Wir danken dem chinesischen Ministerium für Wissenschaft und Technologie (2016YDF0600803) für die finanzielle Unterstützung.

1. Probenvorbereitung <ol><li> Schneiden Sie einen kleinen Gewebeblock (ca. 3 mm x 3 mm x 5 mm) aus der Pflanzenprobe ( zB Pappelstiel). </li><li> Tauchen Sie das Gewebe 30 Minuten lang in kochendes deionisiertes Wasser ein. Sofort 30 Minuten bei Raumtemperatur (RT) auf deionisiertes Wasser übertragen. Wiederholen Sie diesen Schritt, bis das Gewebe auf den Boden des Behälters sinkt, was darauf hinweist, dass die Luft im Gewebe entfernt wurde und dass sich das Gewebe erweicht hat. Hinweis: Für die Proben, die vor diesem Schritt nach unten sinken, wiederholen Sie diesen Zyklus im Allgemeinen 3-5 mal. </li><li> Vorbereitung von 20, 50, 70, 90% (v / v) Aliquoten von Polyethylenglykol (PEG) in deionisiertem Wasser sowie reinem PEG und halten die Lösungen bei 65 ° C. </li><li> Inkubieren Sie das Gewebe in einer Reihe von abgestuften PEG-Bädern, um das Wasser zu verdrängen und lassen Sie das PEG infiltrieren. Hinweis: In der Regel wird PEG mit einem Molekulargewicht 2.000 (PEG2000) zum Einbetten verwendet. <ol><li> Verarbeiten Sie das Gewebe mit gPEG in einem Trockenofen wie folgt: (a) 20% PEG für 1 h, (b) 50% PEG für 1,5 h, (c) 70% PEG für 2 h, (d) 90% PEG für 2 h und (E) 100% PEG für 10 h. </li></ol></li><li> Vorwärmen einer Kassette bei 65 ° C in einem Ofen. Gießen Sie den PEG mit dem Block in die Kassette und legen Sie den Gewebeblock in eine gewünschte Position mit vorgewärmten Pinzetten oder Nadeln. </li><li> Langsam die Kassette abkühlen und das Gewebe bei RT bis zum Gebrauch aufbewahren. </li><li> Den PEG-Block, der das Zielgewebe enthält, in einen kleinen Block (ca. 1 cm x 1 cm x 2 cm) mit einer scharfen Rasierklinge zerlegen und auf das Mikrotom aufbringen. </li><li> Schneiden Sie dünne Abschnitte aus dem PEG-Block (typischerweise 3-10 μm). </li><li> Spülen Sie den Abschnitt mit deionisiertem Wasser in einem Uhrglas 10 mal, um das PEG aus dem Gewebe zu entfernen. </li><li> Tauchen Sie die Sektionen mit Toluol / Ethanol (2: 1, v / v) für 6 h ein, um die Extrakte zu entfernen. Die Reaktionsflüssigkeit wird durch Mischen von 65 ml entionisiertem Wasser, 0,5 ml Essigsäure und 0,6 g Natriumchlorid zubereitetRitus in einem Becher Einen Teil und 3 ml Reaktionsflüssigkeit in eine 5 ml Durchstechflasche geben. Schrauben Sie auf die Oberseite der Durchstechflasche. </li><li> Die Durchstechflasche im Wasserbad bei 75 ° C für 2 h erhitzen, um das Lignin des Gewebes zu entfernen. Spülen Sie den Abschnitt mit deionisiertem Wasser in einem Uhrglas 10 mal aus. </li><li> Den unbehandelten / delignenten Abschnitt auf einen Glasmikroskop-Objektträger übertragen. Füllen Sie den Abschnitt sorgfältig mit Pinsel oder Nadeln. Das extra entionisierte Wasser mit Seidenpapier entfernen. </li><li> Tauchen Sie den Abschnitt in D 2 O ein. Bedecken Sie die Probe mit einem Glasdeckel. Den Deckel mit Nagellack abdichten, um die Verdampfung des D 2 O zu verhindern. </li></ol> 2. Spektrale