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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenomics--technologies+to+relieve+the+phenotyping+bottleneck.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+shoot+imaging+to+study+drought+responses.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Natural+genetic+variation+for+growth+and+development+revealed+by+high-throughput+phenotyping+in+Arabidopsis+thaliana.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-content+behavioral+analysis+of+Caenorhabditis+elegans+in+precise+spatiotemporal+chemical+environments.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Native+environment+modulates+leaf+size+and+response+to+simulated+foliar+shade+across+wild+tomato+species.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+developmental+trajectory+of+leaflet+morphology+in+wild+tomato+species.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shade+Avoidance.">Shade Avoidance.</a> The Arabidopsis Book. , e0157 (2012).</li><li>Smith, H., Kendrick, R. E., Kronenberg, G. H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Photomorphogenesis in Plants. , 377-416 (1994).</li><li>Iwata, H., Ukai, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SHAPE:+a+computer+program+package+for+quantitative+evaluation+of+biological+shapes+based+on+elliptic+Fourier+descriptors.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LAMINA:+a+tool+for+rapid+quantification+of+leaf+size+and+shape+parameters.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LeafAnalyser:+a+computational+method+for+rapid+and+large-scale+analyses+of+leaf+shape+variation.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LEAFPROCESSOR:+a+new+leaf+phenotyping+tool+using+contour+bending+energy+and+shape+cluster+analysis.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+Leaf-shape+determination.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+Processing+with+ImageJ.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=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.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coordination+of+cell+proliferation+and+cell+expansion+in+the+control+of+leaf+size+in+Arabidopsis+thaliana.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth-mediated+stress+escape:+convergence+of+signal+transduction+pathways+activated+upon+exposure+to+two+different+environmental+stresses.">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. Ease of modification of the program is also an advantage of the LeafJ plugin because ImageJ already has a large library of plugins that were written by Java and ImageJ macro languages (<a href="http://imagejdocu.tudor.lu/doku.php?id=tutorial:start" target="_blank">http://imagejdocu.tudor.lu/doku.php?id=tutorial:start</a>). Currently we only tested Arabidopsis leaves, but our algorithm of petiole/leaf blade boundary detection could apply to other dicotyledonous leaves after some modification of the plugin.
During testing LeafJ plugin, we found most of the 14 errors came from human mistakes such as misplacing copied results on datasheets and/or mislabeling of plant genotypes. In rare cases the petiole/leaf blade boundary was called incorrectly, necessitating manual correction and creating additional risk of copy and paste mistakes. We could detect such errors after looking at data (a) by thresholding values (e.g. petioles longer than leaf length) and (b) by finding duplicated sample condition (e.g. "sun" or "shade"), genotype, or position of leaves.
Our touch-screen tablet method facilitated accuracy and speed of measurement. Limitation of our method is that communication between main computer and the touch screen tablet relies on the speed of the wireless local area network (LAN).

<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td></td>
 </tr>
 <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><a href="http://rsbweb.nih.gov/ij/" target="_blank">http://rsbweb.nih.gov/ij/</a> </td>
 </tr>
 <tr>
 <td>LeafJ </td>
 <td>custom </td>
 <td></td>
 <td><a href="http://www.openwetware.org/wiki/Maloof_Lab" target="_blank">http://www.openwetware.org/wiki/Maloof_Lab</a> </td>
 </tr>
 <tr>
 <td>Air Display</td>
 <td>Avatron Software Inc.</td>
 <td></td>
 <td><a href="http://avatron.com/" target="_blank">http://avatron.com/</a></td>
 </tr>
 <tr>
 <td>iPad2 </td>
 <td>Apple Inc. </td>
 <td></td>
 <td><a href="http://www.apple.com/" target="_blank">http://www.apple.com/</a></td>
 </tr>
 </tbody>
</table>

