The authors of the present work are grateful to the authors of the original paper describing the AQRNA-seq technology7. This work was supported by grants from the National Institutes of Health (ES002109, AG063341, ES031576, ES031529, ES026856) and the National Research Foundation of Singapore through the Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance IRG.

NOTE: Figure 1 provides a graphical illustration of the procedures involved in AQRNA-seq library preparation. Detailed information regarding the reagents, chemicals, and columns/kits used in the procedure can be found in the Table of Materials. It is recommended to perform a comprehensive evaluation of purity, integrity, and quantity of the input RNA samples using (i) 3% agarose gel electrophoresis, (ii) automated electrophoresis tools for sample quality control of biomolecu...

Analyzing tRNA Dynamics Using AQRNA-seq

<ol>
	<li>Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D., Craig, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translating+RNA+sequencing+into+clinical+diagnostics:+opportunities+and+challenges.">Translating RNA sequencing into clinical diagnostics: opportunities and challenges.</a> Nat Rev Gen. 17 (5), 257-271 (2016).</li><li>Grillone, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-coding+RNAs+in+cancer:+platforms+and+strategies+for+investigating+the+genomic+&amp;#34;dark+matter.&amp;#34.">Non-coding RNAs in cancer: platforms and strategies for investigating the genomic &amp;#34;dark matter.&amp;#34.</a> J Exp Clin Cancer Res. 39 (1), 117 (2020).</li><li>Hwang, B., Lee, J. H., Bang, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+technologies+and+bioinformatics+pipelines.">Single-cell RNA sequencing technologies and bioinformatics pipelines.</a> Exp Mol Med. 50 (8), 1-14 (2018).</li><li>Goh, J. J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+specific+multiplexed+RNA+imaging+in+tissues+with+split-FISH.">Highly specific multiplexed RNA imaging in tissues with split-FISH.</a> Nat Methods. 17 (7), 689-693 (2020).</li><li>Moses, L., Pachter, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Museum+of+spatial+transcriptomics.">Museum of spatial transcriptomics.</a> Nat Methods. 19 (5), 534-546 (2022).</li><li>Cummings, B. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+genetic+diagnosis+in+Mendelian+disease+with+transcriptome+sequencing.">Improving genetic diagnosis in Mendelian disease with transcriptome sequencing.</a> Sci Transl Med. 9 (386), 5209 (2017).</li><li>Hu, J. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+mapping+of+the+cellular+small+RNA+landscape+with+AQRNA-seq.">Quantitative mapping of the cellular small RNA landscape with AQRNA-seq.</a> Nat Biotech. 39 (8), 978-988 (2021).</li><li>Alon, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Barcoding+bias+in+high-throughput+multiplex+sequencing+of+miRNA.">Barcoding bias in high-throughput multiplex sequencing of miRNA.</a> Genome Res. 21 (9), 1506-1511 (2011).</li><li>Fuchs, R. T., Sun, Z., Zhuang, F., Robb, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bias+in+ligation-based+small+RNA+sequencing+library+construction+is+determined+by+adaptor+and+RNA+structure.">Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure.</a> PLoS One. 10 (5), e0126049 (2015).</li><li>Pang, Y. L. J., Abo, R., Levine, S. S., Dedon, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diverse+cell+stresses+induce+unique+patterns+of+tRNA+up-+and+down-regulation:+tRNA-seq+for+quantifying+changes+in+tRNA+copy+number.">Diverse cell stresses induce unique patterns of tRNA up- and down-regulation: tRNA-seq for quantifying changes in tRNA copy number.</a> Nuc Acids Res. 42 (22), e170 (2014).</li><li>Machnicka, M. A., Olchowik, A., Grosjean, H., Bujnicki, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distribution+and+frequencies+of+post-transcriptional+modifications+in+tRNAs.">Distribution and frequencies of post-transcriptional modifications in tRNAs.</a> RNA Biol. 11 (12), 1619-1629 (2014).</li><li>Li, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulatory+impact+of+RNA+secondary+structure+across+the+Arabidopsis+transcriptome.">Regulatory impact of RNA secondary structure across the Arabidopsis transcriptome.</a> Plant Cell. 24 (11), 4346-4359 (2012).</li><li>Garc&#237;a-Nieto, P. E., Wang, B., Fraser, H. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptome+diversity+is+a+systematic+source+of+variation+in+RNA-sequencing+data.">Transcriptome diversity is a systematic source of variation in RNA-sequencing data.</a> PLOS Comput Biol. 18 (3), e1009939 (2022).</li><li>. FASTQC: a quality control tool for high throughput sequence data Available from: <a target="_blank" href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/">https://www.bioinformatics.babraham.ac.uk/projects/fastqc/</a> (2010)</li><li>Chen, S., Zhou, Y., Chen, Y., Gu, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fastp:+an+ultra-fast+all-in-one+FASTQ+preprocessor.">Fastp: an ultra-fast all-in-one FASTQ preprocessor.</a> Bioinformatics. 34 (17), i884-i890 (2018).</li><li>Love, M. I., Huber, W., Anders, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moderated+estimation+of+fold+change+and+dispersion+for+RNA-seq+data+with+DESeq2.">Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.</a> Genome Biol. 15 (12), 550 (2014).</li><li>R Core Team. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=R:+a+language+and+environment+for+statistical+computing.">R: a language and environment for statistical computing.</a> R Foundation for Statistical Computing. , (2022).</li><li>Ougland, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AlkB+restores+the+biological+function+of+mRNA+and+tRNA+inactivated+by+chemical+methylation.">AlkB restores the biological function of mRNA and tRNA inactivated by chemical methylation.</a> Mol Cell. 16 (1), 107-116 (2004).</li><li>Bernhardt, H. S., Tate, W. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Primordial+soup+or+vinaigrette:+did+the+RNA+world+evolve+at+acidic+pH.">Primordial soup or vinaigrette: did the RNA world evolve at acidic pH.</a> Biol Direct. 7, 4 (2012).</li><li>Altschul, S. F., Gish, W., Miller, W., Myers, E. W., Lipman, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Basic+local+alignment+search+tool.">Basic local alignment search tool.</a> J Mol Biol. 215 (3), 403-410 (1990).</li><li>Martin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cutadapt+removes+adapter+sequences+from+high-throughput+sequencing+reads.">Cutadapt removes adapter sequences from high-throughput sequencing reads.</a> EMBnet. J. 17 (1), 10-12 (2011).</li></ol>

