The authors are grateful to Dr. Thomas Rademacher and Ibrahim Al Amedi for cultivating the plants used in this study. We would like to thank Dr. Richard M. Twyman for his assistance with editing the manuscript. This work was in part funded by the European Research Council Advanced Grant "Future-Pharma", proposal number 269110, the Fraunhofer Zukunftsstiftung (Future Foundation), the Fraunhofer-Gesellschaft Internal Programs under Grant No. Attract 125-600164.

1. Plant Cultivation and Sample Preparation
<ol>
	<li>Flush each mineral wool block with 1-2 L of deionized water and subsequently with 1 L of 0.1% [m/v] fertilizer solution. Place one tobacco (Nicotiana tabacum or N. benthamiana) seed in each block and gently flush with 0.25 L of fertilizer solution without washing away the seed.</li>
	<li>Cultivate the plants for 7 weeks in a greenhouse or phytotron with 70% relative humidity, a 16-h photoperiod (180 &#181;mol s&#8722;1 m&#8722;2; &#955; = 400-700 nm) and a 25/22 &#176;C light/dark temperature regime.</li>
	<li>Move the plants to the measurement apparatus. If the plants are immobile, harvest single leaves for the measurement of thermal properties.</li>
</ol>
2. Determine Leaf Thickness and Density
<ol>
	<li>Determine the leaf thickness
	<ol>
		<li>Prepare a 2% [m/v] agarose solution in phosphate-buffered saline (PBS) and autoclave it. Let the solution cool down to 40 &#176;C and embed a leaf sample placed in a Petri dish. Solidify the agarose by placing the Petri dish in a refrigerator at 4 &#176;C for 30 min.</li>
		<li>Cut the agarose block into 200-&#181;m slices using a vibratome with a razor blade cutting angle of 15&#176;. Use a cutting velocity of 1.0 mm s-1 and an amplitude of 0.5 mm.</li>
		<li>Mount five transversal leaf sections on a glass slide using cyanoacrylate as a fixative. Determine the leaf thickness under a microscope with a 20&#215; objective and an eyepiece with 10&#215; magnification, using the measurement tools built into the microscope software according to the manufacturer&#39;s instructions.</li>
		<li>Determine the leaf thicknesses in sample areas without veins.</li>
		<li>Alternatively, determine the leaf thickness with a dial-gauge at a vein-free area of the leaf blade. Make sure the dial-gauge is held perpendicular to the plane of the leaf blade. 
		CAUTION: Cyanocrylate is a skin irritant and may also glue fingers together if not handled with care.</li>
	</ol>
	</li>
	<li>Determine the leaf density
	<ol>
		<li>Determine the empty mass (m0) of a dry pycnometer, then fill it with water and determine the mass again (m1). Dry the pycnometer completely, place a leaf inside and determine the mass (m2) once more. With the leaf inside, carefully fill up the pycnometer with water and determine the mass (m3).</li>
		<li>Calculate the leaf density (Ps) using Equation 1. 
		Equation 1: <img alt="Equation" src="/files/ftp_upload/54835/54835eq1.jpg" /></li>
	</ol>
	</li>
</ol>
3. Determine the Spectral Transmission and Reflection of Leaves
<ol>
	<li>Place a leaf in the sample chamber of a UV/VIS spectrophotometer by fixing it between sample-holding clamps. For transmission measurements, place the leaf in front of the detector. For reflection measurements place the leaf at the rear of the detection chamber.</li>
	<li>Launch the spectrophotometer control software. Select a spectrum from 900 nm to 1600 nm. Start a new scan and record the values for transmission (&#181;T) and reflection (&#181;R) displayed by the UV/VIS spectrophotometer software, based on the spectral curve.</li>
	<li>Perform all measurements with at least three biological replicates. Increase the number of biological replicates to five or more if a heterogeneous sample quality can be expected, i.e., variation in leaf surface morphology and thickness.</li>
	<li>Calculate the power for transmission (PT) and reflection (PR) by multiplying the measured &#181;T or &#181;R values by the measured laser power PLaser according to Equations 2 and 3. 
	Equation 2: <img alt="Equation" src="/files/ftp_upload/54835/54835eq2.jpg" /> 
	Equation 3: <img alt="Equation" src="/files/ftp_upload/54835/54835eq3.jpg" /> 
	NOTE: The transmission can be also determined with a photodiode sensor during the measurement (see 6.3).</li>
</ol>
4. Set up the Measurement Apparatus
<ol>
	<li>Mount a fiber-coupled single-bar NIR diode laser (wavelength = 1,550 nm) into a 25.4-mm diameter cone on a stainless-steel holder. Connect a controller to set the output power (PLaser) of the NIR laser to 4-6 W.</li>
	<li>Place a bi-convex lens with a focal length of 25.4 mm at the end of the cone to adjust the beam width to 13 mm.</li>
	<li>Place a photodiode power sensor 354 mm below the bottom of the lens. Then attenuate the photodiode by placing a neutral density filter with an optical density of 1.0 and a 22-mm ceramic layer above the sensor.</li>
	<li>Connect the photodiode power sensor to an oscilloscope using a coaxial cable.</li>
	<li>Connect a 10 &#215; 10 cm frame which has a 6 &#215; 6 cm sample exposure area with the scaffold of the measurement setup at a height of 308 mm below the lens (Figure 1). Fix the leaf position in space by mounting it into the 10 &#215; 10 cm frame.</li>
	<li>Connect a NIR detector to a personal computer using a universal serial bus (USB) cable and install the interface software for the detector.</li>
