The authors would like to thank intern Evelyn Von Neida who provided paramount insights regarding the preparation of biomass samples for both of the high-throughput pipelines discussed in this manuscript. Support for the development of this work and manuscript was provided by the BioEnergy Science Center. The BioEnergy Science Center is a U.S. Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. The National Renewable Energy Laboratory (NREL) is a national laboratory of the US DOE Office of Energy Efficiency and Renewable Energy, operated for DOE by the Alliance for Sustainable Energy, LLC. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory.

1. High-throughput Determination of Glucose and Xylose Yields Following Enzymatic Saccharification9,29
<ol>
	<li>Sample Preparation (Grinding, De-starching, Extraction, Pretreatment)
	<ol>
		<li>Grind at least 300 mg of each biomass sample using a Wiley mill, such that the particles pass through a 20 mesh (850 &#181;m) screen. Transfer to anti-static zip-top bags (typically bar-coded) and record sample information to the barcode database.</li>
		<li>Add approximately 250 mg or more of the ground biomass from anti-static bag to a numbered (with pencil, do not use ink or marker) tea-bag, carefully roll up the teabags, being sure to fold the ends over the biomass to prevent loss during de-starching and extraction.</li>
		<li>Wrap the teabag closed using tin-coated copper wire. Record the teabag number for each barcoded sample.</li>
		<li>Prepare de-starching enzyme solution from commercial enzymes (typically, 0.25% (v/v) glucoamylase (~1,600 AGU/L) and 1.5% (v/v) alpha-amylase (~2,900 KNU-S/L) in 0.1 M sodium acetate (pH 5.0)). Prepare 16 ml per g of bulk biomass or 500 ml per 120 teabag samples. Note: Loadings capable of removing starch from ground biomass should be determined empirically by testing for starch after digestion with a range of enzyme loadings and ratios.</li>
		<li>In a plastic container, add 16 ml of de-starching enzyme solution per 1 g of bulk ground biomass. For a batch de-starching protocol, add 120 teabags to 500 ml of the buffer-enzyme solution.</li>
		<li>Incubate in a shaker at 55&#160;&#176;C for 24 (&#177; 4) hr, at 120 rpm to remove possible starch.</li>
		<li>After the de-starching incubation, rinse and soak the biomass in several liters of deionized water for 30 min. Repeat this process two additional times to remove buffer salts that can cause bumping (formation of gas bubbles in the solution that can result in a sudden and violent rise in the level of the solution, causing hot liquid ethanol to cascade out of the reaction vessel) during the extraction.</li>
		<li>Following the exhaustive rinsing of the de-starched biomass, place the teabags into the Soxhlet column for extraction. No thimble is required for this step.</li>
		<li>Set up the Soxhlet reflux, using 95% ethanol and extract the samples for 24 hr.</li>
		<li>Remove the teabags from the Soxhlet reactor and spread out in a single layer on a flat tray.</li>
		<li>Allow the samples to dry overnight at room temperature in a fume hood.</li>
		<li>Unroll the teabags and return the dried biomass to original barcoded anti-static bags. 
		Note: The anti-static bags are efficient at reducing static electricity in the biomass, improving the handling characteristics. If other storage options are used, additional biomass may be needed due to the biomass clinging to the side of the storage container.</li>
		<li>Transfer at least 50 mg of the dried biomass to a barcoded hopper (using more material is better for accurate dispensing). Scan barcodes of anti-static bags and the receiving hopper to ensure accurate sample tracking.</li>
		<li>Load hoppers onto solids weighing robot, paying close attention to the order of the samples in the racks. Load enough reactor plates to contain all the samples and select the dispensing protocol based on the number of plates used.</li>
		<li>Robotically weigh 5 mg of the dried samples (&#177; 0.3 mg) into acid-resistant stainless steel 96-well plates. Weigh out 3 replicates of each sample distributed around the plate to minimize any localized variations.</li>
		<li>Include 8 control biomass samples of a well-characterized biomass standard material in order to track assay performance. For multiple plates, the use of multiple standard biomass hoppers will minimize particle size drift caused by sieving in the hoppers over repeated dispensing cycles. 
		Note: In each plate, there should be 24 samples with 3 replicates, 4 blanks consisting of deionized water and enzyme, and 8 controls, using the previously characterized standard biomass. The 3 corner wells of each of the 4 corners of the plate are reserved for the sugar standards.</li>
		<li>Check blank and sugar standard wells for errant biomass particles and remove if present.</li>
		<li>Add 250 &#181;l of deionized water to each well, and seal the plates with silicone adhesive-backed polytetrafluoroethylene (PTFE) tape.</li>
		<li>Using a 1/8&#8221; soldering iron tip, pierce the PTFE tape at each steam port (117 total) for each plate. Use an empty plate stacked on sealed reactor plate as a guide and to restrict PTFE film movement.</li>
		<li>Clamp the plates tightly with 0.031&#8221; thick glass-reinforced PTFE gaskets (pre-punched with holes for steam ports) between plates and empty plates on top and bottom of stack. Pretreat the samples using a steam reactor set to 180&#160;&#176;C for 17.5 min, or other temperature/time combinations based on desired severity.</li>
		<li>Cool reactor plates to 50&#160;&#176;C by flooding with deionized water.</li>
	</ol>
	</li>
	<li>Enzymatic Saccharification
	<ol>
		<li>Prepare an enzymatic saccharification solution consisting of 8% (v/v) enzyme solution in 1.0 M sodium citrate, pH 5.0. Prepare 5 ml per reactor plate. Note: Dilution required should be determined based on activity and protein content of the specific stock enzyme solution.</li>
		<li>When the plates are cool, centrifuge them in a swinging bucket rotor at 1,500 x g for 20 min. Remove sealing film. Note: These plates are heavy and the centrifuge specifications should be checked for compatibility.</li>
		<li>Add 40 &#181;l of the 8% enzyme stock solution to each well (70 mg enzyme per g biomass).</li>
		<li>Reseal with new PTFE tape. Place sealed plate into magnetic plate clamp.</li>
		<li>Gently mix the samples by inversion (at least 15 times), and incubate at 50&#160;&#176;C for 70 hr.</li>
		<li>When the saccharification has concluded, mix by inversion and centrifuge the plates at 1,500 x g for 20 min.</li>
	</ol>
	</li>
	<li>Sugar Assay
	<ol>
		<li>Prepare a set of 6 glucose and xylose combined calibration standards in 0.014 M citrate buffer (pH 5.0) ranging from 0 to 0.750 mg/ml (0, 0.2, 0.3, 0.45, 0.65, and 0.75 mg/ml recommended) and 0 to 0.600 mg/ml (0, 0.1, 0.2, 0.35, 0.45, and 0.6 mg/ml recommended) for glucose and xylose, respectively.</li>
		<li>Prepare the glucose oxidase/peroxidase (GOPOD) and xylose dehydrogenase (XDH) reagents according to instructions in the kit.</li>
		<li>Using a pipette, remove 200 &#181;l of liquid from the 4 corner wells and edge wells adjacent to the corner wells (12 total wells) of the reactor plate.</li>
		<li>Using a pipette, dispense 180 &#181;l deionized water to each well of a 96-well polystyrene flat-bottom dilution plate. Do not add water to the 12 sugar standard and corner wells (remove those tips if using a 96-channel head).</li>
		<li>Using a pipette, dispense 180 &#181;l GOPOD reagent to each well of the glucose assay plate.</li>
		<li>Using a pipette, dispense 180 &#181;l XDH reagent to each well of the xylose assay plate.</li>
		<li>Using a pipette, transfer 20 &#181;l hydrolysate aliquots from the 96-well reactor plate to the dilution plate. Pipet from the upper section of the wells to avoid biomass solids and residual liquid in corner wells. Mix by trituration for at least 10 cycles.</li>
		<li>Using a pipette, transfer 110 &#181;l of sugar standards, in duplicate, to the corner wells of the dilution plate.</li>