The AQRNA-seq library preparation workflow is designed to maximize the capture of RNAs within a sample and minimize polymerase fall-off during reverse transcription7. Through a two-step linker ligation, novel DNA oligos (Linker 1 and Linker 2) are ligated in excess to fully complement the RNA within the sample. Excess linkers can be efficiently removed with RecJf, a 5&#39; to 3&#39; exonuclease specific to single-stranded DNAs, leaving the ligated products intact. In addition, AlkB treatment reduc...

Next-generation sequencing (NGS), also known as massively parallel sequencing, is a DNA sequencing technology that involves DNA fragmentation, ligation of adaptor oligonucleotides, polymerase chain reaction (PCR)-based amplification, sequencing of the DNA, and reassembly of the fragment sequences into a genome. The adaptation of NGS to sequence RNA (RNA-seq) is a powerful approach to identify and quantify RNA transcripts and their variants1. Innovative developments in RNA library preparation workflows and bioinformatic analysis pipelines, coupled with advancements in laboratory instrumentation, have expanded the repertoire of RNA-seq applications, progressing beyond exome sequencing into advanced functional omics like non-coding RNA profiling2, single cell analysis3, spatial transcriptomics4,5, alternative splicing analysis6, among others. These advanced RNA-seq methods reveal complex RNA functions through quantitative analysis of the transcriptome in normal and diseased cells and tissue.
Despite these advances in RNA-seq, several key technical features limit the quantitative power of the method. While most RNA-seq methods allow precise and accurate quantification of changes in the levels of RNAs between experimental variables (i.e., biological samples and/or physiological states), they cannot provide quantitative comparisons of the levels of RNA molecules within a sample. For example, most RNA-seq methods cannot accurately quantify the relative number of copies of individual tRNA isoacceptor molecules in a cellular pool of expressed tRNAs. As highlighted in the companion publication7, this limitation to RNA-seq arises from several features of RNA structure and the biochemistry of library preparation. For example, the activity of the ligation enzymes used to attach the 3&#39;- and 5&#39;-end sequencing linkers to RNA molecules is strongly influenced by the identity of the terminal nucleotides of the RNA and the sequencing linkers. This leads to large variations in efficiencies of linker ligations and profound artifactual increases in sequencing reads8,9,10.
A second set of limitations arise from the inherent structural properties of RNA molecules. Specifically, RNA secondary structure formation and dynamic changes in the dozens of post-transcriptional RNA modifications of the epitranscriptome&#160;can cause polymerase fall-off or mutation during reverse transcription. These errors result in incomplete or truncated cDNA synthesis or altered RNA sequence. While both of these phenomena can be exploited to map secondary structures or some modifications, they degrade the quantitative accuracy of RNA-seq if subsequent library preparation steps fail to capture truncated cDNAs or if data processing throws out mutated sequences not matching a reference dataset11,12. Furthermore, the immense chemical, length, and structural diversity of RNA transcripts, as well as the lack of tools to uniformly fragment long RNAs, diminishes the applicability of most RNA-seq methods to all RNA species13.
