Funding was provided by a Fiske Award and a Wellesley College Faculty Award.

1. Preparing leaves for a run
<ol>
	<li>Place 8 A. thaliana plants in the dark overnight (&#62; 6 h works for most species) to ensure that the chloroplasts move into their dark position. All replicas start with comparable transmission values.</li>
	<li>Alternatively, place 8 complete leaves into a Petri dish with a moist filter paper at the bottom, close the Petri dish, and wrap it with aluminum foil.</li>
</ol>
2. Testing if the transmission device works
<ol>
	<li>Connect the transmission device to a stable power source and press the power switch (ON/OFF button) of the device to reset the instrument (Figure 1A,B).</li>
	<li>Connect the iPad to a stable power source, press the Home Screen button to activate the screen, and enter the passcode to log in.</li>
	<li>Press the Settings icon, press the Display &#38; Brightness icon, press Auto-Lock, and select Never by pressing this option to ensure that the screen stays on permanently. Otherwise the program will stop running when the screen goes to sleep. Press the Home Screen button to return to the main screen.</li>
	<li>Double press the Home Screen button to see which applications are open, and close all of them by swiping them towards the top of the screen. Press the Home Screen button to return to the main screen.
	<ol>
		<li>Find the LeafSensor app on the main screen or by swiping left or right. Press the icon of the app to open it (see Supplementary File). A green screen with text and white fields will appear to enter the information.</li>
		<li>Ensure the word Connected is seen in the lower part of the screen as it indicates that the app is communicating with the transmission device. If the message &#39;Adafruit NOT Found&#39; appears, check that the device is plugged in and press the start button on the device again.</li>
	</ol>
	</li>
	<li>Fill in the first 4 fields on the app page to name the experiment and to set up the conditions for a brief test run without leaves and with the leaf clips open:
	<ol>
		<li>Name the experiment (use 8 or fewer uppercase letters or numbers) e.g., by typing TEST into the field named Expt Name.</li>
		<li>Choose how many different blue light intensities will be used in the experiment, e.g., by typing 3 into the field named # Light Intensities.</li>
		<li>Choose the blue light intensities for this run (choose an integer between 0 and 3000 and separate each number from the next with a comma; see Supplementary File on how to convert these numbers into actual blue light intensities of LEDs) e.g., by typing 0,100,1000 into the field named Blue Intensities.</li>
		<li>Choose the length of time each blue light intensity will shine onto the leaf (separate each number from the next with a comma) e.g., by typing 2,2,2 into the field named Blue Duration (minutes).</li>
	</ol>
	</li>
	<li>Press Start Experiment in the middle section of the screen. In the lower part of the screen 8 hyphens and the message Starting Experiment will appear.
	<ol>
		<li>Ensure that no light is being emitted from the LEDs for the first two minutes, then weak blue light is being emitted, and after 2 min the blue light intensity is increasing.</li>
		<li>Ensure that intense red light is being emitted from the LEDs once a minute for the measurements. 
		&#8203;NOTE: As the experiment is running, numbers will appear on the app page for each of the eight sensors, and data will be updated once a minute. Ensure the output numbers from the photodiodes are around 1000-1023 (if the room is dark). An update on the lower left shows how many measurements were done so far e.g., Done 1 of 6 measurements (one measurement each minute).</li>
		<li>When the experiment is done, check for the appearance of the Experiment finished on the lower left of the app page. Now the instrument is ready for a run with leaves.</li>
	</ol>
	</li>
	<li>Press twice the Home Screen button, swipe out of the app and open it again. Press the ON/OFF button on the instrument to reset it.</li>
</ol>
3. Setting up leaves in the leaf clips
NOTE: This step has to be done in the dark with a green light source (e.g., place a green filter in front of a light bulb) to avoid inducing chloroplast movement. Alternatively, use very low white light and an extended dark period in the leaf clips. Remember, one part of the leaf clip holds the LED (larger opening), while the other holds the phototransistor (Figure 1C).
<ol>
	<li>If the plants are dark-adapted entire, pick 8 leaves wide enough to cover the LEDs. Otherwise, remove the leaves from the Petri dish. Prepare 8 strips of filter paper about the length of a leaf clip and with a hole punched out at the top to not cover the LED.
	<ol>
		<li>Moisten the filter paper and place it onto the leaf clip part that holds the LED. Repeat this for each of the eight leaf clips.</li>