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 in width at each position along the axis is determined using a running window (the mean width for the seven positions proximal to the focal position is subtracted from the mean width for the seven positions distal to the focal position). The petiole/blade boundary is defined as the first position beyond the leaf base where the change in width is greater than 90% of all calculated width differences. LeafJ does additional checks to increase reliability of this call; specifically LeafJ also requires that 1) the width exceeds the narrowest 5% of positions in the length (this prevents aberrant calls at the petiole base); 2) the region proximal to the focal position does not have a major change in width; and 3) the width of the leaf 20% distal to the focal position is at least 150% wider than at the focal position (true if the focal position is the boundary because 20% proximal to the boundary should be blade and therefore much wider).
Once LeafJ has defined the petiole/blade boundary, built-in ImageJ classes and methods are used to determine blade area, perimeter, and circularity. Built-in ImageJ methods are also used to fit an ellipse to the blade and to calculate the major and minor axes of that ellipse (then used as blade length and width). Petiole length is determined by a line that traces along the center of the petiole area.
2. Detection of Shade-induced Petiole Elongation
To ask whether LeafJ measurements were useful in assaying shade avoidance, we used a mixed effects model with treatment and leaf number as fixed effects and replicate as a random effect. We found that petiole length, blade area, blade length, blade width, and the petiole length/blade length ratio were significantly affected by shade treatment, whereas blade circularity and the blade length/blade width ratio were not (p&lt;0.05). Our data showed that LeafJ plugin is useful for studies on leaf shade avoidance responses (Figure 2).
3. Accuracy and Speed of the LeafJ Plugin
To determine the performance of the LeafJ plugin in larger data sets we compared operation time and accuracy between manual and plugin measurements. For manual measurements we defined the petiole/ blade boundary as the place where the leaf width appeared to rapidly increase. It took an experienced researcher an average of 19 min 3 sec to measure one transparency with five plants (about 50 leaves) by manual measurement, while with LeafJ it took only 3 min 20 sec. Averaged across 5 transparencies, measuring with the plugin was 5.7 times faster than manual measurement. The manual measurement was done by a researcher with experience in making many manual measurements; a beginner would be considerably slower at manual measurement, resulting in an even greater advantage of LeafJ. We assessed accuracy by comparing data from the two methods; the data were highly correlated for all leaf parameters (Figure 3). Of the 3,532 data points there were 172 (4.9%) that showed strong differences between the methods (evidenced by being outliers on the correlation plots). We analyzed the cause of these outliers. Of 172 outliers, 29 were due to errors during LeafJ plugin measurement and 143 were due to errors in manual measurement. This error analysis also showed accuracy of plugin measurement.
4. Cell Size and Cell Number Measurement
Independent of LeafJ we also developed an efficient work flow for measuring cell number and size. Theoretically cell number and size can be used to classify mutant plants into nine categories when compared with wild type; (1) smaller cells size with decreased cell numbers, (2) smaller cells with normal cell numbers, (3) smaller cells with increased cell numbers, (4) normal cell size with decreased cell numbers, (5) normal cell size with normal cell numbers, (6) normal cell size with decreased cell numbers, (7) larger cell size with decreased cell numbers, (8) larger cell size with normal cell numbers, (9) larger cell size with increased cell numbers15,16. We measured leaf cell parameters in 67 genotypes of Arabidopsis thaliana using our tablet-based cell size and cell number measurement method. We measured cell size of 8,629 cells from 877 leaves of 224 plants. Multiplying cell density by leaf area (measured by LeafJ), we estimated total leaf cell number data of 438 leaves from 219 plants. Our analysis placed these genotypes into six of the possible nine categories (Fig. 4). The largest category was (5): normal cell size and number; second largest was (8): larger cell size with normal cell number. Although follow-up work is needed, this suggests that our tablet-based method can be used to categorize mutants based on leaf cell size and number.
<img alt="Figure 1" fo:src="/files/ftp_upload/50028/50028fig1highres.jpg" src="/files/ftp_upload/50028/50028fig1.jpg" /> 
Figure 1. An example of petiole/leaf blade boundary detection and user interface. Note that LeafJ is able to define petiole/leaf blade boundary automatically (Figure 1B). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50028/50028fig1large.jpg" target="_blank"> Click here to view larger figure</a>.
<img alt="Figure 2" fo:src="/files/ftp_upload/50028/50028fig2highres.jpg" src="/files/ftp_upload/50028/50028fig2.jpg" /> 
Figure 2. LeafJ plugin could detect shade avoidance responses in various leaf parameters. Leaf 3 to leaf 6 from seven wild type plants (Arabidopsis thaliana Columbia ecotype) under each condition (sun and shade) were examined. From top left to bottom right, the y-axis units are mm, mm, mm, mm2, ratio of petiole length to blade length, and ratio of blade length to blade width. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50028/50028fig2large.jpg" target="_blank"> Click here to view larger figure</a>.
<img alt="Figure 3" fo:src="/files/ftp_upload/50028/50028fig3highres.jpg" src="/files/ftp_upload/50028/50028fig3.jpg" /> 
Figure 3. LeafJ plugin is highly accurate. Correlation of data from manual measurement and LeafJ plugin measurement from 3,532 data points. Each dot represents one leaf. Green dots indicate the 170 outliers in this graph. Axes of leaf area is mm2 all others are mm. Numbers in front of each parameter represent leaf position (i.e. "3" is the third leaf).
<img alt="Figure 4" fo:src="/files/ftp_upload/50028/50028fig4highres.jpg" src="/files/ftp_upload/50028/50028fig4.jpg" /> 
Figure 4. Scatter plot of leaf cell area and leaf cell number among 67 genotypes of Arabidopsis thaliana. Each point represents the phenotypes of each genotype grown under shade condition. Plants were classified into nine categories based on their difference from wild type (Col) as determined by a linear mixed-effects model and multiple-testing corrected p-values: (1) smaller cell size with decreased cell numbers, (2) smaller cell size with normal cell number, (3) smaller cell size with increased cell number, (4) normal cell size with decreased cell number, (5) normal cell size with normal cell number, (6) normal cell size with decreased cell number, (7) larger cell size with decreased cell number, (8) larger cell size with normal cell number, (9) larger cell size with increased cell number. "*" indicates the phenotype of wild type (Col).

