We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program (BBDP) of Sun City, Arizona for the provision of human brain tissues. The BBDP has been supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the National Institute on Aging (P30AG19610 Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson&#39;s Disease Consortium) and the Michael J. Fox Foundation for Parkinson's Research27. This study was also supported by the DHS and the State of Arizona (ADHS grant # ADHS14-052688). We also thank Andrea Schmitt (Banner Research) and Cynthia Lechuga (TGen) for administrative support.

This research has been performed in compliance with all institutional, national and international guidelines for human welfare. Brain tissues were obtained from the Banner Sun Health Research Institute Brain and Body Donation Program in Sun City, AZ. The operations of the Brain and Body Donation Program are approved by the Western Institutional Review Board (WIRB protocol #20120821). All subjects or their legal representatives signed the informed consent. Commercial (non-brain) biospecimens were purchased from Proteogenex.
1. RNase R Treatment
NOTE: In the following steps, the reaction volume is adjusted to a total volume of 50 &#181;L. This is the minimum sample volume to be used in the RNA cleanup &#38; concentrator kit (see Table of Materials). Additionally, the optimized protocol described here is for an input amount of 4 &#181;g of total RNA. A longer incubation time for RNase R treatment is recommended for an input amount &#62;4 &#181;g.
<ol>
	<li>Dilute total RNA to 4 &#181;g in 39 &#181;L RNase-free water in a microcentrifuge tube and mix well by pipetting.</li>
	<li>In a separate tube, dilute the RNase R to a working concentration of 2 U/&#181;L with 1x RNase R Reaction Buffer. Make only enough for immediate use.</li>
	<li>Pipette 39 &#181;L of total RNA and 5 &#181;L of 10x RNase R Reaction Buffer into a 1.5 mL reaction tube and mix well by pipetting (50 &#181;L will be the total reaction volume). Next, add 6 &#181;L of RNase R (2 U/&#181;L).</li>
	<li>Adjust the pipette to the full reaction volume (50 &#181;L) and mix well by pipetting up and down 10 times.</li>
	<li>Place the tube in a 37 &#176;C water bath for 10 min. Make sure that the full reaction volume is immersed in the water bath.</li>
	<li>Place the tube on ice and immediately proceed with RNA cleanup &#38; concentration (section 2).</li>
</ol>
2. Purifying RNA Using an RNA Cleanup and Concentrator Kit
NOTE: When using high quality RNA (RIN&#62;8, DV200&#62;80%), RNase R treatment may result in loss of approximately 60% of RNA. Using a 4 &#181;g input, it is estimated that 2&#8211;2.5 &#181;g of treated RNA is left after section 1.
<ol>
	<li>Before starting, prepare the RNA Wash Buffer by adding 48 mL of 100% ethanol to the buffer concentrate and mix well by pipetting. Place purification columns into collection tubes (see Table of Materials) and place in a tube rack. 
	NOTE: Use the following centrifugation settings for all following steps: 10,000&#8211;16,000 x g. If DNase I treatment has already been performed, skip DNase I treatment at this stage.</li>
	<li>Add 2 volumes of RNA Binding Buffer to the RNase R treated sample, and mix well by pipetting (total volume: 150 &#181;L).</li>
	<li>Add 1 volume of 100% ethanol to the RNA Binding Buffer and RNase R treated sample mixture, and mix well by pipetting (total volume: 300 &#181;L).</li>
	<li>Transfer the entire volume to the column and centrifuge the column for 30 s. Discard flow through.</li>
	<li>Add 400 &#181;L of RNA Prep Buffer directly to the column, centrifuge the column for 30 s, and discard flow through.</li>
	<li>Add 700 &#181;L of RNA Wash Buffer directly to the column, centrifuge the column for 30 s, and discard flow through.</li>
	<li>Add 400 &#181;L of RNA Wash Buffer directly to the column, centrifuge the column for 2 min, and transfer the column to a fresh RNase-free 1.5 mL tube.</li>
	<li>Add 11 &#181;L of RNase-free water directly to the column by holding the pipette tip right above the column filter and ensuring that water lands only on the column filter.</li>
	<li>Incubate the column for 1 min at room temperature and centrifuge for 1 min.</li>
	<li>Before discarding the column, check for flow through in the RNase-free tube. If elution was successful, store sample at -80 &#176;C or immediately proceed with library preparation. The final total elution volume of approximately 10 &#181;L is used for library construction. 
	NOTE: Stopping point: Leave RNA at -80 &#176;C for up to 7 days before continuing with library preparation.</li>