	</ol>
	</li>
	<li>Place each leaf on the top of the wet filter paper of its leaf clip. Ensure the correct leaf side is facing towards the LED (usually experiments are done with the adaxial leaf surface facing the LED).
	<ol>
		<li>Avoid placing the midribs of the leaf on top of the LEDs, and for more consistent results, place similar parts of each leaf (e.g., the widest section of the leaf) over the LED.</li>
		<li>Place the other leaf clip part with the phototransistor on top. Use a rubber band to hold the two leaf clip parts together if needed (Figure 1C,D).</li>
	</ol>
	</li>
	<li>Place each leaf clip into its &#39;boat&#39; and fill the reservoirs with water using a pipette. Ensure the leaf or at least the filter paper touches the water to avoid dehydration of the leaves during the run (Figure 1D).</li>
</ol>
4. Conducting a run
NOTE: For a standard exploratory run, start out with 4 h of darkness (0 &#181;mol photon m-2 s-1), followed by 7 h of low blue light (2 &#181;mol photon m-2 s-1), followed by 60 min each of 5, 10, 30, 40, 50, 60, 90, 100 &#181;mol photon m-2 s-1 of blue light. This will induce the leaves to exhibit their dark transmission, induce chloroplast movement into the maximum accumulation, and show different degrees of avoidance response.
<ol>
	<li>Set up the LeafSensor app on the iPad using the steps described below.
	<ol>
		<li>Type EXPLORA1 into the field named Expt Name.</li>
		<li>Type 10 into the field named # Light Intensities.</li>
		<li>Type 0,1,60,160,550,750,950,1150,1350,1950 into the field named Blue intensities.</li>
		<li>Type 240,420,60,60,60,60,60,60,60,60 into the field named Blue Duration.</li>
	</ol>
	</li>
	<li>Press Start Experiment. After the first min, the output values (usually between 990 and 820) will appear on the screen. If the values are far off, check if the leaves were placed correctly into the leaf clips.</li>
	<li>When the run is done, ensure that the message Experiment Finished appears at the bottom left of the screen. Data will be saved automatically.
	<ol>
		<li>Put the screen in the upright position (not horizontal). Two new options will appear on the screen, namely Save and Utilities.</li>
		<li>Press Utilities and a list of saved files will appear. Select the file of interest, in this case, EXPLORA1.</li>
		<li>Below the list of files , look for Selected Expt: EXPLORA1. Press Email, enter an email address, and the data file will be automatically attached to the message. Press Send. 
		NOTE: If there is a long delay until the files arrive, restart the app and send the file again.</li>
	</ol>
	</li>
	<li>If a run was aborted, but data up to that point are of interest, press Save before selecting Utilities. After several runs, clean up the memory space: Press Utilities, select one file at a time, swipe left next to file, and press Delete to remove file.</li>
	<li>Press Back to run another experiment or if done, press the Home Screen button, swipe to the main screen, press Settings, press Display &#38; Brightness, press Auto-Lock, and press 2 Minutes.</li>
</ol>
5. Data analysis
<ol>
	<li>Download the file EXPLORA1 from the email, add extension .csv to the file, double-click on the file. Data will be sorted into a spreadsheet with the data for the eight different sensors sorted into separate columns. The last column shows the time (in seconds) at which the data were collected. Delete the first row under the headings (Sensor1-8) if it contains nonsensical data.</li>
	<li>Set up a master data sheet that will contain the results for each sensor in a separate sheet and that converts the output values into % transmission values using the equations obtained from the calibration of each leaf clip and sensor (see Supplementary File).
	<ol>
		<li>Copy each data set into a separate data sheet (e.g., column A contains the time; column C contains the data of Sensor1).</li>
		<li>Set up column B so that it converts time from seconds to minutes. Set up column D so that it contains the formula to convert voltage to % transmission using the equation from the calibration.</li>
		<li>Set up a similar data sheet for each sensor and remember the formula used to convert the voltage output into % transmission values may be different for each sensor.</li>
	</ol>
	</li>
	<li>Plot % transmission (T) against time, min (Figure 2).</li>
	<li>Save the data sheet under a new name so that the master data sheet can be reused.</li>
	<li>To further analyze data (Figure 2), calculate &#916;T (%) accumulation (e.g., change of T at maximum accumulation compared to T at dark), &#916;T (%) avoidance (e.g., change of T at maximum avoidance compared to T at dark), or dT/dt (%/h) (e.g., change in T during the fastest part of the accumulation or avoidance reaction). For further details see 8.</li>
</ol>