The AQRNA-seq (absolute quantification RNA sequencing) method has been developed to remove several of these technical and biological constraints that limit quantitative accuracy7. By minimizing sequence-dependent biases in capture, ligation, and amplification during RNA sequencing library preparation, AQRNA-seq achieves superior linearity compared to other methods, accurately quantifying 75% of a reference library of 963 miRNAs within 2-fold accuracy. This linear correlation of sequencing read count and RNA abundance is also observed in an analysis of a variable length pool of RNA oligonucleotide standards and in reference to orthogonal methods like northern blotting. Establishing linearity between sequencing read count and RNA abundance enables AQRNA-seq to achieve accurate, absolute quantification of all RNA species within a sample.
Here is a description of the protocol for AQRNA-seq library preparation workflow and the accompanying downstream data analytics pipeline. The method was applied to elucidate the dynamics of tRNA abundance during starvation-induced dormancy and subsequent resuscitation in&#160;the Mycobacterium bovis bacilli de Calmette et Gu&#233;rin (BCG) model of tuberculosis. Results were presented for the exploratory visualization of the sequencing data, along with subsequent clustering and differential expression analyses that unveiled discernable patterns in tRNA abundance associated with various phenotypes.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2-ketoglutarate&#160;</td>
			<td>Sigma-Aldrich</td>
			<td>75890</td>
			<td>Prepare a working solution (1 M) and store it at -20&#186;C</td>
		</tr>
		<tr>
			<td>2100 Bioanalyzer Instrument</td>
			<td>Agilent</td>
			<td>G2938C</td>
			<td></td>
		</tr>
		<tr>
			<td>5&#39;-deadenylase (50 U/&#956;L)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0331S&#160;(component #: M0331SVIAL)</td>
			<td>Store at -20 &#176;C</td>
		</tr>
		<tr>
			<td>Adenosine 5&#39;-Triphosphate (ATP)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0437M (component #: N0437AVIAL)</td>
			<td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&#956;L), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM); prepare a working solution (10 mM) and store it at -20&#186;C</td>
		</tr>
		<tr>
			<td>AGAROSE GPG/LE</td>
			<td>AmericanBio</td>
			<td>AB00972-00500</td>
			<td>Store at ambient temperature</td>
		</tr>
		<tr>
			<td>Ammonium iron(II) sulfate hexahydrate</td>
			<td>Sigma-Aldrich</td>
			<td>F2262</td>
			<td>Prepare a working solution (0.25 M) and store it at -20 &#176;C</td>
		</tr>
		<tr>
			<td>Bioanalyzer Small RNA Analysis</td>
			<td>Agilent</td>
			<td>5067-1548&#160;</td>
			<td>The Small RNA Analysis is used for checking the quality of input RNAs and the efficiency of enzymatic reactions (e.g., Linker 1 ligation)</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin (BSA; 10 mg/mL)&#160;</td>
			<td>New England Biolabs</td>
			<td>B9000</td>
			<td>This product was discontinued on 12/15/2022 and is replaced with Recombinant Albumin, Molecular Biology Grade (NEB B9200).</td>
		</tr>
		<tr>
			<td>Chloroform&#160;</td>
			<td>Macron Fine Chemicals</td>
			<td>4441-10</td>
			<td></td>
		</tr>
		<tr>
			<td>Demethylase&#160;</td>
			<td>ArrayStar</td>
			<td>AS-FS-004</td>
			<td>Demethylase comes with the rtStar tRNA Pretreatment &#38; First-Strand cDNA Synthesis Kit (AS-FS-004)</td>
		</tr>
		<tr>
			<td>Deoxynucleotide (dNTP) Solution Mix</td>
			<td>New England Biolabs</td>
			<td>N0447L (component #: N0447LVIAL)</td>
