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><tbody><tr><td>&amp;szlig;-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>&lt;strong&gt;Kit components:&lt;/strong&gt;&lt;br/&gt; 2X Hi-RPM Hybridization Buffer&lt;br/&gt; 25X Fragmentation Buffer&lt;br/&gt; 10X Gene Expression Blocking Agent</td></tr><tr><td>Gene Expression Wash buffer Kit</td><td>Agilent Technologies</td><td>5188-5325</td><td>&lt;strong&gt;Kit components:&lt;/strong&gt;&lt;br/&gt; Gene Expression Wash Buffer 1&lt;br/&gt; Gene Expression Wash Buffer 2&lt;br/&gt; 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>&lt;strong&gt;Kit components:&lt;/strong&gt;&lt;br/&gt; T7 Primer&lt;br/&gt; 5x First Strand Buffer&lt;br/&gt; 0.1M DTT&lt;br/&gt; 10 mM dNTP Mix&lt;br/&gt; AffinityScript RNAse Block Mix&lt;br/&gt; 5x Transcription Buffer&lt;br/&gt; NTP Mix&lt;br/&gt; T7 RNA Polymerase Blend&lt;br/&gt; Nuclease-free water&lt;br/&gt; 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>&lt;strong&gt;Kit components:&lt;/strong&gt;&lt;br/&gt; One color RNA Spike-Mix&lt;br/&gt; 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&lt;br/&gt; 150 mM LiCl&lt;br/&gt; 50 mM EDTA&lt;br/&gt; 1.5% EDTA&lt;br/&gt; 15 ul/mL &amp;beta;-mercaptoethanol</td><td></td><td>We routinely use Roth or Sigma chemicals; Add &amp;beta;-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>&lt;strong&gt;Kit components:&lt;/strong&gt;&lt;br/&gt; RNeasy mini spin column (pink)&lt;br/&gt; 1.5 ml collection tubes&lt;br/&gt; 2 ml collection tubes&lt;br/&gt; Buffer RLT&lt;br/&gt; Buffer RW&lt;br/&gt; Buffer RPE&lt;br/&gt; Nuclease-free water</td></tr><tr><td>RNeasy Plant Mini Kit</td><td>Qiagen</td><td>74904</td><td>&lt;strong&gt;Kit components:&lt;/strong&gt;&lt;br/&gt; RNeasy mini spin column (pink)&lt;br/&gt; QIAshredder spin column (purple)&lt;br/&gt; 1.5 ml collection tubes&lt;br/&gt; 2 ml collection tubes&lt;br/&gt; Buffer RLT&lt;br/&gt; Buffer RLC&lt;br/&gt; Buffer RW1&lt;br/&gt; Buffer RPE&lt;br/&gt; 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&amp;trade; 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 &amp;deg;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

7.4K Views.  Swinburne University of Technology. Deze microarray hybridisatiemethode is handig voor het vergelijken van een groot aantal monsters van een organisme met een bekend genoom. Vergeleken met een hoge doorvoer sequencing aanpak, de hoeveelheid gegevens gegenereerd door deze methode is veel gemakkelijker te hanteren en verder, het kan worden geanalyseerd in een meer kosteneffectieve manier. Het aantonen van de procedure zal Dr.Christiane Seiler een post-doctorale wetenschapper die werkzaam is in de onderzoeksgroep bij Rosis op de I...

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 ...

RNAi-mediated Control of Aflatoxins in Peanut: Method to Analyze Mycotoxin Production and Transgene Expression in the Peanut/Aspergillus Pathosystem

The Food and Agriculture Organization of the United Nations estimates that 25% of the food crops in the world are contaminated with aflatoxins. That represents 100 million tons of food being destroyed or diverted to non-human consumption each year. Aflatoxins are powerful carcinogens normally accumulated by the fungi Aspergillus flavus and A. parasiticus in cereals, nuts, root crops and other agricultural products. Silencing of five aflatoxin-synthesis genes by RNA interference (RNAi) in peanut plants was used to control aflatoxin accumulation following inoculation with A. flavus. Previously, no method existed to analyze the effectiveness of RNAi in individual peanut transgenic events, as these usually produce few seeds, and traditional methods of large field experiments under aflatoxin-conducive conditions were not an option. In the field, the probability of finding naturally contaminated seeds is often 1/100 to 1/1,000. In addition, aflatoxin contamination is not uniformly distributed. Our method uses few seeds per transgenic event, with small pieces processed for real-time PCR (RT-PCR) or small RNA sequencing, and for analysis of aflatoxin accumulation by ultra-performance liquid chromatography (UPLC). RNAi-expressing peanut lines 288-72 and 288-74, showed up to 100% reduction (p&#8804;0.01) in aflatoxin B1 and B2 compared to the control that accumulated up to 14,000 ng.g-1 of aflatoxin B1 when inoculated with aflatoxigenic A. flavus. As reference, the maximum total of aflatoxins allowable for human consumption in the United States is 20 ng.g-1. This protocol describes the application of RNAi-mediated control of aflatoxins in transgenic peanut seeds and methods for its evaluation. We believe that its application in breeding of peanut and other crops will bring rapid advancement in this important area of science, medicine and human nutrition, and will significantly contribute to the international effort to control aflatoxins, and potentially other mycotoxins in major food crops.

