F.S. acknowledges support from Rhenac Green Tec AG through parts of this study. J.S. and B.B. received funding from the DFG (SFB TR175).

This protocol contains some examples of how to analyze growth behavior of A. thaliana plants.
1. Preparation
<ol>
	<li>Before starting, make a careful plan on how many plants will be needed for doing a reliable statistical analysis of the experiments and then prepare the appropriate amounts of pots. 
	NOTE: Always allow for the possibility that some seeds might not germinate.</li>
	<li>Use a climatic chamber with different individually programmable LED levels to compare plant growth under the same general environmental conditions (Materials Table).</li>
</ol>
2. Plant Growth and Set-up of the LED Lights
<ol>
	<li>Prepare the appropriate number (depending on different conditions and/or mutants to be analyzed) of 6 x 7 cm pots with soil and plant a single seed in each of them. 
	NOTE: In this case, 36 plants per conditions were sown.</li>
	<li>Vernalize for two days at 4 &#176;C.</li>
	<li>Set the relative air humidity to 65% and the temperature to 22 &#176;C/16 &#176;C for a 16 h day/ 8 h night cycle. Adjust the light of all levels to an intensity of 200 &#181;M/cm2/s1. Do this by entering the respective values into a program via the touch screen at the front of the climatic chamber. To compare four different light qualities set the LEDs to the following parameters as provided in Table 1.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Identifyier</td>
			<td>395 nm [%]</td>
			<td>440 nm [%]</td>
			<td>3 K [%]</td>
			<td>660 nm [%]</td>
			<td>770 nm [%]</td>
		</tr>
		<tr>
			<td>&#34;sunlight&#34;</td>
			<td>100</td>
			<td>11</td>
			<td>100</td>
			<td>15</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Red and Blue (RB)</td>
			<td>100</td>
			<td>15</td>
			<td>25</td>
			<td>10</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Blue (B)</td>
			<td>100</td>
			<td>15</td>
			<td>25</td>
			<td>2</td>
			<td>25</td>
		</tr>
		<tr>
			<td>Red (R)</td>
			<td>90</td>
			<td>2</td>
			<td>25</td>
			<td>10</td>
			<td>100</td>
		</tr>
	</tbody>
</table>
Table 1: Composition of light intensities emitted from LEDs
<ol start="4">
	<li>Monitor the spectral output continuously, e.g. by using a built-in calibrated spectrometer.</li>
	<li>Place the plants in the climatic chamber on the different levels and keep them covered with a transparent top until well-developed cotyledons are visible. Make sure the plants are sufficiently watered.</li>
	<li>Monitor and document plants by eye and photographically as often as appropriate, depending on how fast the plants grow under the chosen conditions, e.g. every two days. Make sure to use a tripod for the camera to ensure the equal distance between camera and objects for all pictures and thereby enable comparisons. Use a scale bar when appropriate. 
	NOTE: The term DAS (days after sowing) refers to the actual planting of the seeds including vernalization.</li>
</ol>
3. Determination of PSII Yield
<ol>
	<li>Use a pulse-modulated fluorimeter. Set up the camera head at an appropriate distance from the plant so that the complete rosette can be seen on the live window.</li>
	<li>Upon starting the software a &#34;select unit&#34; window appears. Tick &#34;MINI&#34; and then &#34;ok.&#34; Choose color &#34;blue&#34; in the next pop-up window. Click &#34;ok.&#34; 
	NOTE: The pulse-modulated fluorescence measuring light is automatically switched on. On the monitor, the Image window appears showing the fluorescence parameter Ft. A plant placed under the camera can now be seen as an orange image.</li>
	<li>To focus the image and/or choose specific regions of the plant, switch to Live Video by ticking the &#34;live video&#34; box and turn the adjustment ring of the objective lens. Quit the LIVE Video window by clicking the exit box in the upper right corner.</li>
	<li>To measure photosynthetic parameters, define an area of interest (AOI). Use the default setting for this, which is a circle which automatically appears in the middle of the screen. The red box beside it represents the averaged Ft value of all pixels within the AOI. Define the appropriate AOI by choosing from the AOI box at the right panel, where different forms are available.&#160;Click &#8220;add&#8221; in the AOI tab and place the circle within the leaf area. Repeat five times per leaf.</li>
	<li>Maintain the settings (tab at the right) at the default values provided by the manufacturer&#160;(Table 2).</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Meas. Light</td>
			<td>Int.</td>
			<td>Frequency</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>1</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr>
			<td>Act. Light</td>
			<td>Int.</td>
			<td>Width</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>8</td>
			<td>0</td>
			<td></td>
		</tr>
		<tr>
			<td>Image Corrections</td>
			<td>MINI</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Image Transformation</td>
			<td>Battery</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>16.7V</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Gain</td>
			<td>5</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Damping</td>
			<td>1</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sat Pulse</td>
			<td>Int.</td>
			<td>No</td>
			<td>Interval s</td>
		</tr>
		<tr>
			<td></td>
			<td>8</td>
			<td>1</td>
			<td>30</td>
		</tr>
		<tr>
			<td>Slow Induction</td>
			<td>Delay s</td>
			<td>Clock s</td>
			<td>Duration s</td>
		</tr>
		<tr>
			<td></td>
			<td>40</td>
			<td>20</td>
			<td>315</td>
		</tr>
		<tr>
			<td>Absortivity</td>
			<td>Red Gain</td>
			<td>Red Intensity</td>
			<td>NIR Intensity</td>
		</tr>
		<tr>
			<td></td>
			<td>340</td>
			<td>25</td>
			<td>13</td>
		</tr>
		<tr>
			<td>Display</td>
			<td>Color</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>PS Limit</td>
			<td>50</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Inh. Ref. AOI</td>
			<td>1</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Fm Factor (tick)</td>
			<td>1,030</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2: Default Settings for the PAM measurements as provided by the manufacturer.
<ol start="6">
	<li>Apply a saturating light flash to perform a measurement of photosynthetic parameters by fluorescent quenching analysis (Saturation Pulse). Before that, determine the quenching coefficients by measuring the minimal and maximal fluorescence yield of a dark-adapted plant. To this end, place the plant in the dark (e.g. in a drawer or dark box) for a few min. Then place the plant under the camera head, check the boxes Measure and ML (measuring light), in the row below the image&#160;choose &#34;Fv/Fm&#34; by clocking into the circle,&#160;and click Fo,Fm at the bottom of the screen. 
	NOTE: Fo/Fm represents the PSII yield of a dark-adapted plant. Thus, it is the value to which the measurement after applying light is normalized. The current Fo/Fm measurement will remain until New Record is activated. All F and Fm&#39; values determined by Saturation Pulses are related to Fo/Fm and quenching parameters are calculated accordingly.</li>
	<li>Find these results in the report tab and check all the boxes on the right hand side that are relevant to the experiment (e.g. Y(II), qP, qN, etc.).</li>
	<li>Export the results to an analysis software e.g. Excel by clicking on export button in the upper left corner and save under appropriate file name.&#160;Generate at least five AOIs per leaf and measure several leaves of the same plant (do not forget to shortly dark adapt the plant again after each measurement), as well as several plants from one condition, which can then be statistically evaluated.&#160;</li>
	<li>For statistical analysis generate mean values and standard deviations from all AOIs and at least three independent plants for all time points/conditions and perform a student&#39;s t-test to evaluate if the data are significant12. Data are evaluated as significantly different when the p-values are below 0.05.</li>
</ol>
4. Determination of Stomata Density
<ol>
	<li>Collect three fully-expanded rosette leaves from three individual plants per condition into 70% ethanol in a glass petri dish and to extract chlorophyll. Incubate in this solvent overnight at room temperature or store at 4 &#176;C.</li>
	<li>For full clearance from pigments, incubate in chloral hydrate solution (chloral hydrate: water: glycerol = 8: 2: 1 w/v/w) until leaves appear completely white.</li>
	<li>Take differential interference microscopy (DIC) images of the abaxial surface at 40X magnification. Count stomata in the field of vision and extrapolate with the help of the scale bar to stomata per mm&#178;. Repeat this procedure for at least 4 leaves per conditions. Perform statistical analysis by calculating the mean value for all leaves of one condition and from this calculate the mean error.</li>
</ol>
5. Determination of Fresh Weight
<ol>
	<li>Remove all leaves including petioles from rosettes from six plants per condition with a razor blade. Weigh all leaves immediately and subject the data to statistical analyses as described above.</li>
</ol>
6. Determination of Rosette Leaf Area
<ol>
	<li>Use pictures from eight plants per condition to analyze the leaf area graphically. Combine the pictures for one condition in a single image and save as *.jpeg or *.tiff.</li>
	<li>Download the appropriate software (Materials Table). 
	NOTE: It is, of course, possible to apply any other program capable of performing this task.</li>
	<li>Open an image file. Choose the tool &#34;free hand selection.&#34;&#160;Encircle leaves including petioles of one rosette. Click on &#34;Analyze - set measurements&#34; and check &#34;Area,&#34;&#160;&#34;Min &#38; Max Gray value,&#34;&#160;&#34;Integrated density,&#34; and &#34;Mean Gray value.&#34;&#160;Choose appropriate decimal places, best is two or three and click &#34;ok.&#34;</li>
	<li>To obtain mm2 instead of pixels use the &#34;set scale&#34; command. Apply the straight-line selection tool to make a line selection that corresponds to a known distance, e.g. the leave diameter that is easy to calculate from the scale bar of the images, then open the Set Scale dialog and enter this defined distance and unit of measurement. Then go back to &#34;analyze&#34; and click &#34;measure.&#34;</li>
	<li>A new window appears entitled &#34;results&#34; which contains the relevant data for the current rosette. Repeat this procedure with all plants in the image and initialize area measurements for every new plant with Ctrl + M. 
	NOTE: For smaller plants with non-overlapping leaves the &#34;wand tool&#34; can be used to make the process easier and faster. As soon as leaves start covering each other, this tool does not give reliable results.</li>
	<li>Alternatively, cut off all leaves and position them in a way that an overview picture can be taken and then use the wand tool. Take into consideration that when using this method, more plants are needed.</li>
	<li>Choose &#34;File&#34; -&#34;Save as&#34; in the results window and generate an appropriate file name and a location in the computer directory. The file will be automatically saved in Excel format.</li>
</ol>
7. Preparation of RNA
<ol>
	<li>Harvest three samples from ten individual plants for every condition. Extract total RNA using a plant RNA extraction kit according to the manufacturer&#39;s instructions. Determine RNA concentration, purity,&#160;and integrity by using a bioanalyzer. RNA can then be used for downstream applications such as qRT-PCR or gene expression analysis by e.g. RNASeq13.</li>
</ol>

