Ringraziamo il Ministero della Scienza e della Tecnologia della Cina (2016YDF0600803) per il sostegno finanziario.

1. Preparazione del campione <ol><li> Tagliare un piccolo blocco di tessuto (circa 3 mm x 3 mm x 5 mm) dal campione vegetale ( ad es. Un gambo di pioppo). </li><li> Immergere il tessuto in acqua bollente di deionizzazione per 30 min. Trasferire immediatamente in acqua deionizzata a temperatura ambiente (RT) per 30 minuti. Ripetere questo passaggio finché il tessuto non si affonda sul fondo del contenitore, indicando che l&#39;aria nel tessuto è stata rimossa e che il tessuto si è ammorbidito. Nota: Per i campioni che si affondano sul fondo prima di questo passaggio, ripetere generalmente questo ciclo 3-5 volte. </li><li> Preparare 20, 50, 70, 90% (v / v) aliquote di polietilenglicole (PEG) in acqua deionizzata, pure pure PEG e mantenere le soluzioni a 65 ° C. </li><li> Incubare il tessuto in una serie di bagni PEG classificati per spostare l&#39;acqua e consentire l&#39;infiltrazione del PEG. Nota: In genere, PEG con peso molecolare 2.000 (PEG2000) viene utilizzato per l&#39;incorporamento. <ol><li> Processare il tessuto con g(A) 20% PEG per 1h, (b) 50% PEG per 1,5h, (c) 70% PEG per 2h, (d) 90% PEG per 2 ore e E) 100% PEG per 10 h. </li></ol></li><li> Preriscaldare una cassetta a 65 ° C in un forno. Versare il PEG contenente il blocco nella cassetta e quindi posizionare il blocco di tessuto in una posizione desiderata utilizzando pinzette o aghi preriscaldati. </li><li> Lentamente raffreddare la cassetta e conservare il tessuto in RT fino all&#39;uso. </li><li> Sezionare il blocco PEG contenente il tessuto bersaglio in un piccolo blocco (circa 1 cm x 1 cm x 2 cm) usando una lama raso tagliente e montarla sul microtomo. </li><li> Tagliare le sezioni sottili dal blocco PEG (tipicamente 3-10 μm). </li><li> Sciacquare la sezione con acqua deionizzata in un vetro da orologio 10 volte per rimuovere la PEG dal tessuto. </li><li> Immergere le sezioni con toluene / etanolo (2: 1, v / v) per 6 ore per rimuovere gli estrattivi. Preparare il liquido di reazione mescolando 65 ml di acqua deionizzata, 0,5 ml di acido acetico e 0,6 g di sodio chloRito in un bicchiere. Aggiungere una sezione e 3 ml di liquido di reazione a una fiala da 5 ml. Avvitare la parte superiore della fiala. </li><li> Scaldare la fiala in bagno d&#39;acqua a 75 ° C per 2 ore per rimuovere la lignina del tessuto. Sciacquare la sezione con acqua deionizzata in un vetro da orologio 10 volte. </li><li> Trasferire la sezione non trattata / delignificata ad una vetrina del microscopio di vetro. Aprire con cura la sezione con spazzole o aghi. Rimuovere l&#39;acqua extra deionizzata con carta velina. </li><li> Immergere la sezione in D 2 O. Coprire il campione con una slitta di copertura in vetro. Sigillare il coperchio della copertura con smalto per prevenire l&#39;evaporazione del D 2 O. </li></ol> 2. Acquisizione spettrale <ol><li> Aprire il software operativo dello strumento del microscopio Raman confocale. Accendere il laser (lunghezza d&#39;onda = 532 nm), mettere a fuoco sulla superficie del silicio cristallino con un obiettivo microscopico 100X e fare clic sul pulsante di calibrazione per calibrare lo strumento. </li><li> Spostare lo strumentoAlla modalità microscopica ottica e accendere la lampada del microscopio. Montare la diapositiva del microscopio sul palco, con la slitta di copertura rivolta verso l&#39;obiettivo. </li><li> Visualizzare il campione con un obiettivo del microscopio 20X e individuare l&#39;area di interesse. Applicare olio di immersione sul coperchio e passare all&#39;obiettivo del microscopio di immersione (60X, apertura numerica NA = 1,35). Focus sulla superficie del campione. </li><li> Passare lo strumento alla modalità di prova Raman e spegnere la lampada del microscopio. </li><li> Scegliere un&#39;area di mappatura utilizzando uno strumento rettangolare. Modifica la dimensione del passo per determinare il numero di spettri ottenuti. Nota: tenere presente la dimensione del passo (di solito più grande del diametro del punto calcolato dall&#39;apertura numerica dell&#39;obiettivo, teoricamente 1.22λ / NA). Le dimensioni sotto di questo provocheranno un oversampling. </li><li> Impostare i parametri ottimali di spettro per ottenere il miglior rapporto segnale / rumore (SNR) e qualità spettrale in un opportuno tempo di acquisizione (generalmente &lt;8 h), depeSulla conformità del campione. In genere, immettere i parametri di imaging nel software dello strumento come nel seguente modo: laser (532 nm), filtro (100%), foro (300), fessura (100), spettrometro (1.840 cm -1 ), scanalature (1200t) Olio 60X) e tempo di acquisizione (2 s). </li><li> Salvare i dati spettrali prima dell&#39;elaborazione dei dati e convertirli in un formato universale ( ad esempio, file TXT). </li></ol> 3. Analisi dei dati <ol><li> Caricare i dati spettrali (file TXT) nel software di analisi dei dati ( ad esempio, Matlab). Applicare una tecnica di riduzione del rumore sul set di dati per migliorare il SNR ( ad esempio, l&#39;algoritmo Savitzsky-Golay o l&#39;algoritmo wavelet) </li><li> Utilizzare campioni non trattati per produrre le immagini di lignina e polisaccaridi. Per l&#39;imaging di lignina, considerare il picco spettrale di circa 1.600 cm -1 a causa della vibrazione simmetrica di stretching anulare. Per la formazione di polisaccaridi (inclusa la cellulosa e l&#39;emicellulosa), utilizzare il picco spettrale aIntorno a 2.889 cm -1 a causa dei tratti CH e CH2. </li><li> Utilizzare campioni delignificati per generare le immagini di cellulosa e emicellulosa. Eseguire la risoluzione della curva di auto-modellizzazione (SMCR) sugli spettri acquisiti per discriminare lo spettro di cellulosa e emicellulosa e per rappresentare le loro distribuzioni. </li><li> Eseguire l&#39;analisi dei componenti principali (PCA) e l&#39;analisi clustering sui dati acquisiti per distinguere gli spettri Raman da diversi strati di parete cellulare. </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>

