plant height, or qualitative measures of plant color features that may relate to health4,5,6,7. Leaf area was seen here, and in previous research10, to strongly correlate to plant biomass (Figure 8), suggesting this measure could reliably replace the time-consuming, destructive, and error-prone process of manually weighing whole-plant biomass. While a camera alone could be used for capturing the images, the addition of the low-cost SBC increased throughput through automation of image collection and image labeling with necessary metadata15. This method is suitable for one to ~300 plants that are imaged once daily (or less frequently). For larger numbers of plants in an experiment, a conveyer-based, overhead gantry, or robot-enabled imaging system is more suitable since the time to move the plants individually for imaging may be longer than a working day11,13. For more frequent timepoints (for example, an image every hour rather than once per day or less frequently), camera setups inside of a growth chamber are more suitable because they capture images automatically, but are limited by the size of the plants, and need additional SBC15.
While PlantCV is presented as the image analysis option here, other software exists. ImageJ (Fiji) is a free software package for image analysis that can also be used for images of plants through a graphical user interface (GUI)8; however, it is not particularly developed for whole-plant image analysis, is less adaptable to high-throughput computing, and is less easily repeatable than already-working pipelines like those in PlantCV that can be easily applied to new images9. Deep Learning (such as BioDock, see Table of Materials) and Segment Anything22) methods are attractive for their ability to easily segment plants from the background, and could be used for the segmentation tasks described here. However, these Deep Learning models do not provide the plant phenotype measurements described here10, and to make these more complex measurements of quantitative phenotypes may require a large amount of training images (oftentimes greater than 10,000) and computing power beyond a basic laptop or desktop computer10. These models also require significant computer memory and processing power, which are unavailable to many researchers10.
The most critical step of the protocol is arranging the plant imaging setup and ensuring there are no objects (such as other plants, a color card, or plant debris) close to the plant of interest14. If images are not set appropriately, or a color card is not used, they will be impossible to analyze later because there is no mechanism for standardization. While no calibration steps are necessary in this protocol, color correction and size standardization during processing are essential. Always ensure the entire color card is in frame, but not overlapping with the plant (Figure 2). Another common pitfall is uneven lighting, which can cause shadows or bright shots; we recommend using bright, diffuse overhead light and covering windows in the room to prevent uneven lighting (Figure 2). Additional lamps can be added at a face-on angle to the plant if overhead lights are causing shadows. The type of lightbulb is not a concern, given that color will be corrected in the image using a color card. Blurred images are not acceptable for analysis - use auto-focus on the camera, or the manufacturer&#39;s instructions for manual focus to ensure the images are clear.
Similarly, ensuring images are not over-exposed (a common mistake) allows for appropriate analysis (Figure 2); over-exposed images cannot be fixed in a color correction during image analysis because the pixel values are beyond the camera&#39;s detection range with the current settings14. To prevent over-exposure, evaluate the color histograms or RGB values for a test image using the PlantCV workflow described here (Figure 3), and reduce the brightness setting on the camera if the exposure test determines the image is overexposed. The RGB values (and thus the histograms) should not have values at 0 or at 255, which is the value range because it is possible those values are above or below the range of detection and can only record the minimum or maximum rather than the true value. A test image should always be evaluated using these methods for over-exposure (Figure 2 and Figure 3); we suggest utilizing the PlantCV exposure quality control provided in this publication as a check, which determines a quality image to have less than 5 percent of pixels at 0 or 255 for each color. As a rule of thumb, cameras and analysis software provide histograms, which should have equal and complete histogram tails; values cut off below 0 (underexposed) or above 255 (overexposed) should be re-taken with adjusted camera settings. We suggest choosing a location with bright, even, artificial lighting with no windows for illumination, as windows can be too bright, uneven, and frequently change light exposure to the space. Images should appear evenly lit, and the gray squares of a color card should not appear white, which suggests over-exposure.
Troubleshooting may be necessary for the image capture code depending on the IP and security settings of the user&#39;s institution. To troubleshoot, contact the IT department for internet connectivity and file transfer issues. A common mistake is using the wrong size SD card for the SBC, or turning on the SBC before the connected monitor is turned on. To troubleshoot, follow the above protocol precisely, and re-load the SBC software. Online forums are an excellent resource for troubleshooting and finding solutions for the specific camera of choice when using gphoto2. To troubleshoot PlantCV image analysis and use the most up-to-date versioning, utilize the documentation page (https://plantcv.readthedocs.io/en/latest/), or post an issue on GitHub (https://github.com/danforthcenter/plantcv/issues). In particular, if the thresholding method in this protocol does not sufficiently separate the plant from the background, utilize alternative thresholding methods provided in &#8220;photo-studio-SV-notebook.ipynb&#8221;&#160;or on the documentation page.
This method can be applied to any plant species and treatment that can be transported to the assembled phototography setup described. For example, it is not applicable to plants growing in a field, since they cannot be transported to the photography setup without digging up the plant. In this situation, plants could be cut down and subsequently imaged using the protocol, although the measurement would be destructive. Multiple plants can be captured in a single image and analyzed, but a user should beware of keeping plants from touching each other (Figure 2), and plants are placed in the same location on the backdrop each time to ensure the workflow can be run automatically over the entire set of images. If a top-down view is necessary for plants such as Arabidopsis thaliana, use a tripod to move the camera to the top of the plants placed on the photography-grade fabric; the remaining methods for image capture and analysis remain the same.
As demonstrated here with maize, this method is important for both plant breeding and fundamental insights into how plants tolerate abiotic stresses, such as heat and drought. Heat and drought stress combined contribute to 15%-20% yield loss in maize annually worldwide23. Yield losses are only expected to increase with global climate change, as temperatures are expected to increase 1.4-4.4&#176;C by the year 210024. Evaluating maize diversity for stress tolerance, as well as determining the phenotypic, physiological, and molecular mechanisms of abiotic stress tolerance, are critical research aims for global food security25. To address these questions, researchers in all adjacent fields - physiologists, geneticists, breeders, molecular biologists, etc. - must be able to quickly, effectively, and often non-destructively evaluate the phenotypes of maize plants under abiotic stress25. The method and example results shown here address this need, and are an important tool for researchers to address real-world agricultural challenges.
The phenotypes observed here were hypothesized, given a wealth of previous research - drought stress reduces plant size and greenness (measured by hue circular mean)26 (Figure 6, Figure 7, and Table 1). The phenotype of heat stress with well-watering unexpectedly did not significantly reduce leaf area, although with more replicates, this relationship could be better evaluated statistically, and height was reduced as expected (Figure 6). While the hue circular mean was not changed due to heat, the color profile was indeed altered to include both more yellow and darker green pixels, showing that evaluation of just the mean hue is insufficient, and the multi-value-trait histogram of color should be considered (Figure 7). These phenotypic changes describe a plant&#39;s response to the environment, and additional techniques such as measurements of gas exchange, microscopy imaging, and more could be employed to assess the physiological changes that occur in response to stress. This result demonstrates the power of precise, quantitative measurements that would otherwise be missed using manual or qualitative measures of plant health. These insights are critical for breeding and engineering crops resistant to climate change, which significantly reduces global crop yields and is predicted to worsen in future years.

