Name: Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish (Video) | JoVE Journal
Uploaded: 2017-10-27T12:30:00.000+00:00
Duration: 11 min 42 s
Description: Learn RNASeq-based gene expression analysis in zebrafish embryos and larvae for developmental biology. This method enables quantitative comparison of transcriptome changes and pathway analysis.

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5 minutes

This work was supported by R01DK102001 (N.A.Z.), P30DK072488 (N.A.Z.), and T32DK098107 (T.L.H. and J.E.N.).

All animal protocols outlined below are in accordance with and approved by the University of Maryland Institutional Animal Care and Use Committee (IACUC).
1. Embryo Preparation
<ol>
	<li>Generating embryos through natural mating
	<ol>
		<li>Culture embryos to 3 months of age, reproductive maturity5,8.</li>
		<li>Segregate adult male and female fish from the desired strain into divided mating tanks on the evening before embryo collection, and add 2 males and 3 females to each tank. 
		NOTE: Use of the transgenic insulin2a:mCherry fluorescent reporter strain allowed the analysis of pancreatic β-cells.</li>
		<li>Transfer fish to the mating tank with fresh system water and remove the divider immediately after the lights come on the following morning.</li>
		<li>Allow the fish to mate naturally until embryos are observed in bottom tank. Collect embryos in 30 min intervals until the desired amount is collected. Store each collected time point in separate Petri dishes in embryo media at 28.5 °C.</li>
		<li>Perform microinjection of genetic material or placement in experimental culture media6, if desired, and culture the embryos in fresh Hank's embryo medium8 in 10-cm Petri dishes at 28.5 °C. 
		NOTE: For gene expression analysis in morpholino (MO) injected or mutant animals, note that each manipulation may have incidental impacts on gene expression. Mutants can exhibit genetic compensation at the level of transcription that is not observed by MO-based gene targeting9.</li>
	</ol>
	</li>
	<li>Stage embryos
	<ol>
		<li>Culture the embryos in groups of 50–75 embryos per 10-cm Petri dish to promote consistent developmental timing of all embryos.</li>
		<li>Monitor the developmental morphology using dissecting microscope at blastomere, epiboly, and somite stages to ensure developmental progression10. 
		NOTE: Remove any dying or malformed embryos to prevent developmental delay in the dish.</li>
		<li>Separate the embryos based on the developmental age. Measure the embryo age using somite number after segmentation (post-gastrulation, 10.33 h post fertilization (hpf)) until approximately 24 hpf. Stage the embryos and larvae using total body length after 24 hpf. 
		NOTE: The somites are the chevron-shaped mesodermal tissue present on the dorsal part of the embryo.</li>
		<li>Place the sorted embryos into a 28.5 °C incubator and allow development to progress to the desired age.</li>
	</ol>
	</li>
</ol>
2. Single-cell Dissociation: Whole Embryo and Sorted Cell Populations
<ol>
	<li>Whole embryo dissociation
	<ol>
		<li>Euthanize the zebrafish embryos at the desired stage by placing the Petri dish on ice for 5-15 min until no movement is observed.</li>
		<li>Transfer a pool of 20 embryos to a labeled 1.5 mL microcentrifuge tube. Remove any excess embryo media from the microcentrifuge tube.</li>
		<li>Add 200 µL lysis reagent (see Table of Materials) to the tube containing embryos. Homogenize the embryos in lysis reagent mechanically using a pestle. Add 800 µL lysis reagent to bring the total volume to 1 mL. Store at -80 °C until RNA extraction.</li>
	</ol>
	</li>
	<li>Flow-sorted cell isolation11
	<ol>
		<li>Transfer the embryos from the Petri dish into a 1.5 mL microcentrifuge tube and remove as much embryo medium as possible. 
		NOTE: Depending on the embryo age, mechanical dissociation may also be required at this step. With embryos &gt; 48 hpf, we recommend the use of a pestle to disrupt embryo integrity before proceeding.</li>
		<li>Incubate the embryos with 1 mL of dissociation buffer 1 (see Table of Materials) in 1.5 mL microcentrifuge tube. Incubate for 15-30 min at room temperature. Triturate the solution by gently pipetting up and down using a P1000 tip to mix every 3-4 min during the incubation step. DO NOT VORTEX.</li>
		<li>Collect the embryos by centrifugation for 3 min at 300 x g and remove the supernatant. Re-suspend the embryos in 1 mL dissociation buffer 2 (see Table of Materials). Incubate for 15-30 min at room temperature with regular pipet trituration.</li>
		<li>Assess the digestion by diluting 1-2 µL of suspension in fluorescence-activated cell sorting (FACS) buffer under microscope. 