Gli autori non hanno niente da rivelare.

La parete cellulare è un composito organizzato in diversi strati, tra cui l&#39;angolo di cella (CC), la parete secondaria (SW, con gli strati S1, S2 e S3) e la lamella centrale composta (CML, lamella centrale e principale adiacente Parete) che rende difficile ottenere una superficie piana durante la preparazione del campione. Pertanto, i campioni di piante, in particolare l&#39;erba, che hanno una struttura più complessa del legno, spesso devono essere solidificati per consentire una sezione sottile. PEG è una matri...

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 border="0" cellpadding="0" cellspacing="0" width="597">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:199px;">Microtome</td>
			<td style="width:136px;">Thermo Scientific</td>
			<td style="width:199px;">Microm HM430</td>
			<td style="width:64px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Confocal Raman microscope</td>
			<td>Horiba Jobin Yvon</td>
			<td>Xplora</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">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

L&#39;applicazione di Raman imaging alla biomassa vegetale è in aumento in quanto può offrire informazioni spaziali e compositive sulle soluzioni acquose. L&#39;analisi di solito non richiede un&#39;ampia preparazione dei campioni; Informazioni strutturali e chimiche possono essere ottenute senza etichettatura. Tuttavia, ogni immagine Raman contiene migliaia di spettri; Ciò solleva difficoltà quando estrae informazioni nascoste, soprattutto per i componenti con strutture chimiche simili. Questo lavoro introduce un&#39;analisi multivariata per affrontare questo problema. Il protocollo stabilisce un metodo generale per visualizzare i componenti principali, tra cui la lignina, la cellulosa e l&#39;emicellulosa all&#39;interno della parete cellulare delle piante. In questo protocollo sono descritte le procedure per la preparazione del campione, l&#39;acquisizione spettrale e l&#39;elaborazione dei dati. È altamente dipendente dall&#39;abilità dell&#39;operatore durante la preparazione del campione e l&#39;analisi dei dati. Utilizzando questo approccio, un&#39;indagine di Raman può essere eseguita da un utente non specializzato per ottenere higI dati di qualità h e risultati significativi per l&#39;analisi delle pareti cellulari delle piante.

La figura 1 presenta una panoramica di un tipico sistema micro-Raman per l&#39;imaging Raman di una parete cellulare. Ad esempio, gli spettri Raman originali del pioppo ( Populus nigra L.) presentano significative derive e spicchi di base ( Figura 2a ). Dopo aver eseguito il metodo automatico di pre-elaborazione per il set di dati Raman Imaging (APRI), questi due contaminanti spettrali vengono eliminati con successo ( <...