Measuring plant traits, or phenotypes, is critical to answering fundamental and applied questions in plant science. For example, a researcher seeking to understand the impact of a genetic mutation on plant size will need to quantitatively measure plant height and leaf area1. A researcher applying a biotic stress (such as bacterial, viral, or fungal infection) or an abiotic stress (such as drought, heat, cold, or nutrient depletion) will need to investigate the effect of the stress on plant color, or &#34;greenness&#34;, to measure chlorosis2,3.
While measuring these traits is essential to basic plant biology as well as applied plant breeding, measurements of these traits by hand can be time-consuming, imprecise, and inaccurate4,5,6,7. For example, measuring plants for height includes manually using a ruler for each plant, oftentimes in hot or humid environmental conditions, and is subject to error from the viewpoint of the person measuring, as well as what point is considered the &#34;top&#34; of the plant. Furthermore, measurements such as biomass and pigment content are destructive, and the same plant cannot be measured for change over time or used for multiple samplings because it must be destroyed in the process of making these measurements4,5,6,7. The time-consuming, error-prone nature of these measurements hinders progress in evaluating plants for their phenotypic and physiological stress responses.
Capturing images of plants and measuring quantitative traits from the image provides solutions to these problems4,5,6,7. Image analysis improves accuracy and precision, while reducing the time a researcher has to make the measurements, and the same plant can be imaged multiple times or used for other samplings due to the non-destructive trait quantification4,5,6,7. Furthermore, images can be stored and re-analyzed long after an experiment is complete and plants are disposed of4,5,6,7. Image analysis can be completed individually with low-throughput image editing tools, such as ImageJ (Fiji)8, or in higher throughput using computer vision and machine learning with platforms such as PlantCV9. PlantCV is a free, open-source image analysis platform that enables researchers to first mask (or separate) a plant or other object from the background of an image, and make measurements of size, morphology, color, and more of that plant or object9. Using a modular workflow, a researcher first builds a flexible workflow (or pipeline) of different modules with a sample image, then subjects the remaining images to the high-throughput workflow without interacting with each individual image9. Quantitative traits are output and analyzed for statistically significant differences between groups. Deep Learning pipelines can be developed for efficient, automated image analysis&#160;after enough images have been captured and labeled with appropriate trait measurements or metadata10.
&#34;High-throughput&#34; phenotyping can increase the speed and reduce human labor during data collection, and/or during data analysis4,5,6,7. High-throughput phenotyping facilities have enabled the automation of image collection in controlled environments, where plants move on conveyor belts or via robots to an imaging station, and are automatically labeled with the name (i.e., label with metadata) associated with the plant6,11,12,13,14. While these conveyor belts or robot-enabled facilities enable the capture and labeling of large amounts of images without human intervention, there are few in the world, and they can cost millions of dollars to build, making them inaccessible to many researchers. To apply these concepts of automated imaging at a lower cost, Raspberry Pi (here referred to as single-board computers, SBC) are low-cost computers (less than $35 each) that have been used to capture, name, store, and transfer images of plants grown in controlled environments. These setups capture images in automated, timed intervals using an array of up to 72 Raspberry Pi12,15,16. While more cost-effective than conveyor belt systems that can cost millions of dollars, previously described SBC growth chambers are most applicable to a top-down view of growing small plants and still require numerous SBC15. In field environments, gantries, UAS (uncrewed aerial systems) such as drones, and satellites all capture images for analysis of plant traits17,18. Both field systems and high-throughput controlled environment facilities can be prohibitive in their upfront cost, maintenance, and expert knowledge necessary to build and maintain the systems, as well as analyze images17,18. Cell phones and digital cameras used alone for plant photography present time-consuming challenges of image transfer and naming with metadata, which is essential for downstream image analysis.
The methods described here present a low-cost solution to plant imaging for the challenges unaddressed by other systems. A photography setup, where plants are transported individually to the studio, allows for the efficient measurement of plants of all sizes in pots. Using a SBC connected to a digital camera, this method first provides standardized capture, labeling, and transfer of images. Using a keyboard or a QR/barcode scanner to name images as they are being captured circumvents the time-consuming and error-prone activity of renaming images with metadata after they have been captured and stored. The composition of the image and recommended camera settings are important for making downstream image analysis possible, and guidelines are detailed here. Finally, an image analysis pipeline in PlantCV is provided to extract quantitative traits from the captured images, such as leaf area, plant height, width, and color.
Relative to conveyor belt systems, this method is low-cost and requires a single SBC. The phenotyping setup described here was constructed to remove the constraints and costs of a conveyor belt or robotic systems - any size plant can be measured because the setup can be separated from the growth space, only a single SBC is needed, and it works with numerous camera types for maximum flexibility and affordability. Similar approaches to the one presented here include the PhenoBox or PhenoRig12,19. Compared to manual measurements, this method saves time, reduces errors, and does not destroy the plants4,12,15,19.