Watch this Scientific Journal Video about LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement at JoVE.com

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

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 ...

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

Nanyang Technological University

Research

JoVE Journal

Biology

28.2K Views.  University of California Davis. 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.

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

Homemade Site Directed Mutagenesis of Whole Plasmids

Site directed mutagenesis of whole plasmids is a simple way to create slightly different variations of an original plasmid. With this method the cloned target gene can be altered by substitution, deletion or insertion of a few bases directly into a plasmid. It works by simply amplifying the whole plasmid, in a non PCR-based thermocycling reaction. During the reaction mutagenic primers, carrying the desired mutation, are integrated into the newly synthesized plasmid. In this video tutorial we demonstrate an easy and cost effective way to introduce base substitutions into a plasmid. The protocol works with standard reagents and is independent from commercial kits, which often are very expensive. Applying this protocol can reduce the total cost of a reaction to an eighth of what it costs using some of the commercial kits. In this video we also comment on critical steps during the process and give detailed instructions on how to design the mutagenic primers.

Site directed mutagenesis of whole plasmids is a simple way to create slightly different variations of an original plasmid. Here we demonstrate an easy and cost effective way to introduce base substitutions into a plasmid using standard reagents.

Site directed mutagenesis of whole plasmids is a simple way to create slightly different variations of an original plasmid. With this method the cloned ...

Semi-automated Optical Heartbeat Analysis of Small Hearts

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our Semi-automatic Optical Heartbeat Analysis (SOHA) uses a novel movement detection algorithm that is able to detect cardiac movements associated with individual contractile and relaxation events. The program provides a host of physiologically relevant readouts including systolic and diastolic intervals, heart rate, as well as qualitative and quantitative measures of heartbeat arrhythmicity. The program also calculates heart diameter measurements during both diastole and systole from which fractional shortening and fractional area changes are calculated. Output is provided as a digital file compatible with most spreadsheet programs. Measurements are made for every heartbeat in a record increasing the statistical power of the output. We demonstrate each of the steps where user input is required and show the application of our methodology to the analysis of heart function in all three genetically tractable heart models.

We have developed a Semi-automated Optical Heartbeat Analysis method (SOHA) for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts. We demonstrate the application of our methodology to the analysis of heart function in fruit fly and embryonic mouse hearts.

We have developed a method for analyzing high speed optical recordings from Drosophila, zebrafish and embryonic mouse hearts (Fink, et. al., 2009). Our ...

