Fanny Laurent (FARE) is warmly thanked for the preparation of the figures. The Research Federation FRABio (University of Lille, CNRS) is acknowledged for providing the technical environment conducive to achieving this work. Funding was obtained from the French National Research Agency (LIGNOPROG project ANR-14-CE05-0026).

1. Sample preparation
NOTE: Successive steps for preparing the samples are described in Figure 1.
<ol>
	<li>Plant sample preparation
	<ol>
		<li>From wood trunks or fragments, cut 1 cm long samples with razor blades without damaging their structure.</li>
		<li>Embed samples in polyethylene glycol (PEG) medium, by dipping them in successive solutions of increasing PEG concentration diluted in water until reaching 100% PEG.
		<ol>
			<li>Place sample under vacuum to remove water.</li>
			<li>Gently stir samples in 30% v/v PEG for 24 h.</li>
			<li>Gently stir samples in 50% v/v PEG for 24 h.</li>
			<li>Gently stir samples in 100% PEG for 24 h at 70 &#176;C (pure PEG is solid at room temperature).</li>
		</ol>
		</li>
		<li>Place samples in capsules at 70 &#176;C (on a hot plate) and progressively decrease temperature until reaching room temperature.</li>
		<li>Prepare carefully 30-60 &#181;m thickness flat sections from these PEG blocks using a microtome and disposables blades.</li>
		<li>Collect and wash sections in three successive baths of water for 5 min to remove PEG from the sections.</li>
	</ol>
	</li>
	<li>Fluorescent probe preparation
	<ol>
		<li>Prepare buffer solution of 30 mM phosphate at pH 6.0.</li>
		<li>Weigh PEG rhand dextran rhodamine probe powder.</li>
		<li>Dissolve probe powder into the buffer with continuous stirring in a glass vial for 1 h in the dark at a final concentration of 0.1 g/L.</li>
	</ol>
	</li>
	<li>Plant sample staining
	<ol>
		<li>Incubate sections with 500 &#181;L of the fluorescent probe (see 1.2.3) for 72 h (no stirring) in a plastic tube (no more than 3 sections per tube) in the dark.</li>
		<li>Pick-up a section with a brush, adsorb the buffer with a clean sheet (avoid drying the section), and then mount the incubated sections between a cover glass and a #1.5H coverslip.</li>
	</ol>
	</li>
</ol>
2. Fluorescence lifetime and spectral measurement system calibration
NOTE: The complete workflow for sFLIM measurement is presented in Figure 2.
<ol>
	<li>Determine the spectral window width of the sFLIM detector.
	<ol>
		<li>Put urea crystals in a culture box with a 0.17 &#181;m glass bottom.</li>
		<li>Place the culture box on a microscope sample holder and select the 20x objective.</li>
		<li>Select a Ti:Sa laser with a 900 nm wavelength and 2% power, and turn on the scanning.</li>
		<li>Collect the second harmonic signal on the sFLIM detector. 
		NOTE: The second harmonic signal is emitted at exactly half excitation wavelength of 450 nm; while instantaneous, this measure also provides the instrumental response function of the system.</li>
		<li>Gradually adjust the laser excitation wavelength from 900 to 980 nm until the second harmonic signal is collected in the second spectral channel (462.5 nm).</li>
		<li>Determine the spectral window length (12.5 nm).</li>
		<li>If the spectral range differs from what is expected, troubleshoot by doing the following.
		<ol>
			<li>Adjust the laser to 910 nm.</li>
			<li>Move the grating position with a scroll wheel to measure the second harmonic signal on the first channel only (first channel boundary).</li>
			<li>Adjust the laser to 935 nm.</li>
			<li>Move the grating position with a scroll wheel to measure the second harmonic signal on the first channel only (second channel boundary).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Calibration of the overall spectrometer channels
	<ol>
		<li>Insert the mirror on the microscope stage.</li>
		<li>Select the visible continuous laser (458 nm, 1% power).</li>
		<li>Collect the reflection signal on the sFLIM detector and check if photons are measured in the appropriate spectral channel.</li>
		<li>Repeat step 2.2.2 and 2.2.3 with all available continuous lasers (514 nm, 561 nm and 633 nm).</li>
		<li>If the spectral channel differs from what is expected, move the grating position with the scroll wheel and repeat steps 2.1 and 2.2.</li>
	</ol>
	</li>
</ol>
3. Sample fluorescence characterization
<ol>
	<li>In a spectrofluorometer equipped with a film holder accessory (e.g., Jasco FP-8500 instrument), place the plant section sample (not incubated with the fluorescent probe).</li>
	<li>Measure fluorescence emission typically on the range 300-600 nm while exciting the sample typically on the range 250-550 nm.</li>
	<li>Plot the fluorescence intensity versus excitation and emission wavelengths to draw a 3D fluorescence map.</li>
	<li>Determine the maximum excitation/emission area from the plot.</li>
</ol>
4. Spectral FRET measurement
<ol>
	<li>Autofluorescence calibration
	<ol>
		<li>Select the objective 20x (NA: 0.8).</li>
		<li>Set the two-photon laser excitation at 750 nm.</li>