<ol>
	<li>Arabidopsis Genome Initiative. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+the+genome+sequence+of+the+flowering+plant+Arabidopsis+thaliana.">Analysis of the genome sequence of the flowering plant Arabidopsis thaliana.</a> Nature. 408 (6814), 796-815 (2000).</li><li>An, G., Watson, B. D., Chiang, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transformation+of+Tobacco,+Tomato,+Potato,+and+Arabidopsis+thaliana+Using+a+Binary+Ti+Vector+System.">Transformation of Tobacco, Tomato, Potato, and Arabidopsis thaliana Using a Binary Ti Vector System.</a> Plant Physiology. 81 (1), 301-305 (1986).</li><li>Schiml, S., Fauser, F., Puchta, H., Murata, 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> Chromosome and Genomic Engineering in Plants: Methods and Protocols. , 111-122 (2016).</li><li>Rivero, L., Sanchez-Serrano, J. J., Salinas, J., 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=.">.</a> Arabidopsis Protocols. , 3-25 (2014).</li><li>Ubbens, J. R., Stavness, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+Plant+Phenomics:+A+Deep+Learning+Platform+for+Complex+Plant+Phenotyping+Tasks.">Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks.</a> Frontiers in Plant Science. 8 (1190), (2017).</li><li>Cosgrove, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+Suppression+of+Growth+by+Blue+Light:+OCCURRENCE,+TIME+COURSE,+AND+GENERAL+CHARACTERISTICS.">Rapid Suppression of Growth by Blue Light: OCCURRENCE, TIME COURSE, AND GENERAL CHARACTERISTICS.</a> Plant Physiology. 67 (3), 584-590 (1981).</li><li>Seiler, F., Soll, J., B&#246;lter, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+Phenotypical+and+Molecular+Analyses+of+Arabidopsis+Grown+under+Fluorescent+and+LED+Light.">Comparative Phenotypical and Molecular Analyses of Arabidopsis Grown under Fluorescent and LED Light.</a> Plants. 6 (2), 24 (2017).</li><li>Janda, 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=Growth+and+stress+response+in+Arabidopsis+thaliana,+Nicotiana+benthamiana,+Glycine+max,+Solanum+tuberosum+and+Brassica+napus+cultivated+under+polychromatic+LEDs.">Growth and stress response in Arabidopsis thaliana, Nicotiana benthamiana, Glycine max, Solanum tuberosum and Brassica napus cultivated under polychromatic LEDs.</a> Plant Methods. 11 (1), 31 (2015).</li><li>Olle, M., Vir&#353;ile, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+light-emitting+diode+lighting+on+greenhouse+plant+growth+and+quality.">The effects of light-emitting diode lighting on greenhouse plant growth and quality.</a> Agricultural and food science. 22 (2), 12 (2013).</li><li>Lin, K. -. 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+effects+of+red,+blue,+and+white+light-emitting+diodes+on+the+growth,+development,+and+edible+quality+of+hydroponically+grown+lettuce+(Lactuca+sativa+L.+var.+capitata).">The effects of red, blue, and white light-emitting diodes on the growth, development, and edible quality of hydroponically grown lettuce (Lactuca sativa L. var. capitata).</a> Scientia Horticulturae. 150, 86-91 (2013).</li><li>Castronuovo, D., 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=Light+spectrum+affects+growth+and+gas+exchange+of+common+dandelion+and+purple+coneflower+seedlings.">Light spectrum affects growth and gas exchange of common dandelion and purple coneflower seedlings.</a> International Journal of Plant Biology. , (2016).</li><li>Student, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=THE+PROBABLE+ERROR+OF+A+MEAN.">THE PROBABLE ERROR OF A MEAN.</a> Biometrika. 6 (1), 1-25 (1908).</li><li>Database, J. S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Essentials+of+Genetics.+RNA-Seq.">Essentials of Genetics. RNA-Seq.</a> JoVE. , (2017).</li><li>Klermund, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LLM-Domain+B-GATA+Transcription+Factors+Promote+Stomatal+Development+Downstream+of+Light+Signaling+Pathways+in+Arabidopsis+thaliana+Hypocotyls.">LLM-Domain B-GATA Transcription Factors Promote Stomatal Development Downstream of Light Signaling Pathways in Arabidopsis thaliana Hypocotyls.</a> The Plant Cell. 28 (3), 646-660 (2016).</li><li>Annunziata, M. G., 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=Getting+back+to+nature:+a+reality+check+for+experiments+in+controlled+environments.">Getting back to nature: a reality check for experiments in controlled environments.</a> J Exp Bot. 68 (16), 4463-4477 (2017).</li><li>Palus, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Japan's+Massive+Indoor+Farm+Produces+10,000+Heads+of+Fresh+Lettuce+Every+Day.">Japan's Massive Indoor Farm Produces 10,000 Heads of Fresh Lettuce Every Day.</a> Smithonian.com. , (2014).</li></ol>

