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 &#946;-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 &#176;C.</li>
		<li>Perform microinjection of genetic material or placement in experimental culture media6, if desired, and culture the embryos in fresh Hank&#39;s embryo medium8 in 10-cm Petri dishes at 28.5 &#176;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&#8211;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 &#176;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 &#181;L lysis reagent (see Table of Materials) to the tube containing embryos. Homogenize the embryos in lysis reagent mechanically using a pestle. Add 800 &#181;L lysis reagent to bring the total volume to 1 mL. Store at -80 &#176;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 &#62; 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 &#181;L 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 &#181;m 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 &#946;-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 &#176;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 &#176;C.</li>
		<li>Wash with 75% ethanol and centrifuge the samples at 7,500 x g for 5 min at 4 &#176;C. Remove the supernatant completely and allow to air dry at room temperature. Re-suspend the RNA in 15-30 &#181;L 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 &#176;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 &#176;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/&#181;L) 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 &#176;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 &#39;Sort&#39; button and select &#34;Custom Sort&#34; within the spreadsheet containing the differentially expressed genes in the experimental versus control condition.</li>
			<li>Select the box under &#39;Column&#39; in the window that pops up and choose to sort by the &#39;LFC&#39; column. Ensure &#39;Values&#39; is selected in the &#39;Sort On&#39; column. Select to sort from &#39;Largest to Smallest&#39; in the &#39;Order&#39; column and click &#34;OK&#34;. 
			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="Equation" src="/files/ftp_upload/56187/56187eq1.jpg" /> 
			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 &#34;0&#34; indicates to look for an exact match, and if an exact match is found, the function will return a value of &#34;1&#34;, while if no match is found, the function will return an error &#34;N/A&#34;. (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 &#34;1&#34; indicating a match was found, it will return &#34;FALSE&#34;, and if the input is &#34;N/A&#34; indicating a match was not found, it will return &#34;TRUE&#34;. (iii) The &#34;TRUE&#34; or &#34;FALSE&#34; result is then input into the IF function, IF(ISERROR(MATCH(A1,Experimental2_vs_Control!A:A,0)),&#34;&#34;,&#34;Duplicate&#34;), where, if the input is &#34;TRUE&#34; indicating a match was not found, it will return the value in between the first set of quotation marks (&#34;&#34;) which is empty, and therefore the cell will be empty. If the input is &#34;FALSE&#34; indicating a match was found, it will return the value in between the second set of quotation marks (&#34;Duplicate&#34;), and the cell will say &#34;Duplicate&#34;.</li>
			<li>Press &#34;Enter&#34; or &#34;Return&#34; 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 &#39;Feature ID&#39;, to copy the equation into each cell in the column.</li>
			<li>Select &#34;Custom Sort&#34; again, add a second level of sorting by clicking the &#34;+&#34; icon in the lower left corner. In the first level, &#34;Sort by&#34;, under Column select &#34;Duplicate&#34;, under Sort On select &#39;Values&#39;, and under Order select &#39;Z to A&#39;. In the second level, &#34;Then by&#34;, under Column select &#39;LFC&#39;, under Sort On select &#39;Values&#39;, and under Order select &#39;Largest to Smallest&#39; and select &#34;OK&#34;. 
			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 &#34;Edit&#34; then &#34;Replace&#34; in the drop-down &#39;File&#39; menu. Type &#34;(*)&#34; in the &#34;Find what:&#34; bar of the Replace window, and leave the &#34;Replace with:&#34; bar empty.Select &#39;Replace All&#39; 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 &#34;Gene set analysis&#34; on the left sidebar of the webpage, followed by &#34;Over-representation analysis&#34;.</li>