			<td>This dNTP Solution Mix contains equimolar concentrations of dATP, dCTP, dGTP and dTTP (10 mM each)</td>
		</tr>
		<tr>
			<td>Digital Dual Heat Block</td>
			<td>VWR Scientific Products</td>
			<td>13259-052</td>
			<td>Heating block is used with the QIAquick&#160;Gel Extraction Kit&#160;</td>
		</tr>
		<tr>
			<td>DyeEx&#160;2.0 Spin Kit&#160;</td>
			<td>Qiagen</td>
			<td>63204&#160;</td>
			<td>Effective at removing short remnants (e.g., oligos less than 10 bp in length)</td>
		</tr>
		<tr>
			<td>Electrophoresis Power Supply</td>
			<td>Bio-Rad Labrotories</td>
			<td>PowerPac 300</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf PCR Tubes (0.5 mL)</td>
			<td>Eppendorf</td>
			<td>0030124537</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf Safe-Lock Tubes (0.5 mL)</td>
			<td>Eppendorf</td>
			<td>022363611</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf Safe-Lock Tubes (1.5 mL)</td>
			<td>Eppendorf</td>
			<td>022363204</td>
			<td></td>
		</tr>
		<tr>
			<td>Eppendorf Safe-Lock Tubes (2 mL)</td>
			<td>Eppendorf</td>
			<td>022363352</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl alcohol (Ethanol), Pure</td>
			<td>Sigma-Aldrich</td>
			<td>E7023&#160;</td>
			<td>The pure ethanol is used with the Oligo Clean and Concentrator Kit from Zymo Research</td>
		</tr>
		<tr>
			<td>Gel Imaging System</td>
			<td>Alpha Innotech</td>
			<td>FluorChem 8900</td>
			<td></td>
		</tr>
		<tr>
			<td>Gel Loading Dye, Purple (6X), no SDS</td>
			<td>New England Biolabs</td>
			<td>N0556S (component #: B7025SVIAL)</td>
			<td>NEB N0556S contains Quick-Load Purple 50 bp DNA Ladder and Gel Loading Dye, Purple (6X), no SDS</td>
		</tr>
		<tr>
			<td>GENESYS 180 UV-Vis Spectrophotometer</td>
			<td>Thermo Fisher Scientific</td>
			<td>840-309000</td>
			<td>The spectrophotometer is used for measuring the oligo concentrations using the Beer&#39;s law</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Sigma-Aldrich</td>
			<td>H4034</td>
			<td>Prepare a working solution (1 M; pH = 8 with NaOH) and store it at -20 &#176;C</td>
		</tr>
		<tr>
			<td>Hydrochloric acid (HCl)&#160;</td>
			<td>VWR Scientific Products</td>
			<td>BDH3028&#160;</td>
			<td>Prepare a working solution (5 M) and store it at ambient temperature</td>
		</tr>
		<tr>
			<td>Isopropyl Alcohol (Isopropanol), Pure</td>
			<td>Macron Fine Chemicals</td>
			<td>3032-16</td>
			<td>Isopropanol is used with the QIAquick&#160;Gel Extraction Kit&#160;</td>
		</tr>
		<tr>
			<td>L-Ascorbic acid</td>
			<td>Sigma-Aldrich</td>
			<td>A5960</td>
			<td>Prepare a working solution (0.5 M) and store it at -20&#186;C</td>
		</tr>
		<tr>
			<td>Microcentrifuge</td>
			<td>Eppendorf</td>
			<td>5415D</td>
			<td></td>
		</tr>
		<tr>
			<td>NanoDrop 2000 Spectrophotometer</td>
			<td>Thermo Fisher Scientific</td>
			<td>ND-2000</td>
			<td></td>
		</tr>
		<tr>
			<td>NEBuffer 2 (10X)</td>
			<td>New England Biolabs</td>
			<td>M0264L (component #: B7002SVIAL)</td>
			<td>NEB M0264L contains RecJf (30 U/&#956;L) and NEBuffer 2 (10X); store at -20 &#176;C</td>
		</tr>
		<tr>
			<td>Nuclease-Free Water (not DEPC-Treated)</td>
			<td>Thermo Fisher Scientific</td>
			<td>AM9938&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Oligo Clean &#38; Concentrator Kit&#160;</td>
			<td>Zymo Research</td>
			<td>D4061&#160;</td>
			<td>Store at ambient temperature</td>
		</tr>
		<tr>
			<td>PEG 8000 (50% solution)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0437M (component #: B1004SVIAL)</td>
