Name: LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement (Video) | JoVE Journal
Uploaded: 2013-01-21T12:00:00.000+00:00
Duration: 8 min 14 s
Description: Learn to automate leaf shape measurement for high throughput plant phenotyping using the LeafJ ImageJ plugin. This method accelerates screening by analyzing dissected leaves with flatbed scanning and image analysis.

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5 minutes

LeafJ was written by JNM while he was on sabbatical in Dr. Katherine Pollard's lab at the Gladstone Institutes.
This work was supported by a grant from the National Science Foundation (grant number IOS-0923752).

1. Plant Materials
Note that this plant growth protocol is aimed for detecting shade avoidance response. You can grow plants under your favorite condition.
<ol>
	<li>Sprinkle Arabidopsis thaliana seeds on water soaked filter papers in 9 cm Petri dishes and store (stratify) them at 4 &deg;C for four days in the dark.</li>
	<li>Transfer these Petri dishes to simulated sun conditions: 80-100 &mu;E photosynthetically active radiation (PAR) and far-red supplement to bring the R:FR ratio to 1.86. Use long day conditions (16 hr light/8 hr dark) and constant temperature of 22 &deg;C. Incubate in this condition for three days to allow the seeds to germinate.</li>
	<li>Transfer germinated seed to soil and keep plants under sun condition. For large-scale experiments, we recommend preparing small tags for labeling each plants by using Data Merge Manager in Microsoft Word 2004 (or later) for making labels.</li>
	<li>Eleven days after transfer to soil, move half of the plants to shade condition: same as sun but with supplemental far-red light to bring the R/FR ratio to 0.52.</li>
	<li>After an additional twelve days, the plants are ready for leaf imaging. At this stage the older leaves have fully matured whereas younger leaves are still expanding, so that you capture a snapshot of development. You may want to choose a different developmental time depending on your needs.</li>
</ol>
2. Capturing Dissected Leaf Images
<ol>
	<li>Prepare transparency sheets labeled with plant genotype and growth condition with five rectangular frames. One frame corresponds to leaves from one plant. Microsoft Excel can be used to print a consistent grid with labels.</li>
	<li>Dissect leaves of twenty-six day old plants.</li>
	<li>Scan leaves at 600 dpi on a flat-bed scanner. Note that leaves from one plant should be placed vertically within a black window in a sandwich of transparent sheets. Avoid touching leaves to a black window frame and overlapping leaves, which will give errors in following procedures.</li>
</ol>
3. Leaf Image Analysis by LeafJ
<ol>
	<li>Download ImageJ from <a href="http://malooflab.openwetware.org/Resources.html#Software_Tools" target="_blank">http://malooflab.openwetware.org/Resources.html#Software_Tools</a> or <a href="http://bitbucket.org/jnmaloof/leafj" target="_blank">http://bitbucket.org/jnmaloof/leafj</a>. Drag the LeafJ.jar file into the plugins folder of ImageJ.</li>
	<li>Open an image file in ImageJ 1.45s or later14.</li>
	<li>Split the image into three-color channels (red, green, and blue) by "Image &gt; Color &gt; Split Channels" and apply threshold to the image in the blue channel.</li>
	<li>Select all of the leaves from one plant by a rectangle tool (Figure 1A).</li>
	<li>Select "LeafJ" from the plugin menu.</li>
	<li>Select annotation information for this plant from the dialog box that appears. You can edit the default values that appear here by clicking "edit these options".</li>
	<li>After running LeafJ plugin and before clicking "OK" button, edit traced lines from the region of interest (ROI) manager window (if necessary; Figure 1B). A touch-screen tablet (such as an iPad) is useful for this procedure. iPads can be connected to a computer as an external monitor using Air Display software.</li>
	<li>Export measurement results and associated information (file names, flowering time, dissected by, measured by, etc) to Microsoft Excel or equivalent software.</li>
</ol>
4. Leaf Cell Image Analysis in ImageJ
<ol>
	<li>Fix dissected leaves as described in reference15 after scanning (step 2). FAA fixed leaves can be kept in 4 &deg;C for at least 6 months.</li>
	<li>Clear the leaves by changing FAA fixative to chloral hydrate solution and incubate leaves for 1~2 hr before microscopic observation15.</li>
	<li>Mount leaves on microscope slides with trichomes facing up. Using 40x magnification on a compound microscope, image the mesophyll layer of the center of each leaf on either side of the main vein, avoiding cells near trichomes or veins.</li>
	<li>Trace leaf cell outlines by ImageJ ROI manager tool with aid of the touch-screen tablet and a stylus (as described in step 3). Cell image analysis uses the built-in features of ImageJ but does not require LeafJ.</li>
</ol>