		<li>Place the wheat straw (WS) alone plant section between the slide and the coverslip.</li>
		<li>Apply the following parameters in the software. Switch to lambda mode. Select the spectral detector ChS with a 9.7 nm resolution. Select the spectral range between 420 to 722 nm.</li>
		<li>Acquire the image.</li>
		<li>Determine the donor emission peak (470 nm with this setup).</li>
	</ol>
	</li>
	<li>Acceptor&#39;s calibration
	<ol>
		<li>Place rhodamine-based fluorescent probes in a culture box with a 0.17 &#181;m glass bottom.</li>
		<li>Acquire the image with the same setting.</li>
		<li>Determine the acceptor emission peak (570 nm with this setup).</li>
	</ol>
	</li>
	<li>Determine the spectral range with the maximum "WS alone" signal and no acceptor signal as the donor only emission for sFLIM measurements (460-490 nm with this setup).</li>
	<li>Sample measurement
	<ol>
		<li>Place the stained plant section between the slide and the coverslip.</li>
		<li>Acquire the image with the same setting.</li>
		<li>Save the lsm file.</li>
		<li>Qualitatively associate a strong FRET event with a decrease in donor emission peak compared to the "WS alone sample" and an increase in the acceptor emission peak compared to the rhodamine sample.</li>
	</ol>
	</li>
</ol>
5. sFLIM measurements
NOTE: For the sFLIM setup, the system used is a time domain sFLIM setup as described previously10. An upright microscope and various time correlated single photon counting cards and confocal microscope manufacturers can be used and the protocol should be adapted accordingly.
<ol>
	<li>Switch the system to sFLIM mode.
	<ol>
		<li>Set the confocal microscope as described in 4.1.1 and 4.1.2.</li>
		<li>Switch to the Non-Descanned mode in the software to send fluorescent photons to the sFLIM detector.</li>
		<li>Set the sFLIM acquisition to the Enable mode to allow photon counting on the SPC150.</li>
		<li>Select 30 s time collection on SPC150.</li>
		<li>Check that the CFD is between 1 x 105 and 1 x 106. 
		CAUTION: Ensure that the number of photons measured on the TCSPC card is always less than 1% of the laser excitation frequency (in this setup, the laser excitation frequency is 80 Mhz and detection rate cannot exceed 800 kHz in any pixel to avoid a pile up effect11).</li>
	</ol>
	</li>
	<li>Place the "WS alone" plant section between the slide and the coverslip.
	<ol>
		<li>Set the software to Continuous mode to allow the laser scan.</li>
		<li>Choose the measurement area on the sample.</li>
		<li>Click Start on the SPC150.</li>
		<li>Save the sdt file.</li>
		<li>Repeat the process for at least 10 samples per condition.</li>
	</ol>
	</li>
	<li>Place the stained plant section between the slide and the coverslip.
	<ol>
		<li>Repeat steps 5.2.1 - 5.2.5.</li>
	</ol>
	</li>
</ol>
6. sFLIM analysis
<ol>
	<li>Select sFLIM data acquired file on "WS alone" and import them to the SPCImage software using File | Import.</li>
	<li>Select fit parameters.
	<ol>
		<li>From Option | Model, select Incomplete multi-exponentials: 12.5 ns.</li>
		<li>From Option | Preferences, select Calculate Instrumental Response automatically.</li>
		<li>From main panel&#39;s right menu, select the 2 exponential fit model: 
		<img alt="Equation 1" src="/files/ftp_upload/59925/59925equ01.jpg" /></li>
	</ol>
	</li>
	<li>For each channel, apply the fitting model and save the fit parameters to a spreadsheet (a1, a2, t1, t2).</li>
	<li>For comparison between channels and experiments, calculate the mean fluorescence lifetime in a spreadsheet (Tm): 
	<img alt="Equation 2" src="/files/ftp_upload/59925/59925equ02.jpg" /></li>
	<li>Calculate the mean lifetime for all samples (at least 10) in all channels.
	<ol>
		<li>Analyze Tm on donor channels (from channel 1 to 3: 460-490 nm) and determine the dedicated channel (Channel 2 corresponding to 467.5 - 480 nm) that shows the highest photon number and corresponds to lignin maximum emission. Values from channel 2 must be used in step 6.7.</li>
	</ol>
	</li>
	<li>Select sFLIM data acquired file on stained WS and import them to the SPCImage software.
	<ol>
		<li>Repeat steps 6.1-6.4.</li>
	</ol>
	</li>
	<li>Calculate the FRET efficiency EFRET for the donor in the previous selected channel WS Alone (TmD) and from stained plant sample (TmDA): 
	<img alt="Equation 3" src="/files/ftp_upload/59925/59925equ03.jpg" /></li>
	<li>Compare sFLIM and EFRET values between the WS and the sample.
	<ol>
		<li>Consider a positive EFRET associated with a homogeneous lifetime decrease between WS and the sample to be analyzed as a FRET event (see representative results and works by Spriet et al.12 and Terryn et al.10 for details).</li>
		<li>Consider a lifetime decrease distributed differently to be due to a mix of FRET and lignin compaction level and do not interpret as molecular interactions.</li>
		<li>Consider no lifetime modification as an absence of measurable FRET signal, but not as a lack of interaction with lignin</li>
	</ol>
	</li>
</ol>