</ol>
3. circRNA Library Prep
NOTE: See Table of Materials for kit, which contains most reagents used in this section.
<ol>
	<li>rRNA depletion and fragmentation
	<ol>
		<li>Transfer 10 &#181;L of purified RNA from step 2.10 to a clean well in a new 96-well 0.3 mL PCR plate. To the well, add 5 &#181;L of rRNA Binding Buffer followed by 5 &#181;L rRNA Removal Mix. Gently pipette up and down 10 times to mix.</li>
		<li>Seal plate and incubate for 5 min at 68 &#176;C on a pre-programmed, pre-heated thermocycler block. After completion of the 5 min incubation, place plate on bench and incubate at room temperature for 1 min.</li>
		<li>Remove seal from plate. Add 35 &#181;L of vortexed room temperature rRNA removal beads to sample. Adjust the pipette to 45 &#181;L and pipette up and down 10&#8211;20x to mix thoroughly. Incubate plate for 1 min at room temperature.</li>
		<li>Transfer plate to a magnetic stand and incubate on the stand for 1 min or until the solution clears. Transfer all of the supernatant (~45 &#181;L) to new well on the same plate, or new plate (depending on how many samples you are working with).</li>
		<li>Vortex the RNA cleanup beads (see Table of Materials) until well dispersed, and add 99 &#181;L of beads to each sample. Pipette up and down 10x to mix. Incubate the plate at room temperature for 10 min.</li>
		<li>Transfer the plate to the magnetic stand and incubate an additional 5 min or until the solution clears. Remove and discard all of the supernatant from the well.</li>
		<li>With the plate still on the magnetic stand, add 200 &#181;L of freshly prepared 80% EtOH to the well without disrupting the beads. Incubate for 30 s, then remove and discard ethanol. Repeat for a total of 2 washes.</li>
		<li>Add 11 &#181;L of Elution Buffer to each well and pipette up and down 10 times to mix. Incubate at room temperature for 2 min, and then transfer to the magnetic stand until the solution clears (1&#8211;5 min).</li>
		<li>Transfer 8.5 &#181;L of the supernatant from the well to a new well on the same plate or to a new plate. Add 8.5 &#181;L of the Elute, Primer, Fragment High mix to each well containing sample. Pipette up and down 10 times to mix thoroughly.</li>
		<li>Seal plate and incubate for 8 min at 94 &#176;C on a pre-programmed, pre-heated thermocycler block. Remove from thermocycler when it reaches 4 &#176;C and centrifuge briefly. 
		NOTE: Proceed immediately to the Synthesize First Strand cDNA protocol.</li>
	</ol>
	</li>
	<li>Synthesize cDNA
	<ol>
		<li>For each sample being prepared, mix 9 &#181;L First Strand Synthesis Mix with 1 &#181;L of reverse transcriptase (see Table of Materials). Add 8 &#181;L of the mixture to the sample. Pipette up and down 6 times to mix.
		<ol>
			<li>Seal plate and incubate on a pre-programmed, pre-heated thermocycler block using the following parameters: 25 &#176;C for 10 min, 42 &#176;C for 15 min, 70 &#176;C for 15 min, 4 &#176;C hold. Proceed immediately to second strand synthesis.</li>
		</ol>
		</li>
		<li>Add 5 &#181;L of Resuspension buffer to each sample followed by 20 &#181;L of Second Strand Marking master mix. Pipette the entire volume up and down 6 times.</li>
		<li>Seal plate and incubate on a pre-programmed, pre-heated thermocycler block set to 16 &#176;C for 1 h. Following incubation, remove the plate from the thermocycler and let it equilibrate to room temperature.</li>
		<li>Vortex PCR purification beads (see Table of Materials) and add 90 &#181;L beads to each well of sample. Pipette up and down 10 times to mix thoroughly. Incubate at room temperature for 10 min.</li>
		<li>Transfer the bead/sample mix to the magnetic stand and incubate for 5 min or until liquid clears. Remove and discard supernatant.</li>
		<li>Add 200 &#181;L of 80% EtOH to each sample. Incubate samples on the magnetic stand at room temperature for 30 s. Discard supernatant. Repeat 1x.</li>
		<li>Allow beads to dry at room temperature for 6 min, and then remove from magnetic stand.</li>
		<li>Resuspend beads in 19.5 &#181;L of Resuspension buffer. Pipette up and down 10 times to mix thoroughly. Incubate at room temperature for 2 min, then transfer to the magnetic stand and incubate for an additional 1 min or until the liquid clears.</li>
		<li>Transfer 17.5 &#181;L of supernatant to new well/new plate. 
		NOTE: If not proceeding immediately, samples can be stored at -20 &#176;C for up to 7 days.</li>
	</ol>
	</li>
	<li>Library preparation
	<ol>
