We thank the Department of Plant &#38; Soil Science and Cotton Inc. for their partial support of this study.

1. Preparation of plant samples
<ol>
	<li>Collect two-month-old cotton plants from the greenhouse (Figure 1A).</li>
	<li>Flip plant pots gently to separate soil and roots with intact lateral roots by loosening the soil around the plant (Figure 1B).</li>
	<li>Wash the collected plants thoroughly in trays filled with water to remove all the dirt (for root samples) (Figure 1C).</li>
	<li>Use paper towels to dry separated root, stem, and leaf tissues, and label them (Figure 1D). Air dry for 2 days at room temperature to prevent any fungal contamination (Figure 1E).</li>
	<li>Transfer sample tissues to labelled containers/aluminum foils and incubate in a temperature-controlled incubator at 49 &#176;C for 7 to 10 days (Figure 1F). 
	NOTE: Higher temperatures may alter the lignin structure. Alternatively, a freeze dryer can be used to dry samples for 1 to 2 days without causing any chemical changes to the plant biomass.</li>
	<li>Use a blade to cut the incubator dried tissue into 5 mm size pieces or alternatively employ a biomass grinder to grind the plant tissues (Figure 1G, Figure 1H). 
	NOTE: The biomass grinder/blade must be cleaned after each sample was cut/ground.</li>
	<li>Transfer the cut tissue/biomass grounded plant material into grinding vials and grind into fine powder of 1 mm size using a freezer mill or cryogenic grinder with liquid N2.</li>
	<li>Grind samples for three cycles at the rate of 10 CPS (each cycle span of 2 min) into a uniform powder (Figure 1I, 1J, 1K). 
	&#8203;NOTE: The experiment can be paused at this point and samples can be stored at room temperature in airtight containers for long-term storage.</li>
</ol>
2. Washing samples with water, PSB, and methanol
<ol>
	<li>Measure and record the weight of all empty 2 mL microfuge tubes used for lignin content estimation in the lab notebook.</li>
	<li>Transfer 20 mg of the ground sample powder to the pre-weighed tube. Weigh the tube with tissue and tissue powder and record these weights in the lab notebook.</li>
	<li>Incubate all 2 mL microfuge tubes (with open lids) with 20 mg of tissue powder in a heat block or oven at 60 &#176;C for 1 hour.</li>
	<li>After incubation, cool samples for 10 min at room temperature (RT).</li>
	<li>Add 1.8 mL of water to each microfuge tube and mix by vortexing. Then, centrifuge at 25,200 x g (15,000 rpm) for 10 min at RT and discard the supernatant (Figure 2).</li>
	<li>Add 1.8 mL of Protein Solubilization buffer (PSB) (Table 1) to each retained pellet and mix by vortexing. Centrifuge at 25,200 x g (15,000 rpm) for 10 min at RT and discard the supernatant.</li>
	<li>Repeat step 2.6 again for each sample.</li>
	<li>Add 1.8 mL of water to each pellet, mix by vortexing, and centrifuge at 25,200 x g (15,000 rpm) for 10 min. After centrifugation, save the pellet and discard the supernatant.</li>
	<li>To the retained pellet, add 1.8 mL of methanol and incubate in a 60 &#176;C heat block for 20 min. Then, centrifuge at 25,200 x g (15,000 rpm) for 10 min at RT. After centrifugation, discard the supernatant and retain the pellet (Figure 2).</li>
	<li>Repeat step 2.9 again for each sample.</li>
	<li>Air dry the pellet at RT or proceed immediately by vacuum drying. Vacuum dry using vacuum drier at 30 &#176;C for 2 to 3 hours or until the pellet is completely dry. 
	NOTE: The experiment can be paused at this point by air drying over-night or continue by vacuum drying.</li>
	<li>After drying, weigh sample tubes with the dried pellet and record the weight next to the respective empty tube weight in the lab notebook. Estimate the pellet weight by subtracting the two values. These weights will be used for lignin estimation at the end of lignin extraction process.</li>
	<li>At this point of lignin extraction, include the commercial bamboo lignin for generation of the lignin standard curve. Measure commercial bamboo lignin into separate tubes ranging from 0.5 mg to 5 mg in 0.5 mg increments (0.5 mg, 1 mg, 1.5 mg, 2 mg, 3 mg, 3.5 mg, 4 mg, 4.5 mg, and 5.0 mg). Measure each concentration three times for three technical replicates. 