	<li>Place the detector at a 45&#176; angle to the laser beam 135 mm above the ceramic layer. Align the measurement area of the detector to the laser spot on the sample by varying the sensor position and angle until the maximum temperature signal is observed.</li>
	<li>Use the laser control interface software to adjust the output laser power to 5 W and the duration of the laser pulse to 0.5 s. Select the &#34;Current control&#34; command in the control options window below the graphical representation of the laser power and adjust the laser power by typing &#34;5&#34; into the &#34;Power [W]&#34; field. Adjust the laser pulse duration by typing &#34;0.5&#34; into the &#34;Time [s]&#34; field.</li>
	<li>To determine the absolute laser power for each set of experiments, replace the photodiode power sensor with a thermal surface absorber power sensor at the end of each set of experiments and measure the laser output power for 20 s without a sample.</li>
</ol>
5. Prepare the Leaf Samples
<ol>
	<li>Use intact and undamaged leaves for the measurements.</li>
	<li>If relevant for the investigation, mimic typical leaf damage types by piercing the leaf with a scalpel, rubbing the leaf between latex gloves, exposing the leaf to an open flame or a laser beam for 2-3 s, or use other techniques to simulate other types of damage.</li>
	<li>Carefully but quickly mount the leaf sample between sample-holding clamps.</li>
</ol>
6. Take the Temperature Measurements
<ol>
	<li>Avoid direct contact between the leaf and the ceramic attenuator placed above the photodiode sensor to prevent artificial heat transfer that interferes with the calculation of cp,s and &#955; (see section 9).</li>
	<li>Use the temperature measurement software to collect the temperature profile of the leaf sample for a total of 60 s via the NIR detector. First, record the temperature baseline for 10 s, then activate the laser for 0.5 s and continue data collection for 49.5 s.
	<ol>
		<li>Start a measurement by clicking &#34;Measurement&#34; and then &#34;New Measurement&#34;. Afterwards click the green arrow above the graphical representation of the thermal profile. Save the temperature profile by clicking on the &#34;Save&#34; icon (a stylized disk) above the graphical representation of the profile.</li>
	</ol>
	</li>
	<li>Confirm the transmitted laser power using the photodiode power sensor by calculating the difference in signal for measurements with and without a leaf sample using an oscilloscope connected to the photodiode power sensor via a coaxial cable (Figure 2).
	<ol>
		<li>Determine the height of the two flanks (f1,S and f2,S) in the voltage profile acquired with the oscilloscope.</li>
		<li>Repeat the measurement without a leaf sample as a reference (f1,0 and f2,0). Calculate transmission &#181;T as the ratio of these measurements according to Equation 4 (see also Figure 2). 
		Equation 4: <img alt="Equation" src="/files/ftp_upload/54835/54835eq4.jpg" /></li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" src="/files/ftp_upload/54835/54835fig2.jpg" /> 
Figure 2: Measuring leaf transmission using a photodiode power sensor. A. Typical voltage profile for a reference experiment without a leaf sample visualized using an oscilloscope. B. Voltage profile with a leaf sample mounted in the apparatus. In both cases, the transmitted laser power is proportional to each of the two flanks. <a href="http://cloudfront.jove.com/files/ftp_upload/54835/54835fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
7. Calculate the Specific Heat Capacity of the Leaf Sample
<ol>
	<li>Calculate the maximum temperature difference &#916;T [K] during the laser pulse by subtracting the room temperature T0 [K] from the maximum leaf temperature Tmax [K] (Equation 5). 
	Equation 5: <img alt="Equation" src="/files/ftp_upload/54835/54835eq5.jpg" /></li>
	<li>Calculate the energy absorbed by a leaf (ES [J]) based on the effective laser power and laser pulse duration (Equation 6), where PR [W] is the reflected laser power and PT [W] is the transmitted laser power. 
	Equation 6: <img alt="Equation" src="/files/ftp_upload/54835/54835eq6.jpg" /></li>
	<li>Calculate the mass of the heated leaf area (mS [kg]) using Equation 7, where dS [m] is the leaf thickness according to 2.1), rLaser [m] is the radius of the laser spot, VS [m3] is the heated leaf volume, and &#961;S [kg m-3] is the leaf density according to 2.2). 
	Equation 7: <img alt="Equation" src="/files/ftp_upload/54835/54835eq7.jpg" /></li>
	<li>Calculate cp,s[J kg-1 K-1] according to Equation 8 by dividing the absorbed energy ES by the product of the heated leaf area mass mS and maximum temperature difference &#916;T. 
	Equation 8: <img alt="Equation" src="/files/ftp_upload/54835/54835eq8.jpg" /></li>
</ol>
8. Prepare the Temperature Profile Data for Thermal Conductivity Calculations
<ol>
	<li>Use the &#34;Export&#34; command of the NIR sensor control software to export the time and temperature raw data as a *.dat file and open the file in a spreadsheet processor.</li>
	<li>Apply 1:100 data reduction, e.g., using an &#34;IF(MOD(Value;100)=0;&#34;x&#34;;&#34;0&#34;)&#34; command, resulting in a data density of one data point per 0.1 s.</li>
	<li>Calculate the average baseline temperature TB [&#176;C] for each temperature profile over the initial 10 s of a measurement, during which the laser was still off. Then, calculate the difference between TB and the actual ambient temperature T0 [&#176;C].</li>