		<li>Using a pipette, transfer 20 &#181;l aliquots from the dilution plate to the glucose and xylose assay plates. Mix by trituration.</li>
		<li>Incubate the glucose and xylose assay plates at room temperature for 30 min. Carefully break any surface bubbles before reading. A heat gun briefly passed over the surface of the plate works well.</li>
		<li>Using an ultraviolet/visible 96-well plate reader, set the measured wavelength to 510 nm and record the absorbance against a reagent blank. This measurement monitors the formation of quinonimine, which is proportional to glucose concentration. The glucose concentration is calculated from the calibration curve based on the calibration standards prepared in 1.3.1.</li>
		<li>Using an ultraviolet/visible 96-well plate reader, set the measured wavelength to 340 nm and record the absorbance. This measurement monitors the reduction of NAD+ to NADH, which is proportional to xylose concentration. The xylose concentration is calculated from the calibration curve based on the calibration standards prepared in 1.3.1.</li>
	</ol>
	</li>
</ol>
2. High-throughput Determination of Total Lignin and Lignin Monomeric Content Using pyMBMS 28
<ol>
	<li>Sample Preparation
	<ol>
		<li>Grind and extract the biomass using the methods described in steps 1.1.1, and 1.1.6-1.1.8. This includes grinding and extracting of a set of standards to use as experimental controls. 
		Note: The pyMBMS measurements are not particle-size dependent, so if only using the pyMBMS protocol, biomass preparation should be carried out to allow the sample to fit in the sample holder, &#60;4 mm.</li>
		<li>Using a small spatula dispense approximately 4 mg (3 to 5 mg preferred) of the prepared biomass into an 80 &#181;l stainless steel cup designed for the auto-sampler.</li>
		<li>Ensure that at least 10% of the samples being analyzed are control standards such as those available from the National Institute of Standards and Technology (sugarcane bagasse-8491; poplar-8492; pine-8493; or wheat straw-8494). The standard can also be any species analogous to the samples to be analyzed that have already been characterized using standard methods.</li>
		<li>Randomly load the samples into the auto-sampler cups using tweezers to avoid bias due to possible spectrometer drift over time. Note: A typical randomization of the samples would include the measurement of all samples once, followed by a re-randomization of the order of the samples for a duplicate measurement. Randomization programs are available online.</li>
		<li>Using a standard hole-punch, manually produce glass filter discs from type A/D glass fiber sheets, with no binder. Hold the round glass fiber filter with tweezers, center it over the sample cup, and push into the sample using a 3.5 mm Allen wrench to confine the material in each cup during the experiment.</li>
	</ol>
	</li>
	<li>Instrumental Protocol
	<ol>
		<li>Calibrate the mass spectrometer using a known standard that has peak intensities over the entire range of compounds that may exist for the experimental samples. For typical biomass samples, use perfluorotributylamine (PFTBA).</li>
		<li>Set the helium carrier gas flow rate to 0.9 L/min using a gas flow meter.</li>
		<li>Set the auto-sampler furnace to a pyrolysis temperature of 500&#160;&#176;C and the interface temperature to 350&#160;&#176;C using the auto-sampler software. Note: A 1/8&#8221; stainless steel heated transfer line wrapped in heat tape that connects the auto-sampler to the mass spectrometer is controlled using a heat controller at 250&#160;&#176;C.</li>
		<li>Begin data acquisition on the mass spectrometer and wait at least 60 sec to obtain sufficient data for background spectra collection.</li>
		<li>Start the automated auto-sampler method with the specifications from 2.2.2. Note: The auto-sampler drops each sample individually into the auto-sampler furnace. The total data acquisition time is approximately 1.5 min; however, pyrolysis of a typical 4 mg sample is complete after 30 sec.</li>
		<li>Record total ion content (TIC) of each sample using mass spectrometer software at 0.5 sec scan rate. Record intensities between m/z 30 to 450. 
		Note: Typical biomass compounds use soft ionization at 17 eV. The instrument can record larger intervals from m/z 1 to 1,000; however, scan rate will be limited by computer CPU power.</li>
		<li>Remove background from the spectra using the manual enhance feature in the software. Note: A 60 scan portion of the baseline at the beginning of data collection is used to calculate an average background value. This average background spectra is removed from the experimental sample spectra automatically in the mass spectrometer software.</li>
		<li>Import the single column text file created by the mass spectrometer software containing the spectral data for each sample, into a database program and combine all the samples into one database. Add any applicable metadata to the spreadsheet. Import the formatted data (spreadsheet/CSV file) into a statistical software package and mean normalize the spectra to account for variation in the pyrolyzed sample masses.</li>
		<li>Use a statistical software package to perform a principal component analysis (PCA) using the spectral data to analyze grouping of the replicated standard samples used in the measurements, as well as to evaluate which peaks are integral to the classification of chemical compounds in the loadings plot28. 
		Note: PCA groups the samples based on the similarity of their spectra, and allows a check of the standards to gauge experimental error due to instrument drift during the run.</li>
		<li>To calculate the lignin syringyl (S)/guaiacyl (G) ratio, sum the areas of the S peaks (m/z = 154, 167, 168, 182, 194, 208, and 210) and divide by the sum of G peaks at 124, 137, 138, 150, 164, and 178.</li>
		<li>To calculate the total lignin content, sum the lignin peaks with m/z = 120, 124, 137, 138, 150, 152, 154, 164, 167, 178, 180, 182, 194, and 210.</li>
		<li>Calculate a correction factor for scaling the pyMBMS measurement to a standard method for estimating total lignin, such as Klason lignin. Divide the Klason lignin value of the individual standard by the total lignin content measured for that standard sample using pyMBMS.</li>
		<li>Apply this correction factor to all like-species in the data set. Repeat for each type of biomass analyzed. Note: The correction factor can vary significantly based on the S/G of the biomass analyzed.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Perlack, R. D., Stokes, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. , (2011).</li><li>Henry, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ch.+5.">Ch. 5.</a> Plant Resources for Food, Fuel and Conservation. , 53-80 (2009).</li><li>Henry, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+plant+biomass+resources+available+for+replacement+of+fossil+oil.">Evaluation of plant biomass resources available for replacement of fossil oil.</a> Plant Biotechnol. J. 8 (3), 288-293 (2010).</li><li>Furtado, A., 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=Modifying+plants+for+biofuel+and+biomaterial+production.">Modifying plants for biofuel and biomaterial production.</a> Plant Biotechnol. J. 12 (9), 1246-1258 (2014).</li><li>Sluiter, J. B., Ruiz, R. O., Scarlata, C. J., Sluiter, A. D., Templeton, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Compositional+analysis+of+lignocellulosic+feedstocks.+1.+Review+and+description+of+methods.">Compositional analysis of lignocellulosic feedstocks. 1. Review and description of methods.</a> J. Agric. Food Chem. 58 (16), 9043-9053 (2010).</li><li>Lupoi, J. S., Singh, S., Simmons, B. A., Henry, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+Lignocellulosic+Biomass+Using+Analytical+Spectroscopy:+an+Evolution+to+High-Throughput+Techniques.">Assessment of Lignocellulosic Biomass Using Analytical Spectroscopy: an Evolution to High-Throughput Techniques.</a> Bioenerg. Res. 7 (1), 1-23 (2014).