This work was supported by the National Center for Scientific Research (CNRS), The French Alternative Energies and Atomic Energy Commission (CEA) and Paris-Sud University. All library preparation, Illumina sequencing and bioinformatics analyses for this study were performed at the I2BC Next-Generation Sequencing (NGS) facility. The members of the I2BC NGS facility are acknowledged for critical reading of the manuscript and helpful suggestions.

1. Isolation of small RNAs
<ol>
	<li>Extract total RNA using phenol-based reagents or any other method. Verify if the RNA is of good quality.</li>
	<li>Pre-run a 15% TBE-urea gel (see Table of Materials) for 15 min at 200 V.</li>
	<li>While the gel is pre-running, mix 5-20 &#181;g of total RNA in a 5-15 &#181;L volume with an equal volume of formamide loading dye (see Table of Materials; 95% deionized formamide, 0.025% bromophenol blue, 0.025% xylene cyanol, 5 mM EDTA pH 8) in a 200 &#181;L PCR tube. Likewise, mix 10 &#181;L (200 ng) of small-RNA ladder (see Table of Materials) with an equal volume of formamide loading dye. Incubate for 5 min at 65 &#176;C in a thermocycler with heated lid, then place the tubes immediately on ice.</li>
	<li>Load the ladder and the sample on the same gel with at least one lane between them and run at 200 V until the bromophenol blue (dark blue) has migrated about two-third of the gel length (approximately 40 min).</li>
	<li>Prepare a system to elute the RNA from gel as follows: puncture the bottom of a nuclease-free 0.5 mL micro centrifuge tube with a 21-gauge needle. Place the punctured 0.5 mL tube in a nuclease-free round-bottom 2 mL micro centrifuge tube.</li>
	<li>Remove the gel, and incubate at room temperature with 3 &#956;L nucleic acid gel stain (10,000 x concentrate; see Table of Materials) in 30 mL&#160;water for 10-15 min.</li>
	<li>View the gel on a &#39;Dark Reader&#39; trans illuminator (it is strongly recommended to avoid UV as this might damage the RNA) and cut out the sample RNA between the 17 nt and the 29 nt ladder bands. Transfer the gel piece to the 0.5 mL tube from step 1.5.</li>
	<li>Centrifuge the 0.5 mL tube in the 2 mL tube in a micro centrifuge at maximum speed for 2 min. Remove the 0.5 mL tube, which should be empty now.</li>
	<li>Add 300 &#181;L of nuclease-free 0.3 M NaCl to the 2 mL tube containing the crushed gel and rotate for at least 2 h at room temperature or at 4 &#176;C overnight (16 h).</li>
	<li>Transfer the suspension of crushed gel pieces to a spin column (see Table of Materials) and centrifuge for 2 min at maximum speed in a micro centrifuge.</li>
	<li>Add 1 &#181;L (20 &#181;g/&#181;L) of glycogen (see Table of Materials) and 950 &#181;L of room temperature 100% ethanol. Incubate for at least 30 min at -80 &#176;C.</li>
	<li>Centrifuge for 20 min at maximum speed in a micro centrifuge at 4 &#176;C. Remove the supernatant, wash the pellet with 800 &#181;L of cold 80 % ethanol. Centrifuge again for 5 min at 4 &#176;C, and carefully remove all supernatant. Resuspend RNA pellet in 15 &#181;L of nuclease free water. Typically, ~5-20 ng of small RNA should be recovered, depending on the amount of input total RNA (~1 ng of small RNA per 1 &#181;g of input total RNA).</li>
	<li>Recommended additional step: Check the quantity and quality of the recovered sRNA (e.g., by capillary gel electrophoresis using a small RNA kit; see Table of Materials).</li>
</ol>
2. Preparation of preadenylated 3&#39;&#160;HD adapter
NOTE: Preadenylation of 3&#39;&#160;HD adapter was done in a manner similar to the protocol described by Chen et al18. Note that preadenylated adapter can be ordered directly (/5rApp/ modification), but this is quite expensive.
<ol>
	<li>Order 5&#39; phosphorylated, 3&#39; blocked 3&#39; HD adapter oligonucleotide. See Table 1 for sequence and modifications. Note that &#39;3AmMO&#39;&#160;is a 3&#39;&#160;amino modifier group, most suppliers can produce oligonucleotide with this modification. Dilute in nuclease free water to 100 &#181;M.</li>
	<li>Set up a 100 &#181;L reaction containing the following reagents: 10 &#181;L of oligo (100 &#181;M), 10 &#181;L of T4 RNA ligase buffer (10x), 10 &#181;L of ATP (10 mM), 40 &#181;L of 50% PEG8000, 5 &#181;L of T4 RNA ligase 1 (50 units), 25 &#181;L of nuclease-free water. Incubate overnight at 20 &#176;C.</li>
	<li>Perform a classical phenol-chloroform extraction of the preadenylated oligonucleotide followed by ethanol precipitation. Add 100 &#181;L of acid (pH 4.5) phenol:chloroform and vortex. Spin for 5 min at room temperature,&#160;maximum speed. Carefully transfer 90 &#181;L of the upper phase to a new tube; add 10 &#181;L of 3 M sodium acetate pH 5.2, 1 &#181;L of ultra-pure glycogen and 250 &#181;L of cold 100% ethanol.</li>
	<li>Keep at -20 &#176;C for at least 30 min. Centrifuge for 30 min at 4 &#176;C maximum speed. Remove the supernatant, wash the pellet once with 500 &#181;L cold 80% ethanol. As an alternative to phenol-chloroform extraction and ethanol precipitation, a nucleotide removal kit can be used (see Table of Materials). Resuspend in 25&#160;&#181;L water.</li>
	<li>Measure the concentration of the adapter using a kit for specific detection of single-stranded DNA (see Table of Materials). Dilute to 80 ng/&#181;L (10 &#181;M).&#160;</li>
	<li>Recommended additional step: Verify the efficacy of preadenylation by migrating 1 &#181;L of 10 &#181;M adapter on a 15% TBE-urea gel (Table of Materials) along with untreated oligonucleotide. The preadenylated adapter should migrate slightly slower than untreated oligonucleotide. If desired,&#160;the preadenylated adapter can be gel-purified; proceed as described above (steps 1.2-1.12).</li>