		<li>Paste the list of genes in the &#34;Paste a list of gene/protein identifiers&#34; box. Select gene symbol in the &#34;Gene/protein identifier type&#34; box and click &#34;Proceed&#34;. Select the box next to &#34;Pathways as defined by pathway databases&#34; 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 &#34;Find enriched sets&#34; 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 &#34;Set size&#34; (indicating the number of total genes in the pathway), &#34;Candidates contained&#34; (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 &#34;All&#34; under &#34;Select&#34; in the column header above the selection boxes, then select &#34;Visualize selected sets&#34;. 
		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 &#34;relative overlap&#34; and &#34;shared candidates&#34; 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 &#34;apply&#34;. 
		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 &#39;GO Enrichment Analysis tool&#39; in the Gene Ontology Consortium13. Paste the list of gene symbols in the box on the left side of the page under &#34;Your gene IDs here&#8230;&#34;</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) &#39;biological process&#39;. Select &#39;Danio rerio&#39; underneath the GO terms box and click &#34;Submit&#34;. 
		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 &#181;g 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&#39;s specifications.</li>
		<li>Dilute cDNA 1:3 in DEPC-treated water. Store at 4 &#176;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 &#34;design 2 qPCR primers&#34; and prompt the site to create primer set. 
		NOTE: Be sure to include internal control primers, such as &#946;-actin, to normalize comparisons between samples. The &#946;-actin primers are F: 5&#39;-TCGAGCTGTCTTCCCATCCA-3&#39; and R: 5&#39;-TCACCAACGTAGCTGTCTTTCTG-3&#39;.</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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Zebrafish. , (2002).</li><li>Detrich, H. W., Zon, L., Westerfield, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Zebrafish: Disease Models and Chemical Screens. , (2017).</li><li>Detrich, H. W., Zon, L. I., Westerfield, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Zebrafish: Genetics, Genomics, and Transcriptomics. , (2016).</li><li>Detrich, H. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Zebrafish: Genetics, Genomics and Informatics. , (2011).</li><li>Avdesh, 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=Regular+care+and+maintenance+of+a+zebrafish+(Danio+rerio)+laboratory:+an+introduction.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microinjection+of+zebrafish+embryos+to+analyze+gene+function.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Efficient+genome+editing+in+zebrafish+using+a+CRISPR-Cas+system.">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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The zebrafish book. A guide for the laboratory use of zebrafish (Danio rerio). , (2000).</li><li>Rossi, 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=Genetic+compensation+induced+by+deleterious+mutations+but+not+gene+knockdowns.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stages+of+embryonic+development+of+the+zebrafish.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+and+Characterization+of+Single+Cells+from+Zebrafish+Embryos.">Isolation and Characterization of Single Cells from Zebrafish Embryos.</a> J Vis Exp. (109), (2016).</li><li>. IDT Primerquest Tool Available from: <a target="_blank" href="https://www.idtdna.com/Primerquest/Home/Index">https://www.idtdna.com/Primerquest/Home/Index</a> (2017)</li><li>Heid, C. A., Stevens, J., Livak, K. J., Williams, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real+time+quantitative+PCR.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Basal+body+proteins+regulate+Notch+signaling+through+endosomal+trafficking.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+effects+on+beta-cell+mass+by+disruption+of+Bardet-Biedl+syndrome+or+Alstrom+syndrome+genes.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole+organism+transcriptome+analysis+of+zebrafish+models+of+Bardet-Biedl+Syndrome+and+Alstrom+Syndrome+provides+mechanistic+insight+into+shared+and+divergent+phenotypes.">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>Commercial Reagents</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>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Zebrafish Strains</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>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Equipment</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>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Software</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 colspan="2">http://geneontology.org/page/go-enrichment-analysis</td>
			<td></td>
		</tr>
	</tbody>
</table>

