This work has been supported under the CGIAR thematic area Global Rice Agri-Food System CRP, RICE, Stress-Tolerant Rice for Africa and South Asia (STRASA) Phase III, and IZN (Interdisciplinary Centre for Crop Plant Research, Halle (Saale), Germany. We thank Mandy P&#252;ffeld for her excellent technical assistance and Dr. Isabel Maria Mora-Ramirez for sharing information and experience with the wheat chip. We thank Dr. Rhonda Meyer (RG Heterosis, IPK Gatersleben, Germany) for comments and critically reading the manuscript.

<ol>
	<li>Wang, 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=Three-dimensional+intact-tissue+sequencing+of+single-cell+transcriptional+states.">Three-dimensional intact-tissue sequencing of single-cell transcriptional states.</a> Science. 361 (6400), (2018).</li><li>P&#252;ffeld, M., Seiler, C., Kuhlmann, M., Sreenivasulu, N., Butardo, V. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Developing+Rice+Grain+Transcriptome+Using+the+Agilent+Microarray+Platform.">Analysis of Developing Rice Grain Transcriptome Using the Agilent Microarray Platform.</a> Methods in Molecular Biology. 1892, 277-300 (2019).</li><li>Martin, L., Fei, Z., Giovannoni, J., Rose, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Catalyzing+plant+science+research+with+RNA-seq.">Catalyzing plant science research with RNA-seq.</a> Frontiers in Plant Science. 4 (66), (2013).</li><li>Hrdlickova, R., Toloue, M., Tian, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq+methods+for+transcriptome+analysis.">RNA-Seq methods for transcriptome analysis.</a> Wiley Interdisciplinary Reviews. RNA. 8 (1), (2017).</li><li>Radchuk, V. V., 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=Spatiotemporal+profiling+of+starch+biosynthesis+and+degradation+in+the+developing+barley+grain.">Spatiotemporal profiling of starch biosynthesis and degradation in the developing barley grain.</a> Plant Physiology. 150 (1), 190-204 (2009).</li><li>Sreenivasulu, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systems+biology+of+seeds:+deciphering+the+molecular+mechanisms+of+seed+storage,+dormancy+and+onset+of+germination.">Systems biology of seeds: deciphering the molecular mechanisms of seed storage, dormancy and onset of germination.</a> Plant Cell Reports. 36 (5), 633-635 (2017).</li><li>Sreenivasulu, N., Wobus, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Seed-development+programs:+a+systems+biology-based+comparison+between+dicots+and+monocots.">Seed-development programs: a systems biology-based comparison between dicots and monocots.</a> Annual Review of Plant Biology. 64, 189-217 (2013).</li><li>Butardo, V. M., 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=Systems+Genetics+Identifies+a+Novel+Regulatory+Domain+of+Amylose+Synthesis.">Systems Genetics Identifies a Novel Regulatory Domain of Amylose Synthesis.</a> Plant Physiology. 173 (1), 887-906 (2017).</li><li>de Guzman, M. K., 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=Investigating+glycemic+potential+of+rice+by+unraveling+compositional+variations+in+mature+grain+and+starch+mobilization+patterns+during+seed+germination.">Investigating glycemic potential of rice by unraveling compositional variations in mature grain and starch mobilization patterns during seed germination.</a> Scientific Reports. 7 (1), 5854 (2017).</li><li>Sreenivasulu, N., Sunkar, R., Wobus, U., Strickert, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Array+platforms+and+bioinformatics+tools+for+the+analysis+of+plant+transcriptome+in+response+to+abiotic+stress.">Array platforms and bioinformatics tools for the analysis of plant transcriptome in response to abiotic stress.</a> Methods in Molecular Biology. 639, 71-93 (2010).</li><li>Anacleto, 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=Integrating+a+genome-wide+association+study+with+a+large-scale+transcriptome+analysis+to+predict+genetic+regions+influencing+the+glycaemic+index+and+texture+in+rice.">Integrating a genome-wide association study with a large-scale transcriptome analysis to predict genetic regions influencing the glycaemic index and texture in rice.</a> Plant Biotechnology Journal. , (2018).</li><li>Pietsch, C., Sreenivasulu, N., Wobus, U., Roder, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linkage+mapping+of+putative+regulator+genes+of+barley+grain+development+characterized+by+expression+profiling.">Linkage mapping of putative regulator genes of barley grain development characterized by expression profiling.</a> BMC Plant Biology. 9, 4 (2009).</li><li>Druka, 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=An+atlas+of+gene+expression+from+seed+to+seed+through+barley+development.">An atlas of gene expression from seed to seed through barley development.</a> Functional &amp; Integrative Genomics. 6 (3), 202-211 (2006).</li><li>Fujita, M., 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=Rice+Expression+Atlas+In+Reproductive+Development.">Rice Expression Atlas In Reproductive Development.</a> Plant and Cell Physiology. 51 (12), 2060-2081 (2010).</li><li>Wang, 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=A+dynamic+gene+expression+atlas+covering+the+entire+life+cycle+of+rice.">A dynamic gene expression atlas covering the entire life cycle of rice.</a> The Plant Journal. 61 (5), 752-766 (2010).</li><li>Yamakawa, H., Hakata, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Atlas+of+rice+grain+filling-related+metabolism+under+high+temperature:+Joint+analysis+of+metabolome+and+transcriptome+demonstrated+inhibition+of+starch+accumulation+and+induction+of+amino+acid+accumulation.">Atlas of rice grain filling-related metabolism under high temperature: Joint analysis of metabolome and transcriptome demonstrated inhibition of starch accumulation and induction of amino acid accumulation.</a> Plant and Cell Physiology. 51 (5), 795-809 (2010).</li><li>Shakoor, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Sorghum+bicolor+expression+atlas+reveals+dynamic+genotype-specific+expression+profiles+for+vegetative+tissues+of+grain,+sweet+and+bioenergy+sorghums.">A Sorghum bicolor expression atlas reveals dynamic genotype-specific expression profiles for vegetative tissues of grain, sweet and bioenergy sorghums.</a> BMC Plant Biology. 14, 35 (2014).</li><li>Yu, Y. 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=Transcriptome+analysis+reveals+key+differentially+expressed+genes+involved+in+wheat+grain+development.">Transcriptome analysis reveals key differentially expressed genes involved in wheat grain development.</a> Crop Journal. 4 (2), 92-106 (2016).</li><li>Kochevenko, 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=Identification+of+QTL+hot+spots+for+malting+quality+in+two+elite+breeding+lines+with+distinct+tolerance+to+abiotic+stress.">Identification of QTL hot spots for malting quality in two elite breeding lines with distinct tolerance to abiotic stress.</a> BMC Plant Biology. 18 (1), 106 (2018).</li></ol>