</ol>
3. Library preparation - Protocol TS5
NOTE: We present here the modified TS protocol &#39;TS5&#39;&#160;that we described previously12 and that can be performed either with reagents from the kit or with self-provided reagents. It should be noted that we obtained similar or even slightly better results with a different protocol, &#39;TS7&#39;. However, with TS7 it is more difficult to eliminate adapter dimers. We have therefore preferred to describe TS5 in detail, but TS7 can be followed by simply replacing the adapters. For TS7 use the &#39;MidRand-Like (MRL)&#39;&#160;adapter sequences (Table 1). Note that here the randomized regions are in the middle of the adapters. Primers for reverse transcription and PCR will hybridize to the sequences downstream of the randomized region in the 3&#39;&#160;adapter and upstream of the randomized region in the 5&#39;&#160;adapter. Sequencing will start from the first randomized nucleotide in the 5&#39; adapter.
<ol>
	<li>3&#39; adapter ligation.
	<ol>
		<li>Combine 1 &#181;L of preadenylated 3&#39;&#160;HD adapter (10 &#181;M) with 1 &#181;L of purified small RNA (~0.1-1 &#181;M) in a 0.2 mL micro centrifuge tube. Incubate for 2 min at 72 &#176;C in a thermo cycler, then put directly on ice.</li>
		<li>Add 4 &#181;L of 50 % PEG 8000 (viscous solution; pipet slowly), 1 &#181;L of RNA ligase buffer (10x), 1 &#181;L of H2O, 1 &#181;L of T4 RNA ligase2 truncated, and 1 &#181;L of RNase inhibitor. Incubate overnight at 16 &#176;C.</li>
	</ol>
	</li>
	<li>Elimination of unligated 3&#39;&#160;adapter
	<ol>
		<li>Add 10 &#181;L of nuclease-free water and mix well. Add 6 &#181;L of 3 M NaOAc pH 5.2 or &#39;Adapter Depletion Solution&#39;&#160;from the Nf kit (Table of Materials) and mix well. Add 40 &#181;L of magnetic purification beads (Table of Materials) and 60 &#181;L of isopropanol and mix well. Incubate for 5 min at room temperature.</li>
		<li>Put the sample in a magnetic rack until the solution appears clear. Remove and discard the supernatant.</li>
		<li>Add 180 &#181;L of freshly prepared 80 % ethanol. Incubate for ~30 s, then remove. Take care to use freshly prepared 80% ethanol and do not incubate with 80% ethanol for extended periods.</li>
		<li>Briefly spin the tube and remove residual liquid that may have collected at the bottom of the well. Let the beads dry for 2 min, then resuspend in 22 &#181;L of 10 mM Tris pH 8 or resuspension buffer from the Nf kit. Incubate for 2 min, then magnetize the sample until the solution appears clear.</li>
		<li>Add 6 &#181;L of 3 M NaOAc pH 5.2 or &#8216;Adapter Depletion Solution&#8217; from the Nf kit (Table of Materials)&#160;to a new tube. Transfer 20 &#181;L of the supernatant from the previous step to this new tube and mix by pipetting. Add 40 &#181;L of magnetic beads and 60 &#181;L of 100% isopropanol and mix well by pipetting. Incubate for 5 min.</li>
		<li>Magnetize the sample until the solution appears clear, then remove and discard the supernatant.</li>
		<li>Add 180 &#181;L of freshly prepared 80 % ethanol. Incubate for ~30 s, then remove. Take care to use freshly prepared 80% ethanol and do not incubate for with 80% ethanol for extended periods.</li>
		<li>Briefly spin the tube and remove residual liquid that may have collected at the bottom of the well. Let the beads dry for 2 min, and resuspend in 10 &#181;L of nuclease-free water. Incubate for 2 min, then magnetize the sample until the solution appears clear.</li>
		<li>Transfer 9 &#181;L of supernatant to a new tube. Add 1 &#181;L of T4 RNA ligase buffer (10x) and 1 &#181;L of water. Alternatively, add 2 &#181;L of ligase buffer from the TS kit.</li>
	</ol>
	</li>
	<li>Ligation of 5&#39;&#160;adapter.
	<ol>
		<li>Add 1 &#181;L of 5&#39;&#160;HD adapter (10 &#181;M; Table 1) to a 200 &#181;L PCR tube in a thermo cycler with heated lid. Incubate for 2 min at 70 &#176;C, then put the tube directly on ice.</li>
		<li>Add 1 &#181;L of 10 mM ATP and 1 &#181;L of T4 RNA ligase 1. Mix well by gently pipetting. Add 3 &#181;L of this mix to the 3&#39;&#160;ligated RNA from step 3.2.9 and mix by pipetting. Incubate for 1 h at 28 &#176;C.</li>
	</ol>
	</li>
	<li>Reverse transcription (RT)
	<ol>
		<li>Transfer 6 &#181;L of 3&#39;&#160;and 5&#39;&#160;adapter ligated RNA to a new 200 &#181;L PCR tube (keep the remaining ~8 &#181;L at -80 &#176;C for later use if necessary). Add 1 &#181;L of RT primer (10 &#181;M; Table 1) and mix by pipetting. Incubate for 2 min at 70 &#176;C, then put the tube directly on ice.</li>
		<li>Add the following reagents for RT: 2 &#181;L of 5x first strand buffer, 0.5 &#181;L of 12.5 mM dNTP mix, 1 &#181;L of 100 mM DTT, 1 &#181;L of RNase inhibitor, and 1 &#181;L of reverse transcriptase (Table of Materials). Incubate for 1 h at 50 &#176;C.</li>
	</ol>
	</li>
	<li>PCR amplification
	<ol>
		<li>Add the following reagents to the 12.5 &#181;L of RT reaction mixture: 10 &#181;L of PCR polymerase buffer, 2 &#181;L of universal P5 primer (10 &#181;M; Table 1), 2 &#181;L of P7-index primer (10 &#181;M; Table 1), 1 &#181;L of 12.5 mM dNTPs, 0.5 &#181;L of DNA polymerase (Table of Materials), and 22 &#181;L of water. Keep the reaction on ice until use.</li>
		<li>Run the following PCR program: 98 &#176;C for 30 s, 11 cycles (98 &#176;C for 10 s, 60 &#176;C for 30 s and 72 &#176;C for 15 s) and 72 &#176;C for 10 min. Keep the reaction at 4 &#176;C when finished. 
		NOTE: Concerning the number of PCR cycles: we typically perform 11 cycles, but this should be optimized by the user. Try to perform the smallest possible number of PCR cycles.</li>
	</ol>
	</li>
	<li>Gel purification
	<ol>