		<li>Add 12.5 &#181;L of A-Tailing Mix to each well containing supernatant. Pipette the entire volume up and down 10 times to mix.</li>
		<li>Incubate the reaction on a pre-programmed, pre-heated thermocycler block set to 37 &#176;C using the following parameters: 37 &#176;C for 30 min, 70 &#176;C for 5 min, 4 &#176;C hold. When samples reach 4 &#176;C, proceed immediately to adapter ligation.</li>
		<li>To each sample add 2.5 &#181;L of Resuspension buffer, 2.5 &#181;L of a unique RNA adapter, and 2.5 &#181;L of Ligation Mix. Pipette up and down 10x to mix.</li>
		<li>Incubate samples on a pre-programmed, pre-heated thermocycler block at 30 &#176;C for 10 min.</li>
		<li>Add 5 &#181;L of Stop Ligation buffer to each sample and pipette up and down to mix.</li>
		<li>Add 42 &#181;L of mixed PCR purification beads to each sample and mix thoroughly. Follow steps 3.2.6 through 3.2.10, but change the resuspension volume to 52 &#181;L and final elution volume to 50 &#181;L.</li>
		<li>Repeat the PCR purification bead protocol again with the 50 &#181;L elution from step 3.3.6, but change the resuspension volume to 22 &#181;L and final elution volume to 20 &#181;L. 
		NOTE: If not proceeding immediately, samples can be stored at -20 &#176;C for up to 7 days.</li>
		<li>Add 5 &#181;L of PCR Primer Cocktail and 25 &#181;L of PCR Master Mix to each sample. Mix by pipetting up and down 10 times. Incubate the reaction on a pre-programmed, pre-heated thermocycler block using the following parameters: 98 &#176;C for 30 s; then 8 cycles of 98 &#176;C for 10 s, 60 &#176;C for 30 s, and 72 &#176;C for 30 s; then 72 &#176;C for 5 min, then 4 &#176;C hold. 
		NOTE: Optimization of the total number of PCR cycles may be needed to generate sufficient amounts of library for sequencing.</li>
		<li>Follow the protocol for PCR bead purification (steps 3.2.4 through 3.2.9), except add 50 &#181;L of well-mixed PCR purification beads and change the resuspension volume to 32.5 &#181;L with a final elution volume of 30 &#181;L. 
		NOTE: Samples should be stored at -20 &#176;C.</li>
	</ol>
	</li>
	<li>Quantification and Quality Control using a nucleic acid analyzer
	<ol>
		<li>Allow tapes and reagents to equilibrate at room temperature for 30 min.</li>
		<li>Mix 2 &#181;L of library with 2 &#181;L of HS D1000 buffer, and add to a compatible well plate.</li>
		<li>Seal tightly with compatible foil seal, and vortex for 1 min at 2,000 rpm.</li>
		<li>Spin down and load plate on the analyzer following software prompts. 
		NOTE: Libraries should be approximately 260 bp in size.</li>
	</ol>
	</li>
</ol>
4. Data Analysis Workflow
<ol>
	<li>Sequence RNA-seq libraries (see Table of Materials) to generate 82 bp paired-end reads. Convert raw sequencing data in the form of basecall files (.bcl) to FASTQs using the bcl2fastq tool (v0.2.19).</li>
	<li>Detect circRNAs. 
	NOTE: Based on previously reported evidence that an ensemble circRNA detection approach performs better compared to using a single detection tool21,22, we suggest using multiple tools for circRNA detection. Here, circRNAs were identified using six existing circRNA prediction algorithms: find_circ, CIRI, CIRCexplorer, Mapsplice, KNIFE, and DCC, applying the recommended parameter settings for each algorithm.
	<ol>
		<li>Download and install each circRNA detection algorithm on a Linux high performance computing cluster using the instructions provided by the developers.</li>
		<li>Align RNA-seq FASTQs against the reference genome (GRCh37), utilizing the aligner recommended for each tool.</li>
		<li>Following alignment, execute circRNA detection algorithms by applying their respective recommended parameter settings.</li>
		<li>Each tool will output a multi-column results file with the list of detected circRNAs, extract the circRNA co-ordinates and the number of supporting reads from this in order to quantify the number of candidates detected in each sample/test condition.</li>
	</ol>
	</li>
	<li>Convert the circRNA coordinates output by CIRI, Mapsplice, and DCC to 0-based coordinates to be consistent with the other three algorithms.</li>
	<li>Select circRNAs with two or more supporting reads or downstream analyses and comparisons. Table 1 summarizes all the parameters evaluated in our study along with the total number of sequencing reads generated for each sample.</li>
	<li>For each sample/test condition, count the number of detected circRNAs normalized to the number of mapped reads generated for that library, per million. Summarize the results across the various tools/samples in box plots, as detailed in the Representative Results.</li>
</ol>