The authors have no competing financial interests.

The described method provides highly reproducible results for high-yielding and high-quality RNA extracts (Figure 1). Based on our experience, we recommend three biological replicates for one genotype, stage or condition for analysis. To be able to detect statistically meaningful differences, the general abundance of mRNA must be considered. Please note, however, that a starting amount of 200 mg tissue samples is required in triplicates for the RNA extraction step. Hence, for experiments that have a limited amount of samples such as genetically modified plant materials, this method may not be appropriate.
During microarray hybridization, the following steps are most critical: (1) loading the samples to the gasket slide (Step 4.7) and (2) washing the arrays after hybridization (Steps 4.13 and 4.14). Sample loading needs to be done very carefully to avoid bubble formation and liquid spilling. Caution is necessary when putting the microarray slide on the gasket slide containing the loaded samples: the DNA-side (with spotted probes) should be facing down to allow hybridization. For washing the microarrays, the second wash step is especially critical: here, removing the slides from wash buffer 2 needs to be performed very slowly (10 s) to avoid buffer artefacts on the slides, which can interfere with data acquisition and analyses.
In order to receive results from the hybridization experiment that are eligible for down-stream bioinformatics analysis, one should follow a few important criteria. The QC report, which is generated after the performance of the protocol above, gives a good idea of what should be improved, and which data are in a usable condition (Figure 3). The first information received is the visualized grid (Figure 2). Here, one can directly see if all areas for the fluorescence read-out are properly aligned. If there is a visually detectable shift, this will influence all other values (Background, signal intensity, etc.) and the read out of this chip cannot be used. On the overview (Spatial Distribution of all Outliers), one can easily spot any contamination (e.g., dust or hair), which affected the outcome. Dirt can be removed by softly blowing and rescanning the chip. The histogram of signal plot as mentioned above should result in a broad Gauss-shaped curve, with only minor outliers (Figure 2). Depending on the tissue analyzed (Figure 1), this curve can look different and can be appearing to have two peaks, or one predominant peak with a shoulder. This will not influence the quality of data. Only a strong shifted peak, or high signals on the edges indicate problems, such as &#34;green monsters&#34; (too high fluorescence intensities), which cannot be removed by washing and should be reported to the chip manufacturer. Also, the &#34;Spatial Distribution graph for Median Signals&#34; should vary around a common value. This should be in the range between 40 and 100, depending on the general intensity readout of the chip analyzed. Another important quality control is the evaluation of the spike in data: The log-graph for the spike in signals should result in a linear and regular line. Finally, the table of &#34;evaluation metrics&#34; gives a good and reliable view of the received results. Here the manufacturer provides a range of values which can be classified as Excellent, Good and Evaluate (Figure 3). According to our experience, some of the values cannot always be applied for all chips. For rice custom chips, the &#34;nonControl value&#34; was frequently out of range but does not significantly affect further data processing. In addition, the range for &#34;NegControl&#34; might be extended, based on tissue, organism and general output of the Chip to &#60;80. The range for evaluation for value E1 med CV might be extended to &#60;9, instead of &#60;8 according to our experience. Following this, a proper evaluation of all chip read out data is possible. In general, one should always keep in mind that independent biological replicates always give the most reliable result.
The advantage of this technique compared to other methods lies in the fact that it represents a cost-effective, high-throughput method for transcriptional profiling. Moreover, the downstream data analysis pipeline is easier and more established. Despite advantages, there are certain limitations of this technique, such as the number of genes that can be analyzed. The microarray can only facilitate the analysis of genes previously spotted probes on the array. Therefore, this technique is only applicable to plant species with sequenced genomes and fully annotated genes. We envision that the microarray-based transcriptomics will continue to be very useful and relevant not only for gene expression profiling but also for other downstream bioinformatics pipelines such as gene regulatory network analyses and transcriptome-wide association study.

An erratum was issued for:&#160;<a href="https://www.jove.com/video/60602/obtaining-high-quality-transcriptome-data-from-cereal-seeds-modified">Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling</a>. Two&#160;author&#160;names were&#160;updated.
The&#160;names were&#160;corrected from:
Christianne&#160;Seiler 
Nese&#160;Sreeenivasulu
to:
Christiane&#160;Seiler 
Nese&#160;Sreenivasulu

An erratum was issued for:&#160;<a href="https://www.jove.com/video/60602/obtaining-high-quality-transcriptome-data-from-cereal-seeds-modified">Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling</a>. Two&#160;author&#160;names were&#160;updated.

