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.