</li><li>Lapierre, C., Monties, B., Rolando, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thioacidolysis+of+lignin:+comparison+with+acidolysis.">Thioacidolysis of lignin: comparison with acidolysis.</a> J. Wood Chem. Technol. 5 (2), 277-292 (1985).</li><li>DeMartini, J. D., Studer, M. H., Wyman, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small-scale+and+automatable+high-throughput+compositional+analysis+of+biomass.">Small-scale and automatable high-throughput compositional analysis of biomass.</a> Biotechnol. Bioeng. 108 (2), 306-312 (2010).</li><li>Selig, M. J., 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=High+throughput+determination+of+glucan+and+xylan+fractions+in+lignocelluloses.">High throughput determination of glucan and xylan fractions in lignocelluloses.</a> Biotechnol. Lett. 33 (5), 961-967 (2011).</li><li>Selig, M. J., 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=Lignocellulose+recalcitrance+screening+by+integrated+high-throughput+hydrothermal+pretreatment+and+enzymatic+saccharification.">Lignocellulose recalcitrance screening by integrated high-throughput hydrothermal pretreatment and enzymatic saccharification.</a> Ind. Biotechnol. 6 (2), 104-111 (2010).</li><li>Studer, M. H., De Martini, J. D., Brethauer, S., McKenzie, H. L., Wyman, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Engineering+of+a+high-throughput+screening+system+to+identify+cellulosic+biomass,+pretreatments,+and+enzyme+formulations+that+enhance+sugar+release.">Engineering of a high-throughput screening system to identify cellulosic biomass, pretreatments, and enzyme formulations that enhance sugar release.</a> Biotechnol. Bioeng. 105 (2), 231-238 (2009).</li><li>Gjersing, E., Happs, R. M., Sykes, R. W., Doeppke, C., Davis, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+determination+of+sugar+content+in+biomass+hydrolysates+using+nuclear+magnetic+resonance+spectroscopy.">Rapid determination of sugar content in biomass hydrolysates using nuclear magnetic resonance spectroscopy.</a> Biotechnol. Bioeng. 110 (3), 721-728 (2013).</li><li>Templeton, D. W., Sluiter, A. D., Hayward, T. K., Hames, B. R., Thomas, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+corn+stover+composition+and+sources+of+variability+via+NIRS.">Assessing corn stover composition and sources of variability via NIRS.</a> Cellulose (Dordrecht, Netherlands). 16 (4), 621-639 (2009).</li><li>Tucker, M. P., 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=Fourier+transform+infrared+quantification+of+sugars+in+pretreated+biomass+liquors.">Fourier transform infrared quantification of sugars in pretreated biomass liquors.</a> Appl. Biochem. Biotechnol. 84-86, 39-50 (2000).</li><li>Wolfrum, E. J., Sluiter, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+multivariate+calibration+models+for+corn+stover+feedstock+and+dilute-acid+pretreated+corn+stover.">Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover.</a> Cellulose (Dordrecht, Netherlands). 16 (4), 567-576 (2009).</li><li>Ona, 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=Non-destructive+determination+of+wood+constituents+by+Fourier-transform+Raman+spectroscopy.">Non-destructive determination of wood constituents by Fourier-transform Raman spectroscopy.</a> J. Wood Chem. Technol. 17 (4), 399-417 (1997).</li><li>Ona, T., Sonoda, T., Ohshima, J., Yokota, S., Yoshizawa, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid+quantitative+method+to+assess+eucalyptus+wood+properties+for+kraft+pulp+production+by+FT-Raman+spectroscopy.">A rapid quantitative method to assess eucalyptus wood properties for kraft pulp production by FT-Raman spectroscopy.</a> J. Pulp Pap. Sci. 29 (1), 6-10 (2003).</li><li>Hames, B. R., Thomas, S. R., Sluiter, A. D., Roth, C. J., Templeton, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+biomass+analysis.+New+tools+for+compositional+analysis+of+corn+stover+feedstocks+and+process+intermediates+from+ethanol+production.">Rapid biomass analysis. New tools for compositional analysis of corn stover feedstocks and process intermediates from ethanol production.</a> Appl. Biochem. Biotechnol. 105-108, 5-16 (2003).</li><li>Studer, M. 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=Lignin+content+in+natural+Populus+variants+affects+sugar+release.">Lignin content in natural Populus variants affects sugar release.</a> Proc. Natl. Acad. Sci. U. S. A. 108 (15), 6300-6305 (2011).</li><li>Chen, M., Zhao, J., Xia, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+four+different+chemical+pretreatments+of+corn+stover+for+enhancing+enzymatic+digestibility.">Comparison of four different chemical pretreatments of corn stover for enhancing enzymatic digestibility.</a> Biomass Bioenergy. 33 (10), 1381-1385 (2009).</li><li>Davison, B. H., Drescher, S. R., Tuskan, G. A., Davis, M. F., Nghiem, N. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Variation+of+S/G+ratio+and+lignin+content+in+a+Populus.+family+influences+the+release+of+xylose+by+dilute+acid+hydrolysis.">Variation of S/G ratio and lignin content in a Populus. family influences the release of xylose by dilute acid hydrolysis.</a> Appl. Biochem. Biotechnol. 129-132, 427-435 (2006).</li><li>Li, 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=Lignin+monomer+composition+affects+Arabidopsis+cell-wall+degradability+after+liquid+hot+water+pretreatment.">Lignin monomer composition affects Arabidopsis cell-wall degradability after liquid hot water pretreatment.</a> Biotechnol. Biofuels. 3, 27-33 (2010).</li><li>Lupoi, J. 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=High-throughput+prediction+of+eucalypt+lignin+syringyl/guaiacyl+content+using+multivariate+analysis:+a+comparison+between+mid-infrared,+near-infrared,+and+Raman+spectroscopies+for+model+development.">High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development.</a> Biotechnol. Biofuels. 7, 93 (2014).</li><li>Lupoi, J. S., Smith, 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=Characterization+of+woody+and+herbaceous+biomasses+lignin+composition+with+1064+nm+dispersive+multichannel+Raman+spectroscopy.">Characterization of woody and herbaceous biomasses lignin composition with 1064 nm dispersive multichannel Raman spectroscopy.</a> Appl. Spectro. 66 (8), 903-910 (2012).</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=Rapid+determination+of+syringyl:guaiacyl+ratios+using+FT-Raman+spectroscopy.">Rapid determination of syringyl:guaiacyl ratios using FT-Raman spectroscopy.</a> Biotechnol. Bioeng. 109 (3), 647-656 (2012).</li><li>Lupoi, J. 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=High-throughput+prediction+of+Acacia+and+eucalypt+lignin+syringyl/guaiacyl+content+using+FT-Raman+spectroscopy+and+partial+least+squares+modeling.">High-throughput prediction of Acacia and eucalypt lignin syringyl/guaiacyl content using FT-Raman spectroscopy and partial least squares modeling.</a> Bioenerg. Res. in press, (2015).</li><li>Sykes, R., Kodrzycki, B., Tuskan, G., Foutz, K., Davis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Within+tree+variability+of+lignin+composition+in+Populus.">Within tree variability of lignin composition in Populus.</a> Wood Sci. Technol. 42 (8), 649-661 (2008).</li><li>Sykes, R., Mielenz, J. R., 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=Ch.+12.+High-Throughput+Screening+of+Plant+Cell-Wall+Composition+Using+Pyrolysis+Molecular+Beam+Mass+Spectroscopy.">Ch. 12. High-Throughput Screening of Plant Cell-Wall Composition Using Pyrolysis Molecular Beam Mass Spectroscopy.</a> Biofuels: Methods and Protocols. 581, 169-183 (2009).</li><li>Decker, S., Himmel, M. E., 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=Ch.+17.+Reducing+the+effect+of+variable+starch+levels+in+biomass+recalcitrance+screening).">Ch. 17. Reducing the effect of variable starch levels in biomass recalcitrance screening).</a> Biomass Conversion. 908, 181-195 (2012).</li><li>Evans, R. J., Milne, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+characterization+of+the+pyrolysis+of+biomass.">Molecular characterization of the pyrolysis of biomass.</a> Energy Fuels. 1 (2), 123-137 (1987).</li></ol>