Erratum: Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

The transcriptome represents the complete set of ribonucleic acid (RNA) transcripts expressed by the genome of an organism at a given time and in particular environmental and growth conditions. Each cell has its individual transcriptome, which reflects its current physiologic and metabolic state. A collection of cells derived from a similar tissue or organ is used in a typical transcriptome study, but single-cell and spatially-resolved transcriptomics are getting popular1. Transcriptomic analyses start with the extraction of the total RNA from a selected tissue at a specific time point, and in defined growth conditions. For this purpose, we are recommending the use of a newly developed method for the extraction of total RNA from samples high in starch or sugar content, such as cereal seeds2. The comparison of transcriptomes among different samples results in the identification of RNA molecules with different abundance. These RNA molecules are considered as differentially expressed genes (DEGs). The abundance of transcripts derived from specific marker genes can then be used to estimate the developmental status or determine the response of an organism to environmental fluctuations. Genes with no detectable changes in their transcript abundance across the developmental time points under study are often used as reference or housekeeping genes.
The RNA is typically detected and quantified by various methods, such as Northern blotting and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), but current high-throughput transcriptomics methods rely heavily on nucleic acid hybridization using microarray technology as well as RNA sequencing (RNA-Seq). RNA-Seq is very popular at present because it provides several advantages for high throughput transcriptomic applications as reviewed elsewhere3,4. Although an older technology, gene expression profiling using microarray chips is still widely used because it is a more established technology, which requires less of a background in bioinformatics. Compared to RNA-Seq, the data sets generated from microarray experiments are smaller and easier to analyze. In addition, it is more cost effective, especially if dealing with large sample numbers. In our laboratory, we routinely use transcriptomic analyses using the microarray technology to determine the role of central regulatory hubs that govern the molecular networks and pathways involved in the growth, development and metabolism of cereal grains5,6,7,8,9. We also routinely use it to conduct of genome-wide gene expression profiling studies to obtain a mechanistic understanding of the response of cereal grains to abiotic stresses10, as well as in the conduct of transcriptome-wide association (TWAS) and linkage mapping studies to identify genes responsible for cereal grain quality and nutrition11,12. Other groups have also used the microarray technology in providing development-specific gene expression atlas in barley13, rice14,15,16, sorghum17, and wheat18.
The purpose of this publication is to provide a brief textual summary and a detailed visual description of the method we currently employ in our laboratory for the transcriptomic profiling of cereal grains using an Agilent microarray platform. Please note that other microarray platforms are available, but will not be covered in this method. We begin the protocol by presenting a detailed description of RNA extraction from developing or germinating cereal seeds. Based on our experience, obtaining a high quality and high quantity transcriptome amenable to downstream transcriptomic analyses is often the bottleneck when using cereal seed tissues. We have tried several commercially available RNA extraction kits, but none have provided satisfactory results. Hence, we developed a chemical extraction protocol to obtain a crude RNA extract that is then subjected to column purification using a commercially available kit. Using this method, we routinely and reproducibly obtain high quality RNA2 (Figure 1), which can be used for different downstream applications to generate a transcriptome profile.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>&#223;-mercaptoethanol</td>
			<td>Roth</td>
			<td>4227.3</td>
			<td>Add 300 ul to 20 mL RNA Extraction Buffer immediately prior to use</td>
		</tr>
		<tr>
			<td>Bioanalyzer 2100</td>
			<td>Agilent Technologies</td>
			<td></td>
			<td>To determine quality and quantity of RNA extract</td>
		</tr>
		<tr>
			<td>Crushed ice maker</td>
			<td>Various brands</td>
			<td></td>
			<td>To keep samples on ice during sample processing</td>
		</tr>
		<tr>
			<td>Dewar flask</td>
			<td>Various brands</td>
			<td></td>
			<td>Used as liquid nitrogen container</td>
		</tr>
		<tr>
			<td>Ethanol, absolute</td>
			<td>Roth</td>
			<td>9065.1</td>
			<td></td>
		</tr>
		<tr>
			<td>Gene Expression Hybridization Kit</td>
			<td>Agilent Technologies</td>
			<td>5188-5242</td>
			<td>Kit components: 
			2X Hi-RPM Hybridization Buffer 
			25X Fragmentation Buffer 
			10X Gene Expression Blocking Agent</td>
		</tr>
		<tr>
			<td>Gene Expression Wash buffer Kit</td>
			<td>Agilent Technologies</td>
			<td>5188-5325</td>
			<td>Kit components: 
			Gene Expression Wash Buffer 1 
			Gene Expression Wash Buffer 2 
			Triton X-102</td>
		</tr>
		<tr>
			<td>Heat block</td>
			<td>Various brands</td>