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R., 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+phototropin+family+of+photoreceptors.">The phototropin family of photoreceptors.</a> Plant Cell. 13, 993-997 (2001).</li><li>Jarillo, J. 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=Phototropin-related+NPL1+controls+chloroplast+relocation+induced+by+blue+light.">Phototropin-related NPL1 controls chloroplast relocation induced by blue light.</a> Nature. 410, 952-954 (2001).</li><li>Sakai, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Arabidopsis+nph1+and+npl1:+blue+light+receptors+that+mediate+both+phototropism+and+chloroplast+relocation.">Arabidopsis nph1 and npl1: blue light receptors that mediate both phototropism and chloroplast relocation.</a> Proceedings of the National Academy of Sciences, USA. 98 (12), 6969-6974 (2001).</li><li>Wada, M., Kong, S. -. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Actin-mediated+movement+of+chloroplasts.">Actin-mediated movement of chloroplasts.</a> Journal of Cell Science. 131, 1-8 (2018).</li><li>Wen, F., Xing, D., Zhang, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hydrogen+peroxide+is+involved+in+high+blue+light-induced+chloroplast+avoidance+movements+in+Arabidopsis.">Hydrogen peroxide is involved in high blue light-induced chloroplast avoidance movements in Arabidopsis.</a> Journal of Experimental Botany. 59 (10), 2891-2901 (2008).</li><li>Tlalka, M., Fricker, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+calcium+in+blue-light+dependent+chloroplast+movement+in+Lemna+trisulca+L.">The role of calcium in blue-light dependent chloroplast movement in Lemna trisulca L.</a> The Plant Journal. 20, 461-473 (1999).</li><li>K&#246;niger, M., Bollinger, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chloroplast+movement+behavior+varies+widely+among+species+and+does+not+correlate+with+high+light+stress+tolerance.">Chloroplast movement behavior varies widely among species and does not correlate with high light stress tolerance.</a> Planta. 236, 411-426 (2012).</li><li>Higa, T., Wada, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chloroplast+avoidance+movement+is+not+functional+in+plants+grown+under+strong+sunlight.">Chloroplast avoidance movement is not functional in plants grown under strong sunlight.</a> Plant, Cell and Environment. 39, 871-882 (2016).</li><li>. 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The authors have no conflicts of interest.