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

Questo protocollo mira a presentare un metodo generale per la visualizzazione della lignina, della cellulosa e dell&#39;emicellulosa nelle pareti delle cellule vegetali utilizzando l&#39;analisi Raman e l&#39;analisi multivariata.

Combinazione di Raman Imaging e analisi multivariata per la visualizzazione di lignina, cellulosa e emicellulosa nella parete cellulare delle piante

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 Views.  Beijing Forestry University.  Questo protocollo mira a presentare un metodo generale per la visualizzazione della lignina, della cellulosa e dell'emicellulosa nelle pareti delle cellule vegetali utilizzando l'analisi Raman e l'analisi multivariata. 

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

Determination of the Glycogen Content in Cyanobacteria

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

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

Determinazione del contenuto di glicogeno nei cianobatteri

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

Ricerca

Biochimica

Structural Characterization of Mannan Cell Wall Polysaccharides in Plants Using PACE

Plant cell wall polysaccharides are notoriously difficult to analyze, and most methods require expensive equipment, skilled operators, and large amounts of purified material. Here, we describe a simple method for gaining detailed polysaccharide structural information, including resolution of structural isomers. For polysaccharide analysis by gel electrophoresis (PACE), plant cell wall material is hydrolyzed with glycosyl hydrolases specific to the polysaccharide of interest (e.g., mannanases for mannan). Large format polyacrylamide gels are then used to separate the released oligosaccharides, which have been fluorescently labeled. Gels can be visualized with a modified gel imaging system (see Table of Materials). The resulting oligosaccharide fingerprint can either be compared qualitatively or, with replication, quantitatively. Linkage and branching information can be established using additional glycosyl hydrolases (e.g., mannosidases and galactosidases). Whilst this protocol describes a method for analyzing glucomannan structure, it can be applied to any polysaccharide for which characterized glycosyl hydrolases exist. Alternatively, it can be used to characterize novel glycosyl hydrolases using defined polysaccharide substrates.

A protocol for the structural analysis of polysaccharides by gel electrophoresis (PACE), using mannan as an example, is described.

Caratterizzazione strutturale di Mannan polisaccaridi di parete cellulare nelle piante usando passo

Plant cell wall polysaccharides are notoriously difficult to analyze, and most methods require expensive equipment, skilled operators, and large amounts ...

Isolation and Respiratory Measurements of Mitochondria from Arabidopsis thaliana

Mitochondria are essential organelles involved in numerous metabolic pathways in plants, most notably the production of adenosine triphosphate (ATP) from the oxidation of reduced compounds such as nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FADH2). The complete annotation of the Arabidopsis thaliana genome has established it as the most widely used plant model system, and thus the need to purify mitochondria from a variety of organs (leaf, root, or flower) is necessary to fully utilize the tools that are now available for Arabidopsis to study mitochondrial biology. Mitochondria are isolated by homogenization of the tissue using a variety of approaches, followed by a series of differential centrifugation steps producing a crude mitochondrial pellet that is further purified using continuous colloidal density gradient centrifugation. The colloidal density material is subsequently removed by multiple centrifugation steps. Starting from 100 g of fresh leaf tissue, 2 - 3 mg of mitochondria can be routinely obtained. Respiratory experiments on these mitochondria display typical rates of 100 - 250 nmol O2 min-1 mg total mitochondrial protein-1 (NADH-dependent rate) with the ability to use various substrates and inhibitors to determine which substrates are being oxidized and the capacity of the alternative and cytochrome terminal oxidases. This protocol describes an isolation method of mitochondria from Arabidopsis thaliana leaves using continuous colloidal density gradients and an efficient respiratory measurements of purified plant mitochondria.

Isolation and Respiratory Measurements of Mitochondria from Arabidopsis thaliana

As mitochondria are only a small percentage of the plant cell, they need to be purified for a range of studies. Mitochondria can be isolated from a variety of plant organs by homogenization, followed by differential and density gradient centrifugation to obtain a highly purified mitochondrial fraction.

Isolamento e misure respiratoria dei mitocondri da Arabidopsis thaliana

Mitochondria are essential organelles involved in numerous metabolic pathways in plants, most notably the production of adenosine triphosphate (ATP) from ...