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The authors have nothing to disclose.

In this study, two commercially available library preparation kits, pre-treatment options, and input RNA amounts were tested in order to optimize a circRNA enrichment protocol for construction of circRNA sequencing libraries. Based on this study&rsquo;s assessments, a number of key aspects and critical steps in creating circRNA sequencing libraries are apparent. Our evaluation confirms the utility of RNase R pre-treatment, as reflected by the increased number of circRNAs detected. Overall, a higher diversity of circRNAs ...

Circular RNAs (CircRNAs) are endogenous, non-coding RNAs that have gained attention given their pervasive expression in the eukaryotic transcriptome1,2,3. They are formed when exons back-splice to each other and hence were initially considered to be splicing artifacts4,5. However, recent studies have demonstrated that circRNAs exhibit cell type, tissue, and developmental stage specific expression3,6 and are evolutionarily conserved2,3. Furthermore, they are involved in mediation of protein-protein interactions7, micro-RNA (miRNA) binding3,8,9,10, and regulation of parental gene transcription11.
In classical RNA sequencing (RNA-seq), circRNAs may be completely lost during library construction as a result of poly-A selection for mRNAs or may be difficult to isolate given their low abundance. However, recent circRNA characterization studies have incorporated a pre-treatment step using RNase R in order to enrich for circRNAs2,12,13. RNase R is an exoribonuclease that digests linear RNAs, leaving behind circular RNA structures. CircRNA enrichment protocols were optimized by generating and comparing data from two commercially available whole transcriptome library construction kits, with and without an RNase R pre-treatment step, and using varying amounts of total RNA input (1 to 4 &#181;g). The optimized protocol was next used to evaluate the abundance of circRNAs across five different brain regions (cerebellum [BC], inferior parietal lobe [IP], middle temporal gyrus [MG], occipital cortex [OC] and superior frontal gyrus [SF]) and four other tissue types (liver [LV], lung [LU], lymph node [LN] and pancreas [PA]). RNA-seq libraries were paired end sequenced and data was analyzed using six different circRNA prediction algorithms: find_circ3, CIRI14, Mapsplice15, KNIFE16, DCC17, and CIRCexplorer18. Based on our analysis, the highest number of unique circRNAs was detected when using a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g total input RNA. The optimized protocol is described here. As previously reported19,20, the highest enrichment of circRNAs was observed in the brain compared to other tissue types.