		NOTE: Complete digestion has occurred when the examined aliquot shows single cells with minimal cell clusters and pieces of embryonic tissue.</li>
		<li>Collect the cells by centrifugation at 300 x g for 5 min. Remove supernatant. Re-suspend the cells into 1 mL FACS buffer, and gravity filter through a 40 µm cell strainer to remove undigested tissue from the sample.</li>
		<li>Count the cells using a hemocytometer and dilute with FACS buffer to approximately 1x 106 cells/mL in a FACS tube. 
		NOTE: For cell types with a relatively small number per embryo (less than 5% of total), such as pancreatic β-cells, use a minimum of 1,000 stage-matched embryos.</li>
		<li>Provide the FACS sorting facility with the FACS tubes containing 1 mL of cell suspension samples as well as FACS tube containing only lysis reagent or FACS Buffer. Gate the FACS flow-sorter to collect only single cells that express the fluorescent reporter.</li>
		<li>Perform the FACS flow-sorting until the desired cell numbers have been collected. 
		NOTE: To perform RNASeq, there is a minimum total RNA required. We have found that 3,000-5,000 cells are sufficient to isolate high-quality, high-concentration RNA.</li>
		<li>Store the collected cells on ice and proceed immediately to RNA preparation.</li>
	</ol>
	</li>
</ol>
3. RNA Preparation
<ol>
	<li>Extract RNA
	<ol>
		<li>Incubate the collected sample (from step 2) with lysis reagent for 5 min at room temperature.</li>
		<li>Add 0.2 mL of chloroform for each 1.0 mL of lysis reagent used and invert the tubes by hand for 15 s. Incubate for 2-3 min at room temperature. Centrifuge the samples at 12,000 x g for 15 min at 4 °C.</li>
		<li>Transfer the separated, aqueous phase to a fresh tube and add 0.5 mL of isopropanol for each 1.0 mL of lysis reagent used. Incubate for 10 min at room temperature. Centrifuge the samples at 12,000 x g for 10 min at 4 °C.</li>
		<li>Wash with 75% ethanol and centrifuge the samples at 7,500 x g for 5 min at 4 °C. Remove the supernatant completely and allow to air dry at room temperature. Re-suspend the RNA in 15-30 µL diethyl pyrocarbonate (DEPC)-treated water.</li>
	</ol>
	</li>
	<li>Purify high-quality RNA
	<ol>
		<li>Combine the RNA suspension with 1/10 volume 3M sodium acetate (pH 5.5) and 1 volume of isopropanol. Incubate for 20 min at room temperature and centrifuge the samples at 12,500 x g for 10 mins at 4 °C.</li>
		<li>Wash the pellet with ice-cold 70% ethanol in DEPC-treated water and centrifuge the samples at 10,000 x g for 5 min at 4 °C. Repeat the wash step once.</li>
		<li>Remove the supernatant and allow to dry at room temperature. Re-suspend in DEPC-treated water.</li>
		<li>Assay the extracted RNA for the concentration (ng/µL) and purity (260/230 ratio) using an absorption spectrometer. Ensure that the 260/230 value is ~ 2.0 before proceeding with RNASeq. Store at -80 °C until use (up to 6 months).</li>
		<li>Send the RNA samples to a vendor or core for RNASeq and gene expression change analysis based on quantification of sequencing reads. The results returned from the vendor are typically provided as a list of genes exhibiting a significant fold-change in transcript reads between samples. 
		NOTE: A column can be used if the above method yields poor quality RNA. The amount, concentration, and RNA Integrity Number (RIN) for RNA samples should be confirmed with the vendor or core. The vendor or core will also typically assess RIN.</li>
	</ol>
	</li>
</ol>
4. Pathway and GO Term Analysis
NOTE: See Figure 1 for representative output of the gene expression analysis after RNASeq provided by the core or vendor.
<ol>
	<li>Sorting differentially expressed genes
	<ol>
		<li>Comparing a single experimental condition versus control
		<ol>
			<li>Open data (results) in a spreadsheet management software (see Table of Materials).</li>
			<li>Select the drop-down arrow next to the 'Sort' button and select "Custom Sort" within the spreadsheet containing the differentially expressed genes in the experimental versus control condition.</li>
			<li>Select the box under 'Column' in the window that pops up and choose to sort by the 'LFC' column. Ensure 'Values' is selected in the 'Sort On' column. Select to sort from 'Largest to Smallest' in the 'Order' column and click "OK". 