Erfassung <ol><li> Öffnen Sie die Instrumentensoftware des konfokalen Raman-Mikroskops. Schalten Sie den Laser ein (Wellenlänge = 532 nm), fokussieren Sie auf die Oberfläche des kristallinen Siliziums mit einem 100fachen Mikroskopobjektiv und klicken Sie auf die Kalibriertaste, um das Instrument zu kalibrieren. </li><li> Gerät einschaltenZum optischen Mikroskop-Modus und schalten Sie die Mikroskop-Lampe ein. Montieren Sie den Mikroskop-Objektträger auf der Bühne, wobei der Deckel dem Objektiv zugewandt ist. </li><li> Sehen Sie die Probe mit einem 20X-Mikroskop-Objektiv an und suchen Sie den interessanten Bereich. Das Tauchöl auf das Deckglas auftragen und auf das Immersionsmikroskopobjektiv wechseln (60X, numerische Blende NA = 1.35). Konzentriere dich auf die Oberfläche der Probe. </li><li> Schalten Sie das Gerät in den Raman-Testmodus und schalten Sie die Mikroskoplampe aus. </li><li> Wählen Sie einen Mapping-Bereich mit einem rechteckigen Werkzeug. Ändern Sie die Schrittgröße, um die Anzahl der erhaltenen Spektren zu bestimmen. Hinweis: Beachten Sie die Schrittweite (meist größer als der durch die numerische Apertur des Objektivs berechnete Spotdurchmesser, theoretisch 1,22λ / NA). Größen darunter führen zu Überabtastungen. </li><li> Stellen Sie die optimalen Spektralparameter ein, um das beste Signal-Rausch-Verhältnis (SNR) und die spektrale Qualität in einer geeigneten Erfassungszeit (im Allgemeinen &lt;8 h) zu erhaltenNding auf die Eignung der Probe. Im Allgemeinen werden die Bildgebungsparameter in die Instrumentensoftware wie folgt eingegeben: Laser (532 nm), Filter (100%), Loch (300), Schlitz (100), Spektrometer (1.840 cm -1 ), Rillen (1200t), Objektiv ( 60X Öl) und Erfassungszeit (2 s). </li><li> Speichern Sie die Spektraldaten vor der Datenverarbeitung und konvertieren sie in ein universelles Format ( zB TXT-Dateien). </li></ol> 3. Datenanalyse <ol><li> Laden Sie die Spektraldaten (TXT-Dateien) in die Datenanalyse-Software ( zB Matlab). Wenden Sie eine Rauschunterdrückung auf den Datensatz an, um das SNR zu verbessern ( zB der Savitzsky-Golay-Algorithmus oder der Wavelet-Algorithmus) </li><li> Verwenden Sie unbehandelte Proben, um die Bilder von Lignin und Polysacchariden herzustellen. Für die Lignin-Bildgebung betrachten wir den spektralen Peak um 1.600 cm -1 aufgrund der aromatischen Ring-symmetrischen Streckschwingungen. Für die Polysaccharid-Bildgebung (einschließlich Cellulose und Hemicellulose) verwenden Sie den Spektralpeak aRund 2,889 cm -1 wegen der CH- und CH2-Strecken. </li><li> Verwenden Sie delignifizierte Proben, um die Bilder von Cellulose und Hemicellulose zu erzeugen. Führen Sie eine selbstmodellierende Kurvenauflösung (SMCR) auf den erworbenen Spektren durch, um die Spektren von Cellulose und Hemicellulose zu unterscheiden und ihre Verteilungen abzubilden. </li><li> Führen Sie die Hauptkomponentenanalyse (PCA) und die Clusteranalyse auf die erfassten Daten durch, um die Raman-Spektren von verschiedenen Zellwandschichten zu unterscheiden. </li></ol>