<table><tbody><tr><td>1,000 &amp;micro;L pipette tips</td><td>Rainin</td><td>GP-L1000F</td><td></td></tr><tr><td>20 &amp;micro;L pipette tips</td><td>Rainin</td><td>SR L 10F</td><td></td></tr><tr><td>200 &amp;micro;L pipette tips</td><td>Rainin</td><td>SR L 200F</td><td></td></tr><tr><td>2200 TapeStation Accessories (foil covers)</td><td>Agilent Technologies</td><td>5067-5154</td><td></td></tr><tr><td>2200 TapeStation Accessories (tips)</td><td>Agilent Technologies</td><td>5067-5153</td><td></td></tr><tr><td>Adhesive Film for Microplates</td><td>VWR</td><td>60941-064</td><td></td></tr><tr><td>AMPure XP Beads 450 mL</td><td>Beckman Coulter</td><td>A63882</td><td>PCR purification</td></tr><tr><td>Eppendorf twin.tec 96-Well PCR Plates</td><td>VWR</td><td>951020401</td><td></td></tr><tr><td>High Sensitivity D1000 reagents</td><td>Agilent Technologies</td><td>5067-5585</td><td></td></tr><tr><td>High Sensitivity D1000 ScreenTape</td><td>Agilent Technologies</td><td>5067-5584</td><td></td></tr><tr><td>HiSeq 2500 Sequencing System</td><td>Illumina</td><td>SY-401-2501</td><td></td></tr><tr><td>HiSeq 3000/4000 PE Cluster Kit</td><td>Illumina</td><td>PE-410-1001</td><td></td></tr><tr><td>HiSeq 3000/4000 SBS Kit (150 cycles)</td><td>Illumina</td><td>FC-410-1002</td><td></td></tr><tr><td>HiSeq 4000 Sequencing System</td><td>Illumina</td><td>SY-401-4001</td><td></td></tr><tr><td>HiSeq PE PE Rapid Cluster Kit v2</td><td>Illumina</td><td>PE-402-4002</td><td></td></tr><tr><td>HiSeq Rapid SBS Kit v2 (50 cycle)</td><td>Illumina</td><td>FC-402-4022</td><td></td></tr><tr><td>Kapa Total RNA Kit</td><td>Roche</td><td>KK8400</td><td></td></tr><tr><td>Molecular biology grade ethanol</td><td>Fisher Scientific</td><td>BP28184</td><td></td></tr><tr><td>Qubit Assay Tubes</td><td>Supply Center by Thermo Fischer</td><td>Q32856</td><td></td></tr><tr><td>Qubit dsDNA High Sense Assay Kit</td><td>Supply Center by Thermo Fischer</td><td>Q32854</td><td></td></tr><tr><td>RNA cleanup and concentrator - 5</td><td>Zymo</td><td>RCC-100</td><td>Contains purification columns, collection tubes</td></tr><tr><td>RNAClean XP beads</td><td>Beckman Coulter Genomics</td><td></td><td>RNA Cleanup beads</td></tr><tr><td>Rnase R</td><td>Lucigen</td><td>RNR07250</td><td></td></tr><tr><td>SuperScript II Reverse Transcriptase 10,000 units</td><td>ThermoFisher (LifeTech)</td><td>18064014</td><td></td></tr><tr><td>TapeStation 2200</td><td>Agilent Technologies</td><td></td><td>Nucleic Acid analyzer</td></tr><tr><td>TElowE</td><td>VWR</td><td>10128-588</td><td></td></tr><tr><td>TruSeq Stranded Total RNA Library Prep Kit</td><td>Illumina</td><td>20020596</td><td>Kit used in section 3</td></tr><tr><td>Two-Compartment Divided Tray</td><td>VWR</td><td>3054-1004</td><td></td></tr><tr><td>UltraPure Water</td><td>Supply Center by Thermo Fischer</td><td>10977-015</td><td></td></tr><tr><td>Universal control RNA</td><td>Agilent</td><td>740000</td><td></td></tr></tbody></table>

identification of circular rnas using rna sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Data generated using a commercially available universal control RNA (UC) and using two library preparation kits, both of which include a ribo-depletion step in their protocols, was first assessed. Using an analytical workflow (Data analysis workflow, section 4), overall, a higher number of circRNAs was detected in the TruSeq datasets compared to the Kapa ones (Figure 1). Although the ribosomal RNA (rRNA) percentages were below 5% in datasets from both kits for lower input amounts (1, 2 ug), ...