	<li>Use this difference to individually normalize each profile by shifting it towards T0 (y-normalization), e.g., if TB-T0 = 2.0 K, then subtract 2.0 K from each temperature value in the temperature profile (Figure 3A).</li>
	<li>Normalize the time coordinate of each temperature profile (x-normalization) by deleting every data point before the maximum sample temperature (Tmax) and assign new time values starting with t = 0 for Tmax (Figure 3B).</li>
	<li>Screen each profile for sudden temperature shifts, i.e., temperature differences that are more than three times the baseline noise level, which is typically 3 &#215; 0.31 K &#8776; 1.0 K. Remove these regions from the data set because they correspond to measurement artifacts (Figure 3C).</li>
	<li>Fit an exponential decay function (Equation 9) to the data using a spreadsheet processor, where Tt [K] is the fitted leaf sample temperature at time t [s], T0 is the ambient temperature, A [K] is the amplitude and t1 [s] the decay constant (Figure 3D). 
	Equation 9: <img alt="Equation" src="/files/ftp_upload/54835/54835eq9.jpg" /></li>
	<li>Use the fitted function to calculate the temperature decline in the leaf sample from 0-80 s after the laser pulse.</li>
	<li>Transform the temperature data measured in [&#176;C] to the [K] scale by adding a value of 273.15 to each temperature data point (Figure 3E).</li>
</ol>
<img alt="Figure 3" src="/files/ftp_upload/54835/54835fig3.jpg" /> 
Figure 3: Data processing scheme for the calculation of &#955;. A. After data reduction, the temperature profiles are normalized to the ambient temperature. B. Next, all data points before the maximum sample temperature (Tmax) are removed. C. Measurement artifacts (shown in the &#34;inconsistent&#34; data set) are identified based on temperature shifts larger than three times the baseline noise and removed from the dataset prior to fitting to an exponential function. D. The Celsius temperature scale is converted into the Kelvin scale. E. For each time interval, &#955; is calculated based on the temperature profile. F. A window of 20 s is defined in which a relevant temperature change can be observed. G. Based on the selected time window, the average and standard deviation are calculated for &#955;. H. Representative results for two different N. tabacum leaf samples. Orange arrows and lines indicate the effect of the corresponding processing step on the presented data. <a href="http://cloudfront.jove.com/files/ftp_upload/54835/54835fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
9. Calculation of the Thermal Conductivity of the Leaf Sample
<ol>
	<li>Calculate the temperature difference between the leaf sample and the environment for each 0.1-s interval according to Equation 10, where &#916;Tx [K] is the temperature difference, Tt [&#176;C] is the fitted leaf sample temperature and T0 [&#176;C] the ambient temperature (Figure 3E). 
	Equation 10: <img alt="Equation" src="/files/ftp_upload/54835/54835eq10.jpg" /></li>
	<li>Assume that the decline in temperature is due to the combined effect of convective heat transfer, thermal radiation and thermal conduction. Use the corresponding energy balance (Equation 11) as a basis for the calculation of &#955;, where &#916;ETemp[J] is the difference in the thermal energy of the sample at two consecutive time points, &#916;Erad [J] is the energy difference due to thermal radiation, &#916;Econv [J] is the energy difference due to convective heat transfer, and &#916;Econd [J] is the energy difference due to thermal conduction. 
	Equation 11: <img alt="Equation" src="/files/ftp_upload/54835/54835eq11.jpg" /></li>
	<li>Substitute the general terms in the energy balance with the actual physical properties yielding Equation 12, where &#916;Tt [K] is the difference in the fitted leaf sample temperature, &#949; the unitless emissivity, &#963; [kg s-3 K-4] the Stefan-Boltzmann constant, Arad [m2] the area of thermal radiation, h [J s-1 m-2 K-1] the convective heat transfer coefficient, Aconv [m2] the area of convective heat transfer, Acond [m2] the area of thermal conduction and l [m] the characteristic length. 
	Equation 12: 
	<img alt="Equation" src="/files/ftp_upload/54835/54835eq12.jpg" /></li>
	<li>Calculate the characteristic length l based on the correlation: l = V/A.</li>
	<li>Use the heated sample volume VS and the cross-sectional area of the leaf sample to calculate A [m2]. The cross-sectional leaf area corresponds to Acond according to Equation 13, where Acond is the area where conduction occurs, rLaser is the radius of the laser spot and ds is the leaf thickness. 
	Equation 13: <img alt="Equation" src="/files/ftp_upload/54835/54835eq13.jpg" /></li>
	<li>Calculate Arad and Aconv according to Equation 14, where ALaser is the area of the laser spot. 
	Equation 14: <img alt="Equation" src="/files/ftp_upload/54835/54835eq14.jpg" /></li>
	<li>Substitute Equations 9, 12 and 13 into Equation 11 and resolve the latter for &#955;, yielding Equation 15 where tLaser is the laser pulse duration [s]. 
	Equation 15: 
	<img alt="Equation" src="/files/ftp_upload/54835/54835eq15.jpg" /></li>
	<li>Assume a value of 0.94 for &#949; and calculate &#955; for each 0.1-s time interval over the first 20 s of the temperature profile. Average the 200 values for &#955; obtained in this way and calculate the standard deviation (Figure 3F &#8722; H).</li>
</ol>