<ol>
	<li>Gonzalo, G. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacterial+Enzymes+Involved+in+Lignin+Degradation.">Bacterial Enzymes Involved in Lignin Degradation.</a> J. Biotechnol. 236, 110-119 (2016).</li><li>Rosatella, A. A., Afonso, C. A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chapter+2.">Chapter 2.</a> Ionic Liquids in the Biorefinery Concept: Challenges and Perspectives. , 38-64 (2016).</li><li>Sun, L., 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=Understanding+tissue+specific+compositions+of+bioenergy+feedstocks+Through+hyperspectral+Raman+imaging.">Understanding tissue specific compositions of bioenergy feedstocks Through hyperspectral Raman imaging.</a> Bio. 108 (2), 286-295 (2009).</li><li>Tolstik, T., 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=Classification+and+prediction+of+HCC+tissues+by+Raman+imaging+with+identification+of+fatty+acids+as+potential+lipid+biomarkers.">Classification and prediction of HCC tissues by Raman imaging with identification of fatty acids as potential lipid biomarkers.</a> J. Cancer. Res. Clin. Oncol. 141 (3), 407-418 (2015).</li><li>Schrader, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Infrared and Raman spectroscopy: methods and applications. , (2008).</li><li>Gierlinger, N., 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=Imaging+of+plant+cell+walls+by+confocal+Raman+microscopy.">Imaging of plant cell walls by confocal Raman microscopy.</a> Nat. Protoc. 7 (9), 1694-1708 (2012).</li><li>Luca, A. C. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Online+fluorescence+suppression+in+modulated+Raman+spectroscopy.">Online fluorescence suppression in modulated Raman spectroscopy.</a> Anal. Chem. 82 (2), 738-745 (2009).</li><li>Schl&#252;cker, S., 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=Raman+microspectroscopy:+a+comparison+of+point,+line,+and+wide-field+imaging+methodologies.">Raman microspectroscopy: a comparison of point, line, and wide-field imaging methodologies.</a> Anal. Chem. 75 (16), 4312-4318 (2003).</li><li>Cooper, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemometric+analysis+of+Raman+spectroscopic+data+for+process+control+applications.">Chemometric analysis of Raman spectroscopic data for process control applications.</a> Chemometr. Intell. Lab. Syst. 46 (2), 231-247 (1999).</li><li>Cheng, H. J., Hsiau, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+study+of+granular+agglomeration+mechanism.">The study of granular agglomeration mechanism.</a> Powder Technol. 199 (3), 272-283 (2010).</li><li>Zhang, X., 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=Method+for+removing+spectral+contaminants+to+improve+analysis+of+Raman+imaging+data.">Method for removing spectral contaminants to improve analysis of Raman imaging data.</a> Sci. Rep. 6, 39891 (2016).</li><li>Shinzawa, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multivariate+data+analysis+for+Raman+spectroscopic+imaging.">Multivariate data analysis for Raman spectroscopic imaging.</a> J. Raman Spectrosc. 40 (12), 1720-1725 (2009).</li><li>Lawton, W. H., Sylvestre, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self+modeling+curve+resolution.">Self modeling curve resolution.</a> Technometrics. 13, 617-633 (1971).</li><li>Zhang, X., 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=Method+for+automatically+identifying+spectra+of+different+wood+cell+wall+layers+in+Raman+imaging+data+set.">Method for automatically identifying spectra of different wood cell wall layers in Raman imaging data set.</a> Anal. Chem. 87 (2), 1344-1350 (2015).</li><li>Kudelski, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analytical+application+of+Raman+spectroscopy.">Analytical application of Raman spectroscopy.</a> Talanta. 76 (1), 1-8 (2008).</li></ol>