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Cellular processes are regulated by interactions between biological molecules such as proteins, metabolites, and nucleic acids. While the investigation of protein-protein interactions (PPI) is no novelty, experimental approaches aiming to characterize endogenous protein-metabolite interactions (PMI) constitute a rather recent development. Herein, we present a protocol that allows simultaneous characterization of the PPI and PMI of a protein of choice, referred to as bait. Our protocol was optimized for Arabidopsis cell cultures and combines affinity purification (AP) with mass spectrometry (MS)-based protein and metabolite detection. In short, transgenic Arabidopsis lines, expressing bait protein fused to an affinity tag, are first lysed to obtain a native cellular extract. Anti-tag antibodies are used to pull down protein and metabolite partners of the bait protein. The affinity-purified complexes are extracted using a one-step methyl tert-butyl ether (MTBE)/methanol/water method. Whilst metabolites separate into either the polar or the hydrophobic phase, proteins can be found in the pellet. Both metabolites and proteins are then analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS or UPLC-MS/MS). Empty-vector (EV) control lines are used to exclude false positives. The major advantage of our protocol is that it enables identification of protein and metabolite partners of a target protein in parallel in near-physiological conditions (cellular lysate). The presented method is straightforward, fast, and can be easily adapted to biological systems other than plant cell cultures.

Protein-protein and protein-metabolite interactions are crucial for all cellular functions. Herein, we describe a protocol that allows parallel analysis of these interactions with a protein of choice. Our protocol was optimized for plant cell cultures and combines affinity purification with mass spectrometry-based protein and metabolite detection.

2 in 1: purificazione di affinità per l'analisi parallela di proteina-proteina e proteina-metabolita complessi in un unico passaggio

Cellular processes are regulated by interactions between biological molecules such as proteins, metabolites, and nucleic acids. While the investigation of ...

Using In Vitro Fluorescence Resonance Energy Transfer to Study the Dynamics Of Protein Complexes at a Millisecond Time Scale

Proteins are the primary operators of biological systems, and they usually interact with other macro- or small molecules to carry out their biological functions. Such interactions can be highly dynamic, meaning the interacting subunits are constantly associated and dissociated at certain rates. While measuring the binding affinity using techniques such as quantitative pull-down reveals the strength of the interaction, studying the binding kinetics provides insights on how fast the interaction occurs and how long each complex can exist. Furthermore, measuring the kinetics of an interaction in the presence of an additional factor, such as a protein exchange factor or a drug, helps reveal the mechanism by which the interaction is regulated by the other factor, providing important knowledge for the advancement of biological and medical research. Here, we describe a protocol for measuring the binding kinetics of a protein complex that has a high intrinsic association rate and can be dissociated quickly by another protein. The method uses fluorescence resonance energy transfer to report the formation of the protein complex in vitro, and it enables monitoring the fast association and dissociation of the complex in real time on a stopped-flow fluorimeter. Using this assay, the association and dissociation rate constants of the protein complex are quantified.

Protein-protein interactions are critical for biological systems, and studies of the binding kinetics provide insights into the dynamics and function of protein complexes. We describe a method that quantifies the kinetic parameters of a protein complex using fluorescence resonance energy transfer and the stopped-flow technique. &#160;

Utilizzo In Vitro Fluorescence Resonance Energy Transfer per studiare la dinamica dei complessi della proteina ad una scala di tempo di millisecondo

Proteins are the primary operators of biological systems, and they usually interact with other macro- or small molecules to carry out their biological ...

In Vivo Calcium Imaging in C. elegans Body Wall Muscles

The model organism C. elegans provides an excellent system to perform in vivo calcium imaging. Its transparent body and genetic manipulability allow for the targeted expression of genetically encoded calcium sensors. This protocol outlines the use of these sensors for the in vivo imaging of calcium dynamics in targeted cells, specifically the body wall muscles of the worms. By utilizing the co-expression of presynaptic channelrhodopsin, stimulation of acetylcholine release from excitatory motor neurons can be induced using blue light pulses, resulting in muscle depolarization and reproducible changes in cytoplasmic calcium levels. Two worm immobilization techniques are discussed with varying levels of difficulty. Comparison of these techniques demonstrates that both approaches preserve the physiology of the neuromuscular junction and allow for the reproducible quantification of calcium transients. By pairing optogenetics and functional calcium imaging, changes in postsynaptic calcium handling and homeostasis can be evaluated in a variety of mutant backgrounds. Data presented validates both immobilization techniques and specifically examines the roles of the C. elegans sarco(endo)plasmic reticular calcium ATPase and the calcium-activated BK potassium channel in the body wall muscle calcium regulation.

In Vivo Calcium Imaging in C. elegans Body Wall Muscles

This method provides a way to couple optogenetics and genetically encoded calcium sensors to image baseline cytosolic calcium levels and changes in evoked calcium transients in the body wall muscles of the model organism C. elegans.