	&#8203;NOTE: From here on, the standards measured in the above step were processed in the same way as samples that were dried.</li>
</ol>
3. Treatment of pellet with TGA and acid to precipitate lignin
<ol>
	<li>Subject processed samples from the above step, along with measured standards, to TGA (thioglycolic acid) treatment.</li>
	<li>Add 1 mL of 3 N HCl (Table 1) and 100 &#181;L of TGA to each pellet.</li>
	<li>Vortex and incubate in an 80 &#176;C preheated heat block for 3 hours in a fume hood (Figure 2). 
	NOTE: The heating step at 80 &#176;C must be monitored. High pressure buildup may open the lids and can lead to chemical spills. Screw cap tubes are recommended but 2 mL tubes can be loosely capped during this step as an alternative to prevent such spills.</li>
	<li>After incubation, cool tubes at RT for 10-15 min and centrifuge at 25,200 x g (15,000 rpm) for 10 min at RT. 
	NOTE: Waste generated from acid and organic solvents must be separated and stored in glass containers with ventilated caps. Use separate glass containers for the collection of TGA-acid waste and acid waste.</li>
	<li>After centrifugation, discard the supernatant and retain the pellet. Add 1 mL of water, mix by vortexing, and centrifuge at 25,200 x g (15,000 rpm) for 10 min at RT.</li>
	<li>After centrifugation, discard the supernatant and mix the pellet in 1 N NaOH for 24 h at 37 &#176;C shaker/thermal mixer at low speed (Figure 2). 
	NOTE: This incubation time can be reduced to 1 hour7.</li>
	<li>After incubation, centrifuge the 2 mL microfuge tubes at 25,200 x g (15,000 rpm) for 10 min at RT. Retain the supernatant for the following step. 
	&#8203;NOTE: The procedure involves use of strong acids and other chemicals that are corrosive in nature. Hence, wearing proper PPE is recommended throughout the process of lignin estimation. TGA has a strong unpleasant smell and is corrosive in nature. Hence, it is recommended to use only in the fume hood.</li>
</ol>
4. Precipitation of lignin
<ol>
	<li>Transfer the supernatant to a fresh 2 mL microfuge tube and add 200 &#181;L of concentrated HCl. Incubate at 4 &#176;C for 4 hours or overnight (Figure 2). 
	NOTE: The extraction process can be paused at this point by extending the refrigeration step to overnight.</li>
	<li>Centrifuge at 25,200 x g (15,000 rpm) for 10 min at RT and dissolve the pellet in 1 mL of 1 N NaOH.</li>
	<li>Incubate in the shaker at RT for 10 min to suspend the pellet completely in NaOH.</li>
	<li>Finally, measure the absorbance of samples at 280 nm using a spectrophotometer and compare with the standard lignin curve.</li>
	<li>Measure the unknown concentration of lignin by using the calibration curve regression line values, and absorbances of extracted samples at 280 nm.</li>
</ol>
5. Standard curve preparation and lignin estimation in the sample
<ol>
	<li>Process lignin standards in the same way as experimental samples from TGA treatment.</li>
	<li>Measure commercial bamboo lignin standards in 0.5 mg increments starting from 0.5 mg, 1 mg, 1.5 mg, 2 mg, 2.5 mg, 3.0 mg, 3.5 mg, 4.0 mg, 4.5 mg and 5 mg. Then, process by TGA, HCl, dissolve in 1 N NaOH followed by measuring absorbance at 280 nm (Figure 3A).</li>
	<li>Use values of lignin concentration and absorbance readings to generate a scattered plot of standard lignin curve (Figure 3B).</li>
	<li>Use the regression line, y = mx+c generated in the scattered plot, for the estimation of unknown lignin content of prepared samples using &#34;x&#34; values from average absorbances of extracted samples at 280 nm and &#34;m&#34; and &#34;c&#34; values from lignin standard curve regression line.</li>
	<li>Divide the lignin content in the resultant y value by total weight of the vacuum/air dried plant biomass sample after methanol extraction in mg (approximately 15 mg) to obtain lignin concentration per mg. Then, multiply this value by 100 to calculate lignin percentage per mg.</li>
</ol>