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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+grand+challenge+of+cellulosic+biofuels.">The grand challenge of cellulosic biofuels.</a> Nature Biotechnology. 35, 912 (2017).</li><li>Clegg, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+resonance+energy+transfer.">Fluorescence resonance energy transfer.</a> Current Opinion in Biotechnology. 6, 103-110 (1995).</li><li>Ishikawa-Ankerhold, H. C., Ankerhold, R., Drummen, G. P. 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M., Arndt-Jovin, D. J., Kohen, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cell Structure and Function by Microspectrofluorometry. , 99-117 (1989).</li><li>Spriet, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlated+fluorescence+lifetime+and+spectral+measurements+in+living+cells.">Correlated fluorescence lifetime and spectral measurements in living cells.</a> Microscopy Research and Technique. 70 (2), 85-94 (2007).</li><li>Donaldson, L. A., Newman, R. 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The authors have nothing to disclose.

Correlating fluorescence lifetime and emission spectrum measurements allows combining advantages of both methods. Indeed, spectral measurements alone lack sensitivity and remain qualitative. On the other hand, fluorescence lifetime is the method of choice for traditional quantitative FRET measurements, but it was demonstrated that lignin autofluorescence could vary depending on lignin composition and environment16. Thus, lignin interactions with an acceptor or lignin structural changes cannot be discriminated since they both result in a lifetime decrease. As demonstrated, the developed method provides unambiguous, sensitive and quantitative measurement of interactions between lignin and acceptor molecules in plant sections. Even if the different steps of the method were rigorously optimized, caution must be particularly taken for the following points.
Regarding the samples, the quality of the plant section is critical to ensure in-focus imaging. The different steps for PEG embedding should be strictly followed. The final washing step is essential to ensure no interference from PEG remaining in the samples. Furthermore, buffer concentration and pH should be kept strictly identical for all measurements since any change can have a strong impact on fluorescence properties.
Lifetime measurement can also be delicate. Fluorescence lifetime of the donor can be unstable, for instance because of a high excitation. In such a case, tuning the laser power and the zoom can fix the issue. Another well-known source of artefacts in TCSPC measurements is photon pulse pile-up. Indeed, TCSPC cannot measure the speed of two photons emitted between the same two laser pulses. While plant samples are highly structured, the fluorescence is not homogeneous and can lead to a hidden pulse pileup effect. While the number of photons per second remains below the 1% excitation limit, it can be higher in some sample areas. To ensure no pileup experiments, measuring the donor fluorescence lifetime at a different photon flux can be considered. If the lifetime increases while the flux decreases, a pileup effect is altering measurements. Optimal photon flux is reached with donor&#39;s lifetime stability.
Regarding sFLIM decay curve analysis, we recommend relying on each individual curve fit to ensure that the model accurately describes measurements. If it is not the case, first ensure that none of the previously mentioned artefacts have corrupted the data. Then, step 2.1.4 provides the instrumental response function (IRF) of the system. If IRF presents some anomalies, optimize the optical path to minimize them. A measured IRF for fitting during step 6.2 can also be used. Finally, the fitting model&#39;s degrees of freedom can be increased to a three-exponential decay in step 6.2. However, the number of photons required for individual lifetime and proportion determination is high and it is thus recommended only using them to achieve a more stable mean lifetime from step 6.4. More exhaustive information on FLIM, its characterization18, sFLIM and its application12 notably to lignin10, can be found in the literature.
Although challenging, FRET measurement in plant tissues using native autofluorescence shows some advantages. In comparison to FRET measurements performed on living tissues in which interaction studies between biomolecules often requires genetic engineering to express intrinsic fluorescent markers, lignified plant tissues as those presented here, offer natural autofluorescence, and extrinsic partner fluorescent probes can be easily added, saving much time. However, depending on the analyzed plant species and on possible pre-treatments carried out, autofluorescence is likely to be modified, so that it needs to be carefully characterized and fluorophore used as probes might need to be adapted.
The sFLIM method must be applied to fluorescent probes lacking catalytic activity (PEG and dextran). In the frame of plant lignocellulose hydrolysis, interactions of enzymes in various biomass samples could be performed, possibly resulting in the determination of the impact of localization (cells and tissues) on the interaction strength. The next optimization step will be automation of the analysis step. Indeed, with enough analyzed data, a machine learning-based analysis procedure could be deployed for automated phenotype analysis, which would increase its amenability to fast enzyme-biomass efficiency screening. Furthermore, sFLIM does not require fixation of the sample and can be easily applied to dynamic enzyme-lignin interaction studies. Such a unique method is likely to guide enzyme engineering strategy and biomass pre-treatment to optimize lignocellulose valorization.