In Vivo Calcium Imaging in C. elegans Muscoli della parete del corpo

The model organism C. elegans provides an excellent system to perform in vivo calcium imaging. Its transparent body and genetic manipulability allow for ...

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea (Camellia Sinensis L.) Leaves

A platform for studying insecticide metabolism using in vitro tissues of tea plant was developed. Leaves from sterile tea plantlets were induced to form loose callus on Murashige and Skoog (MS) basal media with the plant hormones 2,4-dichlorophenoxyacetic acid (2,4-D, 1.0 mg L-1) and kinetin (KT, 0.1 mg L-1). Callus formed after 3 or 4 rounds of subculturing, each lasting 28 days. Loose callus (about 3 g) was then inoculated into B5 liquid media containing the same plant hormones and was cultured in a shaking incubator (120 rpm) in the dark at 25 &#177; 1 &#176;C. After 3&#8722;4 subcultures, a cell suspension derived from tea leaf was established at a subculture ratio ranging between 1:1 and 1:2 (suspension mother liquid: fresh medium). Using this platform, six insecticides (5 &#181;g mL-1 each thiamethoxam, imidacloprid, acetamiprid, imidaclothiz, dimethoate, and omethoate) were added into the tea leaf-derived cell suspension culture. The metabolism of the insecticides was tracked using liquid chromatography and gas chromatography. To validate the usefulness of the tea cell suspension culture, the metabolites of thiamethoxan and dimethoate present in treated cell cultures and intact plants were compared using mass spectrometry. In treated tea cell cultures, seven metabolites of thiamethoxan and two metabolites of dimethoate were found, while in treated intact plants, only two metabolites of thiamethoxam and one of dimethoate were found. The use of a cell suspension simplified the metabolic analysis compared to the use of intact tea plants, especially for a difficult matrix such as tea.

Study on the Metabolism of Six Systemic Insecticides in a Newly Established Cell Suspension Culture Derived from Tea Camellia Sinensis L. Leaves

This work presents a protocol for establishing a cell suspension culture derived from tea (Camellia sinensis L.) leaves that can be used to study the metabolism of external compounds that can be taken up by the whole plant, such as insecticides.

Studio sul metabolismo di sei insetticidi sistemici in una nuova coltura di sospensione cellulare derivata dal tè (Camellia Sinensis L.) plurale di "leaf"

A platform for studying insecticide metabolism using in vitro tissues of tea plant was developed. Leaves from sterile tea plantlets were induced to form ...

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.

Misurazione delle interazioni tra sonde fluorescenti e lignina nelle sezioni delle piante mediante sFLIM in base all'autofluorescenza nativa

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

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

This protocol presents an integrated Raman spectroscopy-mass spectrometry (MS) platform that is capable of achieving single-cell resolution. Raman spectroscopy can be used to study cellular response to drugs, while MS can be used for targeted and quantitative analysis of drug uptake and metabolism.

Una spettroscopia Raman integrata e una piattaforma di spettrometria di massa per studiare l'assorbimento di farmaci a singola cellula, il metabolismo e gli effetti

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in ...

RNA Blot Analysis for the Detection and Quantification of Plant MicroRNAs

MicroRNAs (miRNAs) are a class of endogenously expressed non-coding, ~21 nt small RNAs involved in the regulation of gene expression in both plants and animals. Most miRNAs act as negative switches of gene expression targeting key genes. In plants, primary miRNAs (pri-miRNAs) transcripts are generated by RNA polymerase II, and they form varying lengths of stable stem-loop structures called pre-miRNAs. An endonuclease, Dicer-like1, processes the pre-miRNAs into miRNA-miRNA* duplexes. One of the strands from miRNA-miRNA* duplex is selected and loaded onto Argonaute 1 protein or its homologs to mediate the cleavage of target mRNAs. Although miRNAs are key signaling molecules, their detection is often carried out by less than optimal PCR-based methods instead of a sensitive northern blot analysis. We describe a simple, reliable, and extremely sensitive northern method that is ideal for the quantification of miRNA levels with very high sensitivity, literally from any plant tissue. Additionally, this method can be used to confirm the size, stability and the abundance of miRNAs and their precursors.

This method demonstrates use of the northern hybridization technique to detect miRNAs from total RNA extract.

RNA Blot Analisi per il rilevamento e la quantificazione dei microRNA vegetali

MicroRNAs (miRNAs) are a class of endogenously expressed non-coding, ~21 nt small RNAs involved in the regulation of gene expression in both plants and ...