The authors have nothing to disclose.

The first step in studying plant growth is setting up the climatic chamber according to the desired conditions. This is easily done by typing all variables into the program mask of the respective software (Figure 1A). At this step, many modifications can be implemented by changing the light regime and/or temperature. Make sure to constantly monitor temperature, humidity, and light conditions (Figure 2) to prevent technical failure from ruining the experiment. This is a critical point for obtaining reproducible results. Though this set-up offers many variables and can be flexibly adjusted, it has its limitations. The currently available LED lights cannot mimic the sunlight a hundred percent and the climatic conditions inside a climatic chamber can never completely reflect what is going on outside15.
Compared to the widely used fluorescent bulbs LED lights are more versatile, need less energy, and show virtually no heat radiation. These advantages have led the large industry of indoor farming to equip climatic chambers and greenhouses with LEDs16. Considering the huge successes reported in this field, the LED technique will surely find many more applications.
When observing the phenotype and especially for determination of the leaf area it is important to take into account that in older plants leaves overlap (Figure 3). Thus, graphical evaluation of whole rosettes tends to be imprecise. In that case, it is much more accurate to cut off all leaves and go from there.
The evaluation of growth behavior and especially differences of growth and development under different conditions depends on a sufficient sample size. In this study, at least six plants were used for determination of e.g. photosynthetic yield (Figure 5), fresh weight (Figure 7), and leaf area (Figure 8A) but 30 individual seeds were planted at the start of the study to ensure that first, sufficient seeds germinate, and second, a choice of &#34;typical&#34; plants can be made. Even within the same population, i.e. plants in single pots in the same tray under the exact same conditions, showed varying phenotypes. This is then of course reflected in the standard deviation during statistical analysis, but the interpretation of data is generally more reliable when small statistical errors are observed (Figure 8B).
Measurement of photosynthetic performance by PAM (Figure 4, Figure 5) can be done for several parameters. In this case, the focus was on PSII yield Y(II) as an example but it is possible to also determine e.g. non-photochemical quenching, the quantum yield of regulated and non-regulated energy dissipation or light remission. Important here is to choose at least five AOIs per leaf evenly distributed across the leaf surface and then measure at least six leaves from different plants. The drawback of this method is that any effects on PSI cannot be detected; for that purpose, different equipment is needed.