Die Autoren haben nichts zu offenbaren.

Die Pflanzenzellwand ist ein Verbundwerkstoff, der in mehrere Schichten organisiert ist, darunter Zell-Ecke (CC), Sekundärwand (SW, mit den S1-, S2- und S3-Schichten) und zusammengesetzte mittlere Lamellen (CML, mittlere Lamellen plus die angrenzende Primär- Wand), was es schwierig macht, bei der Probenvorbereitung eine ebene Oberfläche zu erhalten. So müssen Pflanzenproben, vor allem Gras, die eine kompliziertere Struktur als Holz haben, oft verfestigt werden, um eine Feinschnittung zu ermöglichen. PEG ist eine id...

Plant biomass is the most abundant renewable resource on Earth; is mainly composed of lignin, cellulose, and hemicellulose; and is considered an attractive source of bioenergy and bio-based chemicals1. Unfortunately, it can resist degradation and confer hydrolytic stability or structural robustness to the plant cell wall. Such resistance is attributable to the accessible surface area, biomass particle size, degree of polymerization, cellulose crystallinity, and protective lignin2. A comprehensive understanding of the structural and chemical nature of the plant cell wall is thus significant from the viewpoint of plant biology and chemistry, as well as from that of commercial utilization. Commonly used wet chemistry analyses, such as chromatography, mass spectrometry, and nuclear magnetic resonance spectroscopy, only provide average compositional data of the measured sample. Furthermore, these methods are invasive and destroy the original structure of the plant tissue3. 
The Raman imaging technique is a powerful tool for the nondestructive visualization of spatially resolved chemical information4. It uses a laser light to cause inelastic scattering with a photon and relies on changes in polarizability arising from the molecular vibrations. In this case, water causes weak Raman scattering, which makes this approach suitable for in situ investigations of biological samples5. The application of the Raman imaging technique to the plant cell wall can elucidate the structure and composition of plant cell walls in their native state, with the resolution on the scale of the single cell and even of the cell wall layers6. A typical Raman imaging analysis of a plant cell wall generally consists of three steps: 1) sample preparation, 2) spectral acquisition, and 3) data processing. 
Although one of the major advantages of Raman imaging is the ability to achieve label-free and non-destructive spectra with minimal sample preparation, physical sample sectioning is still necessary to expose the surface of interest. This process should be performed carefully to obtain a flat surface, since the technique depends on maintaining optical focus7. Spectral acquisition requires a balance between image quality and extensive acquisition times8. Data processing aims to effectively extract the chemical information from the image data, especially for the components with similar chemical structures, such as cellulose and hemicellulose. Due to the strong spectral overlap, the exact spectra are difficult to discern. In this case, multivariate analysis is a straightforward approach to effectively uncover the hiding structural and chemical information9. This work presents a general protocol describing the use of Raman imaging to visualize the main components in plant cell walls, including lignin, cellulose, and hemicellulose.

<table><tbody><tr><td>Microtome</td><td>Thermo Scientific</td><td>Microm HM430</td><td></td></tr><tr><td>Confocal Raman microscope</td><td>Horiba Jobin Yvon</td><td>Xplora</td><td></td></tr><tr><td>Oven</td><td>Shanghai ZHICHENG</td><td>ZXFD-A5040</td><td></td></tr></tbody></table>

combining raman imaging and multivariate analysis to visualize lignin, cellulose, and hemicellulose in the plant cell wall

Die Anwendung der Raman-Bildgebung zur Pflanzen-Biomasse nimmt zu, da sie räumliche und kompositorische Informationen über wässrige Lösungen anbieten kann. Die Analyse erfordert in der Regel keine umfangreiche Probenvorbereitung; Strukturelle und chemische Informationen können ohne Etikettierung erhalten werden. Jedoch enthält jedes Raman-Bild Tausende von Spektren; Dies wirft Schwierigkeiten auf, wenn man versteckte Informationen extrahiert, vor allem für Komponenten mit ähnlichen chemischen Strukturen. Diese Arbeit führt eine multivariate Analyse ein, um dieses Problem zu lösen. Das Protokoll stellt eine allgemeine Methode zur Visualisierung der Hauptkomponenten, einschließlich Lignin, Cellulose und Hemicellulose, innerhalb der Pflanzenzellwand dar. In diesem Protokoll werden Verfahren zur Probenvorbereitung, spektrale Erfassung und Datenverarbeitung beschrieben. Es ist sehr abhängig von der Bedienerfertigkeit bei der Probenvorbereitung und der Datenanalyse. Mit diesem Ansatz kann eine Raman-Untersuchung von einem nicht-spezialisierten Benutzer durchgeführt werden, um hig zu erwerbenH-Qualitätsdaten und aussagekräftige Ergebnisse für die Pflanzenzellwandanalyse.

Abbildung 1 zeigt einen Überblick über ein typisches Mikro-Raman-System für die Raman-Bildgebung einer Pflanzenzellwand. Als Beispiel haben die ursprünglichen Raman-Spektren der Pappel ( Populus nigra L.) signifikante Baseline-Drifts und Spikes (Abbildung 2a ). Nach Durchführung des automatischen Vorverarbeitungsverfahrens für den Raman-Bilddatensatz (APRI) werden diese beiden spektralen Verunreinigungen erfolgrei...

Watch this Scientific Journal Video about Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall at JoVE.com

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

Dieses Protokoll zielt darauf ab, ein allgemeines Verfahren zur Visualisierung von Lignin, Cellulose und Hemicellulose in Pflanzenzellwänden unter Verwendung von Raman-Bildgebung und multivariater Analyse vorzustellen.