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The authors have no conflicts of interest to disclose.

The contact-free, non-destructive measurement method described above can be used to determine cp,s and &#654; in a simultaneous and reproducible manner. The calculation of &#654; in particular depends on several parameters that are sensitive to errors. Nevertheless, the impact of these errors was either linear or sub-proportional, and the coefficient of variation for all parameters was found to be less than 10%. Even though the method can thus be regarded as robust, some technical improvements can be ...

The large-scale processing of biological materials often requires heat-treatment steps such as pasteurization. The equipment for such processes can be designed more precisely if the thermal properties of the biological materials are well characterized, including the specific heat capacity (cp,s) and thermal conductivity (&#955;). These parameters can be determined easily for liquids, suspensions and homogenates by calorimetry 1. However, measuring such parameters in solid samples can be labor intensive, and often requires direct contact with the sample or even its destruction 2. For example, photothermal techniques require direct contact between the sample and detector 3. Such limitations are acceptable during food processing, but are incompatible with highly regulated processes such as the production of biopharmaceutical proteins in plants in the context of good manufacturing practice 4. In such a context, repeated (e.g., weekly) monitoring of thermal properties may be required during a seven-week growth period for individual plants as a quality control tool. If such a monitoring would require and consume a leaf for each measurement, there would be no biomass left to process at the time of harvest.
Additionally, using only leaf parts instead would cause wounding to the plant and increase the risk of necrosis or pathogen infection, again diminishing the process yield. The likelihood of pathogen infection may also increase if a method with direct contact to the sample would be used, inducing the risk that an entire batch of plants can be infected through contact with a contaminated sensor device. Similar aspects have to be considered for the monitoring of plant stresses like drought, e.g., in an ecophysiological context. For example, water loss is often monitored by a change in the fresh biomass, which requires an invasive treatment of the plants under investigation 5, e.g., dissecting a leaf. Instead, determining the specific heat capacity, which depends on the water content of a sample, in a non-invasive manner as describe here, can be used as a surrogate parameter for the hydration status of plants. In both scenarios (pharmaceutical production and ecophysiology), artificial stresses induced by destructive or invasive measurement techniques would be deleterious as they can distort the experimental data. Therefore, previously reported flash methods 6 or the placement of samples between silver plates 7 are unsuitable for such processes and experiments because they either require direct contact to the sample or are destructive. The parameters cp,s and &#955; must be determined in order to design the process equipment for a blanching step that can simplify product purification and thus reduce manufacturing costs 8-10. Both cp,s and &#955; can now be rapidly determined by contact-free non-destructive near infrared (NIR) laser probing in a consistent and reproducible manner 11 and this new method will be explained in detail below. The results obtained with this method were successfully used to simulate heat transfer in tobacco leaves 12, allowing the design of appropriate processing equipment and the selection of corresponding parameters such as the blanching temperature.
The method is easy to set up (Figure 1) and has two phases, measurement and analysis, each of which comprises two major steps. In the measurement phase, a leaf sample is first locally heated by a short laser pulse and the maximum sample temperature is recorded. The temperature profile of the sample is then recorded for a duration of 50 s. In the analysis phase, leaf properties such as density (easily and accurately determined by pycnometric measurement) are combined with the maximum sample temperature to calculate cp,s. In the second step, the leaf temperature profile is used as the input for an energy balance equation, taking conduction, convection and radiation into account, to calculate &#955;.
Detailed step-by-step instructions are provided in the protocol section, expanding on the contents of the accompanying video. Typical measurements are then shown in the results section. Finally, the benefits and limitations of the method are highlighted in the discussion section along with potential improvements and further applications.
<img alt="Figure 1" src="/files/ftp_upload/54835/54835fig1.jpg" /> 
Figure 1: Apparatus used to determine leaf thermal properties. A. Photograph of the measurement apparatus used to determine the specific heat capacity and thermal conductivity of leaves. The peripheral devices (computers, oscilloscope) are not shown. B. Schematic representation of the measurement apparatus. The laser and connected equipment are highlighted in red, the NIR detector for temperature measurement is shown in purple, the leaf sample is green and the photodiode power sensor is blue. C. Drawing of the elements of the measurement setup with the same color code as in B. The size bar indicates 0.1 m. D. Screenshot illustrating the typical elements of the laser control software. <a href="http://cloudflare.jove.com/files/ftp_upload/54835/54835fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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			<td>Console for thermal surface absorber sensor</td>
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			<td height="21" style="height:21px;width:411px;">Digital Phosphor Oscilloscope&#160;</td>
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			<td>Freezer</td>
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			<td style="width:259px;">n.a.</td>
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			<td>For plant cultivation</td>
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			<td>Infrared detector</td>
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			<td height="21" style="height:21px;width:411px;">Laser Driver&#160;</td>
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			<td>Light source</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:411px;">Photodiode sensor&#160;</td>
			<td style="width:259px;">Thorlabs</td>
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			<td>Power sensor for transmission measurements</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:411px;">Precision weight Ohaus Analytical Plus</td>
			<td style="width:259px;">Ohaus</td>
			<td style="width:136px;">80251552</td>
			<td>Precision weight</td>
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			<td height="21" style="height:21px;width:411px;">Sample frame</td>
			<td style="width:259px;">Fraunhofer ILT</td>
			<td style="width:136px;">n.a.</td>
			<td>Fixation of the leaf sample</td>
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			<td height="21" style="height:21px;width:411px;">Software Pyro Control</td>
			<td style="width:259px;">Amtron</td>
			<td style="width:136px;">n.a.</td>
			<td>Laser Power Control Software</td>
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			<td height="21" style="height:21px;width:411px;">Stainless-steel-holder</td>
			<td style="width:259px;">n.a.</td>
			<td style="width:136px;">n.a.</td>
			<td>Holder for measurement set-up</td>
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			<td height="21" style="height:21px;width:411px;">Teflon plates 2cm</td>
			<td style="width:259px;">Fraunhofer ILT</td>
			<td style="width:136px;">n.a.</td>
			<td>Teflon attenuation</td>
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			<td height="21" style="height:21px;width:411px;">Thermal surface absorber Power sensor</td>
			<td style="width:259px;">Thorlabs</td>
			<td style="width:136px;">S314C</td>
			<td>Sensor for laser power measurements</td>
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			<td height="21" style="height:21px;width:411px;">Vibratome</td>
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			<td height="21" style="height:21px;width:411px;">Zoc/Pro 6.51&#160;</td>
			<td style="width:259px;">EmTec Innovative Software</td>
			<td style="width:136px;">n.a.</td>
			<td>Laser Control Software&#160;</td>
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a rapid laser probing method facilitates the non-invasive and contact-free determination of leaf thermal properties