The device is extremely easy to use but it is of crucial importance to calibrate each leaf clip set-up of the transmission device independently since the positioning of the LEDs and phototransistors may slightly vary from leaf clip to leaf clip. Ensure the LEDs and phototransistors are inserted stably and re-check the calibration if the data seem off. Avoid getting water onto the device. The leaves in the leaf clips are placed into &#39;boats&#39; filled with water to avoid water stress. Place these boats e.g., into a lo...

Light is essential for photosynthesis, plant growth, and development. It is one of the most dynamic abiotic factors as light intensities not only change over the course of a season or day, but also rapidly and in unpredictable ways depending on the cloud cover. At the leaf level, light intensities are also influenced by the density and nature of the surrounding vegetation and the plant&#39;s own canopy. One important mechanism that allows plants to optimize light interception under variable light conditions is the ability of chloroplasts to move in response to blue light stimuli1,2. Under low light conditions, chloroplasts spread out perpendicular to the light (along the periclinal cell walls) in a so-called accumulation response, maximizing light interception and hence photosynthesis. Under high light conditions, chloroplasts move towards the anticlinal cell wall in a so-called avoidance response, minimizing light interception and the danger of photoinhibition. In many species, chloroplasts also assume a specific dark position, which is distinct from the accumulation and avoidance positions and often intermediary between those two3,4. Various studies have demonstrated that chloroplast movement is not only important for the short-term stress tolerance of leaves5,6,7, but also for the growth and reproductive success of plants, especially under variable light conditions8,9.
Chloroplast movement is readily observed in real time in certain live specimens (e.g., algae or thin-leafed plants like Elodea) using light microscopy1. Studying chloroplast movement in most leaves, however, requires a pretreatment to induce chloroplast movement, chemical fixation, and preparation of cross sections before viewing the samples under a light microscope10. With the introduction of confocal laser microscopy, it also became possible to image the 3D arrangement of chloroplasts in intact or fixed leaves4,11,12. These imaging techniques greatly aid the understanding of chloroplast movement by providing important qualitative information. Quantifying chloroplast positioning (e.g., as a percentage of chloroplasts in the periclinal or anticlinal positions in these images or the percentage of area covered by chloroplasts per total cell surface) is also possible but quite time-consuming, especially if conducted at the intervals necessary to capture rapid changes in positioning10,8. The simplest way to show whether dark-adapted leaves of a certain species or mutants are capable of chloroplast movement into the avoidance response is by covering most of the area of a leaf to keep the chloroplasts in the dark while exposing a strip of the leaf to high light. After a minimum of 20 min of high light exposure, the chloroplasts in the exposed area will have moved into the avoidance position, and the exposed strip will be visibly lighter in color than the rest of the leaf. This is true for wild type A. thaliana but not for some of the chloroplast movement mutants described in more detail later on13. This method and modifications of it (e.g., reversing what parts of the leaf are exposed, changing light intensities) are useful for screening large numbers of mutants and to identify null mutants that lack either the ability to exhibit an avoidance or accumulation response or both. However, it does not provide information about the dynamic changes in chloroplast movement.
In contrast, the method described here allows for the quantification of chloroplast movement in intact leaves using changes in light transmission through a leaf as a proxy for overall chloroplast movement: under conditions when chloroplasts are spread out in the mesophyll cells in the accumulation response, less light is transmitted through the leaf than when many chloroplasts are in an avoidance response, positioning themselves along the anticlinal cell walls. Hence, changes in transmission can be used as a proxy for the overall chloroplast movement in leaves14. The details of the instrument are described elsewhere (see Supplementary File), but basically, the instrument uses blue light to trigger chloroplast movement and measures how much red light is transmitted through that leaf at set intervals.&#160;More recently, a modification of this system has been described, which uses a modified 96-well microplate reader, a blue LED, a computer, and a temperature-controlled incubator15.
The option to use a combination of methods, including the optical assessment of leaves for screening, followed by measuring dynamic changes in transmission and the use of microscopy, has greatly aided our understanding of both the underlying mechanisms and the physiological/ecological significance of chloroplast movement. For example, it led to the discovery and characterization of various mutants, which are impaired in specific aspects of their movements. For example, A. thaliana phot 1 mutants lack the ability to accumulate their chloroplasts in low light, while phot 2 mutants lack the ability to perform an avoidance reaction. These phenotypes are due to an impairment in two respective blue light receptors16,17,18. In contrast, chup1 mutants lack the ability to form proper actin filaments around the chloroplasts which are essential to move the chloroplasts into the desired position within a cell11,19. In addition to mutant studies, researchers have assessed the effects of various inhibitors on chloroplast movement to elucidate the mechanistic aspects of the process. For example, chemicals such as H2O2 and various antioxidants were used to investigate the effects of this signaling molecule on chloroplast movement20. Various inhibitors were used to elucidate the role of calcium in chloroplast movement21. In addition to helping to uncover the mechanisms of chloroplast movement, these methods can be used to compare chloroplast movement in various species or mutants grown in different conditions in an attempt to understand the ecological and evolutionary context of this behavior. For example, it has been shown that the extent of the effects of various mutations in the chloroplast movement pathway are dependent on the growth conditions7,9, and that sun-adapted plants do not seem to move their chloroplasts much. In contrast, movement is very important for shade plants10,22,23.
This methods paper, focused on the model plant A. thaliana, describes how to use a transmission device which is an updated version of a previously developed instrument9. While this instrument is not commercially available, people with a basic understanding of electronics or the help of engineering or physics colleagues and students will be able to build the instrument using affordable parts and following the detailed instructions (see Supplementary File). The open-source platform used to build the instrument has extensive web support and a community forum which offers help should problems arise24.
The protocol focuses on how to use the instrument to determine changes in leaf transmission in a standard exploratory run that exposes a leaf to a wide range of light conditions and captures the dark, accumulation, and avoidance reactions of A. thaliana. These runs can be modified depending on the goal of the experiment and can be used with most plant species. The paper provides examples of transmission data of A. thaliana wildtype and several mutants and shows how to further analyze the data.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Aluminum foil</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Dark adapted leaves</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Filter paper</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>iPad with LeafSensor app installed (see Supplemental Info)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette</td>
			<td>Any</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Petri dish</td>
			<td>Any</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Transmission device (see Supplemental info)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Water</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

using changes in leaf transmission to investigate chloroplast movement in arabidopsis thaliana

Chloroplast movement in leaves has been shown to help minimize photoinhibition and increase growth under certain conditions. Much can be learned about chloroplast movement by studying the chloroplast positioning in leaves using e.g., confocal fluorescence microscopy, but access to this type of microscopy is limited. This protocol describes a method that uses the changes in leaf transmission as a proxy for chloroplast movement. If chloroplasts are spread out in order to maximize light interception, the transmission will be low. If chloroplasts move towards the anticlinal cell walls to avoid light, the transmission will be higher. This protocol describes how to use a straightforward, home-built instrument to expose leaves to different blue light intensities and quantify the dynamic changes in leaf transmission. This approach allows researchers to quantitatively describe chloroplast movement in different species and mutants, study the effects of chemicals and environmental factors on it, or screen for novel mutants e.g., to identify missing components in the process that leads from light perception to the movement of chloroplasts.