<ol>
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The authors declare that they have no conflict of interest.

Lignin plays a significant role in plant growth and development and recently has been extensively studied for biofuel, bioenergy and bioproduct applications. Lignin is rich in aromatic compounds that are stored in all vascular plant secondary cell walls. It has several industrial applications such as wood panel products, bio dispersants, flocculants, polyurethane foams and in resins of circuit boards29,30,31. Most of the lignin ...

Lignin is one of the vital load-bearing components of plant cell walls and the second most abundant polymer on Earth1. Chemically, lignin is a crosslinked heteropolymer made up of high molecular weight complex phenolic compounds that form a natural renewable source of aromatic polymers and synthesis of biomaterials2,3. This natural polymer plays significant roles in plant growth, development, survival, mechanical support, cell wall rigidity, water transport, mineral transport, lodging resistance, tissue and organ development, deposition of energy, and protection from biotic and abiotic stresses4,5,6,7. Lignin is primarily composed of three different monolignols: coniferyl, sinapyl and p-coumaryl alcohols that are derived from the phenyl propanoid pathway8,9. The amount of lignin and the composition of monomers vary based on the plant species, the tissue/organ type, and different stages of plant development10. Lignin is broadly classified into softwood, hardwood, and grass lignin based on the source and monolignol composition. Softwood is primarily composed of 95% coniferyl alcohol with 4% p-coumaryl and 1% sinapyl alcohols. Hardwood has coniferyl and sinapyl alcohols in equal proportions, while grass lignin is composed of various proportions of coniferyl, sinapyl and p-coumaryl alcohols11,12. The composition of monomers is critical as it determines the lignin strength, decomposition, and degradation of the cell wall as well as determining molecular structure, branching, and crosslinking with other polysaccharides13,14.
Lignin research is gaining importance in foraging, textile industries, paper industries, and for bioethanol, biofuel, and bio-products due to its low cost and high abundance15,16. Various chemical methods (e.g., acetyl bromide, acid detergents, Klason, and permanganate oxidation) along with instrumental methods (e.g., near infrared (NIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and ultraviolet (UV) spectrophotometry) were used for lignin quantification9,17. The analysis methods of lignin are generally classified based on electromagnetic radiation, gravimetry, and solubility. The principle behind lignin estimation by&#160;electromagnetic radiation was based on the&#160;chemical property of lignin by which it absorbs light at specific wavelengths. These results were estimated based on the principle that lignin has a stronger UV absorbance than carbohydrates. In 1962, Bolker and Somerville used potassium chloride pellets to estimate lignin content in wood18. However, this method has drawbacks in the estimation of lignin content from herbaceous samples due to the presence of non-lignin phenolic compounds and the absence of an appropriate extinction coefficient. In 1970, Fergus and Goring found that the guaiacyl and syringyl compound absorption maxima were at 280 nm and 270 nm, which corrected the extinction coefficient issue of the Bolker and Somerville method19. Later, infrared spectroscopy, a highly sensitive technique for characterizing phenolics, was also used for lignin estimation with a small amount of plant biomass samples. One example of such technology&#160;was diffuse-reflectance Fourier transform spectrophotometry. This method, however, lacks a proper standard similar to the UV method20. Later, the lignin content was estimated by NIRS (near infrared spectroscopy) and NMR (nuclear magnetic resonance spectroscopy). Though, there are disadvantages in these methods, they do not alter the chemical structure of lignin, retaining its purity20.