<table><tbody><tr><td>15W USB-C Power Supply</td><td>CanaKit</td><td>DCAR-RSP-3A5-C</td><td></td></tr><tr><td>25604 UC-E6 USB Cable</td><td>Nikon</td><td>18208256044</td><td></td></tr><tr><td>3.5 inch pots</td><td>Hummert International</td><td>13006000</td><td></td></tr><tr><td>Berger BM7 35% Bark HP</td><td>Hummert International</td><td>10121500</td><td></td></tr><tr><td>Biodock 2024</td><td></td><td>AI Software Platform</td><td>www.biodock.ai.</td></tr><tr><td>Code editing software</td><td>Jupyter</td><td></td><td>https://jupyter.org/install</td></tr><tr><td>Code editing software</td><td>Visual Studio Code</td><td></td><td>https://code.visualstudio.com/download</td></tr><tr><td>Color Card ColorChecker Classic</td><td>Calibrite</td><td>CCC</td><td></td></tr><tr><td>Color Card ColorChecker Passport</td><td>Calibrite</td><td>CCPP2</td><td></td></tr><tr><td>Conviron Growth Chamber</td><td>Conviron</td><td>BDW80 model</td><td></td></tr><tr><td>Dell 22 Monitor</td><td>Dell</td><td>SE2222H</td><td></td></tr><tr><td>EH-67 AC Adapter</td><td>Nikon</td><td>AZ185</td><td></td></tr><tr><td>Image analysis software</td><td>PlantCV</td><td></td><td>https://plantcv.readthedocs.io/en/latest/installation/</td></tr><tr><td>Jack&#039;s Water Soluble Fertilizer 15-5-15</td><td>Hummert International</td><td>7590200</td><td>liquid fertilizer</td></tr><tr><td>Keyboard and Mouse</td><td>Logitech</td><td>1440638</td><td>MK270 wireless keyboard and mouse</td></tr><tr><td>Maize seed</td><td>USDA-ARS Germplasm Resources Information Network (GRIN)</td><td>B73</td><td>https://www.ars-grin.gov/</td></tr><tr><td>Micro HDMI to HDMI cable</td><td>Amazon</td><td>B07KSDB25X</td><td></td></tr><tr><td>Nikon Coolpix B500 camera</td><td>Nikon</td><td></td><td></td></tr><tr><td>Nikon D7200 camera</td><td>Nikon</td><td></td><td></td></tr><tr><td>Osmocote 14-14-14</td><td>Hummert International</td><td>7630000</td><td></td></tr><tr><td>Photography-grade fabric</td><td>Yayoya</td><td></td><td>Polyester Fabric Black Photo Backdrop for Photo Video Studio Televison</td></tr><tr><td>Raspberry Pi 4 Case with heatsink</td><td>iUniker</td><td>&amp;lrm;B07ZVKN262</td><td></td></tr><tr><td>Raspberry Pi 4 Model B Rev 1.1</td><td>Raspberry Pi Foundation</td><td>RP4B4GB_BP</td><td></td></tr><tr><td>Raspberry Pi 4 PiSwitch (USB-C)</td><td>CanaKit</td><td>RSP-PISWITCH-USBC</td><td></td></tr><tr><td>Raspberry Pi software</td><td>Raspberry Pi Imager</td><td></td><td>https://www.raspberrypi.com/software/</td></tr><tr><td>Statistical analysis software</td><td>Rstudio</td><td></td><td>https://posit.co/downloads/</td></tr><tr><td>Tripod</td><td>K&amp;amp;F Concept</td><td>T255A3+BH-28L</td><td></td></tr><tr><td>Turface MVP</td><td>Hummert International</td><td>10240000</td><td></td></tr><tr><td>Wireless Barcode Scanner</td><td>Netumscan</td><td>B08X4NMX2M</td><td>1D and 2D barcode scanner</td></tr></tbody></table>