The authors have nothing to disclose.

The key sample preparation steps for obtaining accurate and reproducible data when conducting high-throughput screening experiments are as follows:
Sugar Release Assay:
In general, samples are prepared in lots ranging from a few dozen to several thousand at a time. Each major step is typically carried out for all samples prior to moving forward in order to minimize variations in preparation between samples. De-starching was not originally part of the protocol and ca...

As the global supply of non-renewable fuels and their associated products declines, scientists have been challenged to create similar fuels and chemicals from plant-derived sources1. A key aspect of this work is determining which species of plants may be suitable for the production of biofuels and biomaterials2,3. Typically, these feedstocks are evaluated for lignin, cellulose, and hemicellulose content; as well as their susceptibility to deconstruction (recalcitrance) through thermal, mechanical, and/or chemical pretreatment with or without subsequent enzyme saccharification. More detailed analyses are used to determine the specific composition of the lignin and hemicellulose fractions as well as optimal enzyme activities needed. Transgenic modifications of plants that do not intrinsically possess ideal traits for biochemical or thermochemical conversion to desired commodities have provided researchers with a greatly expanded source of potential feedstocks4. The standard analytical methods for quantifying the chemical traits of a plant, while quite useful for small sample sets, are unsuited for the rapid screening of hundreds or thousands of samples5-7. The HTP methods described herein have been developed to rapidly and efficiently evaluate large numbers of biomass variants for changes in cell wall recalcitrance to thermochemical and/or enzymatic degradation.
It is critical to understand that the HTP screening assays described herein have not been designed to maximize conversion or yield. The objective is to determine relative differences in the intrinsic recalcitrance of related biomass samples. As a result, many of the analysis steps differ from the &#8220;typical&#8221; biomass conversion assays, where the objective is to obtain maximum conversion rate or extent. For example, lower pretreatment severities and shorter enzyme hydrolysis times are used to maximize differences between samples. In most cases, relatively high enzyme loadings are used to reduce differences due to experimental variation in enzyme activity, which could skew the results significantly.
Rapid techniques for determining the composition of plant cell-walls and the monomeric sugars liberated following enzymatic saccharification include robotics, customized, thermochemically compatible 96-well plates, and modifications of standard laboratory methods8-11 and instrumental protocols, such as vibrational spectroscopy (infrared (IR), near-infrared (NIR), or Raman) and nuclear magnetic resonance (NMR)12-17. These methodologies are key to isolating feedstocks with high cellulose or low lignin contents, or those expected to yield the highest glucose, xylose, ethanol, etc. These methods have enabled downscaled analyses that employ smaller quantities of biomass and consumables, leading to reductions in experimental expense18. Another feature of this methodological approach is that various experimental conditions can be rapidly, and in some cases simultaneously, evaluated. For example, a variety of different pretreatment strategies or enzyme cocktails can be tested, allowing the most optimal experimental parameters to be quickly identified and employed. Popular feedstocks, such as corn stover9, poplar8,10, sugarcane bagasse8, and switchgrass8 have been successfully evaluated using these HTP methods.
Total lignin and lignin monomeric composition are also commonly quantified biomass traits. Reductions in lignin content have been shown to increase the enzymatic digestibility of polysaccharides19,20. The role that the lignin monomeric ratio (often reported as syringyl/guaiacyl (S/G) content) plays in the deconstruction of the plant cell wall is still under investigation. Some reports have indicated that reductions in the S/G ratio led to increased glucose yields following hydrolysis21, while other studies unveil the opposite trend19,22. High throughput methods for evaluating lignin and its monomers include vibrational spectroscopy (IR, NIR, and Raman23-26) coupled with multivariate analysis, and pyrolysis molecular beam mass spectrometry (pyMBMS)27,28.
When developing HTP methods for screening biomass, several integral considerations need to be kept in mind. One key aspect is the complexity of the method. What is the required skill level for the technique? Chemometric analyses, for example, require specific skills for constructing, evaluating, and maintaining predictive models. The standard methods exhibit undesirable preparatory or data analysis steps or employ toxic reagents. Development of the models is an ongoing process where new data is incorporated into the model over time to increase the model&#8217;s robustness. Another consideration is the cost-savings and decreased experimental analysis times of the proposed high-throughput methods. If the method is quite rapid, but very costly, it may not be a feasible technique for many labs to adopt. The methods illustrated in this manuscript are variants of standardized techniques, modified to amplify the throughput capabilities. These protocols quantitatively measure the biomass traits of interest without necessitating the development of predictive models. This is a key attribute of these techniques, since predictive methods, while exhibiting strong correlations with the standard analyses used to develop the models, are not as accurate as actually measuring the quantity of interest for the samples. Whereas the methods used are essentially scaled down versions of standard bench-scale analytical methods, accuracy and precision are traded for speed and throughput. Mostly, this outcome is due to higher errors in small volume pipetting and weighing; as well as increased sample heterogeneity as sample size is decreased. While large sample sets can be screened and compared, great care must be exercised when making comparisons between separate campaigns and to bench-scale results.