Plants can produce valuable substances such as secondary metabolites and recombinant proteins. The purification of the latter from plant biomass can be streamlined by heat treatment (blanching). A blanching apparatus can be designed more precisely if the thermal properties of the leaves are known in detail, i.e., the specific heat capacity and thermal conductivity. The measurement of these properties is time consuming and labor intensive, and usually requires invasive methods that contact the sample directly. This can reduce the product yield and may be incompatible with containment requirements, e.g., in the context of good manufacturing practice. To address these issues, a non-invasive, contact-free method was developed that determines the specific heat capacity and thermal conductivity of an intact plant leaf in about one minute. The method involves the application of a short laser pulse of defined length and intensity to a small area of the leaf sample, causing a temperature increase that is measured using a near infrared sensor. The temperature increase is combined with known leaf properties (thickness and density) to determine the specific heat capacity. The thermal conductivity is then calculated based on the profile of the subsequent temperature decline, taking thermal radiation and convective heat transfer into account. The associated calculations and critical aspects of sample handling are discussed.

Measurement of Leaf Properties
Using the above microscopic method, a leaf thickness of 0.22-0.29 &#215; 10&#8722;3 m was determined for both N. tabacum (0.25&#177;0.04 &#215; 10&#8722;3 m, n=33) and N. benthamiana (0.26&#177;0.02 &#215; 10&#8722;3 m, n=24), which is well within the 0.20-0.33 &#215; 10&#8722;3</sup...

Watch this Scientific Journal Video about A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties at JoVE.com

A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

A method was developed to determine the specific heat capacity and thermal conductivity of leaf tissue by non-invasive, contact-free near infrared laser probing, which requires less than 1 min per sample.

Plants can produce valuable substances such as secondary metabolites and recombinant proteins. The purification of the latter from plant biomass can be ...

a-rapid-laser-probing-method-facilitates-non-invasive-contact-free

Research

JoVE Journal

Biochemistry

7.2K Views.  Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V.. A method was developed to determine the specific heat capacity and thermal conductivity of leaf tissue by non-invasive, contact-free near infrared laser probing, which requires less than 1 min per sample.