Arabidopsis thaliana has been the model organism for plant researchers of the molecular era for more than two decades. Several characteristics make this small representative of the Brassica family an ideal candidate for genetic and molecular studies: it has a relatively small genome with only five chromosomes (compared to e.g. Nicotiana tabacum with 24 chromosomes) and its genome was completely sequenced in 20001. A. thaliana can be easily genetically modified by Agrobacterium infection2 and is amenable to even the latest genetic tools such as CRISPR/Cas3. Though small, the growth cycle is fast enough to make biochemical experiments feasible where a higher amount of material is needed. The plants grow on agar plates or on soil and can even be cultivated as liquid cultures4. Arabidopsis can be grown in climatically controlled cupboards, e.g. from Percival, in climatic chambers or in greenhouses. To be able to compare growth behavior and analyze phenotypes of mutants it is critical to provide reproducible and at the same time flexible growth conditions5. Depending on the scientific problem that is to be addressed one might need different temperatures and constant light conditions, diverse light intensities or different light qualities at the same temperature. Light is a very critical parameter in plant growth and its influence is often studied in diverse approaches6. To ensure reproducibility and comparability of the obtained data it is crucial to ensure a stable output and apply the same kind of light sources.
The usual light sources in greenhouses and climatic chambers consist of sodium vapor or fluorescent lamps, which promote satisfying plant growth but have several disadvantages. First, they age over time which changes the spectral output not only in intensity but in quality (own observations). However, only the intensity is usually monitored continuously so that a change in light quality might go unnoticed but still have significant effects. Second, both types of lamps generate heat at higher light intensities, which itself has a profound physiological influence on plant growth and might mask any light dependent effect. Third, the spectral output of these light sources is invariable and quite unlike the natural sunlight7. All these drawbacks have been overcome in case of LEDs)8,9,10,11. They have a long lifetime with barely any change in emission, do not produce waste heat even at very high light intensities and they are very flexible concerning their spectral output.
Here we illustrate how to set up a climatic chamber featuring separate LED lights for red, blue, and white light and follow different parameters of plant growth over time. We measure fresh weight, leaf area, stomata density, and photosynthetic performance. At the same time, we demonstrate the importance of properly setting up statistical evaluations.

<table>
	<tbody>
		<tr>
			<td>Climatic chamber equipped with LED panels</td>
			<td>Rhenac Green Tec AG</td>
			<td></td>
			<td>These chambers are custom made.</td>
		</tr>
		<tr>
			<td>Spectrometer&#160;</td>
			<td>OceanOptics</td>
			<td>USB-650</td>
			<td></td>
		</tr>
		<tr>
			<td>Imaging PAM</td>
			<td>Walz</td>
			<td>IMAGING-PAM M-Series</td>
			<td>There are several suitable models depending on the broader use.</td>
		</tr>
		<tr>
			<td>Microscope+ 40x objective</td>
			<td>Leica&#160;</td>
			<td>DM1000</td>
			<td>Other companies also produce suitable microscopes.</td>
		</tr>
		<tr>
			<td>Software ImageJ</td>
			<td></td>
			<td></td>
			<td>Free download from website</td>
		</tr>
		<tr>
			<td>Plant RNA extraction kit</td>
			<td>Qiagen</td>
			<td>74903</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioanalyser</td>
			<td>Agilent</td>
			<td>G2939BA</td>
			<td>Needs an additional computer</td>
		</tr>
	</tbody>
</table>

analysis of arabidopsis thaliana growth behavior in different light qualities

Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light conditions, which are preferably variable in quantity and quality so that studies under different setups can be conducted. These requirements are met by climatic chambers featuring light emitting diodes (LED) lights, which can &#8211; in contrast to fluorescent lights &#8211; be set to different wavelengths. LEDs are energy conserving and emit virtually no heat even at light intensities, which often constitutes a problem with other light sources. The presented protocol provides a step-by-step guidance of how to program a climatic chamber equipped with variable LED lights as well as describing several approaches for in depth analysis of growth phenotypes. Depending on the experimental set-up various characteristics of the growing plants can be observed and analyzed. Here we describe how to determine fresh weight, leaf area, photosynthetic activity, and stomatal density. We demonstrate that in order to obtain reliable data and draw valid conclusions it is mandatory to use a sufficient number of individuals for statistical evaluation. Taking too few plants for this kind of analysis results in high statistical errors and consequently in less clear interpretations of the data.