The most time-consuming steps involve the physical manipulation of the biomass. Grinding samples may take several min per sample, including cleaning out the mill between samples. Manually loading, unloading, and cleaning hoppers and filling and emptying tea bags and sample bags is also very labor intensive. While each step may take a minute or more, doing thousands of samples may take many hours or even days. The robots can load a typical reactor plate with biomass in about 3 to 4 hr or 6 to 8 plates day-1 robot-1. This situation depends on the precision parameters used as well as the type and amount of biomass to be tested. Filling reactor plates with water, dilute acid, or enzyme is quickly done using a liquid handling robot. Pretreatment of a plate stack (1 to 20 reactor plates) takes between 1 and 3 hr when assembly, cool down, and disassembly is included. Enzyme hydrolysis takes 3 days and the sugar analysis requires about 1 hr of prep time plus 10 min per reactor plate to complete the assay and read the results. A weekly schedule of set pretreatment and analysis days accommodates a reasonable work schedule, minimizing odd-hour and weekend efforts for the human component of the assay and allows for processing ~800 to 1,000 samples per week on an ongoing basis. The maximum throughput depends on several factors, mainly how much hardware (robots, reactors plates, etc.) and how much &#8220;software&#8221; (i.e., staffing) are available to do the manual work. The practical upper limit is 2,500 to 3,000 samples/week; however, that output requires 7 day-a-week operation and multiple student interns and technicians. In comparison, 3,000 samples by HPLC would require approximately 125 days of sample analysis plus the additional labor of manually weighing samples into reactors and filtering samples prior to analysis.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Wiley mill</td>
			<td>Thomas Scientific</td>
			<td>3375E15 (Model 4), or 3383L20 (Mini-mill)</td>
			<td></td>
		</tr>
		<tr>
			<td>anti-static bags</td>
			<td>Minigrip*</td>
			<td>MGST4P02503</td>
			<td>2.5x3&#34;, multiple suppliers available</td>
		</tr>
		<tr>
			<td>tin-coated copper wire</td>
			<td>McMaster-Carr</td>
			<td>8871K84</td>
			<td>0.016&#34; diameter, bend-and-stay wire</td>
		</tr>
		<tr>
			<td>tea-bags</td>
			<td>Herbco</td>
			<td>press n&#39; brew teabags</td>
			<td>3.5x5 inches</td>
		</tr>
		<tr>
			<td>gluco-amylase</td>
			<td>Novozymes</td>
			<td>Spirizyme Fuel&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>alpha-amylase</td>
			<td>Novozymes</td>
			<td>Liquozyme SC DS</td>
			<td></td>
		</tr>
		<tr>
			<td align="left">
			<table cellpadding="0" cellspacing="0">
				<tbody>
					<tr>
						<td>sodium acetate trihydrate</td>
					</tr>
				</tbody>
			</table>
			</td>
			<td>any chemical supplier</td>
			<td></td>
			<td>reagent grade</td>
		</tr>
		<tr>
			<td>acetic acid</td>
			<td>any chemical supplier</td>
			<td></td>
			<td>reagent grade</td>
		</tr>
		<tr>
			<td>190 proof (95%) ethanol</td>
			<td>any chemical supplier</td>
			<td></td>
			<td>reagent grade</td>
		</tr>
		<tr>
			<td>hoppers</td>
			<td>Freeslate</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>96-well C-276 Hastelloy plates</td>
			<td>Aspen Machining (Lafayette, Colorado)</td>
			<td>N/A (custom built)</td>
			<td></td>
		</tr>
		<tr>
			<td>1/8&#8221; soldering iron tip</td>
			<td>Sears</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>silicone-adhesive backed Teflon tape</td>
			<td>3M</td>
			<td>5180</td>
			<td>3&#34; wide (36-yard rolls)</td>
		</tr>
		<tr>
			<td>enzyme solution</td>
			<td>Novozymes</td>
			<td></td>
			<td>Cellic CTec2</td>
		</tr>
		<tr>
			<td>citric acid monohydrate</td>
			<td>any chemical supplier</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>trisodium citrate dihydrate</td>
			<td>any chemical supplier</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>disposable, polystyrene 96-well plates</td>
			<td>Greiner Bio-One</td>
			<td>655101</td>
			<td>or equivalent; multiple suppliers available</td>
		</tr>
		<tr>
			<td>glucose oxidase/peroxidase&#160;</td>
			<td>Megazyme</td>
			<td>K-Gluc</td>
			<td>Megazyme D-glucose assay kit</td>
		</tr>
		<tr>
			<td>xylose dehydrogenase</td>
			<td>Megazyme</td>
			<td>K-Xylose</td>
			<td>Megazyme D-xylose assay kit</td>
		</tr>
		<tr>
			<td>glucose standard solution</td>
			<td>Megazyme</td>
			<td>K-Gluc</td>
			<td>Megazyme D-glucose assay kit</td>
		</tr>
		<tr>
			<td>xylose standard solution</td>
			<td>Megazyme</td>
			<td>K-Xylose</td>
			<td>Megazyme D-xylose assay kit</td>
		</tr>
		<tr>
			<td>stainless steel sample cups</td>
			<td>Frontier Laboratories</td>
			<td>PY1-EC80F</td>
			<td></td>
		</tr>
		<tr>
			<td>glass fiber sheets</td>
			<td>Pall</td>
			<td>66227</td>
			<td>8x10&#34; sheets--circles punched with standard hole punch</td>
		</tr>
		<tr>
			<td>Sugarcane Bagasse Whole Biomass Feedstock</td>
			<td>NIST</td>
			<td>8491</td>
			<td></td>
		</tr>
		<tr>
			<td>Eastern Cottonwood (poplar) Whole Biomass Feedstock</td>
			<td>NIST</td>
			<td>8492</td>
			<td></td>
		</tr>
		<tr>
			<td>Monterey Pine Whole Biomass Feedstock</td>
			<td>NIST</td>
			<td>8493</td>
			<td></td>
		</tr>
		<tr>
			<td>Wheat Straw Whole Biomass Feedstock</td>
			<td>NIST</td>
			<td>8494</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