Video: A Rapid Laser Probing Method Facilitates the Non-invasive and Contact-free Determination of Leaf Thermal Properties

Experimental Design for Laser Microdissection RNA-Seq: Lessons from an Analysis of Maize Leaf Development

Genes with important roles in development frequently have spatially and/or temporally restricted expression patterns. Often these gene transcripts are not detected or are not identified as differentially expressed (DE) in transcriptomic analyses of whole plant organs. Laser Microdissection RNA-Seq (LM RNA-Seq) is a powerful tool to identify genes that are DE in specific developmental domains. However, the choice of cellular domains to microdissect and compare, and the accuracy of the microdissections are crucial to the success of the experiments. Here, two examples illustrate design considerations for transcriptomics experiments; a LM RNA-seq analysis to identify genes that are DE along the maize leaf proximal-distal axis, and a second experiment to identify genes that are DE in liguleless1-R (lg1-R) mutants compared to wild-type. Key elements that contributed to the success of these experiments were detailed histological and in situ hybridization analyses of the region to be analyzed, selection of leaf primordia at equivalent developmental stages, the use of morphological landmarks to select regions for microdissection, and microdissection of precisely measured domains. This paper provides a detailed protocol for the analysis of developmental domains by LM RNA-Seq. The data presented here illustrate how the region selected for microdissection will affect the results obtained.

Many developmentally important genes have cell- or tissue-specific expression patterns. This paper describes LM RNA-seq experiments to identify genes that are differentially expressed at the maize leaf blade-sheath boundary and in lg1-R mutants compared to wild-type. The experimental considerations discussed here apply to transcriptomic analyses of other developmental phenomena.

Genes with important roles in development frequently have spatially and/or temporally restricted expression patterns. Often these gene transcripts are not ...

Production, Crystallization and Structure Determination of C. difficile PPEP-1 via Microseeding and Zinc-SAD

New therapies are needed to treat Clostridium difficile infections that are a major threat to human health. The C. difficile metalloprotease PPEP-1 is a target for future development of inhibitors to decrease the virulence of the pathogen. To perform biophysical and structural characterization as well as inhibitor screening, large amounts of pure and active protein will be needed. We have developed a protocol for efficient production and purification of PPEP-1 by the use of E. coli as the expression host yielding sufficient amounts and purity of protein for crystallization and structure determination. Additionally, using microseeding, highly intergrown crystals of PPEP-1 can be grown to well-ordered crystals suitable for X-ray diffraction analysis. The methods could also be used to produce other recombinant proteins and to study the structures of other proteins producing intergrown crystals.

Production, Crystallization and Structure Determination of C. difficile PPEP-1 via Microseeding and Zinc-SAD

Proline-proline endopeptidase-1 (PPEP-1) is a secreted metalloprotease and promising drug-target from the human pathogen Clostridium difficile. Here we describe all methods necessary for the production and structure determination of this protein.

New therapies are needed to treat Clostridium difficile infections that are a major threat to human health. The C. difficile metalloprotease PPEP-1 is a ...

Differential Scanning Calorimetry &#8212; A Method for Assessing the Thermal Stability and Conformation of Protein Antigen

Differential scanning calorimetry (DSC) is an analytical technique that measures the molar heat capacity of samples as a function of temperature. In the case of protein samples, DSC profiles provide information about thermal stability, and to some extent serves as a structural &#8220;fingerprint&#8221; that can be used to assess structural conformation. It is performed using a differential scanning calorimeter that measures the thermal transition temperature (melting temperature; Tm) and the energy required to disrupt the interactions stabilizing the tertiary structure (enthalpy; &#8710;H) of proteins. Comparisons are made between formulations as well as production lots, and differences in derived values indicate differences in thermal stability and structural conformation. Data illustrating the use of DSC in an industrial setting for stability studies as well as monitoring key manufacturing steps are provided as proof of the effectiveness of this protocol. In comparison to other methods for assessing the thermal stability of protein conformations, DSC is cost-effective, requires few sample preparation steps, and also provides a complete thermodynamic profile of the protein unfolding process.

Differential scanning calorimetry measures the thermal transition temperature(s) and total heat energy required to denature a protein. Results obtained are used to assess the thermal stability of protein antigens in vaccine formulations.

Differential scanning calorimetry (DSC) is an analytical technique that measures the molar heat capacity of samples as a function of temperature. In the ...

Determination of the Glycogen Content in Cyanobacteria

Cyanobacteria accumulate glycogen as a major intracellular carbon and energy storage&#160;during photosynthesis. Recent developments in research have highlighted complex mechanisms of glycogen metabolism, including the diel cycle of biosynthesis and catabolism, redox regulation, and the involvement of non-coding RNA. At the same time, efforts are being made to redirect carbon from glycogen to desirable products in genetically engineered cyanobacteria to enhance product yields. Several methods are used to determine the glycogen contents in cyanobacteria, with variable accuracies and technical complexities. Here, we provide a detailed protocol for the reliable determination of the glycogen content in cyanobacteria that can be performed in a standard life science laboratory. The protocol entails the selective precipitation of glycogen from the cell lysate and the enzymatic depolymerization of glycogen to generate glucose monomers, which are detected by a glucose oxidase-peroxidase (GOD-POD) enzyme coupled assay. The method has been applied to Synechocystis sp. PCC 6803 and Synechococcus sp. PCC 7002, two model cyanobacterial species that are widely used in metabolic engineering. Moreover, the method successfully showed differences in the glycogen contents between the wildtype and mutants defective in regulatory elements or glycogen biosynthetic genes.