Hydrolyzing lignocellulose into monomeric sugars such as glucose requires enzymes. Their activity is known to be restrained by the establishment of non-specific interactions between enzymes and lignin1,2, the latter being a hydrophobic polymer and a major constituent of lignocellulose3. Thus, quantitatively measuring such interactions directly at the cellular scale is a challenge that must be addressed to optimize enzyme catalytic activity and lignocellulose pre-treatment4.
F&#246;rster (or fluorescence) resonance energy transfer (FRET) measurement is a method of choice to asses biomolecules interaction in situ. This non radiative energy transfer can occur between a donor and an acceptor fluorophore if they fulfill different conditions. The donor emission spectrum must overlap, at least partially, the acceptors excitation spectrum. The choice of fluorophores is thus crucial for such experiments. Then, the transfer efficiency decreases with the sixth power of their distance5 and only occurs if both molecules are in close vicinity (typically in the nanometer range). Other contingency can alter the transfer efficiency such as dipole-dipole orientation but are mitigated if fluorophores have flexibility.
FRET can be measured by different techniques and detailed descriptions can be found in literature6,7. In brief, the main methods rely on: i) sensitized emission in which the variation in acceptor emission fluorescence is followed and provides mainly qualitative results; ii) acceptor photobleaching in which the donor emission fluorescence is measured before and after acceptor photobleaching (In this case, more precise FRET estimates can be achieved but require strong laser power applied to fixed samples); and iii) lifetime measurement that decreases for the donor in the presence of the acceptor. This method requires specific instruments and allows precise and sensitive interaction measurements between fluorophores. Considering the FRET measurement between fluorescent probes and lignocellulose, the main difficulty is that lignocellulose is not a single well characterized fluorophore: it has a strong native and spectrally-wide autofluorescence, mainly originating from lignin, and dependent on biomass species8,9.
In this paper, we propose a new and original procedure adapted to sections of wood/plant samples. This sFLIM based method opens the way to quantitative FRET measurements between fluorescent probes and lignin in native lignocellulose samples.

<table><tbody><tr><td>Confocal microscope</td><td>ZEISS</td><td>LSM 710 NLO</td><td>for confocal imaging</td></tr><tr><td>fine brush</td><td></td><td></td><td>to collect sections</td></tr><tr><td>glass vials</td><td></td><td></td><td>for incubations</td></tr><tr><td>high precision microscope cover glasses 170+/-5&amp;micro;m n&amp;deg;1,5#</td><td>Marienfeld</td><td>0107032</td><td>saled by Dutscher s.a in France</td></tr><tr><td>Infra-red pulsed laser</td><td>COHERENT</td><td>CHAMELEON VISION</td><td>For sample excitation</td></tr><tr><td>Micropipette Research Plus monocanal 20-200&amp;micro;L</td><td>Eppendorf</td><td></td><td>with corresponding tips</td></tr><tr><td>Micropipette Research Plus monocanal 2-20&amp;micro;L</td><td>Eppendorf</td><td></td><td>with corresponding tips</td></tr><tr><td>Microscope slides ca. 76 x 26 mm ground edges frosted end</td><td>Thermo Fisher Scientific</td><td>LR45D</td><td></td></tr><tr><td>microtome blades disposable (pack of 50)</td><td>Agar Scientific</td><td>T5024</td><td>saled by Oxford Instruments in France</td></tr><tr><td>mPEG-Rhodamine, 10 kDa</td><td>Creative Peg Works</td><td>PSB-2263</td><td></td></tr><tr><td>mPEG-Rhodamine, 20 kDa</td><td>Creative Peg Works</td><td>PSB-22642</td><td></td></tr><tr><td>mPEG-Rhodamine, 5 kDa</td><td>Creative Peg Works</td><td>PSB-2264</td><td></td></tr><tr><td>nail polish</td><td></td><td></td><td>for mounting</td></tr><tr><td>pH meter five easy plus</td><td>Mettler toledo</td><td>FEP20</td><td>with electrode</td></tr><tr><td>poly (ethylene Glycol) average mol wt 1450</td><td>Merck (sigma-Aldrich)</td><td>P5402-1kg</td><td>for sample embedding</td></tr><tr><td>razor blades, single edges blades, stainless steel, box of 100</td><td>Agar Scientific</td><td>T586</td><td>for fragments preparation saled by Oxford Instruments in France</td></tr><tr><td>rotary microtome</td><td>Microm Microtech</td><td>HM 360</td><td></td></tr><tr><td>sFLim detector</td><td>BECKER-HICKL</td><td>SPC 150</td><td>for lifetime acquisition</td></tr><tr><td>sodium dihydrogen phosphate dihydrate NaH2PO4, 2H2O</td><td>Merck (sigma-Aldrich)</td><td>71500</td><td>for buffer solution</td></tr><tr><td>sodium phosphate dibasic dihydrate Na2HPO4,2H2O</td><td>Merck (sigma-Aldrich)</td><td>30435-1kg</td><td>for buffer solution, old reference, the new one is 71643-1kg</td></tr><tr><td>tetramethyl rhodamine isothiocyanate-dextran 10 k Da</td><td>Merck (sigma-Aldrich)</td><td>R8881-100mg</td><td></td></tr></tbody></table>

measuring interactions between fluorescent probes and lignin in plant sections by sflim based on native autofluorescence

In lignocellulosic biomass (LB), the activity of enzymes is limited by the appearance of non-specific interactions with lignin during the hydrolysis process, which maintains enzymes far from their substrate. Characterization of these complex interactions is thus a challenge in complex substrates such as LB. The method here measures molecular interactions between fluorophore-tagged molecules and native autofluorescent lignin, to be revealed by F&#246;rster resonance energy transfer (FRET). Contrary to FRET measurements in living cells using two exogenous fluorophores, FRET measurements in plants using lignin is not trivial due to its complex autofluorescence. We have developed an original acquisition and analysis pipeline with correlated observation of two complementary properties of fluorescence: fluorescence emission and lifetime. sFLIM (spectral and fluorescent lifetime imaging microscopy) provides the quantification of these interactions with high sensitivity, revealing different interaction levels between biomolecules and lignin.