The gravimetric Klason method is a direct and the most reliable analytical method for lignin estimation of woody stems. The basis for gravimetric lignin estimation is the hydrolysis/solubilization of non-lignin compounds and the collection of insoluble lignin for gravimetry21. In this method, the carbohydrates are removed by hydrolysis of the biomass with concentrated H2SO4 to extract lignin residue20,22. The lignin content estimated by this method is known as acid insoluble lignin or Klason lignin. Application of the Klason method depends on the plant species, the tissue type and the cell wall type. The presence of variable amounts of non-lignin components such as tannins, polysaccharides and proteins, results in proportional differences in the estimation of acid insoluble/soluble lignin contents. Hence, the Klason method is only recommended for lignin estimation of high-lignin content biomass such as woody stems17,23. Solubility methods such as acetyl bromide (AcBr), acid-insoluble lignin, and thioglycolic acid (TGA) are most commonly used methods for estimation of the lignin content from various plant biomass sources. Kim et al. established two methods for lignin extraction by solubilization. The first method extracts lignin as an insoluble residue by solubilizing cellulose and hemicellulose, while the second method separates lignin in the soluble fraction, leaving cellulose and hemicellulose as the insoluble residue24.
Similar methods employed in lignin estimation based on the solubility are thioglycolic acid (TGA) and acetyl bromide (AcBr) methods25. Both TGA and acetyl bromide methods estimate the lignin content by measuring the absorbance of the solubilized lignin at 280 nm; however, the AcBr method degrades xylans during the process of lignin solubilization and shows a false increase in the lignin content26. The thioglycolate (TGA) method is the more reliable method, as it depends on specific bonding with the thioether groups of benzyl alcohol groups of lignin with TGA. The TGA bound lignin is precipitated under acidic conditions using HCl, and the lignin content is estimated using its absorbance at 280 nm27. The TGA method has additional advantages of less structural modifications, a soluble form of lignin estimation, less interference from non-lignin components, and precise estimation of lignin due to specific bonding with TGA.
This TGA method is modified based on the kind of plant biomass sample used for lignin content estimation. Here, we modified and adapted the rapid TGA method of rice straws27 to cotton tissues to estimate the lignin content. Briefly, the dried powdered plant samples were subjected to protein solubilization buffer and methanol extraction to remove proteins and the alcohol soluble fraction. The alcohol insoluble residue was treated with TGA and precipitated lignin under acidic conditions. A lignin standard curve was generated using commercial bamboo lignin and a regression line (y = mx+c) was obtained. The &#34;x&#34; value uses average absorbance values of lignin at 280 nm, while &#34;m&#34; and &#34;c&#34; values were entered from the regression line to calculate unknown lignin concentration in cotton plant biomass samples. This method is divided into five phases: 1) preparation of plant samples; 2) washing the samples with water and methanol; 3) treatment of the pellet with TGA and acid to precipitate lignin; 4) precipitation of lignin; and 5) the standard curve preparation and lignin content estimation of the sample. The first two phases are primarily focused on the plant material preparation followed by water, PSB (protein solubilization buffer) and methanol extractions to obtain the alcohol insoluble material. Then, it was treated with TGA (thioglycolic acid) and HCl to form a complex with lignin in the third phase. At the end, HCl was used to precipitate lignin, which was dissolved in sodium hydroxide to measure its absorbance at 280 nm28.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>BioSpectrophotometer kinetic</td>
			<td>Eppendorf kinetic</td>
			<td>6136000010</td>
			<td>For measuring absorbance at 280 nm</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Eppendorf</td>
			<td>5424</td>
			<td>For centrifuging&#160; samples</td>
		</tr>
		<tr>
			<td>Commercial bamboo lignin</td>
			<td>Aldrich</td>
			<td>1002171289</td>
			<td>Used in the preparation of the standard curve</td>
		</tr>
		<tr>
			<td>Distilled water</td>
			<td>Fischer Scientific</td>
			<td>16690382</td>
			<td>Used in the protocol</td>
		</tr>
		<tr>
			<td>Falcon tubes</td>
			<td>VWR</td>
			<td>734-0448</td>
			<td>Containers for solutions</td>
		</tr>
		<tr>
			<td>Freezer mill</td>
			<td>Spex Sample Prep</td>
			<td>68-701-15</td>
			<td>For fine grinding of plant tissue samples</td>
		</tr>
		<tr>
			<td>Heat block/ Thermal mixer</td>
			<td>Eppendorf</td>
			<td>13527550</td>
			<td>For temperature controlled steps during lignin extraction</td>
		</tr>
		<tr>
			<td>Hotplate stirrer</td>
			<td>Walter</td>
			<td>WP1007-HS</td>
			<td>Used for preparation of solutions</td>
		</tr>
		<tr>
			<td>Hydrochloric acid (HCL)</td>
			<td>Sigma</td>
			<td>221677</td>
			<td>Used in the protocol</td>
		</tr>
		<tr>
			<td>Incubator</td>
			<td>Fisherbrand</td>
			<td>150152633</td>
			<td>For thorough drying of plant tissue samples</td>
		</tr>
		<tr>
			<td>Measuring scale</td>
			<td>Mettler toledo</td>
			<td>30243386</td>
			<td>For measuring plant tissue weight, standards and microfuge tubes</td>
		</tr>
		<tr>
			<td>Methanol (100 %)</td>
			<td>Fischer Scientific</td>
			<td>67-56-1</td>
			<td>Used in the protocol</td>
		</tr>
		<tr>
			<td>Microfuge tubes (2 mL)</td>
			<td>Microcentrifuge</td>
			<td>Z628034-500EA</td>
			<td>Containers for extraction of lignin</td>
		</tr>
		<tr>
			<td>Plant biomass gerinder</td>
			<td>Hanchen</td>
			<td>Amazon</td>
			<td>Used for crushing dried samples</td>
		</tr>
		<tr>
			<td>pH meter</td>
			<td>Fisher Scientific</td>
			<td>AE150</td>
			<td>Measuring pH for solutions prepared for lignin extraction</td>
		</tr>
		<tr>
			<td>Temperature controlled incubator/oven</td>
			<td>Fisher Scientific</td>
			<td>15-015-2633</td>
			<td>Used in the protocol</td>
		</tr>
		<tr>
			<td>Thioglycolic acid (TGA)</td>
			<td>Sigma Aldrich</td>
			<td>68-11-1</td>
			<td>Used in the protocol</td>
		</tr>
		<tr>
			<td>Vacuum dryer</td>
			<td>Eppendorf</td>
			<td>22820001</td>
			<td>Used for drying samples</td>
		</tr>
		<tr>
			<td>Vortex mixer</td>
			<td>Eppendorf</td>
			<td>3340001</td>
			<td>For proper mixing of samples</td>
		</tr>
	</tbody>
</table>