			<td></td>
			<td>It is recommended to have at least one heat block for 1.5 ml tubes and another one for 2.0 ml tubes</td>
		</tr>
		<tr>
			<td>Hybridization oven</td>
			<td>Agilent</td>
			<td>G2545A</td>
			<td>Stainless-steel oven designed for microarray hybridization From Sheldon Manufacturing, used to hybridize the sample in microarray overnight.</td>
		</tr>
		<tr>
			<td>Hybridization gasket slide kit</td>
			<td>Agilent</td>
			<td>G2534-60014</td>
			<td></td>
		</tr>
		<tr>
			<td>iQAir air cleaner</td>
			<td></td>
			<td></td>
			<td>To ensure that the experiment is conducted in low-ozone area</td>
		</tr>
		<tr>
			<td>Isopropanol</td>
			<td>Roth</td>
			<td>9866.5</td>
			<td></td>
		</tr>
		<tr>
			<td>Lint-free paper, Kimwipes</td>
			<td>Kimberly-Clark Professional</td>
			<td>KC34155</td>
			<td></td>
		</tr>
		<tr>
			<td>Low Input Quick Amp Labeling Kit</td>
			<td>Agilent Technologies</td>
			<td>5190-2305</td>
			<td>Kit components: 
			T7 Primer 
			5x First Strand Buffer 
			0.1M DTT 
			10 mM dNTP Mix 
			AffinityScript RNAse Block Mix 
			5x Transcription Buffer 
			NTP Mix 
			T7 RNA Polymerase Blend 
			Nuclease-free water 
			Cyanine 3-CTP</td>
		</tr>
		<tr>
			<td>Metal spatula, small</td>
			<td></td>
			<td></td>
			<td>Ensure that the small metal spatula can fit in the microfuge tubes to ensure easy scooping of samples.</td>
		</tr>
		<tr>
			<td>Microarray scanner</td>
			<td>Agilent Technologies</td>
			<td></td>
			<td>Agilent SureScan or Agilent C microarray scanner are recommended</td>
		</tr>
		<tr>
			<td>Microfuge tubes, nuclease-free</td>
			<td>Various brands</td>
			<td></td>
			<td>2.0 mL and 1.5 mL volume</td>
		</tr>
		<tr>
			<td>Microcentrifuge</td>
			<td>Eppendorf</td>
			<td>5810 R</td>
			<td>It is recommended to have at least one ambient and cold temperature microfuge with rotors that can hold 24 each of 1.5 ml and 2.0 ml microfuge tubes</td>
		</tr>
		<tr>
			<td>Mini incubator</td>
			<td>Labnet</td>
			<td>I5110-230V</td>
			<td>Any small incubator will do.</td>
		</tr>
		<tr>
			<td>Mortar and pestle</td>
			<td></td>
			<td></td>
			<td>Any small ceramic or marble mortar and pestle that can withstand cryogenic grinding using liquid nitrogen.</td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>Sigma-Aldrich</td>
			<td>S7653-1KG</td>
			<td></td>
		</tr>
		<tr>
			<td>Nanodrop 1000</td>
			<td>Peqlab / Thermofisher</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Nuclease-free water</td>
			<td>Biozym</td>
			<td>351900302</td>
			<td></td>
		</tr>
		<tr>
			<td>One-Color RNA Spike-In Kit</td>
			<td>Agilent</td>
			<td>5188-5282</td>
			<td>Kit components: 
			One color RNA Spike-Mix 
			Dilution Buffer</td>
		</tr>
		<tr>
			<td>Ozone barrier slide cover kit</td>
			<td>Agilent</td>
			<td>G2505-60550</td>
			<td>Optional but highly recommended</td>
		</tr>
		<tr>
			<td>PCR tube, 0.2 mL, RNase-free</td>
			<td>Stratagene</td>
			<td>Z376426</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenol:chloroform:isoamyl mixture (25:24:1)</td>
			<td>Roth</td>
			<td>A156.2</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette tips, nuclease free, filter tips</td>
			<td>Various brands</td>
			<td></td>
			<td>To accommodate 2, 20, 20, 100, 200 and 1000 ul volumes</td>
		</tr>
		<tr>
			<td>Pipettor set</td>
			<td>Various brands</td>
			<td></td>
			<td>For 2, 20, 20, 100, 200 and 1000 ul volumes</td>
		</tr>
		<tr>
			<td>RNA Extraction Buffer</td>
			<td>10 mM Tri-HCl pH 8.0 
			150 mM LiCl 
			50 mM EDTA 
			1.5% EDTA 
			15 ul/mL &#946;-mercaptoethanol</td>
			<td></td>
			<td>We routinely use Roth or Sigma chemicals; Add &#946;-mercaptoethanol fresh daily</td>
		</tr>
		<tr>
			<td>RNase-free DNase set</td>
			<td>Qiagen</td>
			<td>79254</td>
			<td></td>
		</tr>
		<tr>
			<td>RNeasy Mini Kit</td>
			<td>Qiagen</td>
			<td>74104</td>
			<td>Kit components: 
			RNeasy mini spin column (pink) 
			1.5 ml collection tubes 
			2 ml collection tubes 
			Buffer RLT 
			Buffer RW 
			Buffer RPE 
			Nuclease-free water</td>
		</tr>
		<tr>
			<td>RNeasy Plant Mini Kit</td>
			<td>Qiagen</td>
			<td>74904</td>
			<td>Kit components: 
			RNeasy mini spin column (pink) 
			QIAshredder spin column (purple) 
			1.5 ml collection tubes 
			2 ml collection tubes 
			Buffer RLT 
			Buffer RLC 
			Buffer RW1 
			Buffer RPE 
			Nuclease-free water</td>
		</tr>
		<tr>
			<td>Slide holders for DNA microarray scanner</td>
			<td>Agilent</td>
			<td>G2505-60525</td>
			<td></td>
		</tr>
		<tr>
			<td>Slide staining dish with removable rack</td>
			<td>DWK Life Sciences 900200</td>
			<td>Fisher Scientific 08-812</td>
			<td>We recommend DWK Life Sciences Wheaton&#8482; Glass 20-Slide Staining Dish with Removable Rack (Complete with dish, cover, and glass slide rack)</td>
		</tr>
		<tr>
			<td>Sodium chloride</td>
			<td>Roth</td>
			<td>P029.1</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultralow temperature freezer</td>
			<td>Various brand</td>
			<td></td>
			<td>Capable of storing sample at -80 &#176;C</td>
		</tr>
		<tr>
			<td>Vortex mixer</td>
			<td>Various brands</td>
			<td></td>
			<td>At least one for single tube vortex-mixer and another one for that can vortex-mix multiple tubes</td>
		</tr>
	</tbody>
</table>

obtaining high-quality transcriptome data from cereal seeds by a modified method for gene expression profiling