Here, we present a reliable and easy assay to measure the glycogen content in cyanobacterial cells. The procedure entails precipitation, selectable depolymerization, and the detection of glucose residues. This method is suitable for both wildtype and genetically engineered strains and can facilitate the metabolic engineering of cyanobacteria.

Cyanobacteria accumulate glycogen as a major intracellular carbon and energy storage&#160;during photosynthesis. Recent developments in research have ...

Rapid Isolation of the Mitoribosome from HEK Cells

The human mitochondria possess a dedicated set of ribosomes (mitoribosomes) that translate 13 essential protein components of the oxidative phosphorylation complexes encoded by the mitochondrial genome. Since all proteins synthesized by human mitoribosomes are integral membrane proteins, human mitoribosomes are tethered to the mitochondrial inner membrane during translation. Compared to the cytosolic ribosome the mitoribosome has a sedimentation coefficient of 55S, half the rRNA content, no 5S rRNA and 36 additional proteins. Therefore, a higher protein-to-RNA ratio and an atypical structure make the human mitoribosome substantially distinct from its cytosolic counterpart.
Despite the central importance of the mitoribosome to life, no protocols were available to purify the intact complex from human cell lines. Traditionally, mitoribosomes were isolated from mitochondria-rich animal tissues that required kilograms of starting material. We reasoned that mitochondria in dividing HEK293-derived human cells grown in nutrient-rich expression medium would have an active mitochondrial translation, and, therefore, could be a suitable source of material for the structural and biochemical studies of the mitoribosome. To investigate its structure, we developed a protocol for large-scale purification of intact mitoribosomes from HEK cells. Herein, we introduce nitrogen cavitation method as a faster, less labor-intensive and more efficient alternative to traditional mechanical shear-based methods for cell lysis. This resulted in preparations of the mitoribosome that allowed for its structural determination to high resolution, revealing the composition of the intact human mitoribosome and its assembly intermediates. Here, we follow up on this work and present an optimized and more cost-effective method requiring only ~1010 cultured HEK cells. The method can be employed to purify human mitoribosomal translating complexes, mutants, quality control assemblies and mitoribosomal subunits intermediates. The purification can be linearly scaled up tenfold if needed, and also applied to other types of cells.

Mitochondria have specialized ribosomes that diverged from their bacterial and cytoplasmic counterparts. Here we show how mitoribosomes can be obtained from their native compartment in HEK cells. The method involves isolation of mitochondria from suspension cells and consequent purification of mitoribosomes.

The human mitochondria possess a dedicated set of ribosomes (mitoribosomes) that translate 13 essential protein components of the oxidative ...

Leaf Spray Mass Spectrometry: A Rapid Ambient Ionization Technique to Directly Assess Metabolites from Plant Tissues

Plants produce thousands of small molecules that are diverse in their chemical properties. Mass spectrometry (MS) is a powerful technique for analyzing plant metabolites because it provides molecular weights with high sensitivity and specificity. Leaf spray MS is an ambient ionization technique where plant tissue is used for direct chemical analysis via electrospray, eliminating chromatography from the process. This approach to sampling metabolites allows for a wide range of chemical classes to be detected simultaneously from intact plant tissues, minimizing the amount of sample preparation needed. When used with a high-resolution, accurate mass MS, leaf spray MS facilitates the rapid detection of metabolites of interest. It is also possible to collect tandem mass fragmentation data with this technique to facilitate a compound identification. The combination of accurate mass measurements and fragmentation is beneficial in confirming compound identities. The leaf spray MS technique requires only minor modifications to a nanospray ionization source and is a useful tool to further expand the capabilities of a mass spectrometer. Here, fresh leaf tissue from Sceletium tortuosum (Aizoaceae), a traditional medicinal plant from South Africa, is analyzed; numerous mesembrine alkaloids are detected with leaf spray MS.

Leaf spray mass spectrometry is a direct chemical analysis technique that minimizes the sample preparation and eliminates chromatography, allowing for the rapid detection of small molecules from plant tissues.

Plants produce thousands of small molecules that are diverse in their chemical properties. Mass spectrometry (MS) is a powerful technique for analyzing ...

Probing The Structure And Dynamics Of Nucleosomes Using Atomic Force Microscopy Imaging

Chromatin, which is a long chain of nucleosome subunits, is a dynamic system that allows for such critical processes as DNA replication and transcription to take place in eukaryotic cells. The dynamics of nucleosomes provides access to the DNA by replication and transcription machineries, and critically contributes to the molecular mechanisms underlying chromatin functions. Single-molecule studies such as atomic force microscopy (AFM) imaging have contributed significantly to our current understanding of the role of nucleosome structure and dynamics. The current protocol describes the steps enabling high-resolution AFM imaging techniques to study the structural and dynamic properties of nucleosomes. The protocol is illustrated by AFM data obtained for the centromere nucleosomes in which H3 histone is replaced with its counterpart centromere protein A (CENP-A). The protocol starts with the assembly of mono-nucleosomes using a continuous dilution method. The preparation of the mica substrate functionalized with aminopropyl silatrane (APS-mica) that is used for the nucleosome imaging is critical for the AFM visualization of nucleosomes described and the procedure to prepare the substrate is provided. Nucleosomes deposited on the APS-mica surface are first imaged using static AFM, which captures a snapshot of the nucleosome population. From analyses of these images, such parameters as the size of DNA wrapped around the nucleosomes can be measured and this process is also detailed. The time-lapse AFM imaging procedure in the liquid is described for the high-speed time-lapse AFM that can capture several frames of nucleosome dynamics per second. Finally, the analysis of nucleosome dynamics enabling the quantitative characterization of the dynamic processes is described and illustrated.