			NOTE: This will sort the differentially expressed genes so that those with increased expression (positive LFC) will appear at the top of the list while those with decreased expression (negative LFC) will appear at the bottom of the list.</li>
		</ol>
		</li>
		<li>Compare the multiple experimental conditions to a single control as follows.
		<ol>
			<li>Select the first cell in an empty column on the Experimental 1 versus Control spreadsheet to determine differentially expressed genes found in two experimental conditions. Type the following equation into the cell: 
			<img alt="Excel formula for duplicate detection, equation formula, educational use." src="/files/ftp_upload/56187/56187eq1.jpg" data-altupdatedat="1788250015000" data-alt="Equation"> 
			NOTE: This equation is a combination of the IF, ISERROR, and MATCH functions. (i) The MATCH function, MATCH(A1,Experimental2_vs_Control!A:A,0), will look for the value in A1 (the ENSEMBL ID) in the A column (A:A) of the spreadsheet named Experimental2_vs_Control (Experimental2_vs_Control!). The "0" indicates to look for an exact match, and if an exact match is found, the function will return a value of "1", while if no match is found, the function will return an error "N/A". (ii) The result of the MATCH function is then input into the ISERROR function, ISERROR(MATCH(A1,Experimental2_vs_Control!A:A,0)), where, if the input is "1" indicating a match was found, it will return "FALSE", and if the input is "N/A" indicating a match was not found, it will return "TRUE". (iii) The "TRUE" or "FALSE" result is then input into the IF function, IF(ISERROR(MATCH(A1,Experimental2_vs_Control!A:A,0)),"","Duplicate"), where, if the input is "TRUE" indicating a match was not found, it will return the value in between the first set of quotation marks ("") which is empty, and therefore the cell will be empty. If the input is "FALSE" indicating a match was found, it will return the value in between the second set of quotation marks ("Duplicate"), and the cell will say "Duplicate".</li>
			<li>Press "Enter" or "Return" to run the equation. Select the cell containing the equation and click the box in the bottom right corner of the cell. Keep the mouse clicked and drag the selected area all the way down the column until the last 'Feature ID', to copy the equation into each cell in the column.</li>
			<li>Select "Custom Sort" again, add a second level of sorting by clicking the "+" icon in the lower left corner. In the first level, "Sort by", under Column select "Duplicate", under Sort On select 'Values', and under Order select 'Z to A'. In the second level, "Then by", under Column select 'LFC', under Sort On select 'Values', and under Order select 'Largest to Smallest' and select "OK". 
			NOTE: This will sort the list of genes into 4 groups based upon directionality of expression change. It is important to check the genes found in both experimental conditions to ensure that the change in the expression is in the same direction in both or in opposite directions.</li>
			<li>Remove any parentheses from the gene symbols column before proceeding or the results will not be found in the databases. Select the entire column containing gene symbols. Select "Edit" then "Replace" in the drop-down 'File' menu. Type "(*)" in the "Find what:" bar of the Replace window, and leave the "Replace with:" bar empty.Select 'Replace All' to remove all instances of parentheses. 
			NOTE: The * represents any number of characters, by placing this character between two parentheses the program is prompted to find any instance in the highlighted cells and any instance of parentheses will be replaced with nothing, thereby removing them.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Determining enriched pathways
	<ol>
		<li>Copy the gene symbols of the set for which one wishes to determine the enriched pathways to the clipboard. Go to ConsensusPathDB12. Select "Gene set analysis" on the left sidebar of the webpage, followed by "Over-representation analysis".</li>
		<li>Paste the list of genes in the "Paste a list of gene/protein identifiers" box. Select gene symbol in the "Gene/protein identifier type" box and click "Proceed". Select the box next to "Pathways as defined by pathway databases" under the Pathway-based sets section. 
		NOTE: A number of options for the analysis will appear including the list of available databases to search. We recommend de-selecting all databases except the Kegg and Reactome databases as they are the most established and comprehensive. The minimum overlap with input list and p-value cutoff settings can also be adjusted based on needs. We recommend leaving these settings at the default 2 gene minimum overlap and a 0.01 p-value cutoff.</li>
		<li>Select "Find enriched sets" to obtain the list of pathways containing genes in the input list. 