The characterization of gene expression is dependent on RNA quality. In germinating, developing and mature cereal seeds, the extraction of high-quality RNA is often hindered by high starch and sugar content. These compounds can reduce both the yield and the quality of the extracted total RNA. The deterioration in quantity and quality of total RNA can subsequently have a significant impact on the downstream transcriptomic analyses, which may not accurately reflect the spatial and/or temporal variation in the gene expression profile of the samples being tested. In this protocol, we describe an optimized method for extraction of total RNA with sufficient quantity and quality to be used for whole transcriptome analysis of cereal grains. The described method is suitable for several downstream applications used for transcriptomic profiling of developing, germinating, and mature cereal seeds. The method of transcriptome profiling using a microarray platform is shown. This method is specifically designed for gene expression profiling of cereals with described genome sequences. The detailed procedure from microarray handling to final quality control is described. This includes cDNA synthesis, cRNA labelling, microarray hybridization, slide scanning, feature extraction, and data quality validation. The data generated by this method can be used to characterize the transcriptome of cereals during germination, in various stages of grain development, or at different biotic or abiotic stress conditions. The results presented here exemplify high-quality transcriptome data amenable for downstream bioinformatics analyses, such as the determination of differentially expressed genes (DEGs), characterisation of gene regulatory networks, and conducting transcriptome-wide association study (TWAS).

This method is optimized for extracting cereal seed samples containing significant amounts of contaminating starch or sugars. It is designed to extract total RNA from 24 seed samples per day. It should be conducted continuously in one day, but optional stop and pause points are identified throughout the protocol. Alternatively, the reader can use their preferred RNA extraction kits or manual chemical extraction method. However, based on our previous experience, commercially available plant RNA extraction kits are not appropriate for seeds due to significant amounts of starch, proteins, sugar and/or lipid contamination. In the described method, a chemical extraction of crude RNA is followed by column purification using a commercial RNA extraction kit. This typically provides RNA with higher quality and yield. Figure 1 shows the result of a BioAnalyzer run to test the quality of RNA extraction using the method described here. Results are presented for barley leaf (Samples 1 and 2 representing low starch content samples) and barley seeds (Samples 3 and 4 representing high starch content samples). The RNA integrity (RIN) value for all samples is 10.
Figure 1 shows a representative gel of the purified RNA extract, where the quality was tested using a Bioanalyzer. Samples 1 and 2 are typical results for low starch content samples such as barley leaves, where additional rRNA bands from chloroplasts are evident. Samples 3 and 4 are representative results for high starch content samples such as barley seeds, showing 18S and 28S rRNA. Please note that no automated RIN value can be calculated for green plant tissues such as leaf samples, due to chloroplast rRNA. However, the integrity and high quality can be visually ascertained by to the absence of any degradation products, which typically appears as low molecular smear. The RIN value for high-starch seeds samples such as barley seeds is typically 10 using the protocol described in this paper.
In addition, we also present here a representative data of a time course experiment of two elite barley inbred lines (Sofiara and Victoriana) used for analysis of malting quality19. During the malting process, starch is converted into sugar. Therefore, the samples represent tissues with varying proportions of starch and sugar contents. As the industrial malting process is similar to the germination process, the transcriptomes of two barley lines, differing in their malting quality, were analyzed. RNAs were extracted from germinating seeds at 2, 24, 48, 72, 120, 144 and 196 h after imbibition in biological triplicates. The RNA preparation and hybridization to the customized barley microarray chip were performed as described above. Figure 2 indicates the normalized grid read out from the microarray hybridization given in the quality control (QC) report (Figure 3) and the histogram plot for detected signals. The grid gives the example of derived signals from each corner of the chip, including background and spike-in read out dots used for calibration. The histogram indicates the deviation of detectable dots with respective signal intensities. A successful hybridization gives a broad Gaussian-shaped curve with only minor outliers as shown in the figure. Failed hybridizations can result in a strong shift towards one side (&#34;green monsters&#34;).
Lastly, a representative result of the acceptable values is shown in column 4 of Figure 3. To indicate the reliability of the performed experiments, the results are further evaluated using the GeneSpring software. The collected data are presented as principal component analysis (PCA). The PCA integrates the values of selected dots (genes) as vector. The number of evaluated dots that are used can vary from several hundred to the entire chip and is dependent on the software used. Each chip (sample) results in one value (vector) that resulted from the integrated signal intensities for the analyzed dots. Therefore, the relative position in the graph (PCA) indicates the similarity of the samples to each other. The closer the samples are, the more similar they are. Technical replicates should be closer together than biological ones, and biological replicates of a sample should cluster closer together, than samples from different time points, tissues or conditions.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60602/60602fig1.jpg" /> 
Figure 1: Electrophoresis file run summary obtained after checking the quality of RNA with Bioanalyzer. Samples 1 and 2 are barley leaf tissue as indicated by additional ribosomal bands from chloroplasts. Samples 3 and 4 are barley seed tissue with 18S and 28S rRNA bands shown. A RIN factor is not always calculated for green tissues such as leaf but according to the gel, the quality of RNA is very good. The RIN value for samples 3 and 4 is 10. <a href="https://www.jove.com/files/ftp_upload/60602/60602fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60602/60602fig2.jpg" /> 
Figure 2: QC Report from successful barley microarray hybridization. A + indicates detected signals on the grid from all corners of the chip. The histogram shows the number of signals categorized according to signal intensity (fluorescence) as logarithm after background subtraction. <a href="https://www.jove.com/files/ftp_upload/60602/60602fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60602/60602fig3.jpg" /> 
Figure 3: Summary of quality control (QC) after hybridization and scanning. The values for the hybridized slide are given in column 2 (value) and the range of acceptable values is shown in column 4. <a href="https://www.jove.com/files/ftp_upload/60602/60602fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Wash Chamber Assembly</td>
			<td>Content and Label</td>
			<td>Purpose</td>
		</tr>
		<tr>
			<td>Dish 1</td>
			<td>Empty, leave on lab bench until next day</td>
			<td>Fill with Wash Buffer 1 the next day, used to disassemble the microarray slides</td>
		</tr>
		<tr>
			<td>Dish 2</td>
			<td>Add one microarray slide rack and a small magnetic stir bar; label with &#34;Wash Buffer 1&#34;, leave on lab bench until next day</td>
			<td>Used to wash the microarray slides with Wash Buffer 1 the next day</td>
		</tr>
		<tr>
			<td>Dish 3</td>
			<td>Add one small magnetic stir bar, label with &#34;Wash Buffer 2&#34; and place in a 37 &#176;C mini incubator.</td>
			<td>Used to wash the microarray slides with Wash Buffer 2 the next day inside the 37 &#176;C mini incubator</td>
		</tr>
	</tbody>
</table>
Table 1: Preparation of the wash chamber assemblies.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Steps</td>
			<td>Dish</td>
			<td>Wash Buffer</td>
			<td>Temperature</td>
			<td>Time</td>
		</tr>
		<tr>
			<td>Disassembly</td>
			<td rowspan="2">1</td>
			<td rowspan="2">1</td>
			<td rowspan="2">Ambient</td>
			<td rowspan="2">As fast as possible</td>
		</tr>
		<tr>
			<td>(Step 4.13)</td>
		</tr>
		<tr>
			<td>First wash</td>
			<td rowspan="2">2</td>
			<td rowspan="2">1</td>
			<td rowspan="2">Ambient</td>
			<td rowspan="2">1 min</td>
		</tr>
		<tr>
			<td>(Step 4.14)</td>
		</tr>
		<tr>
			<td>Second wash</td>
			<td rowspan="2">3</td>
			<td rowspan="2">2</td>
			<td rowspan="2">37 &#176;C</td>
			<td rowspan="2">1 min</td>
		</tr>
		<tr>
			<td>(Step 4.15)</td>
		</tr>
	</tbody>
</table>
Table 2: Incubation temperature and time for wash chamber assemblies.