		<li>Run 5, 10 and 20 &#181;L of PCR product on a native 6% TBE gel (see Table of Materials) along with a suitable ladder (see Table of Materials). Run the gel for about 1 h at 145 V (until the bromophenol blue reaches the bottom; this dye migrates at the 65 bp position).</li>
		<li>Remove the gel, and incubate with nucleic acid gel stain (see Table of Materials) in water for 10-15 min.</li>
		<li>View the gel on a &#34;Dark Reader&#34;&#160;trans illuminator (it is strongly recommended to avoid UV as this might damage the RNA) and cut out the library band at 150 bp. Prepare a system to elute the RNA from gel as described in step 1.5 and transfer the gel piece to the 0.5 mL tube.</li>
		<li>Centrifuge in a micro centrifuge at maximum speed for 2 min. Remove the 0.5 mL tube, which should be empty now.</li>
		<li>Add 300 &#181;L of nuclease-free water to the 2 mL tube containing the crushed gel and rotate for at least 2 h at room temperature or at 4 &#176;C overnight.</li>
		<li>Transfer the suspension of crushed gel pieces in water to a spin column and centrifuge for 2 min at maximum speed.</li>
		<li>Add 1 &#181;L of (20 &#181;g/&#181;L) glycogen, 30 &#181;L of 3 M NaOAc and 975 &#181;L of ice cold 100% ethanol. Centrifuge for 20 min at max speed at 4 &#176;C.</li>
		<li>Resuspend the pellet in 20 &#181;L of 10 mM Tris pH8. Use 1 &#181;L for concentration measurement and 1 &#181;L for quality control.</li>
	</ol>
	</li>
</ol>
4. Data analysis
NOTE: The data analysis procedure described below is based on the Linux operating system Ubuntu 16.04 LTS.
<ol>
	<li>Treatment of raw sequence files
	<ol>
		<li>Download the FASTQ sequence file(s) generated during the sequencing run. If required, perform demultiplexing with bcl2fastq2 (version V2.2.18.12; a manual can be downloaded from the following link: http://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software/documentation.html). 
		Use the following command: 
		nohup Pathway_of_bcl2fastq/bcl2fastq --runfolder-dir Pathway_of_Run --ignore-missing-bcl --output-dir Pathway_of_Output_Directory --barcode-mismatches 1 --aggregated-tiles AUTO -r 16 -d 16 -p 16 -w 16</li>
		<li>Remove adapter sequences using Cutadapt19 version 1.15. A manual can be downloaded here: https://cutadapt.readthedocs.io/en/stable/guide.html. 
		Use the following command: 
		Pathway_to_cutadapt/cutadapt -a TGGAATTCTCGGGTGCCAAGG&#160; -n 5 -O 4 -m 10 -j 0 --nextseq-trim 10 -o Output_File_Read1_cutadapt.fastq.gz Input_File_Read1.fastq.gz 
		Note that the sequence in the command corresponds to the 3&#8217; HD adapter without the 4 random nucleotides; these will therefore not be removed during this step.</li>
		<li>Use seqtk (https://github.com/lh3/seqtk) for the removal of the terminal random nucleotides in the sequencing reads. Use the following command (variable names in bold): 
		seqtk trimfq -b 4 -e 4 Output_File_Read1_cutadapt.fastq &#62; Output_File_Read1_trimmed.fastq</li>
		<li>Use the following awk command in order to discard the sequences shorter than 10 nt: 
		awk &#39;BEGIN {FS = &#34;\t&#34; ; OFS = &#34;\n&#34;} {header = $0 ; getline seq ; getline qheader ; getline qseq ; if (length(seq) &#62;= 10) {print header, seq, qheader, qseq}}&#39; Output_File_Read1_trimmed.fastq &#62; Length_Filtered.fastq</li>
	</ol>
	</li>
	<li>Mapping of the trimmed sequences
	<ol>
		<li>Download the database corresponding to the organism of study from miRBase as follows. Go to http://www.mirbase.org/ftp.shtml and download the &#8216;mature.fa&#8217; file. Note that the sequences are indicated in RNA notation. Replace the U residues by T with the following command: 
		sed -i &#39;/^&#62;/! s/U/T/g&#39; mature.fa 
		NOTE: This will yield a complete list of all miRNAs in miRBase, originating from a variety of organisms.</li>
		<li>Select the miRNA sequences of your organism of interest with the following command: 
		awk &#39;name_of_the_organism{print; nr[NR+1]; next}; NR in nr&#39; mature.fa &#62; mature_name_of_the_organism_mirs.fa</li>
		<li>Map the reads to the above-created file using Bowtie220 (version 2.3.0) allowing no mismatches. First, build an index for your file with the following command: 
		bowtie2-build mature_name_of_the_organism_mirs.fa mature_name_of_the_organism_mirs</li>
		<li>Align the sequencing reads to the database, requiring that a read maps entirely to a miRNA of the database, without any mismatches. To this end, use the following tool: 
		bowtie2 -N 0 -L 10 --score-min C,0,0 --end-to-end --time -x mature_name_of_the_organism_mirs -U Length_Filtered.fastq -S Length_Filtered_ALIGNMENT.sam 
		NOTE: The option: --score-min C,0,0 ensures that alignment is without any mismatches. For an explanation of the various parameters in the tool, please visit the following website: http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml</li>
		<li>To discard the reads that did not align, use the following command: 
		samtools view -F 4 Length_Filtered_ALIGNMENT.sam &#62; Reads_aligned_to_Mirs.sam 
		NOTE: As a result of these steps, you should now have obtained the aligned reads, corresponding to miRNAs.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Small RNA library preparation remains challenging due to bias, mainly introduced during adapter ligation steps. RNAs with a 2&#39;-OMe modification at their 3&#39;&#160;end such as plant miRNAs, piRNA in insects, nematodes and mammals, and small interfering RNAs (siRNA) in insects and plants are particularly difficult to study because the 2&#39;-OMe modification inhibits 3&#39;&#160;adapter ligation. A number of solutions have been proposed in the literature to improve sRNA library preparation protocols, but most commerc...