Imaging Cell Shape Change in Living Drosophila Embryos

The developing Drosophila melanogaster embryo undergoes a number of cell shape changes that are highly amenable to live confocal imaging. Cell shape changes in the fly are analogous to those in higher organisms, and they drive tissue morphogenesis. So, in many cases, their study has direct implications for understanding human disease (Table 1)1-5. On the sub-cellular scale, these cell shape changes are the product of activities ranging from gene expression to signal transduction, cell polarity, cytoskeletal remodeling and membrane trafficking. Thus, the Drosophila embryo provides not only the context to evaluate cell shape changes as they relate to tissue morphogenesis, but also offers a completely physiological environment to study the sub-cellular activities that shape cells.
The protocol described here is designed to image a specific cell shape change called cellularization. Cellularization is a process of dramatic plasma membrane growth, and it ultimately converts the syncytial embryo into the cellular blastoderm. That is, at interphase of mitotic cycle 14, the plasma membrane simultaneously invaginates around each of ~6000 cortically anchored nuclei to generate a sheet of primary epithelial cells. Counter to previous suggestions, cellularization is not driven by Myosin-2 contractility6, but is instead fueled largely by exocytosis of membrane from internal stores7. Thus, cellularization is an excellent system for studying membrane trafficking during cell shape changes that require plasma membrane invagination or expansion, such as cytokinesis or transverse-tubule (T-tubule) morphogenesis in muscle.
Note that this protocol is easily applied to the imaging of other cell shape changes in the fly embryo, and only requires slight adaptations such as changing the stage of embryo collection, or using "embryo glue" to mount the embryo in a specific orientation (Table 1)8-19. In all cases, the workflow is basically the same (Figure 1). Standard methods for cloning and Drosophila transgenesis are used to prepare stable fly stocks that express a protein of interest, fused to Green Fluorescent Protein (GFP) or its variants, and these flies provide a renewable source of embryos. Alternatively, fluorescent proteins/probes are directly introduced into fly embryos via straightforward micro-injection techniques9-10. Then, depending on the developmental event and cell shape change to be imaged, embryos are collected and staged by morphology on a dissecting microscope, and finally positioned and mounted for time-lapse imaging on a confocal microscope.

Imaging Cell Shape Change in Living Drosophila Embryos

Early development of the fruit fly, Drosophila melanogaster, is characterized by a number of cell shape changes that are well suited for imaging approaches. This article will describe basic tools and methods required for live confocal imaging of Drosophila embryos, and will focus on a cell shape change called cellularization.

The developing Drosophila melanogaster embryo undergoes a number of cell shape changes that are highly amenable to live confocal imaging.  Cell shape ...

Measurement of Leaf Hydraulic Conductance and Stomatal Conductance and Their Responses to Irradiance and Dehydration Using the Evaporative Flux Method (EFM)

Water is a key resource, and the plant water transport system sets limits on maximum growth and drought tolerance. When plants open their stomata to achieve a high stomatal conductance (gs) to capture CO2 for photosynthesis, water is lost by transpiration1,2. Water evaporating from the airspaces is replaced from cell walls, in turn drawing water from the xylem of leaf veins, in turn drawing from xylem in the stems and roots. As water is pulled through the system, it experiences hydraulic resistance, creating tension throughout the system and a low leaf water potential (&Psi;leaf). The leaf itself is a critical bottleneck in the whole plant system, accounting for on average 30% of the plant hydraulic resistance3. Leaf hydraulic conductance (Kleaf = 1/ leaf hydraulic resistance) is the ratio of the water flow rate to the water potential gradient across the leaf, and summarizes the behavior of a complex system: water moves through the petiole and through several orders of veins, exits into the bundle sheath and passes through or around mesophyll cells before evaporating into the airspace and being transpired from the stomata. Kleaf is of strong interest as an important physiological trait to compare species, quantifying the effectiveness of the leaf structure and physiology for water transport, and a key variable to investigate for its relationship to variation in structure (e.g., in leaf venation architecture) and its impacts on photosynthetic gas exchange. Further, Kleaf responds strongly to the internal and external leaf environment3. Kleaf can increase dramatically with irradiance apparently due to changes in the expression and activation of aquaporins, the proteins involved in water transport through membranes4, and Kleaf declines strongly during drought, due to cavitation and/or collapse of xylem conduits, and/or loss of permeability in the extra-xylem tissues due to mesophyll and bundle sheath cell shrinkage or aquaporin deactivation5-10. Because Kleaf can constrain gs and photosynthetic rate across species in well watered conditions and during drought, and thus limit whole-plant performance they may possibly determine species distributions especially as droughts increase in frequency and severity11-14.
We present a simple method for simultaneous determination of Kleaf and gs on excised leaves. A transpiring leaf is connected by its petiole to tubing running to a water source on a balance. The loss of water from the balance is recorded to calculate the flow rate through the leaf. When steady state transpiration (E, mmol &bull; m-2 &bull; s-1) is reached, gs is determined by dividing by vapor pressure deficit, and Kleaf by dividing by the water potential driving force determined using a pressure chamber (Kleaf= E /- &Delta;&psi;leaf, MPa)15.
This method can be used to assess Kleaf responses to different irradiances and the vulnerability of Kleaf to dehydration14,16,17.