Observation and analysis of plant growth and especially phenotypes from mutant plants rely on stable and reproducible environmental conditions. These can be provided in climatic chambers. Light quantity and especially quality critically depends on the employed light source, which in this study was provided by LED lights.
Figure 1 shows an example of a climatic chamber equipped with LED panels. Figure 1A shows a screenshot of the control panel where all climatic and light conditions can be adjusted. Within 24 h twenty different time frames can be set. In this example, long day conditions with 16 light/8 h dark have been programmed. This chamber features four levels which can be separately programmed so that plant growth at four different light settings can be studied under exactly the same environmental conditions. The upper left level is set to a spectral output mimicking the sunlight as much as technically possible, the upper right level represents elevated red (660 nm) and blue light (440 nm) with reduced white light (3K). The left lower level was set to elevated blue light and the lower right level to predominantly red light. Figure 1B illustrates the LEDs at the different settings as an overview (middle panel) and the respective zoom-ins (outer small panels). The difference in light qualities can be easily seen by the eye.
An inbuilt spectrometer constantly measures, monitors, and adjusts the spectral output. Figure 2 shows the spectrum from the upper left level 1.1, which was set to mimic sun light. Compared to a standard fluorescent light bulb the portion of UV and blue light is much higher7.
In Figure 3 an example of A. thaliana plants from all four conditions 10, 13, and 17 days, respectively, after sowing is depicted. All plants were photographed from the same distance by mounting the camera on a tripod. The scale bar represents 1 cm. After 10 days, not much difference in size or color can be discerned, but after 17 days a faster growth under red light is obvious. In addition to this visual analysis, several physiological analyses were performed.
Figure 4 follows the different steps of PAM measurements, which analyses e.g. photosynthetic capacity. In Figure 4A a screenshot of the live video is shown, which is the setting for bringing the plant into focus to ensure optimal quality of the measurements. Instead of focusing on the whole plant, one can also choose a single leaf to analyze. Figure 4B demonstrates the current fluorescent yield Ft of a dark-adapted plant before the actual measurement was started. In this case, five circular areas of interest (AOIs) were chosen. The numbers in the red boxes beside each AOI directly gives the numerical result, which can also be saved in the form of a table. To initiate a measurement of photosynthetic parameters Fo,Fm needs to set. A screenshot of Ft after doing this is depicted in Figure 4C. Note that now the button &#34;Fo,Fm&#34; is not active anymore. To start a new measurement, &#34;New Record&#34; needs to be clicked to erase the previous normalization. Finally, Figure 4D shows the effective PSII quantum yield Y(II) after giving a saturating light pulse (&#34;SAT-Pulse&#34;). Quantification of exemplary data is shown in Figure 5. Plants grown under sunlight at 200 &#181;M/cm2/s1 (Figure 5A) were analyzed 12, 21, and 28 days after sowing, respectively. Our data demonstrate that PSII yield is significantly higher in leaves from plants grown for three weeks than for 12 days. The difference between 28 and 12 days is still significant, but the p-value is higher. In Figure 4B, PSII yields from plants grown for two weeks from different light qualities were compared. Interestingly, permanent growth under light containing a high portion of blue light leads to a significantly greater yield of PSII. A similar effect was observed for plants cultivated under enriched red light, but the increase was a little lower.
Different light qualities were shown to effect stomata development14. Therefore, the stomatal density was investigated. Figure 6 demonstrates how a leaf after pigment extraction looks. Single epidermal cells can be well distinguished, and stoma can be easily counted. In the figure, individual stomata are indicated by an asterisk. Detailed data about the stomatal density of plants from the different light settings can be found elsewhere9.
In addition to visual inspection (Figure 3) the fresh weight provides a good measure of the growth progress. In this example leaves from plants grown under &#34;sunlight&#34; after 8, 10, and 12 days after sowing, respectively, were weighed. Statistical evaluation of these data can be seen in Figure 7. As expected, the fresh weight increases with time.
Besides fresh weight, the leaf area is a good measure for growth. Here, plant development was followed from 10, 13, and 17 days after sowing (Figure 8A). At least six individual plants were routinely evaluated to obtain reliable statistical data. To demonstrate the importance of a high sample size, the percentage error of the mean value, from analyzing two and six plants, respectively, was calculated (Figure 8B). That means the percentage of the standard deviation with regard to the mean value was determined. It is very clear that in case of a small sample size the error is 5 - 10% higher than in the case of a higher sample size. By increasing the number of plants that are evaluated, the error can be minimized, which makes the interpretation of data much clearer.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57152/57152fig1.jpg" /> 
Figure 1: Different light qualities are provided by LEDs. A) Screenshot from the control panel of the LED chamber. Day length is set to 16 h (upper right corner) and the light intensity is set to 200 &#181;mol cm-2 s-1. The light quality is different on all four levels: 1.1 represents a spectrum as similar to the sunlight as technically possible, 1.2 represents a high percentage of red and blue wavelengths (RB) light, 2.1 is predominantly set to Blue (B), 2.2 represents mainly red light (R). B) The middle panel shows an overview of all levels; the outer panels show the individual levels in a higher zoom. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig1large.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/57152/57152fig2.jpg" /> 
Figure 2: Wavelength spectrum from simulated sunlight settings. A screenshot from the inbuilt spectrometer in the LED chamber is shown, which was positioned at level 1.1. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig2large.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/57152/57152fig3.jpg" /> 
Figure 3: Plant development over a week. Representative A. thaliana plants from all four light conditions from 10, 13, and 17 DAS. Plants were photographed with a digital reflex camera on a tripod. Scale bar represents 1 cm for all images. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig3large.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/57152/57152fig4.jpg" /> 
Figure 4: Screenshots from representative steps of PAM measurements of A. thaliana plants. A) Screenshot from the &#34;Live video&#34; view where the image focus can be adjusted. B) Current fluorescence yield Ft before application of any light pulses. C) Current fluorescence yield Ft after setting of Fo/Fm. D) Effecting PSII quantum yield after setting a saturating light pulse. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig4large.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/57152/57152fig5.jpg" /> 
Figure 5: Graphical representation of Effecting PSII quantum yield (YII). A) Data from plants 12, 21, and 28 days after sowing and grown under 200 &#181;mol/cm2/s1 under simulated sun light (&#34;sunlight&#34;) subjected to PAM analysis were statistically evaluated. Shown are the mean values of five plants and five AOIs per day. One asterisk indicates a significant difference with a p-value &#60;0.05 when compared to day 12, and two asterisks indicate very significant differences with a p-value &#60;0.02 according to students&#39; t-test. B) Data from plants grown at 200 &#181;mol/cm2 /s1 under simulated sunlight (SL), enriched blue (B) or red (R) light, respectively, were statistically evaluated. Shown are the mean values of five plants and five AOIs per day. Significant differences were calculated in comparison to &#34;sunlight.&#34;
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57152/57152fig6.jpg" /> 
Figure 6: Representative image of stomata at the abaxial side of an A. thaliana leaf. Leaves prepared as described above and were visually analyzed under a light microscope with DIC settings at 40X magnification. Stomata are counted in the visible area of at least 4 leaves per condition. The picture was taken with a digital camera connected to the microscope tubus. The number of stomata per mm&#178; is calculated with help of the scale bar. Stars indicate a single stoma. The scale bar represents 200 &#181;m. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig6large.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/57152/57152fig7.jpg" /> 
Figure 7: Graphical representation of fresh weight from A. thaliana plants grown at simulated sunlight/200 &#181;mol/cm2/s1. Rosette leaves were cut from plants eight, ten, and twelve days after sowing. Mean values in mg from six plants per day are depicted. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig7large.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/57152/57152fig8.jpg" /> 
Figure 8: Statistical evaluation of A. thaliana leaf area from plants grown under different light conditions. A) Leaf area from A. thaliana grown for 10, 13, and 17 days was graphically determined with ImageJ and data from n&#160;= 6 plants were statistically evaluated. The leaf area from all six rosettes from each condition was summed up and divided by six to obtain the mean value. With this value, the standard deviation was calculated, and this is represented by the error bars. B) Leaf area was graphically determined with ImageJ and data from either n&#160;= 2 or n&#160;= 6&#160;plants, respectively, were statistically analyzed as described for panel A. Then the error in percent of the mean value was calculated and depicted graphically. Green bars show the percent error from analysis of n&#160;= 6, blue bars from n&#160;= 2 plants. <a href="https://cloudflare.jove.com/files/ftp_upload/57152/57152fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities | Biology | Video

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Here, we present a protocol for studying plant growth behavior and especially phenotypes in a reproducible manner. We show how to provide variable and at the same time stable light conditions. Proper analyses depend on sufficient sample numbers and valid statistical evaluations.