The different parts of the transmission device are shown in Figure 1. The microcontroller is the control unit of the device and controls the light conditions that the leaves, secured in black leaf clips, are experiencing, and stores the light transmission data it receives (Figure 1A,B). A close-up of the control unit of the instrument shows the ON/OFF button, the SD card for data storage capability, the Bluetooth shield (which sends the data to ...

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Using Changes in Leaf Transmission to Investigate Chloroplast Movement in Arabidopsis thaliana

Many plant species change the positioning of chloroplasts to optimize light absorption. This protocol describes how to use a straightforward, home-built instrument to investigate chloroplast movement in Arabidopsis thaliana leaves using changes in the transmission of light through a leaf as a proxy.

Using Changes in Leaf Transmission to Investigate Chloroplast Movement in Arabidopsis thaliana

Chloroplast movement in leaves has been shown to help minimize photoinhibition and increase growth under certain conditions. Much can be learned about ...

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2.1K Views.  Wellesley College. Many plant species change the positioning of chloroplasts to optimize light absorption. This protocol describes how to use a straightforward, home-built instrument to investigate chloroplast movement in Arabidopsis thaliana leaves using changes in the transmission of light through a leaf as a proxy.

Video: Using Changes in Leaf Transmission to Investigate Chloroplast Movement in Arabidopsis thaliana

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently than plants3, but also synthesize advanced biofuels2-4. Generally, atmospheric CO2 is not a sufficient source for supporting maximal algal growth5. On the other hand, the high concentrations of CO2 in industrial exhaust gases have adverse effects on algal physiology. Consequently, both cultivation conditions (such as nutrients and light) and the control of the flue gas flow into the photo-bioreactors are important to develop an efficient &ldquo;flue gas to algae&rdquo; system. Researchers have proposed different photobioreactor configurations4,6 and cultivation strategies7,8 with flue gas. Here, we present a protocol that demonstrates how to use models to predict the microalgal growth in response to flue gas settings. We perform both experimental illustration and model simulations to determine the favorable conditions for algal growth with flue gas. We develop a Monod-based model coupled with mass transfer and light intensity equations to simulate the microalgal growth in a homogenous photo-bioreactor. The model simulation compares algal growth and flue gas consumptions under different flue-gas settings. The model illustrates: 1) how algal growth is influenced by different volumetric mass transfer coefficients of CO2; 2) how we can find optimal CO2 concentration for algal growth via the dynamic optimization approach (DOA); 3) how we can design a rectangular on-off flue gas pulse to promote algal biomass growth and to reduce the usage of flue gas. On the experimental side, we present a protocol for growing Chlorella under the flue gas (generated by natural gas combustion). The experimental results qualitatively validate the model predictions that the high frequency flue gas pulses can significantly improve algal cultivation.

Flue gas from power plants is a cheap CO2 source for algal growth. We have built prototype &#34;flue gas to algal cultivation&#34; systems and described how to scale up the algal cultivation process. We have demonstrated the use of a mass-transfer bio-reaction model to simulate and to design the optimal operation of flue gas for the growth of Chlorella sp. in algal photo-bioreactors.

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently ...

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental changes. Repeated sampling to monitor these changes in forest soils is a relatively new practice that is not well documented in the literature and has only recently been broadly embraced by the scientific community. The objective of this protocol is therefore to synthesize the latest information on methods of soil resampling in a format that can be used to design and implement a soil monitoring program. Successful monitoring of forest soils requires that a study unit be defined within an area of forested land that can be characterized with replicate sampling locations. A resampling interval of 5 years is recommended, but if monitoring is done to evaluate a specific environmental driver, the rate of change expected in that driver should be taken into consideration. Here, we show that the sampling of the profile can be done by horizon where boundaries can be clearly identified and horizons are sufficiently thick to remove soil without contamination from horizons above or below. Otherwise, sampling can be done by depth interval. Archiving of sample for future reanalysis is a key step in avoiding analytical bias and providing the opportunity for additional analyses as new questions arise.

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental ...