estimation of plant biomass lignin content using thioglycolic acid (tga)

Lignin is a natural polymer that is the second most abundant polymer on Earth after cellulose. Lignin is mainly deposited in plant secondary cell walls and is an aromatic heteropolymer primarily composed of three monolignols with significant industrial importance. Lignin plays an important role in plant growth and development, protects&#160;from biotic and abiotic stresses, and in the quality of animal fodder, the wood, and industrial lignin products. Accurate estimation of lignin content is essential for both fundamental understanding of the lignin biosynthesis and industrial applications of biomass. The thioglycolic acid (TGA) method is a highly reliable method of estimating the total lignin content in the plant biomass. This method estimates the lignin content by forming thioethers with the benzyl alcohol groups of lignin, which are soluble in alkaline conditions and insoluble in acidic conditions. The total lignin content is estimated using a standard curve generated from commercial bamboo lignin.

Two different cotton experimental lines were compared for differences in their lignin contents in different tissues. The extracted lignin content of each sample was measured at 280 nm and recorded its respective absorbance values. The average absorbance values of each biological replicate were compared against the regression line of the lignin standard curve (Table 2, Figure 3C). The regression line, y = mx + c, is used to calculate the unknown lignin content of the extracte...

Watch this Scientific Journal Video about Estimation of Plant Biomass Lignin Content using Thioglycolic Acid TGA at JoVE.com

Estimation of Plant Biomass Lignin Content using Thioglycolic Acid TGA

Here, we present a modified TGA method for estimation of lignin content in herbaceous plant biomass. This method estimates the lignin content by forming specific thioether bonds with lignin and presents an advantage over the Klason method, as it requires a relatively small sample for lignin content estimation.

Estimation of Plant Biomass Lignin Content using Thioglycolic Acid (TGA)

Lignin is a natural polymer that is the second most abundant polymer on Earth after cellulose. Lignin is mainly deposited in plant secondary cell walls ...

estimation-of-plant-biomass-lignin-content-using-thioglycolic-acid-tga

Research

JoVE Journal

Biochemistry

9.2K Views.  Texas Tech University. Here, we present a modified TGA method for estimation of lignin content in herbaceous plant biomass. This method estimates the lignin content by forming specific thioether bonds with lignin and presents an advantage over the Klason method, as it requires a relatively small sample for lignin content estimation. 

Video: Estimation of Plant Biomass Lignin Content using Thioglycolic Acid TGA

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 ...

Determination of the Glycogen Content in Cyanobacteria

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

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

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

Spectrophotometric Determination of Phycobiliprotein Content in Cyanobacterium Synechocystis

This is a simple protocol for the quantitative determination of phycobiliprotein content in the model cyanobacterium Synechocystis. Phycobiliproteins are the most important components of phycobilisomes, the major light-harvesting antennae in cyanobacteria and several algae taxa. The phycobilisomes of Synechocystis contain two phycobiliproteins: phycocyanin and allophycocyanin. This protocol describes a simple, efficient, and reliable method for the quantitative determination of both phycocyanin and allophycocyanin in this model cyanobacterium. We compared several methods of phycobiliprotein extraction and spectrophotometric quantification. The extraction procedure as described in this protocol was also successfully applied to other cyanobacteria strains such as Cyanothece sp., Synechococcuselongatus, Spirulina sp., Arthrospira sp., and Nostoc sp., as well as to red algae Porphyridium cruentum. However, the extinction coefficients of specific phycobiliproteins from various taxa can differ and it is, therefore, recommended to validate the spectrophotometric quantification method for every single strain individually. The protocol requires little time and can be performed in any standard life science laboratory since it requires only standard equipment.

Spectrophotometric Determination of Phycobiliprotein Content in Cyanobacterium Synechocystis

Here, we present a protocol to quantitatively determine phycobiliprotein content in the cyanobacterium Synechocystis using a spectrophotometric method. The extraction procedure was also successfully applied to other cyanobacteria and algae strains; however, due to variations in pigment absorption spectra, it is necessary to test the spectrophotometric equations for each strain individually.

This is a simple protocol for the quantitative determination of phycobiliprotein content in the model cyanobacterium Synechocystis. Phycobiliproteins are ...