RNAi-mediated Control of Aflatoxins in Peanut: Method to Analyze Mycotoxin Production and Transgene Expression in the Peanut/Aspergillus Pathosystem

We demonstrate a method for the analysis of aflatoxins and transgene expression in peanut seeds that contain RNA-interference signals for silencing aflatoxin-synthesis genes in the fungus Aspergillus flavus. RNAi-mediated control of mycotoxins in plants has not been reported previously.

The Food and Agriculture Organization of the United Nations estimates that 25% of the food crops in the world are contaminated with aflatoxins. That ...

Environment

Measuring Gene Expression in Bombarded Barley Aleurone Layers with Increased Throughput

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for germination or early seedling development are activated by gibberellin (GA), while genes associated with stress responses are activated by abscisic acid (ABA). The mechanisms of GA and ABA signaling can be interrogated by introducing reporter gene constructs into aleurone cells via particle bombardment, with the resulting transient expression measured using enzyme assays. An improved protocol is reported that partially automates and streamlines the grain homogenization step and the enzyme assays, allowing significantly more throughput than existing methods. Homogenization of the grain samples is carried out using an automated tissue homogenizer, and GUS (&#946;-glucuronidase) assays are carried out using a 96-well plate system. Representative results using the protocol suggest that phospholipase D activity may play an important role in the activation of HVA1 gene expression by ABA, through the transcription factor TaABF1.

An improved protocol is presented for the measurement of transient gene expression from reporter constructs in barley aleurone cells after particle bombardment. The combination of automated grain grinding with 96-well plate enzyme assays provides high throughput for the procedure.

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for ...

Genetics

Isolation of High Purity Tissues from Developing Barley Seeds

Understanding the mechanisms regulating the development of cereal seeds is essential for plant breeding and increasing yield. However, the analysis of cereal seeds is challenging owing to the minute size, the liquid character of some tissues, and the tight inter-tissue connections. Here, we demonstrate a detailed protocol for dissection of the embryo, endosperm, and seed maternal tissues at early, middle, and late stages of barley seed development. The protocol is based on a manual tissue dissection using fine-pointed tools and a binocular microscope, followed by ploidy analysis-based purity control. Seed maternal tissues and embryos are diploid, while the endosperm is triploid tissue. This allows the monitoring of sample purity using flow cytometry. Additional measurements revealed the high quality of RNA isolated from such samples and their usability for high-sensitivity analysis. In conclusion, this protocol describes how to practically dissect pure tissues from developing grains of cultivated barley and potentially also other cereals.

Here we present a protocol for high purity manual isolation and quality control of embryo, endosperm and seed maternal tissues during entire barley seed development.

Understanding the mechanisms regulating the development of cereal seeds is essential for plant breeding and increasing yield. However, the analysis of ...

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

Biology

Polysome Purification from Soybean Symbiotic Nodules

The aim of this protocol is to provide a strategy for studying the eukaryotic translatome of the soybean (Glycine max) symbiotic nodule. This paper describes methods optimized to isolate plant-derived polyribosomes and their associated mRNAs to be analyzed using RNA-sequencing. First, cytoplasmic lysates are obtained through homogenization in polysome- and RNA-preserving conditions from whole, frozen soybean nodules. Then, lysates are cleared by low-speed centrifugation, and 15% of the supernatant is used for total RNA (TOTAL) isolation. The remaining cleared lysate is used to isolate polysomes by ultracentrifugation through a two-layer sucrose cushion (12% and 33.5%). Polysome-associated mRNA (PAR) is purified from polysomal pellets after resuspension. Both TOTAL and PAR are evaluated by highly sensitive capillary electrophoresis to meet the quality standards of sequencing libraries for RNA-seq. As an example of a downstream application, after sequencing, standard pipelines for gene expression analysis can be used to obtain differentially expressed genes at the transcriptome and translatome levels. In summary, this method, in combination with RNA-seq, allows the study of the translational regulation of eukaryotic mRNAs in a complex tissue such as the symbiotic nodule.