An Induction System for Clustered Stomata by Sugar Solution Immersion Treatment in Arabidopsis thaliana Seedlings

Stomatal movement mediates plant gas exchange, which is essential for photosynthesis and transpiration. Stomatal opening and closing are accomplished by a significant increase and decrease in guard cell volume, respectively. Because shuttle transport of ions and water occurs between guard cells and larger neighboring epidermal cells during stomatal movement, the spaced distribution of plant stomata is considered an optimal distribution for stomatal movement. Experimental systems for perturbing the spaced pattern of stomata are useful to examine the spacing pattern's significance. Several key genes associated with the spaced stomatal distribution have been identified, and clustered stomata can be experimentally induced by altering these genes. Alternatively, clustered stomata can be also induced by exogenous treatments without genetic modification. In this article, we describe a simple induction system for clustered stomata in Arabidopsis thaliana seedlings by immersion treatment with a sucrose-containing medium solution. Our method is easy and directly applicable to transgenic or mutant lines. Larger chloroplasts are presented as a cell biological hallmark of sucrose-induced clustered guard cells. In addition, a representative confocal microscopic image of cortical microtubules is shown as an example of intracellular observation of clustered guard cells. The radial orientation of cortical microtubules is maintained in clustered guard cells as in spaced guard cells in control conditions.

An Induction System for Clustered Stomata by Sugar Solution Immersion Treatment in Arabidopsis thaliana Seedlings

The goal of this protocol is to demonstrate how to induce clustered stomata in cotyledons of Arabidopsis thaliana seedlings by immersion treatment with a sugar-containing medium solution and how to observe intracellular structures such as chloroplasts and microtubules in the clustered guard cells using confocal laser microscopy.

Stomatal movement mediates plant gas exchange, which is essential for photosynthesis and transpiration. Stomatal opening and closing are accomplished by a ...

Detached Leaf Assays to Simplify Gene Expression Studies in Potato During Infestation by Chewing Insect Manduca sexta

The multitrophic nature of gene expression studies of insect herbivory demands large numbers of biological replicates, creating the need for simpler, more streamlined herbivory protocols. Perturbations of chewing insects are usually studied in whole plant systems. While this whole organism strategy is popular, it is not necessary if similar observations can be replicated in a single detached leaf. The assumption is that basic elements required for signal transduction are present within the leaf itself. In the case of early events in signal transduction, cells need only to receive the signal from the perturbation and transmit that signal to neighboring cells which are assayed for gene expression.
The proposed method simply changes the timing of the detachment. In whole plant experiments, larvae are confined to a single leaf which is eventually detached from the plant and assayed for gene expression. If the order of excision is reversed, from last in whole plant studies, to first in the detached study, the feeding experiment is simplified.
Solanum tuberosum var. Kennebec is propagated by nodal transfer in a simple tissue culture medium and transferred to soil for further growth if desired. Leaves are excised from the parent plant and relocated to Petri dishes where the feeding assay is conducted with the larval stages of M. sexta. Damaged leaf tissue is assayed for the expression of relatively early events in signal transduction. Gene expression analysis identified infestation-specific Cys2-His2 (C2H2) transcription factors, confirming the success of using detached leaves in early response studies. The method is easier to perform than whole plant infestations and uses less space.

The presented method creates natural herbivore damaged plant tissue through the application of Manduca sexta larvae to detached leaves of potato. The plant tissue is assayed for expression of six transcription factor homologs involved in early responses to insect herbivory.

The multitrophic nature of gene expression studies of insect herbivory demands large numbers of biological replicates, creating the need for simpler, more ...

Shootward Movement of CFDA Tracer Loaded in the Bottom Sink Tissues of Arabidopsis

The symplastic tracer 5(6)-carboxyfluorescein diacetate (CFDA) has been widely applied in living plants to demonstrate the intercellular connection, phloem transport and vascular patterning. This protocol shows bottom-to-top carboxyfluorescein (CF) movement in the Arabidopsis by using the root-cutting and the hypocotyl-pinching procedure respectively. These two different procedures result in different efficiencies of CF movement: about 91% appearance of CF in the shoots with the hypocotyl-pinching procedure, whereas only about 70% appearance of CF with the root-cutting procedure. The simple change of loading sites, resulting in significant changes in the mobile efficiency of this symplastic dye, suggests CF movement might be subject to the symplastic regulation, most probably by the root-hypocotyl junction.

Shootward Movement of CFDA Tracer Loaded in the Bottom Sink Tissues of Arabidopsis

The goal of this protocol is to show how to load the CFDA into different sites of the bottom parts of Arabidopsis. We then present the resulting distribution pattern of CF in the shoots.