imaging and analysis for quantifying maize (zea mays) abiotic stress phenotypes

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic stresses. However, traditional methods of measuring plants can be time-consuming, inaccurate, and destructive, particularly when assessing traits such as biomass and height on an individual basis. Image-based analysis offers an opportunity to reduce labor, improve accuracy and precision, and enable repeated measurements of the same plant over multiple time points. While high-throughput phenotyping facilities provide a solution, they are often prohibitively expensive and limited in availability worldwide. A method is described for capturing plant images using low-cost single-board computers and digital cameras within a photo studio setup, accompanied by an image analysis pipeline based on the free and open-source package PlantCV. This method offers a fast and accurate protocol for image capture, labeling, and transfer, along with best practices for ensuring high-quality image acquisition. Through image analysis, more than 10 phenotypes were quantified for all images in the experiment simultaneously, including traits such as plant size, height, and color. These methods were used to characterize the response of the Zea mays (maize) inbred variety B73 to heat, drought, and combined heat and drought abiotic stress conditions.

To develop this method, B73 maize plants were evaluated for quantitative phenotypes under control temperatures, heat temperatures, well-watered, and drought conditions, as well as their combinations, for a total of four treatments. B73 is a well-known inbred maize variety with a wealth of genetic data21.
Image analysis in PlantCV successfully color-corrected the images, labeled the quantitative results with the plant metadata, and segmented (in other words, separated) the plant from the background, as is demonstrated in Figure 3. An unsuccessful analysis would provide output images that do not show the blue outline or pink shape around the outside of the plant, but rather include additional noise or missing pieces of the plant (Figure 3, low-quality example). A dual-channel threshold was necessary to include as much of the plant as possible in the mask (see alternative single-channel strategies in the &#34;photo-studio-SV-notebook.ipynb&#34;). However, some plants, particularly those with purple-colored stems, did not have a perfect segmentation, but were sufficient for downstream analysis (Figure 3).
The PlantCV analysis provided 16 quantitative, single-value traits, summarized in Figure 4. A single-value-trait provides one value per plant - for example, the leaf area, height, width, and hue circular mean. Leaf area was considered using the &#34;analyze.shape&#34; function (Figure 4) and considered the entire plant. Height and width, however, were considered using the &#34;analyze.bound_horizontal function&#34;, with a bound drawn at the plant soil line. This is because plants that droop over the side of the pot will have a greater height considering the plant as a whole object, and instead should only be measuring from the plant base where it meets the soil. In addition to the single-value traits, PlantCV outputs multi-value traits, which are histograms, particularly of the color values, representing each pixel of the plant. The hue circular mean is a single-value-trait that is the average hue value for the whole plant.
To determine which traits are the most interesting, the variance explained by treatment for each trait was calculated, as well as the correlation between the different traits using the &#34;frem&#34; function in the pcvr R Studio package20 (Figure 5A). Leaf area, height, width, and hue circular mean were selected for downstream analysis because they explained greater than 50% of the variance due to treatment, and are relevant measures of plant growth (area, height, and width) and health (hue). Representative images of the plant with the largest&#160;and smallest leaf area demonstrate the variation in phenotypes collected in this experiment (Figure 5B,C, respectively). The plant with the largest leaf area also had the highest mean hue and was a B73, well-watered, heat-stressed plant (Figure 5B). The smallest leaf area had the smallest mean hue (Figure 5C), and was a B73, drought-stressed, heat-stressed plant.
Water treatment had a significant effect in determining leaf area (F(1,13) = 226.5, p = 1.32 x 10-9), height (F(1,13) = 21.1, p = 0.0005), width (F(1,13) = 75.5, p = 8.92 x 10-7), and hue circular mean (F(1,13) = 27.8, p = 0.0002), determined using a linear regression model for treatment effect and a two-way ANOVA in R Studio (complete results in Table 1). Temperature treatment had a significant effect on height (F(1,13) = 5.94, p = 0.03), but not leaf area, width, or hue circular mean (p &#62; 0.05, Table 1). The interaction between temperature and water status was not a significant factor in any of the traits measured (p &#62; 0.05, Table 1).
Next, individual treatments were compared to the control and each other using linear regression and post-hoc test (estimated marginal means) with a Sidak correction for multiple comparisons (Figure 6). Leaf area, plant height, plant width, and hue circular mean were significantly reduced in drought-stressed plants compared to well-watered under both temperature conditions (p &#60; 0.05, Figure 6). Heat stress while well-watered only reduced plant height (p &#60; 0.05, Figure 6), but did not significantly reduce leaf area, plant width, or hue circular mean. This difference in hue can be further investigated by assessing the multi-value color trait hue, plotted as a histogram in Figure 7, rather than just an average. The reduction in mean hue due to drought was due to a shift from green to yellow pixels, known as chlorosis (Figure 7). While heat did not show a significant difference in average hue, it did exhibit a reduction and increase due to both yellowing (chlorosis) and darkening of the green color (Figure 7).