Affinity Purification of Chloroplast Translocon Protein Complexes Using the TAP Tag

Chloroplast biogenesis requires the import of thousands of nucleus-encoded proteins into the plastid. The import of these proteins depends on the translocon at the outer (TOC) and inner (TIC) chloroplast membranes. The TOC and TIC complexes are multimeric and probably contain yet unknown components. One of the main goals in the field is to establish the complete inventory of TOC and TIC components. For the isolation of TOC-TIC complexes and the identification of new components, the preprotein receptor TOC159 has been modified N-terminally by the addition of the tandem affinity purification (TAP) tag resulting in TAP-TOC159. The TAP-tag is designed for two sequential affinity purification steps (hence "tandem affinity"). The TAP-tag used in these studies consists of a N-terminal IgG-binding domain derived from Staphylococcus aureus Protein A (ProtA) followed by a calmodulin-binding peptide (CBP). Between these two affinity tags, a tobacco etch virus (TEV) protease cleavage site has been included. Therefore, TEV protease can be used for gentle elution of TOC159-containing complexes after binding to IgG beads. In the protocol presented here, the second Calmodulin-affinity purification step was omitted. The purification protocol starts with the preparation and solubilization of total cellular membranes. After the detergent-treatment, the solubilized membrane proteins are incubated with IgG beads for the immunoisolation of TAP-TOC159-containing complexes. Upon binding and extensive washing, TAP-TOC159 containing complexes are cleaved and released from the IgG beads using the TEV protease whereby the S. aureus IgG-binding domain is removed. Western blotting of the isolated TOC159-containing complexes can be used to confirm the presence of known or suspected TOC and TIC proteins. More importantly, the TOC159-containing complexes have been used successfully to identify new components of the TOC and TIC complexes by mass spectrometry. The protocol that we present potentially allows the efficient isolation of any membrane-bound protein complex to be used for the identification of yet unknown components by mass spectrometry.

We here present a proven and tested protocol for the purification of chloroplast protein import complexes (TOC-TIC complex) using the TAP-tag. The one-step affinity-isolation protocol can potentially be applied to any protein and be used to identify new interaction partners by mass spectrometry.

Chloroplast biogenesis requires the import of thousands of nucleus-encoded proteins into the plastid. The import of these proteins depends on the ...

Using High Content Imaging to Quantify Target Engagement in Adherent Cells

Quantitating the interaction of small molecules with their intended protein target is critical for drug development, target validation and chemical probe validation. Methods that measure this phenomenon without modification of the protein target or small molecule are particularly valuable though technically challenging. The cellular thermal shift assay (CETSA) is one technique to monitor target engagement in living cells. Here, we describe an adaptation of the original CETSA protocol, which allows for high throughput measurements while retaining subcellular localization at the single cell level. We believe this protocol offers important advances to the application of CETSA for in-depth characterization of compound-target interaction, especially in heterogeneous populations of cells.

Measurements of drug target engagement are central to effective drug development and chemical probe validation. Here, we detail a protocol for measuring drug-target engagement using high content imaging in a microplate-compatible adaption of the cellular thermal shift assay (CETSA).

Quantitating the interaction of small molecules with their intended protein target is critical for drug development, target validation and chemical probe ...

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;

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

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

To satisfy the ever-growing market for amino acids, high-performance production strains are needed. The amino acid overproducers are conventionally identified by harnessing the competitions between amino acids and their analogs. However, this analog-based method is of low accuracy, and proper analogs for specific amino acids are limited. Here, we present an alternative strategy that enables an accurate, sensitive, and high-throughput screening of amino acid overproducers using rare-codon-rich markers. This strategy is inspired by the phenomenon of codon usage bias in protein translation, for which codons are categorized into common or rare ones based on their frequencies of occurrence in the coding DNA. The translation of rare codons depends on their corresponding rare transfer RNAs (tRNAs), which cannot be fully charged by the cognate amino acids under starvation. Theoretically, the rare tRNAs can be charged if there is a surplus of the amino acids after charging the synonymous common isoacceptors. Therefore, retarded translations caused by rare codons could be restored by feeding or intracellular overproductions of the corresponding amino acids. Under this assumption, a selection or screening system for identifying amino acid overproducers is established by replacing the common codons of the targeted amino acids with their synonymous rare alternatives in the antibiotic resistance genes or the genes encoding fluorescent or chromogenic proteins. We show that the protein expressions can be greatly hindered by the incorporation of rare codons and that the levels of proteins correlate positively with the amino acid concentrations. Using this system, overproducers of multiple amino acids can be readily screened out from mutation libraries. This rare-codon-based strategy only requires a single modified gene, and the host is less likely to escape the selection than in other methods. It offers an alternative approach for obtaining amino acid overproducers.

