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

Podsumowanie

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.

Streszczenie

High throughput phenotyping (phenomics) is a powerful tool for linking genes to their functions (see review¹ and recent examples^2-4). 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 tools^5,6. 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.

Protokół

1. Plant Materials

Note that this plant growth protocol is aimed for detecting shade avoidance response. You can grow plants under your favorite condition.

1. Sprinkle Arabidopsis thaliana seeds on water soaked filter papers in 9 cm Petri dishes and store (stratify) them at 4 °C for four days in the dark.
2. Transfer these Petri dishes to simulated sun conditions: 80-100 μE 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 °C. Incubate in this condition for three days to allow the seeds to germinate.
3. 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.
4. 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.
5. 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.

2. Capturing Dissected Leaf Images

1. 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.
2. Dissect leaves of twenty-six day old plants.
3. 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.

3. Leaf Image Analysis by LeafJ

1. Download ImageJ from http://malooflab.openwetware.org/Resources.html#Software_Tools or http://bitbucket.org/jnmaloof/leafj. Drag the LeafJ.jar file into the plugins folder of ImageJ.
2. Open an image file in ImageJ 1.45s or later¹⁴.
3. Split the image into three-color channels (red, green, and blue) by "Image > Color > Split Channels" and apply threshold to the image in the blue channel.
4. Select all of the leaves from one plant by a rectangle tool (Figure 1A).
5. Select "LeafJ" from the plugin menu.
6. 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".
7. 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.
8. Export measurement results and associated information (file names, flowering time, dissected by, measured by, etc) to Microsoft Excel or equivalent software.

4. Leaf Cell Image Analysis in ImageJ

1. Fix dissected leaves as described in reference¹⁵ after scanning (step 2). FAA fixed leaves can be kept in 4 °C for at least 6 months.
2. Clear the leaves by changing FAA fixative to chloral hydrate solution and incubate leaves for 1~2 hr before microscopic observation¹⁵.
3. 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.
4. 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.

Wyniki

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 ...

Dyskusje

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...

Materiały

Name	Company	Catalog Number	Comments
far-red light LED	Orbitec	custom made
transparency	IKON	HSCA/5
scanner	Epson	Epson Perfection V700 PHOTO
Image J	NIH		http://rsbweb.nih.gov/ij/
LeafJ	custom		http://www.openwetware.org/wiki/Maloof_Lab
Air Display	Avatron Software Inc.		http://avatron.com/
iPad2	Apple Inc.		http://www.apple.com/