The combined effect of the thermochemical pretreatment and subsequent enzyme saccharification is measured as a function of the mass of glucose and xylose released at the end of the assay. The results are reported in terms of milligrams of glucose and xylose released per gram of biomass. This is in stark contrast to data reported from bench-scale assays, which is usually reported as percent theoretical yield based on compositional analysis of the starting material. As it is not yet practical to carry out compositional ana...

Watch this Scientific Journal Video about High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release at JoVE.com

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

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.

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

high-throughput-screening-recalcitrance-variations-lignocellulosic

Research

JoVE Journal

Environment

9.9K visualizações.  National Renewable Energy Laboratory. 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.

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

Determination of Microbial Extracellular Enzyme Activity in Waters, Soils, and Sediments using High Throughput Microplate Assays

Much of the nutrient cycling and carbon processing in natural environments occurs through the activity of extracellular enzymes released by microorganisms. Thus, measurement of the activity of these extracellular enzymes can give insights into the rates of ecosystem level processes, such as organic matter decomposition or nitrogen and phosphorus mineralization. Assays of extracellular enzyme activity in environmental samples typically involve exposing the samples to artificial colorimetric or fluorometric substrates and tracking the rate of substrate hydrolysis. Here we describe microplate based methods for these procedures that allow the analysis of large numbers of samples within a short time frame. Samples are allowed to react with artificial substrates within 96-well microplates or deep well microplate blocks, and enzyme activity is subsequently determined by absorption or fluorescence of the resulting end product using a typical microplate reader or fluorometer. Such high throughput procedures not only facilitate comparisons between spatially separate sites or ecosystems, but also substantially reduce the cost of such assays by reducing overall reagent volumes needed per sample.

Microplate based procedures are described for the colorimetric or fluorometric analysis of extracellular enzyme activity. These procedures allow for the rapid assay of such activity in large numbers of environmental samples within a manageable time frame.

Much of the nutrient cycling and carbon  processing in natural environments occurs through the activity of extracellular  enzymes released by ...

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be assimilated for energy and nutrients. Measuring soil microbial enzyme activity is crucial in understanding soil ecosystem functional dynamics. The general concept of the fluorescence enzyme assay is that synthetic C-, N-, or P-rich substrates bound with a fluorescent dye are added to soil samples. When intact, the labeled substrates do not fluoresce. Enzyme activity is measured as the increase in fluorescence as the fluorescent dyes are cleaved from their substrates, which allows them to fluoresce. Enzyme measurements can be expressed in units of molarity or activity. To perform this assay, soil slurries are prepared by combining soil with a pH buffer. The pH buffer (typically a 50 mM sodium acetate or 50 mM Tris buffer), is chosen for the buffer&#39;s particular acid dissociation constant (pKa) to best match the soil sample pH. The soil slurries are inoculated with a nonlimiting amount of fluorescently labeled (i.e. C-, N-, or P-rich) substrate. Using soil slurries in the assay serves to minimize limitations on enzyme and substrate diffusion. Therefore, this assay controls for differences in substrate limitation, diffusion rates, and soil pH conditions; thus detecting potential enzyme activity rates as a function of the difference in enzyme concentrations (per sample).
Fluorescence enzyme assays are typically more sensitive than spectrophotometric (i.e. colorimetric) assays,&#160;but can suffer from interference caused by impurities and the instability of many fluorescent compounds when exposed to light; so caution is required when handling fluorescent substrates. Likewise, this method only assesses potential enzyme activities under laboratory conditions when substrates are not limiting. Caution should be used when interpreting the data representing cross-site comparisons with differing temperatures or soil types, as in situ soil type and temperature can influence enzyme kinetics.