Watch this Scientific Journal Video about Identification of Circular RNAs using RNA Sequencing at JoVE.com

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

identification-of-circular-rnas-using-rna-sequencing

Research

JoVE Journal

Genetics

12.1K Views.  Translational Genomic Research Institute. Dit protocol is het resultaat van de evaluatie van de belangrijkste aspecten van circRNA-bibliotheekvoorbereiding, zoals het type kit, RNase R-behandeling, invoerbedragen en hun impact op circRNA-detectie. Het maakt een optimale circRNA-detectie mogelijk en levert gegevens over aanpasbare parameters, afhankelijk van de behoeften van de gebruiker. Het identificeren van circRNAs in verschillende monstertypen zal ons helpen om de verdeling van hun expressie beter te begrijpen en stelt de weg in ...

Video: Identification of Circular RNAs using RNA Sequencing

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

In plant mitochondria, some steady-state transcripts have 5&#39; triphosphate derived from transcription initiation (primary transcripts), while the others contain 5&#39; monophosphate generated post-transcriptionally (processed transcripts). To discriminate between the two types of transcripts, several strategies have been developed, and most of them depend on presence/absence of 5&#39; triphosphate. However, the triphosphate at primary 5&#39; termini is unstable, and it hinders a clear discrimination of the two types of transcripts. To systematically differentiate and map the primary and processed transcripts stably accumulated in maize mitochondrion, we have developed a circular RT-PCR (cRT-PCR)-based strategy by combining cRT-PCR, RNA 5&#39; polyphoshpatase treatment, quantitative RT-PCR (RT-qPCR), and Northern blot. As an improvement, this strategy includes an RNA normalization step to minimize the influence of unstable 5&#39; triphosphate.
In this protocol, the enriched mitochondrial RNA is pre-treated by RNA 5&#39; polyphosphatase, which converts 5&#39; triphsophate to monophosphate. After circularization and reverse transcription, the two cDNAs derived from 5&#39; polyphosphatase-treated and non-treated RNAs are normalized by maize 26S mature rRNA, which has a processed 5&#39; end and is insensitive to 5&#39; polyphosphatase. After normalization, the primary and processed transcripts are discriminated by comparing cRT-PCR and RT-qPCR products obtained from the treated and non-treated RNAs. The transcript termini are determined by cloning and sequencing of the cRT-PCR products, and then verified by Northern blot.
By using this strategy, most steady-state transcripts in maize mitochondrion have been determined. Due to the complicated transcript pattern of some mitochondrial genes, a few steady-state transcripts were not differentiated and/or mapped, though they were detected in a Northern blot. We are not sure whether this strategy is suitable to discriminate and map the steady-state transcripts in other plant mitochondria or in plastids.

We present a circular RT-PCR-based strategy by combining circular RT-PCR, quantitative RT-PCR, RNA 5&#39; polyphosphatase-treatment, and Northern blot. This protocol includes a normalization step to minimize the influence of unstable 5&#39; triphosphate, and it is suitable for discriminating and mapping the primary and processed transcripts stably accumulated in maize mitochondrion.

In plant mitochondria, some steady-state transcripts have 5&#39; triphosphate derived from transcription initiation (primary transcripts), while the ...

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as plant microRNAs (miRNAs) carry a 2&#39;-O-methyl (2&#39;-OMe) modification at their 3&#39;&#160;terminal nucleotide. This modification adds another difficulty as it inhibits 3&#39;&#160;adapter ligation. We previously demonstrated that modified versions of the &#39;TruSeq (TS)&#39;&#160;protocol have less bias and an improved detection of 2&#39;-OMe RNAs. Here we describe in detail protocol &#39;TS5&#39;, which showed the best overall performance. TS5 can be followed either using homemade reagents or reagents from the TS kit, with equal performance.

We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2&#39;-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as ...