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light ...

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18.8K Views.  Ludwig-Maximilians-Universität. Here, we present a protocol for studying plant growth behavior and especially phenotypes in a reproducible manner. We show how to provide variable and at the same time stable light conditions. Proper analyses depend on sufficient sample numbers and valid statistical evaluations.

Video: Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Application of Light-cured Dental Adhesive Resin for Mounting Electrodes or Microdialysis Probes in Chronic Experiments

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these acrylics do not bond to the bone, screws have been used as anchors. However, in small experimental animals like finches or mouse, their craniums are very fragile and can not successfully hold the anchors. In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium. Therefore, anchor screws are not required and surgical field can be reduced considerably. Past experiences show that the bonding effect maintains more than 2 months. Conventional resin's window of time when the materials are pliable and workable is a few minutes. However, the window of working time for these dental adhesives is significantly wider and adjustable.

In this report, we propose a new application of light-curing dental resins for mounting base of electrodes or microdialysis probes in chronic experiments. This material allows direct bonding to the cranium.

In chronic recording experiments, self-curing dental acrylic resins have been used as a mounting base of electrodes or microdialysis-probes. Since these ...

Isolation of Protoplasts from Tissues of 14-day-old Seedlings of Arabidopsis thaliana

Protoplasts are plant cells that have had their cell walls enzymatically removed. Isolation of protoplasts from different plant tissues was first reported more than 40 years ago 1 and has since been adapted to study a variety of cellular processes, such as subcellular localization of proteins, isolation of intact organelles and targeted gene-inactivation by double stranded RNA interference (RNAi) 2-5. Most of the protoplast isolation protocols use leaf tissues of mature Arabidopsis (e.g. 35-day-old plants) 2-4. We modified existing protocols by employing 14-day-old Arabidopsis seedlings. In this procedure, one gram of 14-day-old seedlings yielded 5 106-107 protoplasts that remain intact at least 96 hours. The yield of protoplasts from seedlings is comparable with preparations from leaves of mature Arabidopsis, but instead of 35-36 days, isolation of protoplasts is completed in 15 days. This allows decreasing the time and growth chamber space that are required for isolating protoplasts when mature plants are used, and expedites the downstream studies that require intact protoplasts.

Isolation of Protoplasts from Tissues of 14-day-old Seedlings of Arabidopsis thaliana

This video shows a procedure for isolating intact protoplasts from tissues of 14-day-old seedlings of Arabidopsis. Given that the isolated protoplasts remain intact for at least 96h and are isolated from seedlings instead of one-month-old mature plants, this procedure expedites assays requiring intact protoplasts.

Protoplasts are plant cells that have had their cell walls enzymatically removed. Isolation of protoplasts  from different plant tissues was first ...

Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana

Light mediates an array of developmental and adaptive processes throughout the life cycle of a plant. Plants utilize light-absorbing molecules called photoreceptors to sense and adapt to light. The red/far-red light-absorbing phytochrome photoreceptors have been studied extensively. Phytochromes exist as a family of proteins with distinct and overlapping functions in all higher plant systems in which they have been studied1. Phytochrome-mediated light responses, which range from seed germination through flowering and senescence, are often localized to specific plant tissues or organs2. Despite the discovery and elucidation of individual and redundant phytochrome functions through mutational analyses, conclusive reports on distinct sites of photoperception and the molecular mechanisms of localized pools of phytochromes that mediate spatial-specific phytochrome responses are limited. We designed experiments based on the hypotheses that specific sites of phytochrome photoperception regulate tissue- and organ-specific aspects of photomorphogenesis, and that localized phytochrome pools engage distinct subsets of downstream target genes in cell-to-cell signaling. We developed a biochemical approach to selectively reduce functional phytochromes in an organ- or tissue-specific manner within transgenic plants. Our studies are based on a bipartite enhancer-trap approach that results in transactivation of the expression of a gene under control of the Upstream Activation Sequence (UAS) element by the transcriptional activator GAL43. The biliverdin reductase (BVR) gene under the control of the UAS is silently maintained in the absence of GAL4 transactivation in the UAS-BVR parent4. Genetic crosses between a UAS-BVR transgenic line and a GAL4-GFP enhancer trap line result in specific expression of the BVR gene in cells marked by GFP expression4. BVR accumulation in Arabidopsis plants results in phytochrome chromophore deficiency in planta5-7. Thus, transgenic plants that we have produced exhibit GAL4-dependent activation of the BVR gene, resulting in the biochemical inactivation of phytochrome, as well as GAL4-dependent GFP expression. Photobiological and molecular genetic analyses of BVR transgenic lines are yielding insight into tissue- and organ-specific phytochrome-mediated responses that have been associated with corresponding sites of photoperception4, 7, 8. Fluorescence Activated Cell Sorting (FACS) of GFP-positive, enhancer-trap-induced BVR-expressing plant protoplasts coupled with cell-type-specific gene expression profiling through microarray analysis is being used to identify putative downstream target genes involved in mediating spatial-specific phytochrome responses. This research is expanding our understanding of sites of light perception, the mechanisms through which various tissues or organs cooperate in light-regulated plant growth and development, and advancing the molecular dissection of complex phytochrome-mediated cell-to-cell signaling cascades.

Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana

The molecular basis of spatial-specific phytochrome responses is being investigated using transgenic plants that exhibit tissue- and organ-specific phytochrome deficiencies. The isolation of specific cells exhibiting induced phytochrome chromophore depletion by Fluorescence-Activated Cell Sorting followed by microarray analyses is being utilized to identify genes involved in spatial-specific phytochrome responses.

Light mediates an array of developmental and adaptive processes throughout the life cycle of a plant. Plants utilize light-absorbing molecules called ...

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer software.  Video bioinformatics is a powerful quantitative approach for extracting spatio-temporal data from video images using computer software to perform dating mining and analysis. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos collected in a Nikon BioStation CT incubator equipped with a camera for video imaging. In our experiments, hESC colonies that were attached to Matrigel were filmed for 48 hours in the BioStation CT.  To determine the rate of growth of these colonies, recipes were developed using CL-Quant software which enables users to extract various types of data from video images.  To accurately evaluate colony growth, three recipes were created. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony over time.  The three recipes were run in sequence on video data collected in a BioStation CT to analyze the rate of growth of individual hESC colonies over 48 hours. To verify the truthfulness of the CL-Quant recipes, the same data were analyzed manually using Adobe Photoshop software. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies. In addition, other video bioinformatics recipes can be developed in the future for other cell processes such as migration, apoptosis, and cell adhesion.  