Here, we present a protocol to characterize nucleosome particles at the single-molecule level using static and time-lapse atomic force microscopy (AFM) imaging techniques. The surface functionalization method described allows for the capture of the structure and dynamics of nucleosomes in high-resolution at the nanoscale.

Chromatin, which is a long chain of nucleosome subunits, is a dynamic system that allows for such critical processes as DNA replication and transcription ...

Rapid Collection of Floral Fragrance Volatiles using a Headspace Volatile Collection Technique for GC-MS Thermal Desorption Sampling

Fragrances of many flower families have been sampled and the volatiles analyzed. Knowing the compounds that make up the fragrances can be an important step to conservation of flowers that are threatened or endangered. Because floral fragrance is critical for attracting pollinators, this method could be used to better understand or even enhance pollination. We present a protocol using a portable charcoal air filter and vacuum to collect floral fragrance volatiles, which are then analyzed by a GC-MS. By using this method, fragrance volatiles can be sampled using a non-destructive method with a machine that is easily transported. This methodology uses a rapid sampling procedure, cutting sampling time down from 2-3 hours to approximately 10 minutes. Using GC-MS, the fragrance compounds can be identified individually, based on authentic standards. The steps used for collecting fragrance and control data are presented, from material setup to collecting the data output.

Here, we present a protocol for collecting the floral fragrance volatiles from blooming flowers, using a non-destructive sampling procedure.

Fragrances of many flower families have been sampled and the volatiles analyzed. Knowing the compounds that make up the fragrances can be an important ...

Production, Crystallization, and Structure Determination of the IKK-binding Domain of NEMO

NEMO is a scaffolding protein which plays an essential role in the NF-&#954;B pathway by assembling the IKK-complex with the kinases IKK&#945; and IKK&#946;. Upon activation, the IKK complex phosphorylates the I&#954;B molecules leading to NF-&#954;B nuclear translocation and activation of target genes. Inhibition of the NEMO/IKK interaction is an attractive therapeutic paradigm for the modulation of NF-&#954;B pathway activity, making NEMO a target for inhibitors design and discovery. To facilitate the process of discovery and optimization of NEMO inhibitors, we engineered an improved construct of the IKK-binding domain of NEMO that would allow for structure determination of the protein in the apo form and while bound to small molecular weight inhibitors. Here, we present the strategy utilized for the design, expression and structural characterization of the IKK-binding domain of NEMO. The protein is expressed in E. coli cells, solubilized under denaturing conditions and purified through three chromatographic steps. We discuss the protocols for obtaining crystals for structure determination and describe data acquisition and analysis strategies. The protocols will find wide applicability to the structure determination of complexes of NEMO and small molecule inhibitors.

We describe protocols for the structure determination of the IKK-binding domain of NEMO by X-ray crystallography. The methods include protein expression, purification and characterization as well as strategies for successful crystal optimization and structure determination of the protein in its unbound form.

NEMO is a scaffolding protein which plays an essential role in the NF-&#954;B pathway by assembling the IKK-complex with the kinases IKK&#945; and ...

A Planarian Motility Assay to Gauge the Biomodulating Properties of Natural Products

A straightforward, controllable means of using the non-parasitic planarian, Dugesia tigrina, a free-living aquatic flatworm, to study the stimulant and withdrawal properties of natural products is described. Experimental assays benefitting from unique aspects of planarian physiology have been applied to studies on wound healing, regeneration, and tumorigenesis. In addition, because planarians exhibit sensitivity to a variety of environmental stimuli and are capable of learning and developing conditioned responses, they can be used in behavioral studies examining learning and memory. Planarians possess a basic bilateral symmetry and a central nervous system that uses neurotransmitter systems amenable to studies examining the effects of neuromuscular biomodulators. Consequently, experimental systems monitoring planarian movement and motility have been developed to examine substance addiction and withdrawal. Because planarian motility offers the potential for a sensitive, easily standardized motility assay system to monitor the effect of stimuli, the planarian locomotor velocity (pLmV) test was adapted to monitor both stimulation and withdrawal behaviors by planarians through the determination of the number of grid lines crossed by the animals with time. Here, the technique and its application are demonstrated and explained.

Planarian motility is used to gauge the stimulant and withdrawal properties of natural products when compared to the movement of the animals in spring water alone.

A straightforward, controllable means of using the non-parasitic planarian, Dugesia tigrina, a free-living aquatic flatworm, to study the stimulant and ...