Use of Alu Element Containing Minigenes to Analyze Circular RNAs

In addition to linear mRNAs, many eukaryotic genes generate circular RNAs. Most circular RNAs are generated by joining a 5&#39; splice site with an upstream 3&#39; splice site within a pre-mRNA, a process called back-splicing. This circularization is likely aided by secondary structures in the pre-mRNA that bring the splice sites into close proximity. In human genes, Alu elements are thought to promote these secondary RNA structures, as Alu elements are abundant and exhibit base complementarities with each other when present in opposite directions in the pre-mRNA. Here, we describe the generation and analysis of large, Alu element containing reporter genes that form circular RNAs. Through optimization of cloning protocols, reporter genes with up to 20 kb insert length can be generated. Their analysis in co-transfection experiments allows the identification of regulatory factors. Thus, this method can identify RNA sequences and cellular components involved in circular RNA formation.

Use of Alu Element Containing Minigenes to Analyze Circular RNAs

We clone and analyze reporter genes generating circular RNAs. These reporter genes are larger than constructs to analyze linear splicing and contain Alu elements. To investigate the circular RNAs, the constructs are transfected into cells and resulting RNA is analyzed using RT-PCR after removal of linear RNA.

In addition to linear mRNAs, many eukaryotic genes generate circular RNAs. Most circular RNAs are generated by joining a 5&#39; splice site with an ...

Biochemistry

R-Loop Analysis by Dot-Blot

The three-stranded nucleic acid structure, R-loop, is increasingly recognized for its role in gene regulation. Initially, R-loops were thought to be the by-products of transcription; but recent findings of fewer R-loops in diseased cells made it clear that R-loops have functional roles in a variety of human cells. Next, it is critical to understand the roles of R-loops and how cells balance their abundance. A challenge in the field is the quantitation of R-loops since much of the work relies on the S9.6 monoclonal antibody whose specificity for RNA-DNA hybrids has been questioned. Here, we use dot-blots with the S9.6 antibody to quantify R-loops and show the sensitivity and specificity of this assay with RNase H, RNase T1, and RNase III that cleave RNA-DNA hybrids, single-stranded RNA, and double-stranded RNA, respectively. This method is highly reproducible, uses general laboratory equipment and reagents, and provides results within two days. This assay can be used in research and clinical settings to quantify R-loops and assess the effect of mutations in genes such as senataxin on R-loop abundance.

This protocol details a simple method that quantifies R-loop, a three-stranded nucleic acid structure that comprises of an RNA-DNA hybrid and a displaced DNA strand.

The three-stranded nucleic acid structure, R-loop, is increasingly recognized for its role in gene regulation. Initially, R-loops were thought to be the ...

Mapping R-Loops and RNA:DNA Hybrids with S9.6-Based Immunoprecipitation Methods

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological favorability. In recent years, R-loops have been implicated in a variety of adaptive and maladaptive roles and have been linked to genomic instability in the context of human disorders. As a consequence, the accurate mapping of these structures in genomes is of high interest to many investigators. DRIP-seq (DNA:RNA Immunoprecipitation followed by high throughput sequencing) is described here. It is a robust and reproducible technique that permits accurate and semi-quantitative mapping of R-loops. A recent iteration of the method is also described in which fragmentation is accomplished using sonication (sDRIP-seq), which allows strand-specific and high-resolution mapping of R-loops. sDRIP-seq thus addresses some of the common limitations of the DRIP-seq method in terms of resolution and strandedness, making it a method of choice for R-loop mapping.

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological ...

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

The growing role attributed nowadays to long non-coding RNAs (lncRNA) in physiology and pathophysiology makes it crucial to characterize their interactome by identifying their molecular partners, DNA, proteins and/or RNAs. The latter can interact with lncRNA through networks involving proteins, but they can also be engaged in direct RNA/RNA interactions. We, therefore, developed an easy-to-use RNA pull-down procedure that allowed identification of RNAs engaged in direct RNA/RNA interaction with a lncRNA using psoralen, a molecule that cross-links only RNA/RNA interactions. Bioinformatics modeling of the lncRNA secondary structure allowed the selection of several specific antisense DNA oligonucleotide probes with a strong affinity for regions displaying a low probability of internal base pairing. Since the specific probes that were designed targeted accessible regions throughout the length of the lncRNA, the RNA-interaction zones could be delineated in the sequence of the lncRNA. When coupled with a high throughput RNA sequencing, this protocol can be used for the whole direct RNA interactome studies of a lncRNA of interest.