After imaging, the plants were cut at the root-shoot junction, and the weight of the above-ground tissue was recorded. The roots were gently washed and dried with a towel, then weighed, to calculate the total plant biomass (also known as weight) for comparison to the image-based phenotyping method described. Leaf area measured via image analysis strongly correlated to plant biomass (R2 = 0.84, Figure 8).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/67619/67619fig01.jpg" /> 
Figure 1: Image of the photo studio setup with labeled components for collecting plant images. (A) Photography-grade fabric, (B) color card, (C) line of tape for placing pots uniformly, (D) digital camera, (E) single-board computer, (F) monitor displaying image capture process. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/67619/67619fig02.jpg" /> 
Figure 2: Overview of the imaging workflow. A representative &#34;high quality&#34; and &#34;low quality&#34; image is provided. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/67619/67619fig3v2.jpg" /> 
Figure 3: Overview of the image analysis workflow in PlantCV. Representative images of &#34;high&#34;, &#34;acceptable&#34;, and &#34;low&#34; quality segmentation are presented; PlantCV traits are represented using a pink outline of the whole-plant shape, and a blue outline of the plant area. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/67619/67619fig04.jpg" /> 
Figure 4: Summary and visual representation of the shape traits captured from analysis. Shape function in PlantCV. Purple boxes represent traits that do not utilize another trait in their calculation. Orange boxes represent traits that utilize other traits in their calculation; arrows indicate which traits are used in these calculations. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/67619/67619fig05.jpg" /> 
Figure 5: Image-based phenotyping captures phenotypic variance and correlated traits. (A) Variance of traits explained by treatment (right), and correlation of traits to each other (left). Each trait is labeled, followed by its unit of measurement; &#34;NA&#34; means it is a unitless measurement. (B) Image of the plant that had the highest leaf area and highest hue circular mean (degrees). (C) Image of the plant that had the smallest leaf area and lowest hue circular mean (degrees). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/67619/67619fig06.jpg" /> 
Figure 6: Boxplot comparison of treatment effects on plant phenotypes. Leaf area (A), height (B), width (C), and hue circular mean (D) are compared for the effect of drought, heat, and the combined stresses. n = 4. Letters represent statistically significant differences between treatments (p &#60; 0.05); treatments were compared using linear regression and post-hoc test (estimated marginal means) with a Sidak correction for multiple comparisons. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/67619/67619fig07.jpg" /> 
Figure 7: Histograms of color (hue) summed of all pixels of each treatment combination, normalized by the total number of pixels in the treatment combination. Histograms are colored by the color value for the hue degree. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/67619/67619fig08.jpg" /> 
Figure 8: Correlation of leaf area measured from images to whole-plant biomass. Each black point represents one plant. The pink line represents the linear correlation between leaf area (X) and biomass (Y). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/67619/67619fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Trait</td>
			<td>Factor</td>
			<td>degrees of freedom</td>
			<td>F-value</td>
			<td>p-value</td>
		</tr>
		<tr>
			<td>leaf area (cm^2)</td>
			<td>Temperature</td>
			<td>1</td>
			<td>0.5372</td>
			<td>0.4766</td>
		</tr>
		<tr>
			<td>leaf area (cm^2)</td>
			<td>Water</td>
			<td>1</td>
			<td>226.5318</td>
			<td>1.32E-09</td>
		</tr>
		<tr>
			<td>leaf area (cm^2)</td>
			<td>Temperature:Water</td>
			<td>1</td>
			<td>2.1526</td>
			<td>0.1661</td>
		</tr>
		<tr>
			<td>leaf area (cm^2)</td>
			<td>Residuals</td>
			<td>13</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>height (cm)</td>
			<td>Temperature</td>
			<td>1</td>
			<td>5.9426</td>
			<td>0.0298935</td>
		</tr>
		<tr>
			<td>height (cm)</td>
			<td>Water</td>
			<td>1</td>
			<td>21.107</td>
			<td>0.0005029</td>
		</tr>
		<tr>
			<td>height (cm)</td>
			<td>Temperature:Water</td>
			<td>1</td>
			<td>2.1752</td>
			<td>0.1640545</td>
		</tr>
		<tr>
			<td>height (cm)</td>
			<td>Residuals</td>
			<td>13</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>width(cm)</td>
			<td>Temperature</td>
			<td>1</td>
			<td>2.2495</td>
			<td>0.1575</td>
		</tr>
		<tr>
			<td>width(cm)</td>
			<td>Water</td>
			<td>1</td>
			<td>75.5474</td>
			<td>8.92E-07</td>
		</tr>
		<tr>
			<td>width(cm)</td>
			<td>Temperature:Water</td>
			<td>1</td>
			<td>0.0839</td>
			<td>0.7766</td>
		</tr>
		<tr>
			<td>width(cm)</td>
			<td>Residuals</td>
			<td>13</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>hue circular mean (degrees)</td>
			<td>Temperature</td>
			<td>1</td>
			<td>0.9154</td>
			<td>0.3561432</td>
		</tr>
		<tr>
			<td>hue circular mean (degrees)</td>
			<td>Water</td>
			<td>1</td>
			<td>27.7988</td>
			<td>0.0001509</td>
		</tr>
		<tr>
			<td>hue circular mean (degrees)</td>
			<td>Temperature:Water</td>
			<td>1</td>
			<td>3.4792</td>
			<td>0.0848724</td>
		</tr>
		<tr>
			<td>hue circular mean (degrees)</td>
			<td>Residuals</td>
			<td>13</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: Results of the linear regression model and two-way ANOVA analyses examining the effects of water treatment, temperature treatment, and their interaction on plant traits.