<ol><li>Furbank, R. T., Tester, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Phenomics--technologies+to+relieve+the+phenotyping+bottleneck%5BTitle%5D)+AND+%22Trends+Plant+Sci%22%5BJournal%5D)">Phenomics--technologies to relieve the phenotyping bottleneck.</a> Trends Plant Sci. 16, 635-644 (2011).</li>
<li>Berger, B., Parent, B., Tester, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-throughput+shoot+imaging+to+study+drought+responses%5BTitle%5D)+AND+%22J.+Exp.+Bot%22%5BJournal%5D)">High-throughput shoot imaging to study drought responses.</a> J. Exp. Bot. 61, 3519-3528 (2010).</li>
<li>Borevitz, J. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Natural+genetic+variation+for+growth+and+development+revealed+by+high-throughput+phenotyping+in+Arabidopsis+thaliana%5BTitle%5D)+AND+%22G3+%28Bethesda%29%22%5BJournal%5D)">Natural genetic variation for growth and development revealed by high-throughput phenotyping in Arabidopsis thaliana.</a> G3 (Bethesda). 2, 29-34 (2012).</li>
<li>Albrecht, D. R., Bargmann, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-content+behavioral+analysis+of+Caenorhabditis+elegans+in+precise+spatiotemporal+chemical+environments%5BTitle%5D)+AND+%22Nat.+Methods%22%5BJournal%5D)">High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments.</a> Nat. Methods. 8, 599-605 (2011).</li>
<li>Chitwood, D. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Native+environment+modulates+leaf+size+and+response+to+simulated+foliar+shade+across+wild+tomato+species%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">Native environment modulates leaf size and response to simulated foliar shade across wild tomato species.</a> PLoS ONE. 7, e29570(2012).</li>
<li>Chitwood, D. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+developmental+trajectory+of+leaflet+morphology+in+wild+tomato+species%5BTitle%5D)+AND+%22Plant+Physiol%22%5BJournal%5D)">The developmental trajectory of leaflet morphology in wild tomato species.</a> Plant Physiol. 158, 1230-1240 (2012).</li>
<li>Casal, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Shade+Avoidance%5BTitle%5D)+AND+%22The+Arabidopsis+Book%22%5BJournal%5D)">Shade Avoidance.</a> The Arabidopsis Book. , e0157(2012).</li>
<li>Smith, H. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSmith%2C+H+%22Photomorphogenesis+in+Plants%22">Photomorphogenesis in Plants</a>. Kendrick, R. E., Kronenberg, G. H. M. , Kluwer Academic Publishers. 377-416 (1994).</li>
<li>Iwata, H., Ukai, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((SHAPE%3A+a+computer+program+package+for+quantitative+evaluation+of+biological+shapes+based+on+elliptic+Fourier+descriptors%5BTitle%5D)+AND+%22J.+Hered%22%5BJournal%5D)">SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic Fourier descriptors.</a> J. Hered. 93, 384-385 (2002).</li>