Watch this Scientific Journal Video about Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling at JoVE.com

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

A method for transcriptome profiling of cereals is presented. The microarray-based gene expression profiling starts with the isolation of high-quality total RNA from cereal grains and continues with the generation of cDNA. After cRNA labelling and microarray hybridization, recommendations are given for signal detection and quality control.

The characterization of gene expression is dependent on RNA quality. In germinating, developing and mature cereal seeds, the extraction of high-quality ...

obtaining-high-quality-transcriptome-data-from-cereal-seeds-modified

Research

JoVE Journal

Biochemistry

7.3K Views.  Swinburne University of Technology. A method for transcriptome profiling of cereals is presented. The microarray-based gene expression profiling starts with the isolation of high-quality total RNA from cereal grains and continues with the generation of cDNA. After cRNA labelling and microarray hybridization, recommendations are given for signal detection and quality control.

Video: Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

An Efficient Method for the Isolation of Highly Purified RNA from Seeds for Use in Quantitative Transcriptome Analysis

Plant seeds accumulate large amounts of storage reserves comprising biodegradable organic matter. Humans rely on seed storage reserves for food and as industrial materials. Gene expression profiles are powerful tools for investigating metabolic regulation in plant cells. Therefore, detailed, accurate gene expression profiles during seed development are required for crop breeding. Acquiring highly purified RNA is essential for producing these profiles. Efficient methods are needed to isolate highly purified RNA from seeds. Here, we describe a method for isolating RNA from seeds containing large amounts of oils, proteins, and polyphenols, which have inhibitory effects on high-purity RNA isolation. Our method enables highly purified RNA to be obtained from seeds without the use of phenol, chloroform, or additional processes for RNA purification. This method is applicable to Arabidopsis, rapeseed, and soybean seeds. Our method will be useful for monitoring the expression patterns of low level transcripts in developing and mature seeds.

We have succeeded in establishing a method for RNA isolation from plant seeds containing large amounts of oils, proteins, and polyphenols, which have inhibitory effects on high-purity RNA isolation. Our method is suitable for monitoring the expression of genes with low level transcripts in seeds.

Plant seeds accumulate large amounts of storage reserves comprising biodegradable organic matter. Humans rely on seed storage reserves for food and as ...