		NOTE: The output will be in table format including the name of each pathway followed by "Set size" (indicating the number of total genes in the pathway), "Candidates contained" (indicating the number of genes in the input list that are in that pathway), as well as the p-value and q-value (FDR), and the database from which the pathway was identified.</li>
	</ol>
	</li>
	<li>Generation of pathway networks
	<ol>
		<li>Determine enriched pathways as described above.</li>
		<li>Select all of the enriched pathways to visualize in a pathway network by either selecting each box next to the pathway names to be visualized or by clicking "All" under "Select" in the column header above the selection boxes, then select "Visualize selected sets". 
		NOTE: We recommend visualizing all pathways if there are less than 30; if there are greater than 30 pathways we recommend selecting the top 30 most enriched pathways (those containing the greatest number of genes from the input list).</li>
		<li>Adjust the "relative overlap" and "shared candidates" filters, by selecting either box in the top center of the page and inputting desired percent relative overlap or number of shared candidates, to be more stringent in order to make the more pertinent overlaps within the pathway networks clearer, and select "apply". 
		NOTE: We recommend a minimum relative overlap of 0.2, indicating a 20% gene overlap between 2 pathways to connect them, and a minimum of 2 shared candidates. The graph legend can be seen by clicking on the graph legend in the upper left of the page.</li>
	</ol>
	</li>
	<li>Determination of enriched GO terms
	<ol>
		<li>Copy the gene symbols of the group for which one wishes to determine the enriched gene ontologies to the clipboard. Go to the 'GO Enrichment Analysis tool' in the Gene Ontology Consortium13. Paste the list of gene symbols in the box on the left side of the page under "Your gene IDs here…"</li>
		<li>Select which set of GO terms to use, listed underneath the gene IDs box: biological process, molecular function, or cellular component. Choose (recommended) 'biological process'. Select 'Danio rerio' underneath the GO terms box and click "Submit". 
		NOTE: We recommend using the default p-value cutoff of 0.05.</li>
	</ol>
	</li>
</ol>
5. Verification by qRT-PCR
NOTE: Individual genes identified with significant gene expression changes in RNASeq should be verified by targeted qRT-PCR in replicate experiments.
<ol>
	<li>cDNA synthesis
	<ol>
		<li>Extract the RNA from embryos using the method described in Section 3.1. RNA Extraction.</li>
		<li>Convert 1 µg of RNA to cDNA using cDNA conversion kit (see Table of Materials). Combine RNA, dNTPs, and reverse transcriptase enzyme. Perform the thermocycler reaction according to the manufacturer's specifications.</li>
		<li>Dilute cDNA 1:3 in DEPC-treated water. Store at 4 °C for up to 1-2 months.</li>
	</ol>
	</li>
	<li>qRT- PCR verification
	<ol>
		<li>Select genes to verify by qRT-PCR from RNA sequencing results. Identify genes with high fold changes in expression. Determine which of these genes also contain high read counts, as this indicates abundant expression.</li>
		<li>Select 10-12 targets from the high fold change, high read count list of genes. Design primers to amplify the target genes that span at least 1 intron-exon boundary.</li>
		<li>Enter the gene or targeted region of the gene into qPCR primer design site14. Select the "design 2 qPCR primers" and prompt the site to create primer set. 