Small RNAs (sRNAs) are involved in the control of a diversity of biological processes1. Eukaryotic regulatory sRNAs are typically between 20 and 30 nt in size; the three major types are microRNAs (miRNA), piwi-interacting RNAs (piRNA) and small interfering RNAs (siRNA). Aberrant miRNA expression levels have been implicated in a variety of diseases2. This underscores the importance of miRNAs in health and disease and the requirement for accurate, quantitative research tools to detect sRNAs in general.
Next-generation sequencing (NGS) is a widely used method to study sRNAs. Main advantages of NGS as compared with other approaches, such as quantitative PCR or microarray techniques (qPCR), are that it does not need a priori knowledge of the sRNA sequences and can therefore be used to discover novel RNAs, and in addition it suffers less of background signal and saturation effects. Further, it can detect single nucleotide differences and has a higher throughput than microarrays. However, NGS also has some drawbacks; the cost of a sequencing run remains relatively high and the multistep process required to convert a sample into a library for sequencing may introduce biases. In a typical sRNA library preparation process, a 3&#39;&#160;adapter is first ligated to the sRNA (often gel-purified from total RNA) using a truncated version of RNA ligase 2 (RNL2) and a preadenylated 3&#39;&#160;adapter (Figure 1) in the absence of ATP. This increases the efficiency of sRNA-adapter ligation and reduces the formation of side reactions such as sRNA circularization or concatemerization. Subsequently, a 5&#8217; adapter is ligated by RNA ligase 1 (RNL1), followed by reverse transcription (RT) and PCR amplification. All these steps may introduce bias3,4. Consequently, read numbers may not reflect actual sRNA expression levels leading to artificial, method-dependent expression patterns. Specific sRNAs may be either over- or underrepresented in a library, and strongly underrepresented sRNAs may escape detection. The situation is particularly complicated with plant miRNAs, siRNAs in insects and plants, and piRNAs in insects, nematodes and mammals, in which the 3&#39;&#160;terminal nucleotide has a 2&#39;-O-methyl (2&#39;-OMe) modification1. This modification strongly inhibits 3&#39;&#160;adapter ligation5, making library preparation for these types of RNA a difficult task.
Previous work demonstrated that adapter ligation introduces serious bias, due to RNA sequence/structure effects6,7,8,9,10,11. Steps downstream of adapter ligation such as reverse transcription and PCR do not significantly contribute to bias6,11,12. Ligation bias is likely due to the fact that adapter molecules with a given sequence will interact with sRNA molecules in the reaction mixture to form co-folds, that may either lead to favorable or unfavorable configurations for ligation (Figure 2). Data from Sorefan et al7 suggest that RNL1 prefers a single stranded context, while RNL2 prefers a double strand for ligation. The fact that the adapter/sRNA co-fold structures are determined by the specific adapter and sRNA sequences explains why specific sRNA are over- or underrepresented with a given adapter set. It is also important to note that within a series of sRNA libraries to be compared, the same adapter sequences should be used. Indeed, it has previously been observed that changing adapters by the introduction of different barcode sequences alters miRNA profiles in sequencing libraries9,13.
Randomization of adapter sequences near the ligation junction likely reduces these biases. Sorefan and colleagues7 used adapters with 4 random nucleotides at their extremities, designated &#34;High Definition&#34;&#160;(HD) adapters, and showed that the use of these adapters lead to libraries that better reflect true sRNA expression levels. More recent work confirmed these observations and revealed that the randomized region does not need to be adjacent to the ligation junction11. This novel type of adapters was named &#34;MidRand&#34;&#160;adapters. Together, these results demonstrate that improved adapter design can reduce bias.
Instead of modifying the adapters, bias can be suppressed through the optimisation of reaction conditions. Polyethylene glycol (PEG), a macromolecular crowding agent known to increase ligation efficiency14, has been shown to significantly reduce bias15,16. Based on these results, several &#34;low bias&#34;&#160;kits appeared on the market. These include kits that use PEG in the ligation reactions, either in combination with classical adapters or HD adapters. Other kits avoid ligation altogether, and use 3&#39;&#160;polyadenylation and template switching for 3&#39;&#160;and 5&#39;&#160;adapter addition, respectively12. In yet another strategy, 3&#39;&#160;adapter ligation is followed by a circularization step, thus omitting 5&#39;&#160;adapter ligation17.
In a previous study, we searched for a sRNA library preparation protocol with the lowest possible levels of bias and the best detection of 2&#39;-OMe RNAs12. We tested some of the above-mentioned &#39;low bias&#39;&#160;kits, which had a better detection of 2&#39;-OMe RNAs than the standard protocol (TS). Surprisingly however, upon modification (the use of randomized adapters, PEG in the ligation reactions and removal of excess 3&#39;&#160;adapter by purification) the latter outperformed the other protocols for the detection of 2&#39;-OMe RNAs. Here, we describe in detail a protocol based on the TS protocol, &#39;TS5&#39;, which had the best overall detection of 2&#39;-OMe RNAs. The protocol can be followed using reagents from the TS kit and one reagent from the &#39;Nf&#39;&#160;kit or, to save money, using homemade reagents, with equal performance. We also provide a detailed protocol for the purification of sRNA from total RNA and the preparation of preadenylated 3&#39;&#160;adapter. &#160;