A Filtration-based Method of Preparing High-quality Nuclei from Cross-linked Skeletal Muscle for Chromatin Immunoprecipitation

Chromatin immunoprecipitation (ChIP) is a powerful method to determine protein binding to chromatin DNA. Fiber-rich skeletal muscle, however, has been a challenge for ChIP due to technical difficulty in isolation of high-quality nuclei with minimal contamination of myofibrils. Previous protocols have attempted to purify nuclei before cross-linking, which incurs the risk of altered DNA-protein interaction during the prolonged nuclei preparation process. In the current protocol, we first cross-linked the skeletal muscle tissue collected from mice, and the tissues were minced and sonicated. Since we found that ultracentrifugation was not able to separate nuclei from myofibrils using cross-linked muscle tissue, we devised a sequential filtration procedure to obtain high-quality nuclei devoid of significant myofibril contamination. We subsequently prepared chromatin by using an ultrasonicator, and ChIP assays with anti-BMAL1 antibody revealed robust circadian binding pattern of BMAL1 to target gene promoters. This filtration protocol constitutes an easily applicable method to isolate high-quality nuclei from cross-linked skeletal muscle tissue, allowing consistent sample processing for circadian and other time-sensitive studies. In combination with next-generation sequencing (NGS), our method can be deployed for various mechanistic and genomic studies focusing on skeletal muscle function.

We present a filtration-based protocol to isolate high-quality nuclei from cross-linked mouse skeletal muscle wherein we removed the need for ultracentrifugation, making it easily applicable. We show that chromatin prepared from the nuclei is suitable for chromatin immunoprecipitation and likely chromatin immunoprecipitation sequencing studies.

Chromatin immunoprecipitation (ChIP) is a powerful method to determine protein binding to chromatin DNA. Fiber-rich skeletal muscle, however, has been a ...

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

Caffeine Extraction, Enzymatic Activity and Gene Expression of Caffeine Synthase from Plant Cell Suspensions

Caffeine (1,3,7-trimethylxanthine) is a purine alkaloid present in popular drinks such as coffee and tea. This secondary metabolite is regarded as a chemical defense because it has antimicrobial activity and is considered a natural insecticide. Caffeine can also produce negative allelopathic effects that prevent the growth of surrounding plants. In addition, people around the world consume caffeine for its analgesic and stimulatory effects. Due to interest in the technological applications of caffeine, research on the biosynthetic pathway of this compound has grown. These studies have primarily focused on understanding the biochemical and molecular mechanisms that regulate the biosynthesis of caffeine. In vitro tissue culture has become a useful system for studying this biosynthetic pathway. This article will describe a step-by-step protocol for the quantification of caffeine and for measuring the transcript levels of the gene (CCS1) encoding caffeine synthase (CS) in cell suspensions of C. arabica L. as well as its activity.

This protocol describes an efficient methodology for the extraction and quantification of caffeine in cell suspensions of C. arabica L. and an experimental process for evaluating the enzymatic activity of caffeine synthase with the expression level of the gene that encodes this enzyme.

Caffeine (1,3,7-trimethylxanthine) is a purine alkaloid present in popular drinks such as coffee and tea. This secondary metabolite is regarded as a ...

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing approaches suffer from significant limitations, we have developed a new method for determining TF-DNA binding affinities with high sensitivity on a large scale. The assay relies on the established fluorescence anisotropy (FA) principle but introduces important technical improvements. First, we measure a full FA competitive titration curve in a single well by incorporating TF and a fluorescently labeled reference DNA in a porous agarose gel matrix. Unlabeled DNA oligomer is loaded on the top as a competitor and, through diffusion, forms a spatio-temporal gradient. The resulting FA gradient is then read out using a customized epifluorescence microscope setup. This improved setup greatly increases the sensitivity of FA signal detection, allowing both weak and strong binding to be reliably quantified, even for molecules of similar molecular weights. In this fashion, we can measure one titration curve per well of a multi-well plate, and through a fitting procedure, we can extract both the absolute dissociation constant (KD) and active protein concentration. By testing all single-point mutation variants of a given consensus binding sequence, we can survey the entire binding specificity landscape of a TF, typically on a single plate. The resulting position weight matrices (PWMs) outperform those derived from other methods in predicting in vivo TF occupancy. Here, we present a detailed guide for implementing HiP-FA on a conventional automated fluorescent microscope and the data analysis pipeline.

Here we present a novel method for determining binding affinities at equilibrium and in solution with high sensitivity on a large scale. This improves the quantitative analysis of transcription factor-DNA binding. The method is based on automated fluorescence anisotropy measurements in a controlled delivery system.

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing ...

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade

Flavonols are a major subclass of flavonoids with a variety of biological and pharmacological activities. Here, we provide a method for the in vitro enzymatic synthesis of a flavonol. In this method, Atf3h and Atfls1, two key genes in the biosynthetic pathway of the flavonols, are cloned and overexpressed in Escherichia coli. The recombinant enzymes are purified via an affinity column and then a bienzymatic cascade is established in a specific synthetic buffer. Two flavonols are synthesized in this system as examples and determined by TLC and HPLC/LC/MS analyses. The method displays obvious advantages in the derivation of flavonols over other approaches. It is time- and labor-saving and highly cost-effective. The reaction is easy to be accurately controlled and thus scaled up for mass production. The target product can be purified easily due to the simple components in the system. However, this system is usually restricted to the production of a flavonol from a flavanone.

The derivation of a flavonol is crucial for its application in healthcare and the food industry. Here, we provide a detailed protocol for the biosynthesis of a flavonol from a flavanone and discuss the crucial steps and its advantages over other approaches.

Flavonols are a major subclass of flavonoids with a variety of biological and pharmacological activities. Here, we provide a method for the in vitro ...