		NOTE: Be sure to include internal control primers, such as β-actin, to normalize comparisons between samples. The β-actin primers are F: 5'-TCGAGCTGTCTTCCCATCCA-3' and R: 5'-TCACCAACGTAGCTGTCTTTCTG-3'.</li>
		<li>Combine the cDNA, forward primer, reverse primer, and polymerase. Perform qRT-PCR amplification to determine the relative expression of genes15.</li>
		<li>Analyze the relative mRNA expression of genes of interest by relative quantification determination15.</li>
		<li>Compare the qRT-PCR to RNASeq results to ensure that the directionality of differential expression is consistent.</li>
	</ol>
	</li>
</ol>

<ol><li>Nusslein-Volhard, C., Dahm, R. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aNusslein-Volhard%2C+C+%22Zebrafish%22">Zebrafish</a>. , Oxford University Press. (2002).</li>
<li>Detrich, H. W., Zon, L., Westerfield, M. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aDetrich%2C+HW+%22The+Zebrafish%3A+Disease+Models+and+Chemical+Screens%22">The Zebrafish: Disease Models and Chemical Screens</a>. , 4th ed, Academic Press. (2017).</li>
<li>Detrich, H. W., Zon, L. I., Westerfield, M. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aDetrich%2C+HW+%22The+Zebrafish%3A+Genetics%2C+Genomics%2C+and+Transcriptomics%22">The Zebrafish: Genetics, Genomics, and Transcriptomics</a>. , 4th ed, Academic Press. (2016).</li>
<li>Detrich, H. W. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aDetrich%2C+HW+%22The+Zebrafish%3A+Genetics%2C+Genomics+and+Informatics%22">The Zebrafish: Genetics, Genomics and Informatics</a>. , 3rd ed, Academic Press. (2011).</li>
<li>Avdesh, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Regular+care+and+maintenance+of+a+zebrafish+%28Danio+rerio%29+laboratory%3A+an+introduction%5BTitle%5D)+AND+%22J+Vis+Exp%22%5BJournal%5D)">Regular care and maintenance of a zebrafish (Danio rerio) laboratory: an introduction.</a> J Vis Exp. (69), e4196(2012).</li>
<li>Rosen, J. N., Sweeney, M. F., Mably, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Microinjection+of+zebrafish+embryos+to+analyze+gene+function%5BTitle%5D)+AND+%22J+Vis+Exp%22%5BJournal%5D)">Microinjection of zebrafish embryos to analyze gene function.</a> J Vis Exp. (25), (2009).</li>
<li>Hwang, W. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Efficient+genome+editing+in+zebrafish+using+a+CRISPR-Cas+system%5BTitle%5D)+AND+%22Nat+Biotechnol%22%5BJournal%5D)">Efficient genome editing in zebrafish using a CRISPR-Cas system.</a> Nat Biotechnol. 31 (3), 227-229 (2013).</li>
<li>Westerfield, M. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aWesterfield%2C+M+%22The+zebrafish+book.+A+guide+for+the+laboratory+use+of+zebrafish+%28Danio+rerio%29%22">The zebrafish book. A guide for the laboratory use of zebrafish (Danio rerio)</a>. , 4th ed, Univ. of Oregon Press. Eugene. (2000).</li>
<li>Rossi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genetic+compensation+induced+by+deleterious+mutations+but+not+gene+knockdowns%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Genetic compensation induced by deleterious mutations but not gene knockdowns.</a> Nature. 524 (7564), 230-233 (2015).</li>
<li>Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B., Schilling, T. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stages+of+embryonic+development+of+the+zebrafish%5BTitle%5D)+AND+%22Dev+Dyn%22%5BJournal%5D)">Stages of embryonic development of the zebrafish.</a> Dev Dyn. 203 (3), 253-310 (1995).</li>
<li>Samsa, L. A., Fleming, N., Magness, S., Qian, L., Liu, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Isolation+and+Characterization+of+Single+Cells+from+Zebrafish+Embryos%5BTitle%5D)+AND+%22J+Vis+Exp%22%5BJournal%5D)">Isolation and Characterization of Single Cells from Zebrafish Embryos.</a> J Vis Exp. (109), (2016).</li>
<li>MPIMG. ConsenusPathDB. , Available from:
 <a target="_blank" href="http://cpdb.molgen.mpg.de/">http://cpdb.molgen.mpg.de/</a> (2017).</li>
<li>Consortium, G. O. Enrichment analysis Tool. , Available from:
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<li>IDT. IDT Primerquest Tool. , Available from:
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<li>Heid, C. A., Stevens, J., Livak, K. J., Williams, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Real+time+quantitative+PCR%5BTitle%5D)+AND+%22Genome+Res%22%5BJournal%5D)">Real time quantitative PCR.</a> Genome Res. 6 (10), 986-994 (1996).</li>
<li>Leitch, C. C., Lodh, S., Prieto-Echague, V., Badano, J. L., Zaghloul, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Basal+body+proteins+regulate+Notch+signaling+through+endosomal+trafficking%5BTitle%5D)+AND+%22J+Cell+Sci%22%5BJournal%5D)">Basal body proteins regulate Notch signaling through endosomal trafficking.</a> J Cell Sci. 127 (Pt 11), 2407-2419 (2014).</li>
<li>Lodh, S., Hostelley, T. L., Leitch, C. C., O'Hare, E. A., Zaghloul, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Differential+effects+on+beta-cell+mass+by+disruption+of+Bardet-Biedl+syndrome+or+Alstrom+syndrome+genes%5BTitle%5D)+AND+%22Hum+Mol+Genet%22%5BJournal%5D)">Differential effects on beta-cell mass by disruption of Bardet-Biedl syndrome or Alstrom syndrome genes.</a> Hum Mol Genet. 25 (1), 57-68 (2016).</li>
<li>Hostelley, T. L., Lodh, S., Zaghloul, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Whole+organism+transcriptome+analysis+of+zebrafish+models+of+Bardet-Biedl+Syndrome+and+Alstrom+Syndrome+provides+mechanistic+insight+into+shared+and+divergent+phenotypes%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">Whole organism transcriptome analysis of zebrafish models of Bardet-Biedl Syndrome and Alstrom Syndrome provides mechanistic insight into shared and divergent phenotypes.</a> BMC Genomics. 17, 318(2016).</li>
</ol>

The authors have nothing to disclose.