Video bioinformatics is the automated processing, analysis, understanding, and data mining of biological spatio-temporal data extracted from microscopic videos. The purpose of this article is to demonstrate a method for measuring human embryonic stem cell colony growth using a video bioinformatics method.

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer ...

Floral-dip Transformation of Arabidopsis thaliana to Examine pTSO2::&beta;-glucuronidase Reporter Gene Expression

The ability to introduce foreign genes into an organism is the foundation for modern biology and biotechnology. In the model flowering plant Arabidopsis thaliana, the floral-dip transformation method1-2 has replaced all previous methods because of its simplicity, efficiency, and low cost. Specifically, shoots of young flowering Arabidopsis plants are dipped in a solution of Agrobacterium tumefaciens carrying specific plasmid constructs. After dipping, the plants are returned to normal growth and yield seeds, a small percentage of which are transformed with the foreign gene and can be selected for on medium containing antibiotics. This floral-dip method significantly facilitated Arabidopsis research and contributed greatly to our understanding of plant gene function. In this study, we use the floral-dip method to transform a reporter gene, &beta;-glucuronidase (GUS), under the control of TSO2 promoter. TSO2, coding for the Ribonucleotide Reductase (RNR) small subunit3, is a cell cycle regulated gene essential for dNDP biosynthesis in the S-phase of the cell cycle. Examination of GUS expression in transgenic Arabidopsis seedlings shows that TSO2 is expressed in actively dividing tissues. The reported experimental method and materials can be easily adapted not only for research but also for education at high school and college levels.

Floral-dip Transformation of Arabidopsis thaliana to Examine pTSO2::&#946;-glucuronidase Reporter Gene Expression

This article illustrates the floral-dip method of Agrobacterium tumefaciens -mediated transformation of Arabidopsis thaliana. By introducing a cell-cycle regulated promoter-reporter, pTSO2::&beta;-glucuronidase (GUS), into Arabidopsis, we illustrates how one detects GUS reporter expression in transgenic seedlings.

The ability to introduce foreign genes into an organism is the foundation for modern biology and biotechnology. In the model flowering plant Arabidopsis ...

Arabidopsis thaliana Polar Glycerolipid Profiling by Thin Layer Chromatography (TLC) Coupled with Gas-Liquid Chromatography (GLC)

Biological membranes separate cells from the environment. From a single cell to multicellular plants and animals, glycerolipids, such as phosphatidylcholine or phosphatidylethanolamine, form bilayer membranes which act as both boundaries and interfaces for chemical exchange between cells and their surroundings. Unlike animals, plant cells have a special organelle for photosynthesis, the chloroplast. The intricate membrane system of the chloroplast contains unique glycerolipids, namely glycolipids lacking phosphorus: monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), sulfoquinovosyldiacylglycerol (SQDG)4. The roles of these lipids are beyond simply structural. These glycolipids and other glycerolipids were found in the crystal structures of photosystem I and II indicating the involvement of glycerolipids in photosynthesis8,11. During phosphate starvation, DGDG is transferred to extraplastidic membranes to compensate the loss of phospholipids9,12.
Much of our knowledge of the biosynthesis and function of these lipids has been derived from a combination of genetic and biochemical studies with Arabidopsis thaliana14. During these studies, a simple procedure for the analysis of polar lipids has been essential for the screening and analysis of lipid mutants and will be outlined in detail. A leaf lipid extract is first separated by thin layer chromatography (TLC) and glycerolipids are stained reversibly with iodine vapor. The individual lipids are scraped from the TLC plate and converted to fatty acyl methylesters (FAMEs), which are analyzed by gas-liquid chromatography coupled with flame ionization detection (FID-GLC) (Figure 1). This method has been proven to be a reliable tool for mutant screening. For example, the tgd1,2,3,4 endoplasmic reticulum-to-plastid lipid trafficking mutants were discovered based on the accumulation of an abnormal galactoglycerolipid: trigalactosyldiacylglycerol (TGDG) and a decrease in the relative amount of 18:3 (carbons : double bonds) fatty acyl groups in membrane lipids 3,13,18,20. This method is also applicable for determining enzymatic activities of proteins using lipids as substrate6.

Arabidopsis thaliana Polar Glycerolipid Profiling by Thin Layer Chromatography TLC Coupled with Gas-Liquid Chromatography GLC

Composition of polar lipid extracts and the fatty acid composition of individual glycerolipids are determined in a simple and robust lipid profiling experiment. For this purpose, glycerolipids are isolated by thin layer chromatography and subjected to transmethylation of their acyl groups. Fatty acyl methylesters are quantified by gas-liquid chromatography.

Biological membranes separate cells from the environment. From a single cell to multicellular plants and animals, glycerolipids, such as ...

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

In flowering plants, the embryo develops within a nourishing tissue - the endosperm - surrounded by the maternal seed integuments (or seed coat). As a consequence, the isolation of plant embryos at early stages (1 cell to globular stage) is technically challenging due to their relative inaccessibility. Efficient manual dissection at early stages is strongly impaired by the small size of young Arabidopsis seeds and the adhesiveness of the embryo to the surrounding tissues. Here, we describe a method that allows the efficient isolation of young Arabidopsis embryos, yielding up to 40 embryos in 1 hr to 4 hr, depending on the downstream application. Embryos are released into isolation buffer by slightly crushing 250-750 seeds with a plastic pestle in an Eppendorf tube. A glass microcapillary attached to either a standard laboratory pipette (via a rubber tube) or a hydraulically controlled microinjector is used to collect embryos from droplets placed on a multi-well slide on an inverted light microscope. The technical skills required are simple and easily transferable, and the basic setup does not require costly equipment. Collected embryos are suitable for a variety of downstream applications such as RT-PCR, RNA sequencing, DNA methylation analyses, fluorescence in situ hybridization (FISH), immunostaining, and reporter gene assays.