<table border="0" cellpadding="0" cellspacing="0" style="width:873px;" width="874">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">2100 Bioanalyzer Instrument&#160;</td>
			<td style="width:159px;">Agilent</td>
			<td style="width:169px;">G2939BA</td>
			<td style="width:172px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Acid-Phenol:Chloroform, pH 4.5 (with IAA, 125:24:1)</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">AM9720</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Adenosine 5&#39;-Triphosphate (ATP)</td>
			<td style="width:159px;">Nex England Biolabs</td>
			<td style="width:169px;">P0756S</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Agencourt AMPure XP beads</td>
			<td style="width:159px;">Beckman Coulter</td>
			<td style="width:169px;">A63880</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Bioanalyzer High Sensitivity DNA Kit</td>
			<td style="width:159px;">Agilent</td>
			<td style="width:169px;">5067-4626</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Bioanalyzer Small RNA Kit</td>
			<td style="width:159px;">Agilent</td>
			<td style="width:169px;">5067-1548</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Corning Costar Spin-X centrifuge tube filters</td>
			<td style="width:159px;">Sigma Aldrich</td>
			<td>CLS8162-96EA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Dark Reader transilluminator</td>
			<td style="width:159px;">various suppiers</td>
			<td style="width:169px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">HotStart PCR Kit, with dNTPs</td>
			<td style="width:159px;">Kapa Biosystems</td>
			<td>KK2501</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">NEXTflex small RNA-seq V3 kit</td>
			<td style="width:159px;">BIOO Scientific</td>
			<td style="width:169px;">NOVA-5132-05</td>
			<td>optional</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Novex TBE gels 6%</td>
			<td style="width:159px;">ThermoFisher</td>
			<td>EC6265BOX</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Novex TBE Urea gels 15%</td>
			<td style="width:159px;">ThermoFisher</td>
			<td>EC6885BOX</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">QIAquick Nucleotide Removal Kit</td>
			<td style="width:159px;">Qiagen&#160;</td>
			<td style="width:169px;">28304</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Qubit 4 Quantitation Starter Kit</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">Q33227</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">Qubit ssDNA Assay Kit</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">Q10212</td>
			<td></td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;width:374px;">RNA Gel Loading Dye (2X)</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">R0641</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">RNA Gel Loading Dye (2X)</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">R0641</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">RNase Inhibitor, Murine&#160;</td>
			<td style="width:159px;">Nex England Biolabs</td>
			<td style="width:169px;">M0314S</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">SuperScript IV Reverse Transcriptase</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">18090200</td>
			<td></td>
		</tr>
		<tr height="26">
			<td height="26" style="height:28px;width:374px;">SYBR Gold Nucleic Acid Gel Stain</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">S11494</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">T4 RNA Ligase 1 (ssRNA Ligase)</td>
			<td style="width:159px;">Nex England Biolabs</td>
			<td style="width:169px;">M0204S</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">T4 RNA Ligase 2, truncated</td>
			<td style="width:159px;">Nex England Biolabs</td>
			<td style="width:169px;">M0242S</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TrackIt 50 bp DNA ladder</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">10488043</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">TruSeq Small RNA Library Prep Kit</td>
			<td style="width:159px;">Illumina</td>
			<td>RS-200-0012/24/36/48</td>
			<td>optional</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">UltraPure Glycogen</td>
			<td style="width:159px;">ThermoFisher</td>
			<td>10814010</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">XCell SureLock Mini-Cell</td>
			<td style="width:159px;">ThermoFisher</td>
			<td style="width:169px;">EI0001</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">XCell SureLock Mini-Cell</td>
			<td style="width:159px;">ThermoFisher</td>
			<td>EI0001</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;">ZR small RNA ladder</td>
			<td style="width:159px;">Zymo Research</td>
			<td style="width:169px;">R1090</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:374px;"></td>
			<td style="width:159px;"></td>
			<td style="width:169px;"></td>
			<td>the last two numbers correspond to the set of indexes</td>
		</tr>
	</tbody>
</table>