An easy-to-use RNA pull-down protocol is designed for the identification of RNAs engaged in direct RNA/RNA interaction with a long non-coding RNA. The protocol uses psoralen as a fixative to cross-link only RNA/RNA interactions and provides the whole direct RNA interactome of a long non-coding RNA when coupled with RNA sequencing.

The growing role attributed nowadays to long non-coding RNAs (lncRNA) in physiology and pathophysiology makes it crucial to characterize their interactome ...

Analyzing tRNA Dynamics Using AQRNA-seq

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate quantification of the pool of small RNAs. The AQRNA-seq library preparation procedure described here involves the use of custom-designed sequencing linkers and a step for reducing methylation RNA modifications that block reverse transcription processivity, which results in an increased yield of full-length cDNAs. In addition, a detailed implementation of the accompanying bioinformatics pipeline is presented. This demonstration of AQRNA-seq was conducted through a quantitative analysis of the 45 tRNAs in Mycobacterium bovis BCG harvested on 5 selected days across a 20-day time course of nutrient deprivation and 6 days of resuscitation. Ongoing efforts to improve the efficiency and rigor of AQRNA-seq will also be discussed here. This includes exploring methods to obviate gel purification for mitigating primer dimer issues after PCR amplification and to increase the proportion of full-length reads to enable more accurate read mapping. Future enhancements to AQRNA-seq will be focused on facilitating automation and high-throughput implementation of this technology for quantifying all small RNA species in cell and tissue samples from diverse organisms.

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Absolute quantification RNA sequencing (AQRNA-seq) is a technology developed to quantify the landscape of all small RNAs in biological mixtures. Here, both the library preparation and data processing steps of AQRNA-seq are demonstrated, quantifying changes in the transfer RNA (tRNA) pool in Mycobacterium bovis BCG during starvation-induced dormancy.

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate ...

Quantification of Circular RNAs Using Digital Droplet PCR

Digital droplet polymerase chain reaction (dd-PCR) is one of the most sensitive quantification methods; it fractionates the reaction into nearly 20,000 water-in-oil droplets, and the PCR occurs in the individual droplets. The dd-PCR has several advantages over conventional real-time qPCR, including increased accuracy in detecting low-abundance targets, omitting reference genes for quantification, eliminating technical replicates for samples, and showing high resilience to inhibitors in the samples. Recently, dd-PCR has become one of the most popular methods for accurately quantifying target DNA or RNA for gene expression analysis and diagnostics. Circular RNAs (circRNAs) are a large family of recently discovered covalently closed RNA molecules lacking 5' and 3' ends. They have been shown to regulate gene expression by acting as sponges for RNA-binding proteins and microRNAs. Furthermore, circRNAs are secreted into body fluids, and their resistance to exonucleases makes them serve as biomarkers for disease diagnosis. This article aims to show how to perform divergent primer design, RNA extraction, cDNA synthesis, and dd-PCR analysis to accurately quantify specific circular RNA (circRNA) levels in cells. In conclusion, we demonstrate the precise quantification of circRNAs using dd-PCR.

This protocol describes the detailed method of digital droplet PCR (dd-PCR) for precise quantification of circular RNA (circRNA) levels in cells using divergent primers.

Digital droplet polymerase chain reaction (dd-PCR) is one of the most sensitive quantification methods; it fractionates the reaction into nearly 20,000 ...

Biology

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with&#160;pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

The protocol submitted here explains the complete in silico pipeline needed to predict and functionally characterize circRNAs from RNA-sequencing transcriptome data studying host-pathogen interactions.

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as ...

RNA-Seq

Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.Here, JoVE presents the basic principles behind RNA-seq. We then discuss the experimental and analytical steps of a general RNA-seq protocol. Finally, we examine how researchers are currently using RNA-seq, for example, to compare gene expression between different biological samples, or to characterize protein-RNA interactions.

Next-Gen Transcriptomics Using RNA-Seq

Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed ...

Identification of Circular RNAs using RNA Sequencing

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