The approach described in this protocol offers a relatively rapid and cost-effective strategy for transcriptome-level analysis of whole animals or specific sorted cell populations. The zebrafish provides an advantageous model for this type of study because of the ease and rapidity in generating large amounts of starting material, the ease of implementing genetic or environmental experimental conditions, and the availability of a large spectrum of transgenic reporter lines allowing for isolation of cell-type specific and ...

Comparative analysis of global gene expression is a valuable tool to identify novel genes contributing to observed phenotypes. Such analyses typically rely on quantitative assessment of transcript abundance compared between experimental and control samples. Targeted approaches, such as qRT-PCR are relatively rapid and accurate for investigation of single gene expression changes. RNA sequencing (RNASeq) offers a broad, hypothesis-free approach to identify significant changes in gene expression between samples, making it now the standard for such investigations across experimental systems.
Zebrafish have emerged as a prominent model across many disease areas. Originally developed for their utility in developmental biology studies, due to their high fecundity and relatively low cost of maintenance, experimental use of zebrafish has evolved to include a broad range of phenotypes from embryonic to adult stages as well as a wide array of molecular assays1,2,3. Indeed, these advantages make molecular mechanistic studies rapid and cost-effective because of the ease of acquiring large amounts of material combined with the ease of both genetic and environmental manipulation at all stages of life. Moreover, the transparent nature of zebrafish embryos and larvae make it ideal for generating cell- and tissue-specific transgenic reporter lines allowing in vivo visualization of discrete cell populations4. Exploitation of such lines permits global gene expression analysis in specific isolated cell types based on reporter gene expression.
Here we present a comprehensive protocol for global gene expression analysis using RNASeq after culture of zebrafish embryos. Genetic experimental manipulations, including morpholino (MO)-based transient gene knockdown or CRISPR-mediated genome editing, have been presented elsewhere5,6,7. We therefore focus on a detailed protocol for isolation of RNA from whole embryos or sorted transgenic reporter-expressing cells followed by simple computational analysis of RNASeq results using pathway tools and gene ontology (GO) terms. Finally, we have included a strategy for validation of gene expression changes by quantitative reverse transcriptase PCR (qRT-PCR). These protocols are applicable to zebrafish embryos subjected to a wide range of experimental conditions, including comparison of genetic mutants or environmental conditions.