To measure potential rates of soil extracellular enzyme activities, synthetic substrates that are bound to a fluorescent dye are added to soil samples. Enzyme activity is measured as the fluorescent dye is released from the substrate by an enzyme-catalyzed reaction, where higher fluorescence indicates more substrate degradation.

Microbes in soils and other environments produce extracellular enzymes to depolymerize and hydrolyze organic macromolecules so that they can be ...

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce (Lactuca sativa) Germplasm Collections

This protocol describes a method by which a large collection of the leafy green vegetable lettuce (Lactuca sativa L.) germplasm was screened for likely drought-tolerance traits. Fresh water availability for agricultural use is a growing concern across the United States as well as many regions of the world. Short-term drought events along with regulatory intervention in the regulation of water availability coupled with the looming threat of long-term climate shifts that may lead to reduced precipitation in many important agricultural regions has increased the need to hasten the development of crops adapted for improved water use efficiency in order to maintain or expand production in the coming years. This protocol is not meant as a step-by-step guide to identifying at either the physiological or molecular level drought-tolerance traits in lettuce, but rather is a method developed and refined through the screening of thousands of different lettuce varieties. The nature of this screen is based in part on the streamlined measurements focusing on only three water-stress indicators: leaf relative water content, wilt, and differential plant growth following drought-stress. The purpose of rapidly screening a large germplasm collection is to narrow the candidate pool to a point in which more intensive physiological, molecular, and genetic methods can be applied to identify specific drought-tolerant traits in either the lab or field. Candidates can also be directly incorporated into breeding programs as a source of drought-tolerance traits.

Semi-High Throughput Screening for Potential Drought-tolerance in Lettuce Lactuca sativa Germplasm Collections

This protocol was developed to screen a large germplasm collection of the leafy vegetable lettuce (Lactuca sativa L.) for drought-tolerance, in order to identify a small candidate pool of lettuce for use in physiological, molecular, and genetic studies to identify underlying drought-tolerance traits along with breeding programs.

This protocol describes a method by which a large collection of the leafy green vegetable lettuce (Lactuca sativa L.) germplasm was screened for likely ...

Pretreatment of Lignocellulosic Biomass with Low-cost Ionic Liquids

A number of ionic liquids (ILs) with economically attractive production costs have recently received growing interest as media for the delignification of a variety of lignocellulosic feedstocks. Here we demonstrate the use of these low-cost protic ILs in the deconstruction of lignocellulosic biomass (Ionosolv pretreatment), yielding cellulose and a purified lignin. In the most generic process, the protic ionic liquid is synthesized by accurate combination of aqueous acid and amine base. The water content is adjusted subsequently. For the delignification, the biomass is placed into a vessel with IL solution at elevated temperatures to dissolve the lignin and hemicellulose, leaving a cellulose-rich pulp ready for saccharification (hydrolysis to fermentable sugars). The lignin is later precipitated from the IL by the addition of water and recovered as a solid. The removal of the added water regenerates the ionic liquid, which can be reused multiple times. This protocol is useful to investigate the significant potential of protic ILs for use in commercial biomass pretreatment/lignin fractionation for producing biofuels or renewable chemicals and materials.

The pretreatment of lignocellulosic biomass with protic low-cost ionic liquids is shown, resulting in a delignified cellulose-rich pulp and a purified lignin. The pulp gives rise to high glucose yields after enzymatic saccharification.

A number of ionic liquids (ILs) with economically attractive production costs have recently received growing interest as media for the delignification of ...

Ammonia Fiber Expansion (AFEX) Pretreatment of Lignocellulosic Biomass

Lignocellulosic materials are plant-derived feedstocks, such as crop residues (e.g., corn stover, rice straw, and sugar cane bagasse) and purpose-grown energy crops (e.g., miscanthus, and switchgrass) that are available in large quantities to produce biofuels, biochemicals, and animal feed. Plant polysaccharides (i.e., cellulose, hemicellulose, and pectin) embedded within cell walls are highly recalcitrant towards conversion into useful products. Ammonia fiber expansion (AFEX) is a thermochemical pretreatment that increases accessibility of polysaccharides to enzymes for hydrolysis into fermentable sugars. These released sugars can be converted into fuels and chemicals in a biorefinery. Here, we describe a laboratory-scale batch AFEX process to produce pretreated biomass on the gram-scale without any ammonia recycling. The laboratory-scale process can be used to identify optimal pretreatment conditions (e.g., ammonia loading, water loading, biomass loading, temperature, pressure, residence time, etc.) and generates sufficient quantities of pretreated samples for detailed physicochemical characterization and enzymatic/microbial analysis. The yield of fermentable sugars from enzymatic hydrolysis of corn stover pretreated using the laboratory-scale AFEX process is comparable to pilot-scale AFEX process under similar pretreatment conditions. This paper is intended to provide a detailed standard operating procedure for the safe and consistent operation of laboratory-scale reactors for performing AFEX pretreatment of lignocellulosic biomass.

Ammonia Fiber Expansion AFEX Pretreatment of Lignocellulosic Biomass

Ammonia fiber expansion (AFEX) is a thermochemical pretreatment technology that can convert lignocellulosic biomass (e.g., corn stover, rice straw, and sugarcane bagasse) into a highly digestible feedstock for both biofuels and animal feed applications. Here, we describe a laboratory-scale method for conducting AFEX pretreatment on lignocellulosic biomass.

Lignocellulosic materials are plant-derived feedstocks, such as crop residues (e.g., corn stover, rice straw, and sugar cane bagasse) and purpose-grown ...