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.

Critical steps are the isolation of the small RNA fraction of the starting total RNA material (Figure 3) and the desired final library product (Figure 4). Both steps involve polyacrylamide gel purification; small RNA is isolated from 15% TBE urea gels, while the final libraries are isolated from 6% native TBE gels. Small RNA isolated from gel can be analyzed on a small RNA capillary electrophoresis chip (Table of Materials; ...

Watch this Scientific Journal Video about Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs at JoVE.com

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

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 ...

improving-small-rna-seq-less-bias-better-detection-2-o-methyl

Research

JoVE Journal

Genetics

7.5K Views.  Université Paris-Saclay. We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2'-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

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

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids

RNAs are emerging as important biomarkers and therapeutic targets. Thus, there is great potential in developing chemical probes and therapeutic ligands for the recognition of RNA sequence and structure. Chemically modified Peptide Nucleic Acid (PNA) oligomers have been recently developed that can recognize RNA duplexes in a sequence-specific manner. PNAs are chemically stable with a neutral peptide-like backbone. PNAs can be synthesized relatively easily by the manual Boc-chemistry solid-phase peptide synthesis method. PNAs are purified by reverse-phase HPLC, followed by molecular weight characterization by matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Non-denaturing polyacrylamide gel electrophoresis (PAGE) technique facilitates the imaging of the triplex formation, because carefully designed free RNA duplex constructs and PNA bound triplexes often show different migration rates. Non-denaturing PAGE with ethidium bromide post staining is often an easy and informative technique for characterizing the binding affinities and specificities of PNA oligomers. Typically, multiple RNA hairpins or duplexes with single base pair mutations can be used to characterize PNA binding properties, such as binding affinities and specificities. 2-Aminopurine is an isomer of adenine (6-aminopurine); the 2-aminopurine fluorescence intensity is sensitive to local structural environment changes, and is suitable for the monitoring of triplex formation with the 2-aminopurine residue incorporated near the PNA binding site. 2-Aminopurine fluorescence titration can also be used to confirm the binding selectivity of modified PNAs towards targeted double-stranded RNAs (dsRNAs) over single-stranded RNAs (ssRNAs). UV-absorbance-detected thermal melting experiments allow the measurement of the thermal stability of PNA-RNA duplexes and PNA&#183;RNA2 triplexes. Here, we describe the synthesis and purification of PNA oligomers incorporating modified residues, and describe biochemical and biophysical methods for characterization of the recognition of RNA duplexes by the modified PNAs.