<table><tbody><tr><td>&lt;strong&gt;Commercial Reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>TriZol</td><td>Thermo Scientific</td><td>15596026</td><td>lysis reagent</td></tr><tr><td>TrypLE</td><td>Gibco</td><td>12604013</td><td>dissociation buffer 1</td></tr><tr><td>FACSMax</td><td>Genlantis</td><td>T200100</td><td>dissociation buffer 2</td></tr><tr><td>DEPC-treated water</td><td>Sigma</td><td>95284</td><td></td></tr><tr><td>FirstStrand cDNA conversion</td><td>Thermo Scientific</td><td>K1621</td><td>cDNA conversion kit</td></tr><tr><td>2X SYBR Green Master Mix</td><td>Roche</td><td>4707516001</td><td>qRT-PCR Master Mix</td></tr><tr><td>FACS buffer</td><td>Fisher Scientific</td><td>50-105-9042</td><td></td></tr><tr><td>chloroform</td><td>Sigma Aldrich</td><td>288306</td><td></td></tr><tr><td>sodium acetate</td><td>Sigma Aldrich</td><td>S2889</td><td></td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Zebrafish Strains&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Tuebingen</td><td>ZIRC</td><td>ZL57</td><td></td></tr><tr><td>ins2a:mCherry</td><td>ZIRC</td><td>ZL1483</td><td></td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>40 micron cell strainer</td><td>Sigma</td><td>CLS431750</td><td></td></tr><tr><td>FACS tube</td><td>BD Falcon</td><td>352063</td><td></td></tr><tr><td>hemocytometer</td><td>Sigma</td><td>Z359629</td><td></td></tr><tr><td>Dissecting Microscope</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Inverted Microscope</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Nanodrop</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>Illumina HiSeq</td><td>Illumina</td><td></td><td></td></tr><tr><td>LightCycler 480</td><td>Roche</td><td></td><td></td></tr><tr><td>Mating tanks 1.0L Crossing Tank Set</td><td>Aquaneering</td><td>ZHCT100</td><td></td></tr><tr><td>FACS tube 5 mL polypropylene tube</td><td>BD Falcon</td><td>352063</td><td></td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Excel</td><td>Microsoft</td><td></td><td></td></tr><tr><td>Consensus Path DB</td><td>http://cpdb.molgen.mpg.de/</td><td></td><td></td></tr><tr><td>GO Enrichment Analysis</td><td>http://geneontology.org/page/go-enrichment-analysis</td><td></td><td></td></tr></tbody></table>

sample preparation and analysis of rnaseq-based gene expression data from zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

Sorting of Differentially Expressed Genes:
To identify differentially expressed genes in the larval stage of zebrafish models of Alström Syndrome and Bardet-Biedl Syndrome (BBS), we targeted either alms1 or bbs1 transcripts by injecting previously validated splice-blocking MOs into wild-type zebrafish embryos16,17. At 5 days post fertilization (dpf), two r...

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

The overall goal of this RNASeq analysis in zebrafish larval samples is to identify gene expression profiles for zebrafish embryos and larvae and to make quantitative comparative statements about gene expression changes between samples. This method can help answer key questions in any field for which zebrafish embryos or larvae are informative, such as how suppression of one targeted gene results in gene expression changes or a disruption of certain pathways relative to other conditions. The main advantage of this technique is that zebrafish are amenable to production of large numbers of animals, and whole animal transcriptome data can be easily obtained.In addition, isolation of specific cell types can be achieved with ease using sorting of transgenic animals. Demonstrating the procedures will be Lain Hostelley, a graduate student, and Jessica Nesmith, a post-doc from my laboratory. To begin, culture embryos to three months of age, which is reproductive maturity.Segregate two adult male and three female fish from the desired strain into divided mating tanks of fresh system water on the evening before embryo collection. The following morning, after the lights come on, remove the divider, and allow the fish to mate naturally until embryos are observed in the bottom of the tank. Collect embryos in 30 minute intervals in separate Petri dishes of embryo medium until the desired number are collected.To stage the embryos, culture them in groups of 50 to 75 per 10 centimeter Petri dish to promote consistent developmental timing of all embryos. Then, keep the plates at 28.5 degrees Celsius. Measure the embryo age using somite number after segmentation until approximately 24 hours post-fertilization, or HPF, and separate the embryos based on developmental age.After euthanizing embryos at the desired stage according to the text protocol, transfer a pool of 20 embryos to a labeled 1.5 milliliter micro-centrifuge tube. Then, remove any excess embryo medium from the micro-centrifuge tube. Add 200 microliters of lysis reagent to the tube.And use a pestle to mechanically homogenize the embryos. Then, add an additional 800 microliters of lysis reagent to bring the total volume to one milliliter. To extract the RNA, add lysis reagent to the collected sample and incubate it at room temperature for 5 minutes.Add 0.2 milliliters of chloroform for each 1 milliliter of lysis reagent used, and invert the tubes by hand for 15 seconds. After incubating the samples at room temperature for two to three minutes, centrifuge the samples at 12, 000 times G and 4 degrees Celsius for 15 minutes. Transfer the separated aqueous phase to a fresh tube and add 0.5 milliliters of isopropanol for each 1 milliliter of lysis reagent used.After incubating the tubes at room temperature for 10 minutes, centrifuge the samples for 10 minutes. Next, add 75%ethanol to the RNA and centrifuge the tubes at 7, 500 times G and 4 degrees Celsius for 5 minutes. Completely remove the supernatant and allow the