To demonstrate the sFLIM capability to dissect molecular interactions between lignin and fluorescently-tagged molecules, we first used three different samples (Figure 3): native wheat straw (WS), native wheat straw incubated with PEG tagged with rhodamine (PR10), and native wheat straw with dextran tagged with rhodamine (DR10). PR10 is known to interact with lignin while DR10 is supposed to be inert13,14,15. sFLIM curves (Figure 3, top) show some modifications that can be achieved between the reference sample (WS) and two interaction cases (DR10 and PR10). Indeed, one can first easily notice the fluorescence increase in spectral regions corresponding to the rhodamine emission range. Careful observation of the three first channel photon decay curves also reveals a stronger inflection for DR10 than for PR10. After fitting the photon decay curves and calculating each channel mean fluorescence lifetime, the FRET signature becomes more obvious (Figure 3, bottom). Indeed, while the fluorescence lifetime alternatively increases and decreases along the fluorescence spectrum for the WS sample, a clear FRET signature is observed for both PR10 and DR10 with: 1) a constant lifetime value in the donor-only emission channel (3 first channels, in blue); and 2) an increasing fluorescence lifetime in the spectral channel corresponding to an increasing contribution of the high lifetime donor fluorophore.
Once unambiguous FRET has been determined, comparison of lignin fluorescence lifetime (channel 2) allows quantification of the lifetime decrease between WS (0.47 ns), DR10 (0.42 ns) and PR10 (0.36 ns) and thus, reveals molecular interactions between lignin and both PR10 and DR10 with a stronger affinity for PR10.
To illustrate the relevance of this method to quantify different interaction levels, we choose three other samples, mimicking enzyme accessibility upon treated plant samples: acid-treated WS (AWS), in combination with PR of two contrasted molecular weights of 5 kDa and 20 kDa (PR5 and PR20). After careful inspection of sFLIM signatures, lignin fluorescence lifetime is extracted (Figure 4). As previously stated, lignin fluorescence lifetime can be altered by its environment16,17. After acid treatment, the fluorescence lifetime measures in AWS (0.28 ns) becomes lower than previously measured for WS (0.47 ns), which confirms the requirement of the sFLIM procedure for unambiguous interpretation of lifetime decrease and the need for negative controls for each tested condition. As expected, a strong lifetime decrease is observed when adding PR to the AWS while it interacts with the lignin. Furthermore, the interaction is stronger with PR5 (0.14 ns) compared to PR20 (0.16 ns), which is consistent with their hydrodynamic radius measurement (2.3 nm and 4.8 nm for PR5 and PR20, respectively), inducing different steric constraints and thus higher accessibility of PR5 to lignin.
Both experiments demonstrate the relevancy of this method to finely assess lignin interactions with enzymes, depending on their size and on plant samples pre-treatment.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59925/59925fig01.jpg" /> 
Figure 1: Different preparation steps of the samples. Wheat straw (WS) (A) is first reduced in size (B) to be embedded in PEG medium (C). The block is cut using a microtome equipped with disposable blades (D). After washing, resulting sections (E) are placed for incubation in PEG or dextran tagged-rhodamine solution (F). Labelled sections are mounted for sFLIM measurements (G). Scale bars are 2 cm (A), 1 cm (B and C), 200 &#181;m (E and G). <a href="https://www.jove.com/files/ftp_upload/59925/59925fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59925/59925fig02.jpg" /> 
Figure 2: Complete workflow of spectral FRET- based interactions measurements. The figure presents the setup combining spectral fluorescence intensity and lifetime measurements. Spectral fluorescence images are acquired with a confocal microscope, and sequentially fluorescence lifetimes for each spectral range are measured with the sFLIM detector. Analysis of photon decay curves allows accurate determination of interactions between the sample and molecules of interest. Calibrations have to be processed to avoid artefacts. First, the sFLIM detector needs to be spectrally calibrated and its instrumental response function has to be checked. Second, the complex autofluorescence signal has to be precisely calibrated for each sample to determine the fluorescence lifetime in each channel before addition of an acceptor molecule. <a href="https://www.jove.com/files/ftp_upload/59925/59925fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59925/59925fig03.jpg" /> 
Figure 3: Representative sFLIM measurements. sFLIM curves (top panel) were acquired on native wheat straw (WS), WS incubated with PEG tagged with rhodamine (WS+PEG) and native wheat straw with dextran tagged with rhodamine (WS+DEX). For each sample, sFLIM curves were fitted with a bi-exponential decay model and the mean fluorescence lifetime was calculated for each channel (bottom panel). WS+PEG and WS+DEX samples present a decrease in the fluorescence lifetime in the channel corresponding to autofluorescence only (three first bars) associated with a lifetime increase in channel corresponding to a mixed emission of autofluorescence and rhodamine. This behavior is characteristic of a FRET event between lignin and rhodamine-tagged molecules. <a href="https://www.jove.com/files/ftp_upload/59925/59925fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59925/59925fig04.jpg" /> 
Figure 4: Fluorescence lifetime analysis of the channel corresponding to lignin autofluorescence. After validating a FRET event based on sFLIM signature, mean fluorescence lifetime was measured on acid-treated WS (AWS), in combination with PR5 or PR20 (5 kDa and 20 kDa, respectively). While both PR samples present a lifetime decrease, the smaller is characterized by a stronger lifetime reduction, revealing a stronger molecular interaction with lignin. The method is thus sensitive enough to discriminate between both (mean value and standard error are represented, n&#62;10 per condition). <a href="https://www.jove.com/files/ftp_upload/59925/59925fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Measuring Interactions between Fluorescent Probes and Lignin in Plant Sections by sFLIM Based on Native Autofluorescence