This protocol describes a method for eukaryotic polysome purification from intact soybean nodules. After sequencing, standard pipelines for gene expression analysis can be used to identify differentially expressed genes at the transcriptome and translatome levels.

The aim of this protocol is to provide a strategy for studying the eukaryotic translatome of the soybean (Glycine max) symbiotic nodule. This paper ...

Developmental Biology

CRISPR-Based Efficient Genome Editing for Resistant Starch in Rice Breeding

Breeding by Design for Functional Rice with Genome Editing Technologies

The conventional approaches to crop breeding, which rely predominantly on time-consuming and labor-intensive methods such as traditional hybridization and mutation breeding, face challenges in efficiently introducing targeted traits and generating diverse plant populations. Conversely, the emergence of genome editing technologies has ushered in a paradigm shift, enabling the precise and expedited manipulation of plant genomes to intentionally introduce desired characteristics. One of the most widespread editing tools is the CRISPR/Cas system, which has been used by researchers to study important biology-related problems. However, the precise and effective workflow of genome editing has not been well-defined in crop breeding. In this study, we demonstrated the entire process of breeding rice varieties enriched with high levels of resistant starch (RS), a functional trait that plays a crucial role in preventing diseases such as diabetes and obesity. The workflow encompassed several key steps, such as the selection of functional SBEIIb gene, designing the single-guide RNA (sgRNA), selecting an appropriate genome editing vector, determining the vector delivery method, conducting plant tissue culture, genotyping mutation and phenotypic analysis. Additionally, the time frame necessary for each stage of the process has been clearly demonstrated. This protocol not only streamlines the breeding process but also enhances the accuracy and efficiency of trait introduction, thereby accelerating the development of functional rice varieties.

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits 

The protocol describes breeding resistant starch rice varieties by design using genome editing technologies in a precise, efficient, and technically simple way.

The conventional approaches to crop breeding, which rely predominantly on time-consuming and labor-intensive methods such as traditional hybridization and ...

Bioengineering

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

Soybean (Glycine max (L.) Merr.) is an important crop species and has become a legume model for the studies of genetic and biochemical pathways. Therefore, it is important to establish an efficient transient gene expression system in soybean. Here, we report a simple protocol for the preparation of soybean protoplasts and its application for transient functional analyses. We found that young unifoliate leaves from soybean seedlings resulted in large quantities of high quality protoplasts. By optimizing a PEG-calcium-mediated transformation method, we achieved high transformation efficiency using soybean unifoliate protoplasts. This system provides an efficient and versatile model for examination of complex regulatory and signaling mechanisms in live soybean cells and may help to better understand diverse cellular, developmental and physiological processes of legumes.

We developed a simple and efficient protocol for the preparation of large quantities of soybean protoplasts to study complex regulatory and signaling mechanisms in live cells.

Soybean (Glycine max (L.) Merr.) is an important crop species and has become a legume model for the studies of genetic and biochemical pathways. ...

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

The development of a complex multicellular organism is governed by distinct cell types that have different transcriptional profiles. To identify transcriptional regulatory networks that govern developmental processes it is necessary to measure the spatial and temporal gene expression profiles of these individual cell types. Therefore, insight into the spatio-temporal control of gene expression is essential to gain understanding of how biological and developmental processes are regulated. Here, we describe a laser-capture microdissection (LCM) method to isolate small number of cells from three barley embryo organs over a time-course during germination followed by transcript profiling. The method consists of tissue fixation, tissue processing, paraffin embedding, sectioning, LCM and RNA extraction followed by real-time PCR or RNA-seq. This method has enabled us to obtain spatial and temporal profiles of seed organ transcriptomes from varying numbers of cells (tens to hundreds), providing much greater tissue-specificity than typical bulk-tissue analyses. From these data we were able to define and compare transcriptional regulatory networks as well as predict candidate regulatory transcription factors for individual tissues. The method should be applicable to other plant tissues with minimal optimization.

Presented here is a protocol for laser-capture microdissection (LCM) of plant tissues. LCM is a microscopic technique for isolating areas of tissue in a contamination-free manner. The procedure includes tissue fixation, paraffin embedding, sectioning, LCM and RNA extraction. RNA is used in the downstream tissue-specific, temporally resolved analysis of transcriptomes.

The development of a complex multicellular organism is governed by distinct cell types that have different transcriptional profiles. To identify ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

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

Samenvatting

Meer video's verkennen