This study presents an alternative strategy to the conventional toxic analog-based method in identifying amino acid overproducers by using rare-codon-rich markers to achieve accuracy, sensitivity, and high-throughput simultaneously.

To satisfy the ever-growing market for amino acids, high-performance production strains are needed. The amino acid overproducers are conventionally ...

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.

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

Kinetic Screening of Nuclease Activity using Nucleic Acid Probes

Nucleases are a class of enzymes that break down nucleic acids by catalyzing the hydrolysis of the phosphodiester bonds that link the ribose sugars. Nucleases display a variety of vital physiological roles in prokaryotic and eukaryotic organisms, ranging from maintaining genome stability to providing protection against pathogens. Altered nuclease activity has been associated with several pathological conditions including bacterial infections and cancer. To this end, nuclease activity has shown great potential to be exploited as a specific biomarker. However, a robust and reproducible screening method based on this activity remains highly desirable.
Herein, we introduce a method that enables screening for nuclease activity using nucleic acid probes as substrates, with the scope of differentiating between pathological and healthy conditions. This method offers the possibility of designing new probe libraries, with increasing specificity, in an iterative manner. Thus, multiple rounds of screening are necessary to refine the probes&#39; design with enhanced features, taking advantage of the availability of chemically modified nucleic acids. The considerable potential of the proposed technology lies in its flexibility, high reproducibility, and versatility for the screening of nuclease activity associated with disease conditions. It is expected that this technology will allow the development of promising diagnostic tools with a great potential in the clinic.

Altered nuclease activity has been associated with different human conditions, underlying its potential as a biomarker. The modular and easy to implement screening methodology presented in this paper allows the selection of specific nucleic acid probes for harnessing nuclease activity as a biomarker of disease.

Nucleases are a class of enzymes that break down nucleic acids by catalyzing the hydrolysis of the phosphodiester bonds that link the ribose sugars. ...

Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC

The phosphate esters of myo-inositol, also termed inositol phosphates (InsPs), are a class of cellular regulators playing important roles in plant physiology. Due to their negative charge, low abundance and susceptibility to hydrolytic activities, the detection and quantification of these molecules is challenging. This is particularly the case for highly phosphorylated forms containing &#8216;high-energy&#8217; diphospho bonds, also termed inositol pyrophosphates (PP-InsPs). Due to its high sensitivity, strong anion exchange high-performance liquid chromatography (SAX-HPLC) of plants labeled with [3H]-myo-inositol is currently the method of choice to analyze these molecules. By using [3H]-myo-inositol to radiolabel plant seedlings, various InsP species including several non-enantiomeric isomers can be detected and discriminated with high sensitivity. Here, the setup of a suitable SAX-HPLC system is described, as well as the complete workflow from plant cultivation, radiolabeling and InsP extraction to the SAX-HPLC run and subsequent data analysis. The protocol presented here allows the discrimination and quantification of various InsP species, including several non-enantiomeric isomers and of the PP-InsPs, InsP7 and InsP8, and can be easily adapted to other plant species. As examples, SAX-HPLC analyses of Arabidopsis thaliana and Lotus japonicus seedlings are performed and complete InsP profiles are presented and discussed. The method described here represents a promising tool to better understand the biological roles of InsPs in plants.

Here we describe strong anion exchange high-performance liquid chromatography of [3H]-myo-inositol-labeled seedlings which is a highly sensitive method to detect and quantify inositol polyphosphates in plants.