A method is presented for low-cost imaging of plants to measure phenotypes, along with best practices for image capture and an image analysis pipeline for quantifying plant traits. These methods were applied to measure maize (Zea mays) phenotypes under heat, drought, and combined abiotic stress conditions.

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic ...

imaging-analysis-for-quantifying-maize-zea-mays-abiotic-stress

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

551 Views.  Donald Danforth Plant Science Center. A method is presented for low-cost imaging of plants to measure phenotypes, along with best practices for image capture and an image analysis pipeline for quantifying plant traits. These methods were applied to measure maize (Zea mays) phenotypes under heat, drought, and combined abiotic stress conditions.

Video: Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Quantifying Mixing using Magnetic Resonance Imaging

Mixing is a unit operation that combines two or more components into a homogeneous mixture. This work involves mixing two viscous liquid streams using an in-line static mixer. The mixer is a split-and-recombine design that employs shear and extensional flow to increase the interfacial contact between the components. A prototype split-and-recombine (SAR) mixer was constructed by aligning a series of thin laser-cut Poly (methyl methacrylate) (PMMA) plates held in place in a PVC pipe. Mixing in this device is illustrated in the photograph in Fig. 1. Red dye was added to a portion of the test fluid and used as the minor component being mixed into the major (undyed) component. At the inlet of the mixer, the injected layer of tracer fluid is split into two layers as it flows through the mixing section. On each subsequent mixing section, the number of horizontal layers is duplicated. Ultimately, the single stream of dye is uniformly dispersed throughout the cross section of the device.
 Using a non-Newtonian test fluid of 0.2% Carbopol and a doped tracer fluid of similar composition, mixing in the unit is visualized using magnetic resonance imaging (MRI). MRI is a very powerful experimental probe of molecular chemical and physical environment as well as sample structure on the length scales from microns to centimeters. This sensitivity has resulted in broad application of these techniques to characterize physical, chemical and/or biological properties of materials ranging from humans to foods to porous media 1, 2. The equipment and conditions used here are suitable for imaging liquids containing substantial amounts of NMR mobile 1H such as ordinary water and organic liquids including oils. Traditionally MRI has utilized super conducting magnets which are not suitable for industrial environments and not portable within a laboratory (Fig. 2). Recent advances in magnet technology have permitted the construction of large volume industrially compatible magnets suitable for imaging process flows. Here, MRI provides spatially resolved component concentrations at different axial locations during the mixing process. This work documents real-time mixing of highly viscous fluids via distributive mixing with an application to personal care products.

Magnetic resonance imaging (MRI) provides a powerful tool to evaluate the effectiveness of process equipment during operation. We discuss the use of MRI to visualize mixing in a static mixer. The application is relevant to personal care products, but can be applied to a broad range of food, chemical, biomass and biological fluids.

Mixing is a unit operation that combines two or more components into a homogeneous mixture.  This work involves mixing two viscous liquid streams using an ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

Analysis of Oxidative Stress in Zebrafish Embryos

High levels of reactive oxygen species (ROS) may cause a change of cellular redox state towards oxidative stress condition. This situation causes oxidation of molecules (lipid, DNA, protein) and leads to cell death. Oxidative stress also impacts the progression of several pathological conditions such as diabetes, retinopathies, neurodegeneration, and cancer. Thus, it is important to define tools to investigate oxidative stress conditions not only at the level of single cells but also in the context of whole organisms. Here, we consider the zebrafish embryo as a useful in vivo system to perform such studies and present a protocol to measure in vivo oxidative stress. Taking advantage of fluorescent ROS probes and zebrafish transgenic fluorescent lines, we develop two different methods to measure oxidative stress in vivo: i) a &#8220;whole embryo ROS-detection method&#8221; for qualitative measurement of oxidative stress and ii) a &#8220;single-cell&#160;ROS detection method&#8221; for quantitative measurements of oxidative stress. Herein, we demonstrate the efficacy of these procedures by increasing oxidative stress in tissues by oxidant agents and physiological or genetic methods. This protocol is amenable for forward genetic screens and it will help address cause-effect relationships of ROS in animal models of oxidative stress-related pathologies such as neurological disorders and cancer.