We report the protocols for the synthesis and purification of Peptide Nucleic Acid (PNA) oligomers incorporating modified residues. The biochemical and biophysical methods for the characterization of the recognition of RNA duplexes by the modified PNAs are described.

RNAs are emerging as important biomarkers and therapeutic targets. Thus, there is great potential in developing chemical probes and therapeutic ligands ...

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for protein synthesis by translating codons in mRNA into amino acid sequences. tRNAs were long considered as house-keeping molecules that lacked regulatory functions. However, a growing body of evidence indicates that cellular tRNA levels fluctuate in correspondence to varying conditions such as cell type, environment, and stress. The fluctuation of tRNA expression directly influences gene translation, favoring or repressing the expression of particular proteins. Ultimately comprehending the dynamic of protein synthesis requires the development of methods able to deliver high-quality tRNA profiles. The method that we present here is named SPOt, which stands for Streamlined Platform for Observing tRNA. SPOt consists of three steps starting with metabolic labeling of cell cultures with radioactive orthophosphate, followed by guanidinium thiocyanate-phenol-chloroform extraction of radioactive total RNAs and finally hybridization on in-house printed macroarrays. tRNA levels are estimated by quantifying the radioactivity intensities at each probe spot. In the protocol presented here we profile tRNAs in Mycobacterium smegmatis mc2155, a nonpathogenic bacterium often used as a model organism to study tuberculosis.

We describe an original transfer RNA analysis platform named SPOt (Streamlined Platform for Observing tRNA). SPOt simultaneously measures cellular levels of all tRNAs in biological samples, in only three steps, and in less than 24 hours.

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for ...

CRISPR-Mediated Reorganization of Chromatin Loop Structure

Recent studies have clearly shown that long-range, three-dimensional chromatin looping interactions play a significant role in the regulation of gene expression, but whether looping is responsible for or a result of alterations in gene expression is still unknown. Until recently, how chromatin looping affects the regulation of gene activity and cellular function has been relatively ambiguous, and limitations in existing methods to manipulate these structures prevented in-depth exploration of these interactions. To resolve this uncertainty, we engineered a method for selective and reversible chromatin loop re-organization using CRISPR-dCas9 (CLOuD9). The dynamism of the CLOuD9 system has been demonstrated by successful localization of CLOuD9 constructs to target genomic loci to modulate local chromatin conformation. Importantly, the ability to reverse the induced contact and restore the endogenous chromatin conformation has also been confirmed. Modulation of gene expression with this method establishes the capacity to regulate cellular gene expression and underscores the great potential for applications of this technology in creating stable de novo chromatin loops that markedly affect gene expression in the contexts of cancer and development.

Chromatin looping plays a significant role in gene regulation; however, there have been no technological advances that allow for selective and reversible modification of chromatin loops. Here we describe a powerful system for chromatin loop re-organization using CRISPR-dCas9 (CLOuD9), demonstrated to selectively and reversibly modulate gene expression at targeted loci.

Recent studies have clearly shown that long-range, three-dimensional chromatin looping interactions play a significant role in the regulation of gene ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Quantification of DNA methylation can be achieved using bisulfite sequencing, which takes advantage of the property of sodium bisulfite to convert unmethylated cytosine into uracil, in a single-stranded DNA context. Bisulfite sequencing can be targeted (using PCR) or performed on the whole genome and provides absolute quantification of cytosine methylation at the single base-resolution. Given the distinct nature of nuclear- and mitochondrial DNA, notably in the secondary structure, adaptions of bisulfite sequencing methods for investigating cytosine methylation in mtDNA should be made. Secondary and tertiary structure of mtDNA can indeed lead to bisulfite sequencing artifacts leading to false-positives due to incomplete denaturation poor access of bisulfite to single-stranded DNA. Here, we describe a protocol using an enzymatic digestion of DNA with BamHI coupled with bioinformatic analysis pipeline to allow accurate quantification of cytosine methylation levels in mtDNA. In addition, we provide guidelines for designing the bisulfite sequencing primers specific to mtDNA, in order to avoid targeting undesirable NUclear MiTochondrial segments (NUMTs) inserted into the nuclear genome.

Here, we present a protocol to allow accurate quantification of mitochondrial DNA (mtDNA) methylation. In this protocol, we describe an enzymatic digestion of DNA with BamHI coupled with a bioinformatic analysis pipeline which can be used to avoid overestimation of mtDNA methylation levels caused by the secondary structure of mtDNA.

Quantification of DNA methylation can be achieved using bisulfite sequencing, which takes advantage of the property of sodium bisulfite to convert ...