			<td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&#956;L), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM); prepare a working solution (10 mM) and store it at -20&#186;C</td>
		</tr>
		<tr>
			<td>Peltier Thermal Cycler</td>
			<td>MJ Research</td>
			<td>PTC-200</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenol:choloroform:isoamyl&#160;alcohol 25:24:1 pH = 5.2&#160;</td>
			<td>Thermo Fisher Scientific</td>
			<td>J62336&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>PrimeScript Buffer (5X)</td>
			<td>TaKaRa</td>
			<td>2680A&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>PrimeScript Reverse Transcriptase</td>
			<td>TaKaRa</td>
			<td>2680A&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>QIAquick&#160;Gel Extraction Kit&#160;</td>
			<td>Qiagen</td>
			<td>28704</td>
			<td>This kit requires a heating block and isopropanol to work with</td>
		</tr>
		<tr>
			<td>Quick-Load Purple 100 bp DNA Ladder</td>
			<td>New England Biolabs</td>
			<td>N0551S (component #: N0551SVIAL)</td>
			<td></td>
		</tr>
		<tr>
			<td>Quick-Load Purple 50 bp DNA Ladder</td>
			<td>New England Biolabs</td>
			<td>N0556S (component #: N0556SVIAL)</td>
			<td>NEB N0556S contains Quick-Load Purple 50 bp DNA Ladder and Gel Loading Dye, Purple (6X), no SDS</td>
		</tr>
		<tr>
			<td>RecJf&#160;(30 U/&#956;L)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0264L (component #: M0264LVIAL)</td>
			<td>NEB M0264L contains RecJf (30 U/&#956;L) and NEBuffer 2 (10X); store at -20 &#176;C</td>
		</tr>
		<tr>
			<td>RNase Inhibitor (murine; 40 U/&#956;L)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0314L&#160;(component #: M0314LVIAL)</td>
			<td>Store at -20 &#176;C</td>
		</tr>
		<tr>
			<td>SeqAMP&#160;DNA Polymerase&#160;</td>
			<td>TaKaRa</td>
			<td>638509&#160;</td>
			<td>TaKaRa 638509 contains SeqAMP&#160;DNA Polymerase&#160;and SeqAMP&#160;PCR Buffer (2X)</td>
		</tr>
		<tr>
			<td>SeqAMP&#160;PCR Buffer (2X)&#160;</td>
			<td>TaKaRa</td>
			<td>638509&#160;</td>
			<td>TaKaRa 638509 contains SeqAMP&#160;DNA Polymerase&#160;and SeqAMP&#160;PCR Buffer (2X)</td>
		</tr>
		<tr>
			<td>Shrimp Alkaline Phosphatase (1 U/&#956;L)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0371L&#160;(component #: M0371LVIAL)</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium hydroxide (NaOH)</td>
			<td>Sigma-Aldrich</td>
			<td>&#160;S5881</td>
			<td>Prepare a working solution (5 M) and store it at ambient temperature</td>
		</tr>
		<tr>
			<td>T4 DNA Ligase (400 U/&#956;L)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0202L&#160;(component #: M0202LVIAL)</td>
			<td>NEB M0202L contains T4 DNA Ligase (400 U/&#956;L) and T4 DNA Ligase Reaction Buffer (10X)</td>
		</tr>
		<tr>
			<td>T4 DNA Ligase Reaction Buffer (10X)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0202L&#160;(component #: B0202SVIAL)</td>
			<td>NEB M0202L contains T4 DNA Ligase (400 U/&#956;L) and T4 DNA Ligase Reaction Buffer (10X)</td>
		</tr>
		<tr>
			<td>T4 RNA Ligase 1 (30 U/&#956;L)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0437M (component #: M0437MVIAL)</td>
			<td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&#956;L), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM)</td>
		</tr>
		<tr>
			<td>T4 RNA Ligase Reaction Buffer (10X)&#160;</td>
			<td>New England Biolabs</td>
			<td>M0437M (component #: B0216SVIAL)</td>
			<td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&#956;L), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM)</td>
		</tr>
	</tbody>
</table>