This protocol describes an original setup combining spectral and fluorescence lifetime measurements to evaluate F&#246;rster resonance energy transfer (FRET) between rhodamine-based fluorescent probes and lignin polymer directly in thick plant sections.

In lignocellulosic biomass (LB), the activity of enzymes is limited by the appearance of non-specific interactions with lignin during the hydrolysis ...

measuring-interactions-between-fluorescent-probes-lignin-plant

Research

JoVE Journal

Biochemistry

5.5K Views.  Université de Reims Champagne-Ardenne. In lignocellulosebiomassa wordt de activiteit van enzymen die polystochariden analyseren beperkt door het bestaan van niet-specifieke interacties met lignine. FRET-analyse is een relevante benadering voor evolutie op nanoschaal. Het verkrijgen van alle secties na kan een uitdaging zijn, afhankelijk van de biomassa.Neem de tijd om de sectie met de microtome te doen en het uitvoeren van zachte wassen. Het samenslepen van fluorescerende levensduur- en spectrummetingen maakt een kwantitati...

Video: Measuring Interactions between Fluorescent Probes and Lignin in Plant Sections by sFLIM Based on Native Autofluorescence

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The analysis does not usually require extensive sample preparation; structural and chemical information can be obtained without labeling. However, each Raman image contains thousands of spectra; this raises difficulties when extracting hidden information, especially for components with similar chemical structures. This work introduces a multivariate analysis to address this issue. The protocol establishes a general method to visualize the main components, including lignin, cellulose, and hemicellulose within the plant cell wall. In this protocol, procedures for sample preparation, spectral acquisition, and data processing are described. It is highly dependent upon operator skill at sample preparation and data analysis. By using this approach, a Raman investigation can be performed by a non-specialist user to acquire high-quality data and meaningful results for plant cell wall analysis.

This protocol aims to present a general method to visualize lignin, cellulose, and hemicellulose in plant cell walls using Raman imaging and multivariate analysis.

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The ...

Visualizing Lignification Dynamics in Plants with Click Chemistry: Dual Labeling is BLISS!

Lignin is one of the most prevalent biopolymers on the planet and a major component of lignocellulosic biomass. This phenolic polymer plays a vital structural and protective role in the development and life of higher plants. Although the intricate mechanisms regulating lignification processes in vivo strongly impact the industrial valorization of many plant-derived products, the scientific community still has a long way to go to decipher them. In a simple three-step workflow, the dual labeling protocol presented herein enables bioimaging studies of actively lignifying zones of plant tissues. The first step consists in the metabolic incorporation of two independent chemical reporters, surrogates of the two native monolignols that give rise to lignin H- and G-units. After incorporation into growing lignin polymers, each reporter is then specifically labeled with its own fluorescent probe via a sequential combination of bioorthogonal SPAAC/CuAAC click reactions. Combined with lignin autofluorescence, this approach leads to the generation of three-color localization maps of lignin within plant cell walls by confocal fluorescence microscopy and provides precise spatial information on the presence or absence of active lignification machinery at the scale of plant tissues, cells and different cell wall layers.

BLISS, a dual labeling protocol for studying lignification dynamics, was developed. Using synthetic monolignol reporters and a sequential combination of SPAAC and CuAAC bioorthogonal click reactions, this methodology paves the way to in-depth analysis of the factors that regulate the biogenesis of lignins in planta.

Lignin is one of the most prevalent biopolymers on the planet and a major component of lignocellulosic biomass. This phenolic polymer plays a vital ...