Kombinieren von Raman Imaging und multivariater Analyse zur Visualisierung von Lignin, Cellulose und Hemicellulose in der Pflanzenzellwand

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

combining-raman-imaging-multivariate-analysis-to-visualize-lignin

Universidad de Cartagena UDEC

Research

JoVE Journal

Biochemistry

11.8K Aufrufe.  Beijing Forestry University.  Dieses Protokoll zielt darauf ab, ein allgemeines Verfahren zur Visualisierung von Lignin, Cellulose und Hemicellulose in Pflanzenzellwänden unter Verwendung von Raman-Bildgebung und multivariater Analyse vorzustellen. 

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

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release

The conversion of lignocellulosic biomass to fuels, chemicals, and other commodities has been explored as one possible pathway toward reductions in the use of non-renewable energy sources. In order to identify which plants, out of a diverse pool, have the desired chemical traits for downstream applications, attributes, such as cellulose and lignin content, or monomeric sugar release following an enzymatic saccharification, must be compared. The experimental and data analysis protocols of the standard methods of analysis can be time-consuming, thereby limiting the number of samples that can be measured. High-throughput (HTP) methods alleviate the shortcomings of the standard methods, and permit the rapid screening of available samples to isolate those possessing the desired traits. This study illustrates the HTP sugar release and pyrolysis-molecular beam mass spectrometry pipelines employed at the National Renewable Energy Lab. These pipelines have enabled the efficient assessment of thousands of plants while decreasing experimental time and costs through reductions in labor and consumables.

Plant cell wall structure and chemistry traits are evaluated to identify ideal feedstocks for biofuels and bio-materials. Standard methods have limitations when applied to large data sets. These high-throughput pretreatment, enzyme saccharification, and pyrolysis-molecular beam mass spectrometry methods compare large numbers of biomass samples with decreased experimental time and cost.

Hochdurchsatz-Screening von Widerspenstigkeit Variationen in Lignocellulose-reicher Biomasse: Gesamt Lignin, Lignin-Monomeren und enzymatische Zuckerfreisetzung

The conversion of lignocellulosic biomass to fuels, chemicals, and other commodities has been explored as one possible pathway toward reductions in the ...

Forschung

Umwelt

Preparation of Fungal and Plant Materials for Structural Elucidation Using Dynamic Nuclear Polarization Solid-State NMR

This protocol shows how uniformly 13C, 15N-labeled fungal materials can be produced and how these soft materials should be proceeded for solid-state NMR and sensitivity-enhanced DNP experiments. The sample processing procedure of plant biomass is also detailed. This method allows the measurement of a series of 1D and 2D 13C-13C/15N correlations spectra, which enables high-resolution structural elucidation of complex biomaterials in their native state, with minimal perturbation. The isotope-labeling can be examined by quantifying the intensity in 1D spectra and the polarization transfer efficiency in 2D correlation spectra. The success of dynamic nuclear polarization (DNP) sample preparation can be evaluated by the sensitivity enhancement factor. Further experiments examining the structural aspects of the polysaccharides and proteins will lead to a model of the three-dimensional architecture. These methods can be modified and adapted to investigate a wide range of carbohydrate-rich materials, including the natural cell walls of plants, fungi, algae and bacteria, as well as synthesized or designed carbohydrate polymers and their complex with other molecules.

A protocol for preparing 13C,15N-labeled fungal and plant samples for multidimensional solid-state NMR spectroscopy and dynamic nuclear polarization (DNP) investigations is presented.

Vorbereitung von Pilz und Pflanze-Materialien für strukturelle Aufklärung mittels dynamischen atomare Polarisation Festkörper-NMR

This protocol shows how uniformly 13C, 15N-labeled fungal materials can be produced and how these soft materials should be proceeded for solid-state NMR ...

Biochemie

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.

Visualisierung von Lignification Dynamik in Anlagen mit Klicken Sie auf Chemie: doppelte Kennzeichnung ist Glückseligkeit!

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

Chemie

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.

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.