Here we report a protocol to measure oxidative stress in living zebrafish embryos. This procedure allows reactive oxygen species (ROS) detection in both whole embryo tissues and single-cell populations. This protocol will accomplish both qualitative and quantitative analyses.

High levels of reactive oxygen species (ROS) may cause a change of cellular redox state towards oxidative stress condition. This situation causes ...

Lateral Root Inducible System in Arabidopsis and Maize

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is however tedious, because it occurs only in a few cells inside the root and in an unpredictable manner. To circumvent this problem, a Lateral Root Inducible System (LRIS) has been developed. By treating seedlings consecutively with an auxin transport inhibitor and a synthetic auxin, highly controlled lateral root initiation occurs synchronously in the primary root, allowing abundant sampling of a desired developmental stage. The LRIS has first been developed for Arabidopsis thaliana, but can be applied to other plants as well. Accordingly, it has been adapted for use in maize (Zea mays). A detailed overview of the different steps of the LRIS in both plants is given. The combination of this system with comparative transcriptomics made it possible to identify functional homologs of Arabidopsis lateral root initiation genes in other species as illustrated here for the CYCLIN B1;1 (CYCB1;1) cell cycle gene in maize. Finally, the principles that need to be taken into account when an LRIS is developed for other plant species are discussed.

Lateral Root Inducible System in Arabidopsis and Maize

The Lateral Root Inducible System (LRIS) allows for synchronous induction of lateral roots and is presented for Arabidopsis thaliana and maize.

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is ...

Rapid Analysis of Circadian Phenotypes in Arabidopsis Protoplasts Transfected with a Luminescent Clock Reporter

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable biotic and abiotic stress. Conversely, disruption of circadian timekeeping severely compromises plant health and reduces agricultural crop yields. It is therefore imperative that we understand the intricate regulation of circadian rhythms in plants, including the factors that affect motion of the transcriptional clockwork itself.
Testing circadian defects in the model plant Arabidopsis thaliana (Arabidopsis) traditionally involves crossing specific mutant lines to a line rhythmically expressing firefly luciferase from a circadian clock gene promoter. This approach is laborious, time-consuming, and could be fruitless if a mutant has no circadian phenotype. The methodology presented here allows a rapid initial assessment of circadian phenotypes. Protoplasts derived from mutant and wild-type Arabidopsis are isolated, transfected with a rhythmically expressed luminescent reporter, and imaged under constant light conditions for 5 days. Luminescent traces will directly reveal whether the free-running period of mutant plants is different from wild-type plants. The advantage of the method is that any Arabidopsis line can efficiently be screened, without the need for generating a stably transgenic luminescent clock marker line in that mutant background.

The circadian clock regulates about a third of the Arabidopsis transcriptome, but the percentage of genes that feed back into timekeeping remains unknown. Here we visualize a method to rapidly assess circadian phenotypes in any mutant line of Arabidopsis using luminescent imaging of a circadian reporter transiently expressed in protoplasts.

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable ...

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Growth analyses are often used in plant science to investigate contrasting genotypes and the effect of environmental conditions. The cellular aspect of these analyses is of crucial importance, because growth is driven by cell division and cell elongation. Kinematic analysis represents a methodology to quantify these two processes. Moreover, this technique is easy to use in non-specialized laboratories. Here, we present a protocol for performing a kinematic analysis in monocotyledonous maize (Zea mays) leaves. Two aspects are presented: (1) the quantification of cell division and expansion parameters, and (2) the determination of the location of the developmental zones. This could serve as a basis for sampling design and/or could be useful for data interpretation of biochemical and molecular measurements with high spatial resolution in the leaf growth zone. The growth zone of maize leaves is harvested during steady-state growth. Individual leaves are used for meristem length determination using a DAPI stain and cell-length profiles using DIC microscopy. The protocol is suited for emerged monocotyledonous leaves harvested during steady-state growth, with growth zones spanning at least several centimeters. To improve the understanding of plant growth regulation, data on growth and molecular studies must be combined. Therefore, an important advantage of kinematic analysis is the possibility to correlate changes at the molecular level to well-defined stages of cellular development. Furthermore, it allows for a more focused sampling of specified developmental stages, which is useful in case of limited budget or time.

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Quantifying cell division and expansion is of crucial importance to the understanding of whole-plant growth. Here, we present a protocol to calculate cellular parameters determining maize leaf growth rates and highlight the use of these data for investigating molecular growth regulatory mechanisms by directing developmental stage-specific sampling strategies.

