RNA-seqのトロンビン処理におけるトランスクリプトームの解析と制御ヒト肺微小血管内皮細胞

The overall goal of this procedure is to present a complete and detailed process to apply RNA-Seq a powerful next generation DNA sequencing technology to profile transcriptomes. This is accomplished by first isolating total RNA from human pulmonary microvascular endothelial cells with or without thrombin treatment and checking the RNA quality. The second step is to construct DNA libraries from those RNA samples.

Streamline the cluster generation using the cbot instrument and carry out the DNA sequencing task on the HighSeq 1000. Next data analysis is performed to ultimately identify and display differentially expressed gene transcripts. The final step is to validate RN aeq results by RT TP CR.The main advantage of IC over existing methods like a DNA micro array for transcriptome analysis is that IC can profile a complete transcriptome, provide digital type of data, and not rely on any known genomic ization of gene expression in cells.

While measurement of MRA level is a useful tool in determining how the transcription of machinery of the cell is affected by external signals or how cells differ between a health state and a disease state. In this protocol, we'll demonstrate IC analysis of transcriptomes in trobin treated and the control human pulmonary microvascular Indo syn cells. This protocol is based on our recent published study in which we successfully performed the first complete transcriptome analysis of human pulmonary microvascular endothelial cells treated with thrombin using RNA-Seq on the High SEQ 1000, a popular next generation DNA sequencing platform.Mrs.

Suman Shaha will demonstrate DNA sequencing on the high SEQ 1000 instrument, as well as the R-T-P-C-R validation experiment. Using the VS seven R-T-P-C-R system, Dr.Denova will demonstrate the thrombin treatment of human pulmonary, microvascular endothelial cells and total cell RNA isolation.Mrs. Margaret Gibson will demonstrate the Experian system to check RNA quality and DNA library as well as cluster generation on the cbot.

And Dr.Dimitri Gregor will demonstrate data analysis To begin this protocol culture. Human lung microvascular endothelial cells to between 90 and 100%confluence in six well plates in EGM two medium with 5%FBS growth factors and antibiotics. Change media to the starvation media 30 minutes prior to treatment with thrombin.

After 30 minutes, treat the cells with 0.05 units per milliliter, thrombin, or leave untreated as a control. Incubate the cells for six hours at 37 degrees Celsius and 5%carbon dioxide. Following a six hour treatment.

Isolate total RNA from the treated and control cells using the Nirvana kit according to manufacturer's instructions. Assess the quality of the RNA with an Experian standard sense eukaryotic, RNA chip according to the standard protocol on the Experian automated electrophoresis station. Finally, quantify the RNA using a standard spectro photometric method for library construction.

Use one microgram of high quality total RNA per sample as starting material to construct the library. Follow the manufacturer's standard protocol. In this protocol, two rounds of poly containing Mr.mRNA selections are performed.

To remove our RNA so as to minimize our RNA sequencing, assess the quality of the libraries using an Experian DNA one K chip. According to the standard protocol on the Experian automated electrophoresis station. Quantify the library using quantitative real-time polymerase chain reaction.

Abbreviated Q-R-T-P-C-R as described in the written protocol. Accompanying this video, run the Q-R-T-P-C-R according to the cyber green MM protocol and calculate the original stock concentration of each library. Dilute the library stocks to 10 nanomolar and store at 20 degrees Celsius.

When ready to cluster a flow cell thaw the cbot reagent plate in a water bath. CBOT is an instrument used to streamline the cluster generation process. After washing the cbot instrument, denature the libraries by first combining 13 microliters of one XTE and six microliters of 10 nanomolar.

Library then to the side of each tube, add one microliter of one normal sodium hydroxide, vortex the tubes spin down and incubate it room temperature for five minutes. Then place the denatured libraries on ice. Next, dilute the Denatured libraries with pre chilled hybridization buffer by combining 996 microliters of buffer and 4.0 microliters of Denatured library for a final concentration of 12 pico molar.