<li>Bylesjo, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((LAMINA%3A+a+tool+for+rapid+quantification+of+leaf+size+and+shape+parameters%5BTitle%5D)+AND+%22BMC+Plant+Biol%22%5BJournal%5D)">LAMINA: a tool for rapid quantification of leaf size and shape parameters.</a> BMC Plant Biol. 8, 82(2008).</li>
<li>Weight, C., Parnham, D., Waites, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((LeafAnalyser%3A+a+computational+method+for+rapid+and+large-scale+analyses+of+leaf+shape+variation%5BTitle%5D)+AND+%22Plant+J%22%5BJournal%5D)">LeafAnalyser: a computational method for rapid and large-scale analyses of leaf shape variation.</a> Plant J. 53, 578-586 (2008).</li>
<li>Backhaus, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((LEAFPROCESSOR%3A+a+new+leaf+phenotyping+tool+using+contour+bending+energy+and+shape+cluster+analysis%5BTitle%5D)+AND+%22New+Phytol%22%5BJournal%5D)">LEAFPROCESSOR: a new leaf phenotyping tool using contour bending energy and shape cluster analysis.</a> New Phytol. 187, 251-261 (2010).</li>
<li>Tsukaya, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanisms+of+Leaf-shape+determination%5BTitle%5D)+AND+%22Annual+Review+of+Plant+Biology%22%5BJournal%5D)">Mechanisms of Leaf-shape determination.</a> Annual Review of Plant Biology. 57, 477-496 (2006).</li>
<li>Abramoff, M. D., Magalhaes, P. J., Ram, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Image+Processing+with+ImageJ%5BTitle%5D)+AND+%22Biophotonics+International%22%5BJournal%5D)">Image Processing with ImageJ.</a> Biophotonics International. 11, 36-42 (2004).</li>
<li>Horiguchi, G., Fujikura, U., Ferjani, A., Ishikawa, N., Tsukaya, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Large-scale+histological+analysis+of+leaf+mutants+using+two+simple+leaf+observation+methods%3A+identification+of+novel+genetic+pathways+governing+the+size+and+shape+of+leaves%5BTitle%5D)+AND+%22Plant.+J%22%5BJournal%5D)">Large-scale histological analysis of leaf mutants using two simple leaf observation methods: identification of novel genetic pathways governing the size and shape of leaves.</a> Plant. J. 48, 638-644 (2006).</li>
<li>Horiguchi, G., Ferjani, A., Fujikura, U., Tsukaya, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Coordination+of+cell+proliferation+and+cell+expansion+in+the+control+of+leaf+size+in+Arabidopsis+thaliana%5BTitle%5D)+AND+%22J.+Plant.+Res%22%5BJournal%5D)">Coordination of cell proliferation and cell expansion in the control of leaf size in Arabidopsis thaliana.</a> J. Plant. Res. 119, 37-42 (2006).</li>
<li>Pierik, R., de Wit, M., Voesenek, L. A. C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Growth-mediated+stress+escape%3A+convergence+of+signal+transduction+pathways+activated+upon+exposure+to+two+different+environmental+stresses%5BTitle%5D)+AND+%22New.+Phytol%22%5BJournal%5D)">Growth-mediated stress escape: convergence of signal transduction pathways activated upon exposure to two different environmental stresses.</a> New. Phytol. 189, 122-134 (2011).</li>
</ol>