Growth analyses are often used in plant science to investigate contrasting genotypes and the effect of environmental conditions. The cellular aspect of ...

Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects on the lifespan across species. However, the molecular causes of aging and death remain elusive. To gain a systematic understanding of the molecular mechanisms underlying yeast aging, we need high-throughput methods to measure lifespan and to quantify various cellular and molecular phenotypes in single cells. Previously, we developed microfluidic devices to track budding yeast mother cells throughout their lifespan while flushing away newborn daughter cells. This article presents a method for preparing microfluidic chips and for setting up microfluidic experiments. Multiple channels can be used to simultaneously track cells under different conditions or from different yeast strains. A typical setup can track hundreds of cells per channel and allow for high-resolution microscope imaging throughout the lifespan of the cells. Our method also allows detailed characterization of the lifespan, molecular markers, cell morphology, and the cell cycle dynamics of single cells. In addition, our microfluidic device is able to trap a significant amount of fresh mother cells that can be identified by downstream image analysis, making it possible to measure the lifespan with higher accuracy.

This article presents a protocol optimized for the production of microfluidic chips and the setup of microfluidic experiments to measure the lifespan and cellular phenotypes of single yeast cells.

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects ...

Time-lapse Imaging of Bacterial Swarms and the Collective Stress Response

Swarming is a form of surface motility observed in many bacterial species including Pseudomonas aeruginosa and Escherichia coli. Here, dense populations of bacteria move over large distances in characteristic tendril-shaped communities over the course of hours. Swarming is sensitive to several factors including medium moisture, humidity, and nutrient content. In addition, the collective stress response, which is observed in P. aeruginosa that are stressed by antibiotics or bacteriophage (phage), repels swarms from approaching the area containing the stress. The methods described here address how to control the critical factors that affect swarming. We introduce a simple method to monitor swarming dynamics and the collective stress response with high temporal resolution using a flatbed document scanner, and describe how to compile and perform a quantitative analysis of swarms. This simple and cost-effective method provides precise and well-controlled quantification of swarming and may be extended to other types of plate-based growth assays and bacterial species.

We detail a simple method to produce high-resolution time-lapse movies of Pseudomonas aeruginosa swarms that respond to bacteriophage (phage) and antibiotic stress using a flatbed document scanner. This procedure is a fast and simple method for monitoring swarming dynamics and may be adapted to study the motility and growth of other bacterial species.

Swarming is a form of surface motility observed in many bacterial species including Pseudomonas aeruginosa and Escherichia coli. Here, dense populations ...

In Vitro Analysis of E3 Ubiquitin Ligase Function

The covalent attachment of ubiquitin (Ub) to internal lysine residue(s) of a substrate protein, a process termed ubiquitylation, represents one of the most important post-translational modifications in eukaryotic organisms. Ubiquitylation is mediated by a sequential cascade of three enzyme classes including ubiquitin-activating enzymes (E1 enzymes), ubiquitin-conjugating enzymes (E2 enzymes), and ubiquitin ligases (E3 enzymes), and sometimes, ubiquitin-chain elongation factors (E4 enzymes). Here, in vitro protocols for ubiquitylation assays are provided, which allow the assessment of E3 ubiquitin ligase activity, the cooperation between E2-E3 pairs, and substrate selection. Cooperating E2-E3 pairs can be screened by monitoring the generation of free poly-ubiquitin chains and/or auto-ubiquitylation of the E3 ligase. Substrate ubiquitylation is defined by selective binding of the E3 ligase and can be detected by western blotting of the in vitro reaction. Furthermore, an E2~Ub discharge assay is described, which is a useful tool for the direct assessment of functional E2-E3 cooperation. Here, the E3-dependent transfer of ubiquitin is followed from the corresponding E2 enzyme onto free lysine amino acids (mimicking substrate ubiquitylation) or internal lysines of the E3 ligase itself (auto-ubiquitylation). In conclusion, three different in vitro protocols are provided that are fast and easy to perform to address E3 ligase catalytic functionality.

In Vitro Analysis of E3 Ubiquitin Ligase Function

The present study provides detailed in vitro ubiquitylation assay protocols for the analysis of E3 ubiquitin ligase catalytic activity. Recombinant proteins were expressed using prokaryotic systems such as Escherichia coli culture.

The covalent attachment of ubiquitin (Ub) to internal lysine residue(s) of a substrate protein, a process termed ubiquitylation, represents one of the ...

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Chapters in this video

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

Quantifying Mixing using Magnetic Resonance Imaging

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Analysis of Oxidative Stress in Zebrafish Embryos

Lateral Root Inducible System in Arabidopsis and Maize

Rapid Analysis of Circadian Phenotypes in Arabidopsis Protoplasts Transfected with a Luminescent Clock Reporter

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells

Time-lapse Imaging of Bacterial Swarms and the Collective Stress Response

In Vitro Analysis of E3 Ubiquitin Ligase Function