Nitropeptide Profiling and Identification Illustrated by Angiotensin II

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in eukaryotic cells. Precise identification of nitration sites on proteins is crucial for understanding the physiological and pathological processes related to protein nitration, such as inflammation, aging, and cancer. Since the nitrated proteins are of low abundance in cells even under induced conditions, no universal and efficient methods have been developed for the profiling and identification of protein nitration sites. Here we describe a protocol for nitropeptide enrichment by using a chemical reduction reaction and biotin labeling, followed by high resolution mass spectrometry. In our method, nitropeptide derivatives can be identified with high accuracy. Our method exhibits two advantages compared to the previously reported methods. First, dimethyl labeling is used to block the primary amine on nitropeptides, which can be used to generate quantitative results. Second, a disulfide bond containing NHS-biotin reagent is used for the enrichment, which can be further reduced and alkylated to enhance the detection signal on a mass spectrometer. This protocol has been successfully applied to the model peptide Angiotensin II in the current paper.

Proteomic profiling of tyrosine-nitrated proteins has been a challenging technique due to the low abundance of the 3-nitrotyrosine modification. Here we describe a novel approach for nitropeptide enrichment and profiling by using Angiotensin II as the model. This method can be extended for other in vitro or in vivo systems.

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of ...

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

DNA primase synthesizes short RNA primers that initiate DNA synthesis of Okazaki fragments on the lagging strand by DNA polymerase during DNA replication. The binding of prokaryotic DnaG-like primases to DNA occurs at a specific trinucleotide recognition sequence. It is a pivotal step in the formation of Okazaki fragments. Conventional biochemical tools that are used to determine the DNA recognition sequence of DNA primase provide only limited information. Using a high-throughput microarray-based binding assay and consecutive biochemical analyses, it has been shown that 1) the specific binding context (flanking sequences of the recognition site) influences the binding strength of the DNA primase to its template DNA, and 2) stronger binding of primase to the DNA yields longer RNA primers, indicating higher processivity of the enzyme. This method combines PBM and primase activity assay and is designated as high-throughput primase profiling (HTPP), and it allows characterization of specific sequence recognition by DNA primase in unprecedented time and scalability.&#160;

Protein binding microarray (PBM) experiments combined with biochemical assays link the binding and catalytic properties of DNA primase, an enzyme that synthesizes RNA primers on template DNA. This method, designated as high-throughput primase profiling (HTPP), can be used to reveal DNA-binding patterns of a variety of enzymes.

DNA primase synthesizes short RNA primers that initiate DNA synthesis of Okazaki fragments on the lagging strand by DNA polymerase during DNA replication. ...

High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

Proteins generally exert biological functions through interactions with other proteins, either in dynamic protein assemblies or as a part of stably formed complexes. The latter can be elegantly resolved according to molecular size using native polyacrylamide gel electrophoresis (BN-PAGE). Coupling of such separations to sensitive mass spectrometry (BN-MS) has been well-established and theoretically allows for exhaustive assessment of the extractable complexome in biological samples. However, this approach is rather laborious and provides limited complex size resolution and sensitivity. Also, its application has remained restricted to abundant mitochondrial and plastid proteins. Thus, for a majority of proteins, information regarding integration into stable protein complexes is still lacking. Presented here is an optimized approach for complexome profiling comprising preparative-scale BN-PAGE separation, sub-millimeter sampling of broad gel lanes by cryomicrotome slicing, and mass spectrometric analysis with label-free protein quantification. The procedures and tools for critical steps are described in detail. As an application, the report describes complexome analysis of a solubilized endosome-enriched membrane fraction from mouse kidneys, with 2,545 proteins profiled in total. The results demonstrate identification of uniform, low-abundance membrane proteins such as intracellular ion channels as well as high resolution, complex protein assembly patterns, including glycosylation isoforms. The results are in agreement with independent biochemical analyses. In summary, this methodology allows for comprehensive and unbiased identification of protein (super)complexes and their subunit composition, providing a basis for investigating stoichiometry, assembly, and interaction dynamics of protein complexes in any biological system.

A versatile cryoslicing BN-MS protocol using a microtome is presented for high-resolution complexome profiling.

Proteins generally exert biological functions through interactions with other proteins, either in dynamic protein assemblies or as a part of stably formed ...

Preparation of High-Quality Fermented Fish Product

This protocol provides a method for preparation of industrialized fermented fish product with sturgeon (Aquilaria sinensis) meat product. The procedures were: (1) pretreatment of farmed sturgeon including decapitation, evisceration, skinning-off, cleaning and cutting; (2) marinating fish cubes in 6-12% (w/v) salt solution (1:1, fish cube mass to solution volume); (3) drying fish cubes to a water content of 50-60% by hot air (40-60 &#176;C) or by vacuum; (4) fermentation involving inoculating fish cubes with 0.4-1.6% (w/w) S. cerevisiae in flavor solution to fish cubes and fermenting at 25-35 &#176;C for 6-10 h; (5) sealing fish cubes in vacuum packages with marinating and fermenting solutions; (6) sterilizing at 115-121 &#176;C for 10-20 min. The sturgeon meat product prepared by this method has delicious taste which is mellow and thick, has various types and large amounts of volatile flavor compounds such as alcohols and esters which could mask musty and unpleasant odor from fish, has moderate salt content but good texture properties such as high springiness, gumminess and chewiness, and has bright russet color and attractive appearance. This new technique could also be applied in the processing of other fish to provide convenient fish snack foods which could be stored at room temperature. It is appropriate for both marine and freshwater fish.

The goal of the protocol is to provide an industrialized fish fermentation technique based on inoculation of Saccharomyces cerevisiae.

This protocol provides a method for preparation of industrialized fermented fish product with sturgeon (Aquilaria sinensis) meat product. The procedures ...