Place the denatured diluted libraries on. Then invert each row of tubes of the cbot plate, ensuring that all the reagents are thawed. After spinning down the plate, remove the foil seal from the row of the sodium hydroxide tubes and load onto the cbot aliquot.

120 microliters of the diluted denatured libraries to a strip tube labeled one through eight. Add 1.2 microliters of the diluted denatured PHI X control library into each tube as a spike in control. After vortexing and spinning down the tubes, load them on the cbot in the correct orientation with tube number one to the right.

Load a flow cell and manifold onto the cbot. Complete the flow check and begin the clustering run. After the run is complete, check reagent delivery across all lanes.

Make note of any abnormalities. Either start the sequencing run immediately or store the flow cell in the provided tube at four degrees Celsius. To begin sequencing.

Thaw the sequencing by synthesis reagents. Load the reagents to the appropriate spots on the reagent trays, making sure not to touch the other reagents. After touching the cleavage mix, using a nons sequencing flow cell prime the reagent lines twice thoroughly clean the sequencing flow cell with 70%ethanol and Kim wipes, followed by 70%ethanol and lens paper.

Inspect the flow cell for any streaks and reclean it if necessary. Load the flow cell onto the sequencer and perform a flow check to ensure that the seal between the manifolds and the flow cell is tight. Start the sequencing run.

Assess the quality metrics as they become available during the run Monitor intensity throughout the run. After 101 cycles are completed. Perform turnaround chemistry to complete the second read.

First father paired and reagents, and the second read incorporation buffer. Then load the reagents, continue the sequencing, run assessing. Second, read intensity Q3 and other quality metrics as the run progresses.

To begin data analysis, use the latest version of cassava to convert the base call files to FAST Q files. Setting fast Q cluster count to zero to ensure the creation of a single fast Q file. For each sample, unzip the fast Q files for downstream analysis.

Perform paired and alignment using the latest version of top hat, which aligns RNA-Seq reads to mammalian size genomes using the ultra high throughput short read and SAM tools, which implements various utilities for post-processing alignments In the SAM format. The reference human transcriptome can be downloaded from I genomes in running top hat all default parameter settings were used, including the library type option as fragment unstranded using the program cuff diff part of the cufflinks software package. Compare the thrombin treated cells to the control cells.

Screen out the differentially expressed gene transcripts in the thrombin treated cells based on the human reference transcriptome. Then use a spreadsheet to visualize the result in table form. In this case, all default parameter settings were used and those gene transcripts with FPKM less than 0.05 and p greater than 0.05 were filtered out to detect novel isoforms run cuff links without a reference.

Transcriptome compare the sample transcript files to the reference genome using cuff compare. Test the differential expression with cuff diff using the combined thrombin transcript files as the reference genome for one analysis and the combined control transcript files as the reference genome for a second analysis, again, use a spreadsheet to visualize the result in tabular format. As before, those gene transcripts with FPKM less than 0.05 and p greater than 0.05 were filtered out.

After this step, investigators may opt to upload a list of newly reported transcripts to the uc SC genome browser website to verify their validity by a manual inspection. Lists of differentially expressed genes can also be submitted to ingenuity pathway analysis for characterization of the genes and pathways affected by the thrombin treatment. In this step, investigators may opt to use cummerbund an R package that is designed to aid and simplify the task of analyzing cufflinks RN aeq output to help manage, visualize, and integrate all of the data produced by a cuff diff analysis.

Validation of the RN aeq results is then performed by Q-R-T-P-C-R first, perform total RNA isolation from control and thrombin treated human lung microvascular endothelial cells, RNA quality assessment and RNA quantification as demonstrated earlier in the video. Then generate complimentary DNA from one microgram of total RNA of each sample with superscript three first strand synthesis system rt following the manufacturer's instructions. Finally, perform Q-R-T-P-C-R analysis on a VI a seven real-time PCR system using the TAC MAN assay on demand designed nucleotides listed in the written protocol accompanying this video and measure quantitation as described there.