Cell Based Assays of SINEUP Non-coding RNAs That Can Specifically Enhance mRNA Translation

Targeted-protein enhancement is of importance not only for the study of biological processes but also for therapeutic and biotechnological applications. Here, we present a method to selectively up-regulate protein expression of desired genes in cultured cells by means of synthetic antisense non-coding RNAs known as SINEUPs. This positive control of gene expression is at the post-transcriptional level and exerted by an inverted short interspersed nuclear element (SINE) repeat at the 3&#697; end of SINEUPs that comprises its effector domain (ED). SINEUPs can specifically bind to any protein-coding mRNA of choice through its binding domain (BD), a region designed to complement the sequence within the 5&#697; untranslated region (5&#697; UTR) and around the start codon of the mRNA. Target-specific SINEUPs designed in this manner are transfected to cultured cells, and protein and RNA are extracted for downstream analyses, generally 24-48 h post-transfection. SINEUP-induced protein up-regulation is detected by Western-blot analysis and RNA expression is measured using real-time quantitative reverse transcription PCR. We have observed that BD design is critical for achieving optimum SINEUP activity and that testing different BD sizes and positions with respect to the start codon of the target mRNA is recommended. Therefore, we describe here a semi-automated high-throughput imaging method based on fluorescence detection that can be implemented to target mRNA fused with green fluorescent protein (GFP). SINEUPs specifically enhance translation within normal physiological range of the cell, without altering the target transcript level. This method has been successfully employed against a range of endogenous and exogenous targets, in a wide variety of human, mouse, and insect cell lines along with in vivo systems. Moreover, SINEUPs have been reported to increase antibody production and work as an RNA therapeutic against haploinsufficient genes. The versatile and modular nature of SINEUPs makes them a suitable tool for gene-specific translational control.

SINEUPs are synthetic antisense non-coding RNAs, which contain a binding domain (BD) and an effector domain (ED) and up-regulate translation of target mRNA. Here, we describe detection methods for SINEUPs in cultured cell lines, analysis of their translation-promoting activity by Western-blot and a semi-automated high throughput imaging system.

Targeted-protein enhancement is of importance not only for the study of biological processes but also for therapeutic and biotechnological applications. ...

Overexpressing Long Noncoding RNAs Using Gene-activating CRISPR

Long noncoding RNA (lncRNA) biology is a new and exciting field of research, with the number of publications from this field growing exponentially since 2007. These studies have confirmed that lncRNAs are altered in almost all diseases. However, studying the functional roles for lncRNAs in the context of disease remains difficult due to the lack of protein products, tissue-specific expression, low expression levels, complexities in splice forms, and lack of conservation among species. Given the species-specific expression, lncRNA studies are often restricted to human research contexts when studying disease processes. Since lncRNAs function at the molecular level, one way to dissect lncRNA biology is to either remove the lncRNA or overexpress the lncRNA and measure cellular effects. In this article, a written and visualized protocol to overexpress lncRNAs in vitro is presented. As a representative experiment, an lncRNA associated with inflammatory bowel disease, Interferon Gamma Antisense 1 (IFNG-AS1), is shown to be overexpressed in a Jurkat T-cell model. To accomplish this, the activating clustered regularly interspaced short palindromic repeats (CRISPR) technique is used to enable overexpression at the endogenous genomic loci. The activating CRISPR technique targets a set of transcription factors to the transcriptional start site of a gene, enabling a robust overexpression of multiple lncRNA splice forms. This procedure will be broken down into three steps, namely (i) guide RNA (gRNA) design and vector construction, (ii) virus generation and transduction, and (iii) colony screening for overexpression. For this representative experiment, a greater than 20-fold enhancement in IFNG-AS1 in Jurkat T cells was observed.

Traditional cDNA-based overexpression techniques have a limited applicability for the overexpression of long noncoding RNAs due to their multiple splice forms with potential functionality. This review reports a protocol using CRISPR technology to overexpress multiple splice variants of a long noncoding RNA.

Long noncoding RNA (lncRNA) biology is a new and exciting field of research, with the number of publications from this field growing exponentially since ...

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.

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 ...

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported for in vitro differentiation of keratinocytes cultured in two-dimensional (2D) submerged manners using various induction conditions. Described here is a procedure for 2D in vitro keratinocyte differentiation method by contact inhibition and subsequent molecular characterization by RNA-seq. In brief, keratinocytes are grown in defined keratinocyte medium supplemented with growth factors until they are fully confluent. Differentiation is induced by close contacts between the keratinocytes and further stimulated by excluding growth factors in the medium. Using RNA-seq analyses, it is shown that both 1) differentiated keratinocytes exhibit distinct molecular signatures during differentiation and 2) the dynamic gene expression pattern largely resembles cells during epidermal stratification. As for comparison to normal keratinocyte differentiation, keratinocytes carrying mutations of the transcription factor p63 exhibit altered morphology and molecular signatures, consistent with their differentiation defects. In conclusion, this protocol details the steps for 2D in vitro keratinocyte differentiation and its molecular characterization, with an emphasis on bioinformatic analysis of RNA-seq data. Because RNA extraction and RNA-seq procedures have been well-documented, it is not the focus of this protocol. The experimental procedure of in vitro keratinocyte differentiation and bioinformatic analysis pipeline can be used to study molecular events during epidermal differentiation in healthy and diseased keratinocytes.

Presented here is a stepwise procedure for in vitro differentiation of human primary keratinocytes by contact inhibition followed by characterization at the molecular level by RNA-seq analysis.

Human primary keratinocytes are often used as in vitro models for studies on epidermal differentiation and related diseases. Methods have been reported ...

Methods Article

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