Chemistry

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

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

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

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

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

Biology

Label-free in situ Imaging of Lignification in Plant Cell Walls

Meeting growing energy demands safely and efficiently is a pressing global challenge. Therefore, research into biofuels production that seeks to find cost-effective and sustainable solutions has become a topical and critical task. Lignocellulosic biomass is poised to become the primary source of biomass for the conversion to liquid biofuels1-6. However, the recalcitrance of these plant cell wall materials to cost-effective and efficient degradation presents a major impediment for their use in the production of biofuels and chemicals4. In particular, lignin, a complex and irregular poly-phenylpropanoid heteropolymer, becomes problematic to the postharvest deconstruction of lignocellulosic biomass. For example in biomass conversion for biofuels, it inhibits saccharification in processes aimed at producing simple sugars for fermentation7. The effective use of plant biomass for industrial purposes is in fact largely dependent on the extent to which the plant cell wall is lignified. The removal of lignin is a costly and limiting factor8 and lignin has therefore become a key plant breeding and genetic engineering target in order to improve cell wall conversion.
Analytical tools that permit the accurate rapid characterization of lignification of plant cell walls become increasingly important for evaluating a large number of breeding populations. Extractive procedures for the isolation of native components such as lignin are inevitably destructive, bringing about significant chemical and structural modifications9-11. Analytical chemical in situ methods are thus invaluable tools for the compositional and structural characterization of lignocellulosic materials. Raman microscopy is a technique that relies on inelastic or Raman scattering of monochromatic light, like that from a laser, where the shift in energy of the laser photons is related to molecular vibrations and presents an intrinsic label-free molecular "fingerprint" of the sample. Raman microscopy can afford non-destructive and comparatively inexpensive measurements with minimal sample preparation, giving insights into chemical composition and molecular structure in a close to native state. Chemical imaging by confocal Raman microscopy has been previously used for the visualization of the spatial distribution of cellulose and lignin in wood cell walls12-14. Based on these earlier results, we have recently adopted this method to compare lignification in wild type and lignin-deficient transgenic Populus trichocarpa (black cottonwood) stem wood15. Analyzing the lignin Raman bands16,17 in the spectral region between 1,600 and 1,700 cm-1, lignin signal intensity and localization were mapped in situ. Our approach visualized differences in lignin content, localization, and chemical composition. Most recently, we demonstrated Raman imaging of cell wall polymers in Arabidopsis thaliana with lateral resolution that is sub-&mu;m18. Here, this method is presented affording visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species without staining or labeling of the tissues.

Label-free in situ Imaging of Lignification in Plant Cell Walls

A method based on confocal Raman microscopy is presented that affords label-free visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species.

Meeting growing energy demands safely and efficiently is a pressing global challenge. Therefore, research into biofuels production that seeks to find ...

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many parameters that efficiently measure photosynthesis. Leaf reflectance analysis provides several vegetation indices in ecology and agriculture, including the photochemical reflectance index (PRI), which can be used as an indicator of thermal energy dissipation during photosynthesis because it correlates with non-photochemical quenching (NPQ). However, since NPQ is a composite parameter, its validation is required to understand the nature of the PRI parameter. To obtain physiological evidence for evaluation of the PRI parameter, we simultaneously measured chlorophyll fluorescence and leaf reflectance in xanthophyll cycle defective mutant (npq1) and wild-type Arabidopsis plants. Additionally, the qZ parameter, which likely reflects the xanthophyll cycle, was extracted from the results of chlorophyll fluorescence analysis by monitoring relaxation kinetics of NPQ after switching the light off. These simultaneous measurements were carried out using a pulse-amplitude modulation (PAM) chlorophyll fluorometer and a spectral radiometer. The fiber probes from both instruments were positioned close to each other to detect signals from the same leaf position. An external light source was used to activate photosynthesis, and the measuring lights and saturated light were provided from the PAM instrument. This experimental system enabled us to monitor light-dependent PRI in the intact plant and revealed that light-dependent changes in PRI differ significantly between the wild type and npq1 mutant. Furthermore, PRI was strongly correlated with qZ, meaning that qZ reflects the xanthophyll cycle. Together, these measurements demonstrated that simultaneous measurement of leaf reflectance and chlorophyll fluorescence is a valid approach for parameter evaluation.

We describe a new technical approach to study photosynthetic responses in higher plants involving simultaneous measurements of chlorophyll a fluorescence and leaf reflectance using a PAM and a spectral radiometer for the detection of signals from the same leaf area in Arabidopsis.

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many ...

Bioengineering

Protein-protein Interactions Visualized by Bimolecular Fluorescence Complementation in Tobacco Protoplasts and Leaves

Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular fluorescence complementation (BiFC) is an in vivo method to monitor such interactions in plant cells. In the presented protocol the investigated candidate proteins are fused to complementary halves of fluorescent proteins and the respective constructs are introduced into plant cells via agrobacterium-mediated transformation. Subsequently, the proteins are transiently expressed in tobacco leaves and the restored fluorescent signals can be detected with a confocal laser scanning microscope in the intact cells. This allows not only visualization of the interaction itself, but also the subcellular localization of the protein complexes can be determined. For this purpose, marker genes containing a fluorescent tag can be coexpressed along with the BiFC constructs, thus visualizing cellular structures such as the endoplasmic reticulum, mitochondria, the Golgi apparatus or the plasma membrane. The fluorescent signal can be monitored either directly in epidermal leaf cells or in single protoplasts, which can be easily isolated from the transformed tobacco leaves. BiFC is ideally suited to study protein-protein interactions in their natural surroundings within the living cell. However, it has to be considered that the expression has to be driven by strong promoters and that the interaction partners are modified due to fusion of the relatively large fluorescence tags, which might interfere with the interaction mechanism. Nevertheless, BiFC is an excellent complementary approach to other commonly applied methods investigating protein-protein interactions, such as coimmunoprecipitation, in vitro pull-down assays or yeast-two-hybrid experiments.

Formation of protein complexes in vivo can be visualized by bimolecular fluorescence complementation. Interaction partners are fused to complementary parts of fluorescent tags and transiently expressed in tobacco leaves, resulting in a reconstituted fluorescent signal upon close proximity of the two proteins.