The symplastic tracer 5(6)-carboxyfluorescein diacetate (CFDA) has been widely applied in living plants to demonstrate the intercellular connection, ...

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for describing the vegetation structure in the fields of ecology, forestry, and agriculture. Therefore, procedures of three commercially used methods (litter traps, needle technique, and a plant canopy analyzer) for performing LAI estimation were presented step-by-step. Specific methodological approaches were compared, and their current advantages, controversies, challenges, and future perspectives were discussed in this protocol. Litter traps are usually deemed as the reference level. Both the needle technique and the plant canopy analyzer (e.g., LAI-2000) frequently underestimate LAI values in comparison with the reference. The needle technique is easy to use in deciduous stands where the litter completely decomposes each year (e.g., oak and beech stands). However, calibration based on litter traps or direct destructive methods is necessary. The plant canopy analyzer is a commonly used device for performing LAI estimation in ecology, forestry, and agriculture, but is subject to potential error due to foliage clumping and the contribution of woody elements in the field of view (FOV) of the sensor. Eliminating these potential error sources was discussed. The plant canopy analyzer is a very suitable device for performing LAI estimations at the high spatial level, observing a seasonal LAI dynamic, and for long-term monitoring of LAI.

An accurate estimation of leaf area index (LAI) is crucial for many models of material and energy fluxes within plant ecosystems and between an ecosystem and the atmospheric boundary layer. Therefore, three methods (litter traps, needle technique, and PCA) for taking precise LAI measurements were in the presented protocol.

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for ...

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Leaf area index (LAI) is an essential canopy variable describing the amount of foliage in an ecosystem. The parameter serves as the interface between green components of plants and the atmosphere, and many physiological processes occur there, primarily photosynthetic uptake, respiration, and transpiration. LAI is also an input parameter for many models involving carbon, water, and the energy cycle. Moreover, ground-based in situ measurements serve as the calibration method for LAI obtained from remote sensing products. Therefore, straightforward indirect optical methods are necessary for making precise and rapid LAI estimates. The methodological approach, advantages, controversies, and future perspectives of the newly developed LP 110 optical device based on the relation between radiation transmitted through the vegetation canopy and canopy gaps were discussed in the protocol. Furthermore, the instrument was compared to the world standard LAI-2200 Plant Canopy Analyzer. The LP 110 enables more rapid and more straightforward processing of data acquired in the field, and it is more affordable than the Plant Canopy Analyzer. The new instrument is characterized by its ease of use for both above- and below-canopy readings due to its greater sensor sensitivity, in-built digital inclinometer, and automatic logging of readings at the correct position. Therefore, the hand-held LP 110 device is a suitable gadget for performing LAI estimation in forestry, ecology, horticulture, and agriculture based on the representative results. Moreover, the same device also enables the user to take accurate measurements of incident photosynthetically active radiation (PAR) intensity.

Fast and precise leaf area index (LAI) estimation in terrestrial ecosystems is crucial for a wide range of ecological studies and calibrating remote sensing products. Presented here is the protocol for using the new LP 110 optical device for taking ground-based in situ LAI measurements.

Leaf area index (LAI) is an essential canopy variable describing the amount of foliage in an ecosystem. The parameter serves as the interface between ...

Exploring Long-Term Shifts in Phytoplankton by Conversion of Microscopic Images

Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton

Oceanographic time series provide&#160;an important perspective on environmental processes in ecosystems. The Narragansett Bay Long-Term Plankton Time Series (NBPTS) in Narragansett Bay, Rhode Island, USA, represents one of the longest plankton time series (1959-present) of its kind in the world and presents a unique opportunity to visualize long-term change within an aquatic ecosystem. Phytoplankton represent&#160;the base of the food web in most marine systems, including Narragansett Bay. Therefore, communicating their importance to the 2.4 billion people who live within the coastal ocean is critical. We developed a protocol with the goal of visualizing the diversity and magnitude of phytoplankton by utilizing Adobe Illustrator to convert microscopic images of phytoplankton collected from the NBPTS into vector graphics that could be conformed into repetitive visual patterns through time. Numerically abundant taxa or those that posed economic and health threats, such as the harmful algal bloom taxa, Pseudo-nitzschia spp., were selected for image conversion. Patterns of various phytoplankton images were then created based on their relative abundance for select decades of data collected (1970s, 1990s, and 2010s). Decadal patterns of phytoplankton biomass informed the outline of each decade while a background color gradient from blue to red was used to reveal a long-term temperature increase observed in Narragansett Bay. Finally, large, 96-inch by 34-inch panels were printed with repeating phytoplankton patterns to illustrate potential changes in phytoplankton abundance over time. This project enables visualization of literal shifts in phytoplankton biomass, that are typically invisible to the naked eye while leveraging real-time series data (e.g., phytoplankton biomass and abundance) within the art piece itself. It represents an approach that can be utilized for many other plankton time series for data visualization, communication, education, and outreach efforts.