A Novel Method for the Pentosan Analysis Present in Jute Biomass and Its Conversion into Sugar Monomers Using Acidic Ionic Liquid

Recently, ionic liquids (ILs) are used for biomass valorization into valuable chemicals because of their remarkable properties such as thermal stability, lower vapor pressure, non-flammability, higher heat capacity, and tunable solubility and acidity. Here, we demonstrate a method for the synthesis of C5 sugars (xylose and arabinose) from the pentosan present in jute biomass in a one-pot process by utilizing a catalytic amount of Br&#248;nsted acidic 1-methyl-3-(3-sulfopropyl)-imidazolium hydrogen sulfate IL. The acidic IL is synthesized in the lab and characterized using NMR spectroscopic techniques for understanding its purity. The various properties of BAIL are measured such as acid strength, thermal and hydrothermal stability, which showed that the catalyst is stable at a higher temperature (250 &#176;C) and possesses very high acid strength (Ho 1.57). The acidic IL converts over 90% of pentosan into sugars and furfural. Hence, the presenting method in this study can also be employed for the evaluation of pentosan concentration in other kinds of lignocellulosic biomass.

We present a protocol for the synthesis of C5 sugars (xylose and arabinose) from a renewable non-edible lignocellulosic biomass (i.e., jute) with the presence of Br&#248;nsted acidic ionic liquids (BAILs) as the catalyst in water. The BAILs catalyst exhibited better catalytic performance than conventional mineral acid catalysts (H2SO4 and HCl).

Recently, ionic liquids (ILs) are used for biomass valorization into valuable chemicals because of their remarkable properties such as thermal stability, ...

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Here, we present a microscale protocol for processing grain samples and for incorporating this microscale approach into a high-throughput analytical pipeline. This is a higher throughput adaptation of currently available protocols.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole ...

A 3-dimensional (3D)-printed Template for High Throughput Zebrafish Embryo Arraying

The zebrafish is a globally recognized fresh water organism frequently used in developmental biology, environmental toxicology, and human disease related research fields. Thanks to its unique features, including large fecundity, embryo translucency, rapid and simultaneous development, etc., zebrafish embryos are often used for large scale toxicity assessment of chemicals and drug/compound screening. A typical screening procedure involves adult zebrafish spawning, embryos selection, and arraying the embryos into multi-well plates. From there, embryos are subjected to exposure and the toxicity of chemical, or the effectiveness of the drugs/compounds can be evaluated relatively quickly based on phenotypic observations. Among these processes, embryos arraying is one of the most time-consuming and labor-intensive steps that limits the throughput level. In this protocol, we present an innovative approach that makes use of a 3D-printed arraying template coupled with vacuum manipulation to speed up this laborious step. The protocol herein describes the overall design of the arraying template, a detailed experimental setup and step-by-step procedure, followed by representative results. When implemented, this approach should prove beneficial in a variety of research applications using zebrafish embryos as testing subjects.

A 3-dimensional 3D-printed Template for High Throughput Zebrafish Embryo Arraying

Here, we present a protocol to design and fabricate a zebrafish embryo arraying template, followed by a detailed procedure on the use of such template for high throughput zebrafish embryo arraying into a 96-well plate.

The zebrafish is a globally recognized fresh water organism frequently used in developmental biology, environmental toxicology, and human disease related ...

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and maintenance issues. On the contrary, the nematode Caenorhabditis elegans (C. elegans) has advantages to make it an ideal choice for toxicity testing: a short lifespan, easy cultivation, and efficient reproduction. Here, we describe a protocol for the automatic phenotypic profiling of C. elegans in a 384-well plate. The nematode worms are cultured in a 384-well plate with liquid medium and chemical treatment, and videos are taken of each well to quantify the chemical influence on 33 worm features. Experimental results demonstrate that the quantified phenotype features can classify and predict the acute toxicity for different chemical compounds and establish a priority list for further traditional chemical toxicity assessment tests in a rodent model.

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A quantitative method has been developed to identify and predict the acute toxicity of chemicals by automatically analyzing the phenotypic profiling of Caenorhabditis elegans. This protocol describes how to treat worms with chemicals in a 384-well plate, capture videos, and quantify toxicological related phenotypes.

Applying toxicity testing of chemicals in higher order organisms, such as mice or rats, is time-consuming and expensive, due to their long lifespan and ...

High-throughput Siderophore Screening from Environmental Samples: Plant Tissues, Bulk Soils, and Rhizosphere Soils

Siderophores (low-molecular weight metal chelating compounds) are important in various ecological phenomenon ranging from iron (Fe) biogeochemical cycling in soils, to pathogen competition, plant growth promotion, and cross-kingdom signaling. Furthermore, siderophores are also of commercial interest in bioleaching and bioweathering of metal-bearing minerals and ores. A rapid, cost effective, and robust means of quantitatively assessing siderophore production in complex samples is key to identifying important aspects of the ecological ramifications of siderophore activity, including, novel siderophore producing microbes. The method presented here was developed to assess siderophore activity of in-tact microbiome communities, in environmental samples, such as soil or plant tissues. The samples were homogenized and diluted in a modified M9 medium (without Fe), and enrichment cultures were incubated for 3 days. Siderophore production was assessed in samples at 24, 48, and 72 hours (h) using a novel 96-well microplate CAS (Chrome azurol sulphonate)-Fe agar assay, an adaptation of the traditionally tedious and time-consuming colorimetric method of assessing siderophore activity, performed on individual cultivated microbial isolates. We applied our method to 4 different genotypes/Lines of wheat (Triticum aestivum L.), including Lewjain, Madsen, and PI561725, and PI561727 commonly grown in the inland Pacific Northwest. Siderophore production was clearly impacted by the genotype of wheat, and in the specific types of plant tissues observed. We successfully used our method to rapidly screen for the influence of plant genotype on siderophore production, a key function in terrestrial and aquatic ecosystems. We produced many technical replicates, yielding very reliable statistical differences in soils and within plant tissues. Importantly, the results show the proposed method can be used to rapidly examine siderophore production in complex samples with a high degree of reliability, in a manner that allows communities to be preserved for later work to identify taxa and functional genes.

We present a protocol for rapid screening of environmental samples for siderophore potential contributing to micronutrient bioavailability and turnover in terrestrial systems.

Siderophores (low-molecular weight metal chelating compounds) are important in various ecological phenomenon ranging from iron (Fe) biogeochemical cycling ...