Many proteins interact transiently with other proteins or are integrated into multi-protein complexes to perform their biological function. Bimolecular ...

Multicellular Spheroid Formation for FLIM-Based Metabolic and Hypoxia Analysis

Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids

Multicellular tumor spheroids are a popular 3D tissue microaggregate&#160;model for reproducing tumor microenvironment, testing and optimizing drug therapies&#160;and using bio- and nanosensors in a 3D context. Their ease of production, predictable size, growth, and observed nutrient and metabolite gradients are important to recapitulate the 3D niche-like&#160;cell microenvironment. However, spheroid heterogeneity and variability of their production methods can influence overall cell metabolism, viability, and drug response. This makes it difficult to choose the most appropriate&#160;methodology, considering the requirements in size, variability, needs of biofabrication, and use as in vitro 3D tissue models in stem and cancer cell biology. In particular, spheroid production can influence their compatibility with quantitative live microscopies, such as optical metabolic imaging, fluorescence lifetime imaging microscopy (FLIM), monitoring of spheroid&#160;hypoxia with nanosensors, or viability. Here, a number of conventional spheroid formation protocols are presented, highlighting their compatibility with the live widefield, confocal, and two-photon microscopies. The follow-up imaging to analysis&#160;pipeline with multiplexed autofluorescence FLIM and, using various types of cancer and stem cell spheroids, is also presented.

Author Spotlight: Standardizing Spheroid Formation Methods for Metabolic and Oxygenation Analysis Using Fluorescence Lifetime Imaging Microscopy

Here, we describe different multicellular spheroid formation methods to perform follow-up multi-parameter live cell microscopy. Using fluorescence lifetime imaging microscopy (FLIM), cellular autofluorescence, staining dyes, and nanoparticles, the approach for analysis of cell metabolism, hypoxia, and cell death in live three-dimensional (3D) cancer and stem cell-derived spheroids is demonstrated.

Multicellular tumor spheroids are a popular 3D tissue microaggregate&#160;model for reproducing tumor microenvironment, testing and optimizing drug ...

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in &#181;Manager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in &#181;Manager and data analysis is undertaken in FLIMfit.

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions ...

Live-cell Imaging of Fungal Cells to Investigate Modes of Entry and Subcellular Localization of Antifungal Plant Defensins

Small cysteine-rich defensins are one of the largest groups of host defense peptides present in all plants. Many plant defensins exhibit potent in vitro antifungal activity against a broad-spectrum of fungal pathogens and therefore have the potential to be used as antifungal agents in transgenic crops. In order to harness the full potential of plant defensins for diseases control, it is crucial to elucidate their mechanisms of action (MOA). With the advent of advanced microscopy techniques, live-cell imaging has become a powerful tool for understanding the dynamics of the antifungal MOA of plant defensins. Here, a confocal microscopy based live-cell imaging method is described using two fluorescently labeled plant defensins (MtDef4 and MtDef5) in combination with vital fluorescent dyes. This technique enables real-time visualization and analysis of the dynamic events of MtDef4 and MtDef5 internalization into fungal cells. Importantly, this assay generates a wealth of information including internalization kinetics, mode of entry and subcellular localization of these peptides. Along with other cell biological tools, these methods have provided critical insights into the dynamics and complexity of the MOA of these peptides. These tools can also be used to compare the MOA of these peptides against different fungi.

Plant defensins play an important role in plant defense against pathogens. For effective use of these antifungal peptides as antifungal agents, understanding their modes of action (MOA) is critical. Here, a live-cell imaging method is described to study critical aspects of the MOA of these peptides.

Small cysteine-rich defensins are one of the largest groups of host defense peptides present in all plants. Many plant defensins exhibit potent in vitro ...

FLIM-FRET Measurements of Protein-Protein Interactions in Live Bacteria.

Protein-protein interactions (PPIs) control various key processes in cells. Fluorescence lifetime imaging microscopy (FLIM) combined with F&#246;rster resonance energy transfer (FRET) provide accurate information about PPIs in live cells. FLIM-FRET relies on measuring the fluorescence lifetime decay of a FRET donor at each pixel of the FLIM image, providing quantitative and accurate information about PPIs and their spatial cellular organizations. We propose here a detailed protocol for FLIM-FRET measurements that we applied to monitor PPIs in live Pseudomonas aeruginosa in the particular case of two interacting proteins expressed with highly different copy numbers to demonstrate the quality and robustness of the technique at revealing critical features of PPIs. This protocol describes in detail all the necessary steps for PPI characterization - starting from bacterial mutant constructions up to the final analysis using recently developed tools providing advanced visualization possibilities for a straightforward interpretation of complex FLIM-FRET data.

We describe here a protocol to characterize protein-protein interactions between two highly-differently expressed proteins in live Pseudomonas aeruginosa using FLIM-FRET measurements. The protocol includes bacteria strain constructions, bacteria immobilization, imaging and post-imaging data analysis routines.

Protein-protein interactions (PPIs) control various key processes in cells. Fluorescence lifetime imaging microscopy (FLIM) combined with F&#246;rster ...

Measuring Interactions between Fluorescent Probes and Lignin in Plant Sections by sFLIM Based on Native Autofluorescence

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