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

AQRNA-seq for Quantifying Small RNAs

AQRNA sequencing quantitatively maps small RNAs such as microRNAs, tRNAs, and tRNA fragments in virtually any cell or tissue sample. It can be used to map some of the 170 known tRNA modifications, but there are other methods that are more specific for mapping. You can use AQRNA-seq to discover disease biomarkers in the RNome and explore protein translation mechanisms at the systems level.Most RNA-seq methods can't accurately quantify the abundance of individual RNA molecules in a sample. This is caused by both structural properties of the RNA, such as secondary structures, post transcriptional modifications, as well as the biochemistry of library preparation such as ligation biases of a thousand fold induced by the terminal nucleotides on the RNAs. AQRNA-seq was developed to overcome several technical and biological challenges, limiting the accurate quantification of RNA abundance in the sample.Compared to other matters, it achieves linearity between recounts and copy numbers of RNA molecules, accurately, it quantifies 75%of a reference library, compressing 963 microRNAs with twofold accuracy. AQRNA-seq is unique in providing absolute quantification of small RNAs. This links the sequencing recounts directly to the number of copies of an RNA molecule in the sample.This level of accuracy is crucial for comparing dozens to hundreds of tRNAs in a sample, and it's unlike regular small RNA-seq, which only permits relative quantification across conditions and samples. We designed AQRNA-seq for an unmet need in understanding protein translation. We found that cells reprogrammed dozens of tRNA modifications in the number of copies of the modified tRNAs, to allow selective translation of messenger RNAs that are enriched with codons matching those tRNAs.AQRNA-seq allows us to quantify changes in the tRNA pool as part of this mechanism. We've used it extensively to validate this new model for protein translation.

Mycobacterium bovis BCG (bacilli de Calmette et Gu&#233;rin) strain 1173P2 undergoing exponential growth were subject to a time series (0, 4, 10, and 20 days) of nutrient starvation, followed by a 6-day resuscitation in nutrient-rich medium as previously presented in Hu et al.7. Small RNAs were isolated from bacterial culture, with three biological replicates, at each of the five designated time points. Illumina libraries were constructed using the above-described AQRNA-seq library prepar...

Watch this Scientific Journal Video about Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms at JoVE.com

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 ...