sample to air dry at room temperature.Then, re-suspend the RNA in 15 to 30 microliters of DEPC treated water. To purify high quality RNA after extracting the pellet and washing the sample according to the text protocol, use an absorption spectrophotometer to assay the extracted RNA for concentration and purity. Ensure that the 260 over 230 value is about 2.0 before sending the RNA samples to a vendor or corps for RNASeq and gene expression change analysis based on quantification of sequencing reads.To compare a single experimental condition versus a control, open the data of differentially expressed genes in a spreadsheet management software. Select the drop down arrow next to the sort button, and select custom sort within the spreadsheet. In the window that pops up, select the box under column and choose to sort by the LFC column.In the sort on column, ensure values is selected. In the order column, select to sort from largest to smallest and click okay. To determine differentially expressed genes found in two experimental conditions, on the experimental one versus control spreadsheet, select the first cell in an empty column.Then, type the following equation into the cell. Press enter or return to run the equation. Select the cell containing the equation and click the box in the bottom right corner of the cell.Keep the mouse clicked and drag the selected area all the way down the column until selecting the last feature ID to copy the equation into each cell in the column. Select custom sort again and add a second level of sorting by clicking the plus icon in the lower left corner. In the first level, sort by, under column, select duplicate.Under sort on, select values, and under order, select Z to A.In the second level, then by, under column, select LFC. Under sort on, select values, and under order, select largest to smallest, and select okay. To remove any parentheses from the gene symbols column before proceeding, select the entire column containing gene symbols.Select edit, then replace in the drop down file menu. Type open parenthesis, star, closed parenthesis, in the find what bar of the replace window, and lease the replace with bar empty. Select replace all to remove all instances of parentheses.To determine enriched pathways, copy the desired gene symbols to the clipboard. Go to ConsensusPathDB, then select gene set analysis on the left sidebar of the webpage, followed by over-representation analysis. In the paste a list of gene and protein identifiers box, paste the list of genes.Select the gene symbol in the gene/protein identifier type box and click proceed. Under the pathway-based sets section, select the box next to pathways as defined by pathway databases. Select find enriched sets to obtain the list of pathways containing genes in the input list.Select all of the enriched pathways to visualize in a pathway network by either selecting each box next to the pathway names or, under select in the column header above the selection boxes, click all. Then, select visualize selected sets. Adjust the relative overlap and shared candidates filters by selecting either box in the top center of the page and inputting the desired percent, relative overlap, or number of shared candidates, then select apply.To determine the enriched gene ontologies, copy the gene symbols the respective group to the clipboard. Go to the GO Enrichment analysis tool in the Gene Ontology Consortium. On the left side of the page, under your gene IDs here, paste the list of gene symbols in the box.Underneath the gene IDs box, select the go term, biological process. Then, select danio rerio underneath the go terms box and click submit. Carry out QRT PCR and compare to RNASeq, according to the text protocol.Sequencing of extracted RNA from larvae injected with Morpholinos against either alms1 or bbs1 revealed the genes that were uniquely up and downregulated in the Alstrom and BBS models, as well as the genes that were significantly changed in both models. To more clearly elucidate the molecular profile of the Alstrom model, the pathways and gene ontologies enriched in the differentially expressed genes were identified. As shown here, 31 total pathways were upregulated.Aside from the broad grouping of metabolism, the most highly affected pathways were the innate and adaptive immune system, with 32 and 20 genes respectively. Downstream B cell receptor signaling events were also enriched. Several signaling pathways were also enriched among the upregulated genes, consistent with the association of Alstrom Syndrome with primary cilium dysfunction.In addition, three pathways associated with insulin secretion were upregulated, fatty acids bound to GPR40, free fatty acids, and acetylcholine. Finally, six GO terms were enriched among the upregulated genes in the Alstrom model, including erythrocyte differentiation, erythrocyte homeostasis, myeloid cell homeostasis, and homeostatic processes. Once mastered, the pathway and GO term analysis can be done in less than an hour.While attempting this procedure, it's important to remember that selection of pathway parameters and cutoff values are the most important considerations in interpreting results of RNASeq expression comparisons. Following this procedure, other methods, like application with mutant lines and transcriptome analysis of single cell types can be performed in order to answer additional questions, like transcriptome profiling of cell types and gene expression changes under the control of specific genes. After watching this video, you should have a good understanding of how to use zebrafish transcriptomic data generated by RNASeq for identification of significant gene expression changes between experimental samples.

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