Measurement of Leaf Hydraulic Conductance and Stomatal Conductance and Their Responses to Irradiance and Dehydration Using the Evaporative Flux Method EFM

We describe a relatively rapid (30 min) and realistic method for simultaneously measurement of leaf hydraulic conductance (Kleaf) and stomatal conductance (gs) for transpiring excised leaves. The method can be modified to measure the light and dehydration responses of Kleaf and gs.

Water is a key resource, and the plant water transport system sets limits on maximum growth and drought tolerance. When plants open their stomata to ...

RNA Secondary Structure Prediction Using High-throughput SHAPE

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.

High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed ...

An Innovative Method for Exosome Quantification and Size Measurement

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about their complex pathways, their bioavailability and their diverse functions in health and disease. Current work focuses on the presence and the behavior of exosomes (in vitro as well as in vivo) in the context of different human disorders, especially in the fields of oncology, gynecology and cardiology.
Unfortunately, neither a consensus regarding a gold standard for exosome isolation exists, nor is there an agreement on such a method for their quantitative analysis. As there are many methods for the purification of exosomes and also many possibilities for their quantitative and qualitative analysis, it is difficult to determine a combination of methods for the ideal approach. 
Here, we demonstrate nanoparticle tracking analysis (NTA), a semi-automated method for the characterization of exosomes after isolation from human plasma by ultracentrifugation. The presented results show that this approach for isolation, as well as the determination of the average number and size of exosomes, delivers reproducible and valid data, as confirmed by other methods, such as scanning electron microscopy (SEM).

A method for isolation of exosomes from whole blood and further analysis by nanoparticle tracking using a semi-automatic instrument is presented in this article. The presented technology provides an extremely sensitive method for visualizing and analyzing particles in liquid suspension.

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about ...

An Improved Method for Accurate and Rapid Measurement of Flight Performance in Drosophila

Drosophila has proven to be a useful model system for analysis of behavior, including flight. The initial flight tester involved dropping flies into an oil-coated graduated cylinder; landing height provided a measure of flight performance by assessing how far flies will fall before producing enough thrust to make contact with the wall of the cylinder. Here we describe an updated version of the flight tester with four major improvements. First, we added a &#34;drop tube&#34; to ensure that all flies enter the flight cylinder at a similar velocity between trials, eliminating variability between users. Second, we replaced the oil coating with removable plastic sheets coated in Tangle-Trap, an adhesive designed to capture live insects. Third, we use a longer cylinder to enable more accurate discrimination of flight ability. Fourth we use a digital camera and imaging software to automate the scoring of flight performance. These improvements allow for the rapid, quantitative assessment of flight behavior, useful for large datasets and large-scale genetic screens.

An Improved Method for Accurate and Rapid Measurement of Flight Performance in Drosophila

Here we describe a method for rapid and accurate measurement of flight performance in Drosophila, enabling high-throughput screening.

Drosophila has proven to be a useful model system for analysis of behavior, including flight. The initial flight tester involved dropping flies into an ...