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression 

Here, we present a protocol for converting phytoplankton microscopic images into vector graphics and repetitive patterns to enable visualization of shifts in phytoplankton taxa and biomass over 60 years. This protocol represents an approach that can be utilized for other plankton time series and datasets globally.

Oceanographic time series provide&#160;an important perspective on environmental processes in ecosystems. The Narragansett Bay Long-Term Plankton Time ...

Rhizobacterial Colonization Detection on Arabidopsis thaliana

Measuring Bacterial Colonization on Arabidopsis thaliana Roots in Hydroponic Condition

Measuring bacterial colonization on Arabidopsis thaliana root is one of the most frequent experiments in plant-microbe interaction studies. A standardized method for measuring bacterial colonization in the rhizosphere is necessary to improve reproducibility. We first cultured sterile A.thaliana in hydroponic conditions and then inoculated the bacterial cells in the rhizosphere at a final concentration of OD600 of 0.01. At 2 days post-inoculation, the root tissue was harvested and washed three times in sterile water to remove the uncolonized bacterial cells. The roots were then weighed, and the bacterial cells colonized on the root were collected by vortex. The cell suspension was diluted in a gradient with a phosphate-buffered saline (PBS) buffer, followed by plating onto a Luria-Bertani (LB) agar medium. The plates were incubated at 37 &#176;C for 10 h, and then, the single colonies on LB plates were counted and normalized to indicate the bacterial cells colonized on roots. This method is used to detect bacterial colonization in the rhizosphere in mono-interaction conditions, with good reproducibility.

Author Spotlight: Enhancing Rhizobacteria Colonization on Plant Roots for Improved Microbial Fertilizer Efficiency

Colonization of plant growth-promoting rhizobacteria (PGPR) in the rhizosphere is essential for its growth-promoting effect. It is necessary to standardize the method of detection of bacterial rhizosphere colonization. Here, we describe a reproducible method for quantifying bacterial colonization on the root surface.

Measuring bacterial colonization on Arabidopsis thaliana root is one of the most frequent experiments in plant-microbe interaction studies. A standardized ...

Digital Leaf Physiognomy for Paleoclimate and Paleoecological Reconstruction

Reconstructing Terrestrial Paleoclimate and Paleoecology with Fossil Leaves Using Digital Leaf Physiognomy and Leaf Mass Per Area

Climate and environment strongly influence the size, shape, and toothiness (physiognomy) of plants&#39; leaves. These relationships, particularly in woody non-monocotyledonous angiosperms, have been used to develop leaf-based proxies for paleoclimate and paleoecology that have been applied to reconstruct ancient terrestrial ecosystems for the last ~120 million years of Earth&#39;s history. Additionally, given that these relationships have been documented in living plants, they are important for understanding aspects of plant evolution and how plants respond to climatic and environmental changes. To conduct these types of analyses on modern and fossil plants, leaf physiognomy must be measured accurately using a reproducible methodology. This protocol describes a computer-based method for measuring and analyzing a variety of leaf physiognomic variables in modern and fossil leaves. This method allows for the measurement of leaf physiognomic traits, in particular variables related to leaf serrations, leaf area, leaf dissection, and linearity that are used in the digital leaf physiognomy proxy for reconstructing paleoclimate, as well as petiole width and leaf area, which are used for reconstructing leaf mass per area, a paleoecological proxy. Because this digital leaf trait measurement method can be applied to fossil and living plants, it is not limited to applications related to reconstructing paleoclimate and paleoecology. It can also be used to explore leaf traits that may be informative for understanding the function of leaf morphology, leaf development, phylogenetic relationships of leaf traits, and plant evolution.

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

The protocol presented shows digital measurement and analysis of continuous leaf physiognomic traits on fossil leaves to reconstruct paleoclimate and paleoecology using the digital leaf physiognomy and leaf mass per area reconstruction methods.

Climate and environment strongly influence the size, shape, and toothiness (physiognomy) of plants&#39; leaves. These relationships, particularly in woody ...