No conflicts of interest declared.

Our "LeafJ" plugin enables measurement of petiole length semi-automatically, increasing throughput nearly 6 times over manual measurement. Petiole length is an important index of SAS and is also a landmark of other phenomena, such as submergence resistance and hyponastic growth17. Therefore this plugin may be useful to a wide range of plant researchers.
Our plugin is implemented in a well-established java-based free software, ImageJ. This enables easy cross-platform installation. E...

<table><tbody><tr><td>far-red light LED </td><td>Orbitec</td><td>custom made</td><td></td></tr><tr><td>transparency</td><td>IKON</td><td>HSCA/5</td><td></td></tr><tr><td>scanner </td><td>Epson</td><td>Epson Perfection V700 PHOTO</td><td></td></tr><tr><td>Image J </td><td>NIH</td><td></td><td>&lt;a href=&quot;http://rsbweb.nih.gov/ij/&quot; target=&quot;_blank&quot;&gt;http://rsbweb.nih.gov/ij/&lt;/a&gt; </td></tr><tr><td>LeafJ </td><td>custom</td><td></td><td>&lt;a href=&quot;http://www.openwetware.org/wiki/Maloof_Lab&quot; target=&quot;_blank&quot;&gt;http://www.openwetware.org/wiki/Maloof_Lab&lt;/a&gt; </td></tr><tr><td>Air Display</td><td>Avatron Software Inc.</td><td></td><td>&lt;a href=&quot;http://avatron.com/&quot; target=&quot;_blank&quot;&gt;http://avatron.com/&lt;/a&gt;</td></tr><tr><td>iPad2 </td><td>Apple Inc.</td><td></td><td>&lt;a href=&quot;http://www.apple.com/&quot; target=&quot;_blank&quot;&gt;http://www.apple.com/&lt;/a&gt;</td></tr></tbody></table>

leafj: an imagej plugin for semi-automated leaf shape measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the primary photosynthetic organ, and their size and shape vary developmentally and environmentally within a plant. For these reasons studies on leaf morphology require measurement of multiple parameters from numerous leaves, which is best done by semi-automated phenomics tools5,6. Canopy shade is an important environmental cue that affects plant architecture and life history; the suite of responses is collectively called the shade avoidance syndrome (SAS)7. Among SAS responses, shade induced leaf petiole elongation and changes in blade area are particularly useful as indices8. To date, leaf shape programs (e.g. SHAPE9, LAMINA10, LeafAnalyzer11, LEAFPROCESSOR12) can measure leaf outlines and categorize leaf shapes, but can not output petiole length. Lack of large-scale measurement systems of leaf petioles has inhibited phenomics approaches to SAS research. In this paper, we describe a newly developed ImageJ plugin, called LeafJ, which can rapidly measure petiole length and leaf blade parameters of the model plant Arabidopsis thaliana. For the occasional leaf that required manual correction of the petiole/leaf blade boundary we used a touch-screen tablet. Further, leaf cell shape and leaf cell numbers are important determinants of leaf size13. Separate from LeafJ we also present a protocol for using a touch-screen tablet for measuring cell shape, area, and size. Our leaf trait measurement system is not limited to shade-avoidance research and will accelerate leaf phenotyping of many mutants and screening plants by leaf phenotyping.

1. Leaf Images Showing Estimates of the Petiole and Leaf Blade Boundary, and Their Measurement Window
One of the most useful features of LeafJ is automated detection of leaf blade/petiole boundary (Figure 1). The LeafJ algorithm works as follows: the built-in ImageJ ParticleAnalyzer functionality is used to find and determine the orientation of the leaves inside of the user selection. For each leaf the width of the leaf is determined along the leaf's entire axis. Then the change ...

Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review1 and recent examples2-4). Leaves are the ...