Messung von Wechselwirkungen zwischen fluoreszierenden Sonden und Lignin in Pflanzenabschnitten durch sFLIM basierend auf nativer Autofluoreszenz

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

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part I: Lignin

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation1 and other products such as biocomposite materials7. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses (matrix polysaccharides, and the polyphenol lignin6 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls provide tensile strength to cells and the entire plants, ward off pathogens, and allow water to be transported throughout the plant; in the case of trees up to more the 100 m above ground level. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerization by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis. In this first part we focus on the analysis of the polyphenol lignin (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its lignin content by acetylbromide solubilization3, and its lignin composition in terms of its syringyl, guaiacyl- and p-hydroxyphenyl units5. The protocol for analyzing the carbohydrates in lignocellulosic biomass including cellulose content and matrix polysaccharide composition is discussed in Part II2.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part I: Lignin

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of the polyphenolic lignin.

Umfassende Analyse der Zusammensetzung von pflanzlichen Zellwänden (Lignozellulose) Teil I: Lignin

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st ...

Biologie

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation2 and other products such as biocomposite materials6. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses, and the polyphenol lignin5 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls contribute extensively to the strength and structural integrity of the entire plant. Despite its necessary rigidity, the cell wall is a highly dynamic entity that is metabolically active and plays crucial roles in numerous cell activities such as plant growth and differentiation5. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerisation by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its monosaccharide composition of the hemicelluloses and other matrix polysaccharides1, and its content of crystalline cellulose7. The protocol for analyzing the lignin components in lignocellulosic biomass is discussed in Part I3.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part II: Carbohydrates

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of wall derived carbohydrates.

Umfassende Analyse der Zusammensetzung von pflanzlichen Zellwänden (Lignozellulose) Teil II: Kohlenhydrate

Ultrafast Lignin Extraction from Unusual Mediterranean Lignocellulosic Residues

Pretreatment is still the most expensive step in lignocellulosic biorefinery processes. It must be made cost-effective by minimizing chemical requirements as well as power and heat consumption and by using environment-friendly solvents. Deep eutectic solvents (DESs) are key, green, and low-cost solvents in sustainable biorefineries. They are transparent mixtures characterized by low freezing points resulting from at least one hydrogen bond donor and one hydrogen bond acceptor. Although DESs are promising solvents, it is necessary to combine them with an economic heating technology, such as microwave irradiation, for competitive profitability. Microwave irradiation is a promising strategy to shorten the heating time and boost fractionation because it can rapidly attain the appropriate temperature. The aim of this study was to develop a one-step, rapid method for biomass fractionation and lignin extraction using a low-cost and biodegradable solvent. 
In this study, a microwave-assisted DES pretreatment was conducted for 60 s at 800 W, using three kinds of DESs. The DES mixtures were facilely prepared from choline chloride (ChCl) and three hydrogen-bond donors (HBDs): a monocarboxylic acid (lactic acid), a dicarboxylic acid (oxalic acid), and urea. This pretreatment was used for biomass fractionation and lignin recovery from marine residues (Posidonia leaves and aegagropile), agri-food byproducts (almond shells and olive pomace), forest residues (pinecones), and perennial lignocellulosic grasses (Stipa tenacissima). Further analyses were conducted to determine the yield, purity, and molecular weight distribution of the recovered lignin. In addition, the effect of DESs on the chemical functional groups in the extracted lignin was determined by Fourier-transform infrared (FTIR) spectroscopy. The results indicate that the ChCl-oxalic acid mixture affords the highest lignin purity and the lowest yield. The present study demonstrates that the DES-microwave process is an ultrafast, efficient, and cost-competitive technology for lignocellulosic biomass fractionation.

Deep eutectic solvent-based, microwave-assisted pretreatment is a green, fast, and efficient process for lignocellulosic fractionation and high-purity lignin recovery.

Ultraschnelle Ligninextraktion aus ungewöhnlichen mediterranen Lignocellulose-Rückständen

Pretreatment is still the most expensive step in lignocellulosic biorefinery processes. It must be made cost-effective by minimizing chemical requirements ...

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.

Label-freie In situ Imaging der Verholzung in pflanzlichen Zellwänden

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

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

Glycan Profiling of Plant Cell Wall Polymers mit Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

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

Histochemical Staining of Arabidopsis thaliana Secondary Cell Wall Elements

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

Histochemische Färbung von<em&gt; Arabidopsis thaliana</em&gt; Sekundärzellwand-Elemente

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