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 Alstr&#246;m 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), ...

Watch this Scientific Journal Video about Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish at JoVE.com

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

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 ...

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Research

JoVE Journal

Genetics

10.7K Views.  University of Maryland School of Medicine. 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.

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

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental changes. Considerable progress in our understanding of the tight connection between the circadian clock and most aspects of physiology has been made in the field over the last decade. However, unraveling the molecular basis that underlies the function of the circadian oscillator in humans stays of highest technical challenge. Here, we provide a detailed description of an experimental approach for long-term (2-5 days) bioluminescence recording and outflow medium collection in cultured human primary cells. For this purpose, we have transduced primary cells with a lentiviral luciferase reporter that is under control of a core clock gene promoter, which allows for the parallel assessment of hormone secretion and circadian bioluminescence. Furthermore, we describe the conditions for disrupting the circadian clock in primary human cells by transfecting siRNA targeting CLOCK. Our results on the circadian regulation of insulin secretion by human pancreatic islets, and myokine secretion by human skeletal muscle cells, are presented here to illustrate the application of this methodology. These settings can be used to study the molecular makeup of human peripheral clocks and to analyze their functional impact on primary cells under physiological or pathophysiological conditions.

Here, we describe settings to monitor in parallel circadian bioluminescence and the secretory activity of human islet cells and primary myotubes. For this, we employed lentiviral gene delivery of a luciferase core clock reporter, followed by in vitro synchronization and collection of outflow medium by continuous cell perifusion.

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental ...

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such as cancer. Cell cycle-regulated gene expression analysis depends on cell synchronization into specific phases. Here we describe a method utilizing two complementary synchronization protocols that is commonly used for studying periodic variation of gene expression during the cell cycle. Both procedures are based on transiently blocking the cell cycle in one defined point. The synchronization protocol by hydroxyurea (HU) treatment leads to cellular arrest in late G1/early S phase, and release from HU-mediated arrest provides a cellular population uniformly progressing through S and G2/M. The synchronization protocol by thymidine and nocodazole (Thy-Noc) treatment blocks cells in early mitosis, and release from Thy-Noc mediated arrest provides a synchronized cellular population suitable for G1 phase and S phase-entry studies. Application of both procedures requires monitoring of the cell cycle distribution profiles, which is typically performed after propidium iodide (PI) staining of the cells and flow cytometry-mediated analysis of DNA content. We show that the combined use of two synchronization protocols is a robust approach to clearly determine the transcriptional profiles of genes that are differentially regulated in the cell cycle (i.e. E2F1 and E2F7), and consequently to have a better understanding of their role in cell cycle processes. Furthermore, we show that this approach is useful for the study of mechanisms underlying drug-based therapies (i.e. mitomycin C, an anticancer agent), because it allows to discriminate genes that are responsive to the genotoxic agent from those solely affected by cell cycle perturbations imposed by the agent.

We report two cell synchronization protocols that provide a context for studying events related to specific phases of the cell cycle. We show that this approach is useful for analyzing the regulation of specific genes in an unperturbed cell cycle or upon exposure to agents affecting the cell cycle.

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such ...

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral gene therapy studies, IS assays, in combination with next-generation sequencing, have been used as a cell-tracking tool to characterize clonal stem cell populations sharing the same IS. For the accurate comparison of repopulating stem cell clones within and across different samples, the detection sensitivity, data reproducibility, and high-throughput capacity of the assay are among the most important assay qualities. This work provides a detailed protocol and data analysis workflow for bidirectional IS analysis. The bidirectional assay can simultaneously sequence both upstream and downstream vector-host junctions. Compared to conventional unidirectional IS sequencing approaches, the bidirectional approach significantly improves IS detection rates and the characterization of integration events at both ends of the target DNA. The data analysis pipeline described here accurately identifies and enumerates identical IS sequences through multiple steps of comparison that map IS sequences onto the reference genome and determine sequencing errors. Using an optimized assay procedure, we have recently published the detailed repopulation patterns of thousands of Hematopoietic Stem Cell (HSC) clones following transplant in rhesus macaques, demonstrating for the first time the precise time point of HSC repopulation and the functional heterogeneity of HSCs in the primate system. The following protocol describes the step-by-step experimental procedure and data analysis workflow that accurately identifies and quantifies identical IS sequences.

This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral ...

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 ...

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling pathway is one of such essential molecular machinery for cell-to-cell communications, which plays key roles in normal development of embryos. This pathway involves a cell-to-cell transfer of oscillatory information with ultradian rhythms, but despite the progress in molecular biology techniques, it has been challenging to elucidate the impact of multicellular interactions on oscillatory gene dynamics. Here, we present a protocol that permits optogenetic control and live monitoring of gene expression patterns in a precise temporal manner. This method successfully revealed that intracellular and intercellular periodic inputs of Notch signaling entrain intrinsic oscillations by frequency tuning and phase shifting at the single-cell resolution. This approach is applicable to the analysis of the dynamic features of various signaling pathways, providing a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Here, we present a protocol to analyze cell-to-cell transfer of oscillatory information by optogenetic control and live monitoring of gene expression. This approach provides a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling ...

An Ecdysone Receptor-based Singular Gene Switch for Deliberate Expression of Transgene with Robustness, Reversibility, and Negligible Leakiness

Precise control of transgene expression is desirable in biological and clinical studies. However, because the binary feature of currently employed gene switches requires the transfer of two therapeutic expression units concurrently into a single cell, the practical application of the system for gene therapy is limited. To simplify the transgene expression system, we generated a gene switch designated as pEUI(+) encompassing a complete set of transgene expression modules in a single vector. Comprising of the GAL4 DNA-binding domain and modified EcR (GvEcR), a minimal VP16 activation domain fused with a GAL4 DNA-binding domain, as well as a modified Drosophila ecdysone receptor (EcR), the newly developed singular gene switch is highly responsive to the administration of a chemical inducer in a time- and dosage-dependent manner. The pEUI(+) vector is a potentially powerful tool for improving the control of transgene expression in both biological research and pre-clinical studies. Here, we present a detailed protocol for modulation of a transient and stable transgene expression using pEUI(+) vector by the treatment of tebufenozide (Teb). Additionally, we share important guidelines for the use of Teb as a chemical inducer.

Here, we present a protocol for the modulation of transgene expression using pEUI(+) singular gene switch by tebufenozide treatment.

Precise control of transgene expression is desirable in biological and clinical studies. However, because the binary feature of currently employed gene ...

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to abiotic stresses and diseases. As the plant microbiome can be highly complex, low-cost, high-throughput methods such as amplicon-based sequencing of the 16S rRNA gene are often preferred for characterizing its microbial composition and diversity. However, the selection of appropriate methodology when conducting such experiments is critical for reducing biases that can make analysis and comparisons between samples and studies difficult. This protocol describes in detail a standardized methodology for the collection and extraction of DNA from soil, rhizosphere, and root samples. Additionally, we highlight a well-established 16S rRNA amplicon sequencing pipeline that allows for the exploration of the composition of bacterial communities in these samples, and can easily be adapted for other marker genes. This pipeline has been validated for a variety of plant species, including sorghum, maize, wheat, strawberry, and agave, and can help overcome issues associated with the contamination from plant organelles.

Here, we describe a protocol to obtain amplicon sequence data of soil, rhizosphere, and root endosphere microbiomes. This information can be used to investigate the composition and diversity of plant-associated microbial communities, and is suitable for the use with a wide range of plant species.

The intimate interaction between plant host and associated microorganisms is crucial in determining plant fitness, and can foster improved tolerance to ...

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

Preparation and Gene Modification of Nonhuman Primate Hematopoietic Stem and Progenitor Cells

Hematopoietic stem and progenitor cell (HSPC) transplantation has been a cornerstone therapy for leukemia and other cancers for nearly half a century, underlies the only known cure of human immunodeficiency virus (HIV-1) infection, and shows immense promise in the treatment of genetic diseases such as beta thalassemia. Our group has developed a protocol to model HSPC gene therapy in nonhuman primates (NHPs), allowing scientists to optimize many of the same reagents and techniques that are applied in the clinic. Here, we describe methods for purifying CD34+ HSPCs and long-term persisting hematopoietic stem cell (HSC) subsets from primed bone marrow (BM). Identical techniques can be employed for the purification of other HSPC sources (e.g., mobilized peripheral blood stem cells [PBSCs]). Outlined is a 2 day protocol in which cells are purified, cultured, modified with lentivirus (LV), and prepared for infusion back into the autologous host. Key readouts of success include the purity of the CD34+ HSPC population, the ability of purified HSPCs to form morphologically distinct colonies in semisolid media, and, most importantly, gene modification efficiency. The key advantage to HSPC gene therapy is the ability to provide a source of long-lived cells that give rise to all hematopoietic cell types. As such, these methods have been used to model therapies for cancer, genetic diseases, and infectious diseases. In each case, therapeutic efficacy is established by enhancing the function of distinct HSPC progeny, including red blood cells, T cells, B cells, and/or myeloid subsets. The methods to isolate, modify, and prepare HSPC products are directly applicable and translatable to multiple diseases in human patients.

The goal of this protocol is to isolate nonhuman primate CD34+ cells from primed bone marrow, to gene-modify these cells with lentiviral vectors, and to prepare a product for infusion into the autologous host. The total protocol length is approximately 48 h.

Hematopoietic stem and progenitor cell (HSPC) transplantation has been a cornerstone therapy for leukemia and other cancers for nearly half a century, ...