Semi-automated Imaging of Tissue-specific Fluorescence in Zebrafish Embryos

Zebrafish embryos are a powerful tool for large-scale screening of small molecules. Transgenic zebrafish that express fluorescent reporter proteins are frequently used to identify chemicals that modulate gene expression. Chemical screens that assay fluorescence in live zebrafish often rely on expensive, specialized equipment for high content screening. We describe a procedure using a standard epifluorescence microscope with a motorized stage to automatically image zebrafish embryos and detect tissue-specific fluorescence. Using transgenic zebrafish that report estrogen receptor activity via expression of GFP, we developed a semi-automated procedure to screen for estrogen receptor ligands that activate the reporter in a tissue-specific manner. In this video we describe procedures for arraying zebrafish embryos at 24-48 hours post fertilization (hpf) in a 96-well plate and adding small molecules that bind estrogen receptors. At 72-96 hpf, images of each well from the entire plate are automatically collected and manually inspected for tissue-specific fluorescence. This protocol demonstrates the ability to detect estrogens that activate receptors in heart valves but not in liver.

Described here is a protocol for semi-automated imaging of tissue-specific fluorescence in zebrafish embryos.

Zebrafish embryos are a powerful tool for large-scale screening of small molecules. Transgenic zebrafish that express fluorescent reporter proteins are ...

Measurement of Heme Synthesis Levels in Mammalian Cells

Heme serves as the prosthetic group for a wide variety of proteins known as hemoproteins, such as hemoglobin, myoglobin and cytochromes. It is involved in various molecular and cellular processes such as gene transcription, translation, cell differentiation and cell proliferation. The biosynthesis levels of heme vary across different tissues and cell types and is altered in diseased conditions such as anemia, neuropathy and cancer. This technique uses [4-14C] 5-aminolevulinic acid ([14C] 5-ALA), one of the early precursors in the heme biosynthesis pathway to measure the levels of heme synthesis in mammalian cells. This assay involves incubation of cells with [14C] 5-ALA followed by extraction of heme and measurement of the radioactivity incorporated into heme. This procedure is accurate and quick. This method measures the relative levels of heme biosynthesis rather than the total heme content. To demonstrate the use of this technique the levels of heme biosynthesis were measured in several mammalian cell lines.

Altered intracellular heme levels are associated with common diseases such as cancer. Thus, there is a need to measure heme biosynthesis levels in diverse cells. The goal of this protocol is to provide a fast and sensitive method to measure and compare the levels of heme synthesis in different cells.

Heme serves as the prosthetic group for a wide variety of proteins known as hemoproteins, such as hemoglobin, myoglobin and cytochromes. It is involved in ...

Relating Stomatal Conductance to Leaf Functional Traits

Leaf functional traits are important because they reflect physiological functions, such as transpiration and carbon assimilation. In particular, morphological leaf traits have the potential to summarize plants strategies in terms of water use efficiency, growth pattern and nutrient use. The leaf economics spectrum (LES) is a recognized framework in functional plant ecology and reflects a gradient of increasing specific leaf area (SLA), leaf nitrogen, phosphorus and cation content, and decreasing leaf dry matter content (LDMC) and carbon nitrogen ratio (CN). The LES describes different strategies ranging from that of short-lived leaves with high photosynthetic capacity per leaf mass to long-lived leaves with low mass-based carbon assimilation rates. However, traits that are not included in the LES might provide additional information on the species&#39; physiology, such as those related to stomatal control. Protocols are presented for a wide range of leaf functional traits, including traits of the LES, but also traits that are independent of the LES. In particular, a new method is introduced that relates the plants&#8217; regulatory behavior in stomatal conductance to vapor pressure deficit. The resulting parameters of stomatal regulation can then be compared to the LES and other plant functional traits. The results show that functional leaf traits of the LES were also valid predictors for the parameters of stomatal regulation. For example, leaf carbon concentration was positively related to the vapor pressure deficit (vpd) at the point of inflection and the maximum of the conductance-vpd curve. However, traits that are not included in the LES added information in explaining parameters of stomatal control: the vpd at the point of inflection of the conductance-vpd curve was lower for species with higher stomatal density and higher stomatal index. Overall, stomata and vein traits were more powerful predictors for explaining stomatal regulation than traits used in the LES.

Unraveling how physiology and morphology are linked allows a deeper understanding of mechanistic functioning of plant leaves. We present both a procedure to derive parameters of stomatal regulation from stomatal conductance measurements and correlations with traditional functional leaf traits.