The 28 s to 18 S ratio is traditionally used as an indicator of RNA degradation To more accurately quantify the degradation, the experience system calculates an RNA quality indicator or RQI number. The RQI algorithm compares the electropherogram of RNA samples to data from a series of standardized degraded RNA samples and automatically returns a number between 10 and one. The RNA sample should have an RQI of at least seven and ideally greater than eight.

These Experian results indicate a high quality RNA sample with an RQI of 8.4. The libraries should have a broadband at approximately 250 to 300 base pairs as shown in this figure of Experian results for a high quality library. Here, the Q-R-T-P-C-R results of standard curve samples and one unknown sample are shown.

The progress and quality of the sequencing run should be constantly observed throughout the run. This figure shows the appropriate cluster density during the first cycle imaging step. This is the first indication of the run.

Quality clusters should be bright and focused. An example of the first base report generated after completion of the first cycle is shown. It is important to assess the estimated cluster density intensity levels and focus quality at this point.

The next quality checkpoint after cycle four is shown here. This shows the absolute cluster density for each lane. The cluster density should not be above 850 K per square millimeter after cycle 13.

Phasing and pre phasing stats are calculated. Typical numbers are between 0.1 and 0.25. The major quality assessment is possible after cycle 24 when several quality metrics are calculated.

The percent of reads above Q3 is a measure of the confidence in the base. Calling a read with a Q score of three means that there is a one in 1000 chance the base call is wrong. The Q scores will decrease as the run progresses, but should start out with greater than 95%of the reads meeting or exceeding Q3.The clusters passing filter or pf are the clusters from which the actual sequence data will be taken.

Ideally, this should be above 85%The cluster pf is based on many factors including phasing, pre phasing intensity, and Q3.It will not change as the run progresses. The percent aligned is a measure of the reads that align real time to the FI x genome. Since approximately 1%FI x library was spiked into the sample libraries, the percent aligned should be between 0.5 and one.

This statistic shows that the library content is represented well by the clusters and there was no cluster generation bias listed here are expressed genes and isoforms in both control and thrombin treated human pulmonary microvascular endothelial cells. Notably, there are about 26, 000 novel isoforms detected, which illustrates the strength of RN aeq. It can identify unknown RNAs.

Alternatively, spliced transcripts and alternative promoter usage, which are not detectable by microarray techniques. RNA-Seq can also measure the less abundant transcripts that are inaccurately, quantified or not detected by microarrays to validate the RNA-Seq results using an alternative approach. A-Q-R-T-P-C-R experiment was performed to assay three different genes in RNA-Seq data TR one was upregulated by 7.96 fold self.

One was downregulated by 1.16 fold and thanked two was downregulated by 1.70 fold in Q-R-T-P-C-R data, these corresponding numbers are plus 7.25 fold minus 1.15 fold and minus 2.07 fold respectively. The results of these three genes assayed by RNA-Seq and Q-R-T-P-C-R are in good agreement, which corroborates the RNA-Seq results. This protocol is specific to IC at a one time point of trobin treated human pulmonary microvascular in the senior cells, but it could easily be adapted to a multi-time point study or studies in other cells tissues treated with different stimuli or inhibitors or comparison of transcriptomes in cell or tissues between a healthy state and the disease state.

While it is specific to the high SQ 1000, this protocol is applicable to any of the HighSeq family or the genome analyzer. Two instruments with minor modifications of the cluster generation steps and sequencing reagents. Other next generation DNA sequencing platforms such as the solid series.

The GS systems, as well as some emerging newer systems are also employed for the purpose of RNA-Seq. Although their library construction and sequencing procedures may be slightly different, the RNA handling tips, the data analysis portions and the validation by R-T-P-C-R presented in this protocol can be of reference value to their RNA-Seq applications.