The aim of this procedure is to automate leaf shape measurement for high throughput experiments. This is accomplished by first dissecting the leaves and imaging them with a flatbed scanner. The second step of the procedure is to use the leaf J plugin to detect and measure the leaves.The third step is to prepare the leaves for microscopy and photograph them using a compound microscope. The final step is to trace sellout lines on the acquired images using a touchscreen tablet. Ultimately, the results can show differences in leaf shape, cell size and cell number through this combination of image analysis.The main advantage of this technique over existing methods is its speed. It's about six times faster than manual measurement. Individuals knew this method may struggle because proper leaf placement is critical for downstream analysis, and because mounting leaves on slides can be technically challenging.I first had the idea for this method when I realized we had about 15, 000 leaves to analyze, and doing it by hand would take many months and be incredibly boring, demonstrating the plant dissection technique would be causing neuro zue. A research scientist from my laboratory Begin by preparing 8.5 by 11 inch transparency sheets. Each sheet should be split into rectangular frames, large enough to accommodate the leaves from one plant, taper second transparency sheet to the printed sheet to serve as a cover to prevent the leaves from drying out.Label with the plant, genotype and growth condition. Dissect the leaves of the 26th dayle plants. Arrange the leaves on the transparency in tactic order from youngest to oldest.Position the leaves vertically within the frame without touching the border or overlapping other leaves. Otherwise, problems will ensue During analysis, It's important to oriental leaves vertically not touching, frame, not overlapping With the leaves arranged. Sandwich them with the second blank transparency sheet and a rack to keep the leaves flat.Then generate an image using a flatbed scanner with at least 600 dots per inch of resolution. Begin by installing the plugin. Download image J and drag the leaf jja file into the plugins folder of image J.When image J is used, the leaf J plugin will now be available.Proceed with opening the leaf image file in image J.Make sure image J is version 1.45 s or later. Split the image into three color channels, red, green, and blue by clicking image color split channels. Next, apply a threshold to the image in the blue channel.Now select all of the leaves from one plant using the rectangular selection tool and then select the leaf J plugin. The plugin will now open a box that allows the various conditions of the experiment to be set, such as an experimental group and genotype. Before proceeding, edit these options, then select the conditions for selected leaves in the region of interest and provide a descriptive name.Press continue and leaf J will automatically outline each leaf with a unique ROI image. J routines fit in ellipse to the blade and calculate the major and minor axes of that ellipse if necessary. Edit the trace lines from using the ROI manager window.A touchscreen tablet connected as an external monitor can be useful for this procedure. With the boundaries correctly defined, execute the analysis by pressing. Okay, now leaf J outputs the petal length and various geometries of the leaf blade.Ultimately, export the measurement results and associated information to a spreadsheet. After scanning the leaves, fix them in F-A-A-F-A. A fixed leaves can be stored at four degrees Celsius for up to six months.Moving forwards an hour or two before imaging the leaves clear the leaves by changing the FAA fixative to chlorohydrate solution. Once cleared, mount leaves on slides with trichomes facing up using 40 x magnification on a compound microscope image, the mesil layer of the center of each leaf on either side of the main vein, avoiding cells near trichomes or veins. To avoid difficulties in image processing, it's important to remove all air bubbles from slides when mounting leaves and also to get in-focus images in the next step.After collecting the desired images, open one in image J and trace the leaf cell outlines by image JROI manager tool with aid of the touchscreen tablet and a stylus. This does not utilize the LEAF J plugin. Prior to using LEAF J for experimentation, the accuracy and speed of the plugin was compared to that of an experienced researcher.Overall.Leaf J finished 5.7 more quickly than the manual calculations and comparing the data from the two methods showed that the results were tightly correlated for all measured parameters of the 3, 532 compared. Data points 172 were outliers. Of these outliers, 29 were caused by the LEAF J plugin much fewer than the 143 caused by human error.During manual measurement, leaf J has since been updated to further reduce the number of errors. Taking confidence in LEAF J measurements, it was used to assay shade avoidance using a mixed effects model. Treatment and leaf number were fixed effects and replicate was as a random effect.Many parameters were significantly affected by shade treatment. Alternately, some parameters were not significantly affected by shade. Using a tablet assisted imaged analysis of cell parameters, 67 genotypes of Arabidopsis ANA were examined totaling 8, 629 cells from 877 leaves of 224 plants.The genotypes were then grouped into six classes. Most genotypes have a normal cell size and number. However, several had larger cell size with normal cell number.While attempting this procedure is important to use randomization and experimental design when you growing your plants, Remember that working with formaldehyde and chlorhydrate can be extremely hazardous. So precautions such as wearing gloves and working in the fume hood should be taken when doing this procedure. After watching this video, you should have a good understanding of how to do high throughput screening of leaf and cell shape phenotypes using your own experimental conditions Following this procedure.Additional methods like RNA sequencing can be used to answer questions like which genes effectively shape.

leafj-an-imagej-plugin-for-semi-automated-leaf-shape-measurement

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Learn to automate leaf shape measurement for high throughput plant phenotyping using the LeafJ ImageJ plugin. This method accelerates screening by analyzing dissected leaves with flatbed scanning and image analysis.

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

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