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

We report an efficient and simple method to isolate embryos at early stages of development from Arabidopsis thaliana seeds. Up to 40 embryos can be isolated in 1 hr to 4 hr, depending on the downstream application. The procedure is suitable for transcriptome, DNA methylation, reporter gene expression, immunostaining and fluorescence in situ hybridization analyses.

In flowering plants, the embryo develops within a nourishing tissue - the endosperm - surrounded by the maternal seed integuments (or seed coat). As a ...

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling processes. Extracting sterol-enriched membrane microdomains from plant cells for proteomic analysis is a difficult task mainly due to multiple preparation steps and sources for contaminations from other cellular compartments. The plasma membrane constitutes only about 5-20% of all the membranes in a plant cell, and therefore isolation of highly purified plasma membrane fraction is challenging. A frequently used method involves aqueous two-phase partitioning in polyethylene glycol and dextran, which yields plasma membrane vesicles with a purity of 95% 1. Sterol-rich membrane microdomains within the plasma membrane are insoluble upon treatment with cold nonionic detergents at alkaline pH. This detergent-resistant membrane fraction can be separated from the bulk plasma membrane by ultracentrifugation in a sucrose gradient 2. Subsequently, proteins can be extracted from the low density band of the sucrose gradient by methanol/chloroform precipitation. Extracted protein will then be trypsin digested, desalted and finally analyzed by LC-MS/MS. Our extraction protocol for sterol-rich microdomains is optimized for the preparation of clean detergent-resistant membrane fractions from Arabidopsis thaliana cell cultures.
We use full metabolic labeling of Arabidopsis thaliana suspension cell cultures with K15NO3 as the only nitrogen source for quantitative comparative proteomic studies following biological treatment of interest 3. By mixing equal ratios of labeled and unlabeled cell cultures for joint protein extraction the influence of preparation steps on final quantitative result is kept at a minimum. Also loss of material during extraction will affect both control and treatment samples in the same way, and therefore the ratio of light and heave peptide will remain constant. In the proposed method either labeled or unlabeled cell culture undergoes a biological treatment, while the other serves as control 4.

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Here we describe a robust method for the fractionation of plant plasma membranes into detergent resistant and detergent soluble membranes based on a mixture of unlabeled and in vivo fully 15N labeled Arabidopsis thaliana cell cultures. The procedure is applied for comparative proteomic studies to understand signaling processes.

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling ...

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana

The Arabidopsis endosperm consists of a single cell layer surrounding the mature embryo and playing an essential role to prevent the germination of dormant seeds or that of nondormant seeds irradiated by a far red (FR) light pulse. In order to further gain insight into the molecular genetic mechanisms underlying the germination repressive activity exerted by the endosperm, a &#34;seed coat bedding&#34; assay (SCBA) was devised. The SCBA is a dissection procedure physically separating seed coats and embryos from seeds, which allows monitoring the growth of embryos on an underlying layer of seed coats. Remarkably, the SCBA reconstitutes the germination repressive activities of the seed coat in the context of seed dormancy and FR-dependent control of seed germination. Since the SCBA allows the combinatorial use of dormant, nondormant and genetically modified seed coat and embryonic materials, the genetic pathways controlling germination and specifically operating in the endosperm and embryo can be dissected. Here we detail the procedure to assemble a SCBA.

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana

We present the procedure to assemble a seed coat bedding assay (SCBA) from Arabidopsis thaliana seeds. The SCBA was shown to be a powerful tool to explore genetically and in vitro how the endosperm controls seed germination in dormant seeds and in response to light cues. The SCBA is in principle applicable under any situation where the endosperm is suspected to influence embryonic growth.

The Arabidopsis endosperm consists of a single cell layer surrounding the mature embryo and playing an essential role to prevent the germination of ...

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

Arabidopsis thaliana is a model organism commonly used to understand and manipulate various cellular processes in plants, and it has been used extensively in the study of secondary cell wall formation. Secondary cell wall deposition occurs after the primary cell wall is laid down, a process carried out exclusively by specialized cells such as those forming vessel and fiber tissues. Most secondary cell walls are composed of cellulose (40&#8211;50%), hemicellulose (25&#8211;30%), and lignin (20&#8211;30%). Several mutations affecting secondary cell wall biosynthesis have been isolated, and the corresponding mutants may or may not exhibit obvious biochemical composition changes or visual phenotypes since these mutations could be masked by compensatory responses. Staining procedures have historically been used to show differences on a cellular basis. These methods are exclusively visual means of analysis; nevertheless their role in rapid and critical analysis is of great importance. Congo red and calcofluor white are stains used to detect polysaccharides, whereas M&#228;ule and phloroglucinol are commonly used to determine differences in lignin, and toluidine blue O is used to differentially stain polysaccharides and lignin. The seemingly simple techniques of sectioning, staining, and imaging can be a challenge for beginners. Starting with sample preparation using the A. thaliana model, this study details the protocols of a variety of staining methodologies that can be easily implemented for observation of cell and tissue organization in secondary cell walls of plants.

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

Plant cell wall composition varies between tissue types and can include lignin, cellulose, hemicelluloses, and pectin. Various staining techniques have been developed to visualize differences at the cell-type level. This paper is a compilation of commonly used cell wall staining techniques.

Arabidopsis thaliana is a model organism commonly used to understand and manipulate various cellular processes in plants, and it has been used extensively ...

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

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Application of Light-cured Dental Adhesive Resin for Mounting Electrodes or Microdialysis Probes in Chronic Experiments

Isolation of Protoplasts from Tissues of 14-day-old Seedlings of Arabidopsis thaliana

Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth

Floral-dip Transformation of Arabidopsis thaliana to Examine pTSO2::β-glucuronidase Reporter Gene Expression

Arabidopsis thaliana Polar Glycerolipid Profiling by Thin Layer Chromatography (TLC) Coupled with Gas-Liquid Chromatography (GLC)

Efficient and Rapid Isolation of Early-stage Embryos from Arabidopsis thaliana Seeds

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

A Seed Coat Bedding Assay to Genetically Explore In Vitro How the Endosperm Controls Seed Germination in Arabidopsis thaliana

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements