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.

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>

P.C.D. is an inventor on two patents (PCT/US2019/013714, US 2019/0284624 A1) relating to the published work.

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><tbody><tr><td>2-ketoglutarate&amp;nbsp;</td><td>Sigma-Aldrich</td><td>75890</td><td>Prepare a working solution (1 M) and store it at -20&amp;ordm;C</td></tr><tr><td>2100 Bioanalyzer Instrument</td><td>Agilent</td><td>G2938C</td><td></td></tr><tr><td>Adenosine 5&#039;-Triphosphate (ATP)&amp;nbsp;</td><td>New England Biolabs</td><td>M0437M (component #: N0437AVIAL)</td><td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&amp;mu;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&amp;ordm;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 &amp;deg;C</td></tr><tr><td>Bioanalyzer Small RNA Analysis</td><td>Agilent</td><td>5067-1548&amp;nbsp;</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)&amp;nbsp;</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&amp;nbsp;</td><td>Macron Fine Chemicals</td><td>4441-10</td><td></td></tr><tr><td>Demethylase&amp;nbsp;</td><td>ArrayStar</td><td>AS-FS-004</td><td>Demethylase comes with the rtStar tRNA Pretreatment &amp;amp; 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&amp;nbsp;Gel Extraction Kit&amp;nbsp;</td></tr><tr><td>DyeEx&amp;nbsp;2.0 Spin Kit&amp;nbsp;</td><td>Qiagen</td><td>63204&amp;nbsp;</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&amp;nbsp;</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&#039;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 &amp;deg;C</td></tr><tr><td>Hydrochloric acid (HCl)&amp;nbsp;</td><td>VWR Scientific Products</td><td>BDH3028&amp;nbsp;</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&amp;nbsp;Gel Extraction Kit&amp;nbsp;</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&amp;ordm;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/&amp;mu;L) and NEBuffer 2 (10X); store at -20 &amp;deg;C</td></tr><tr><td>Nuclease-Free Water (not DEPC-Treated)</td><td>Thermo Fisher Scientific</td><td>AM9938&amp;nbsp;</td><td></td></tr><tr><td>Oligo Clean &amp;amp; Concentrator Kit&amp;nbsp;</td><td>Zymo Research</td><td>D4061&amp;nbsp;</td><td>Store at ambient temperature</td></tr><tr><td>PEG 8000 (50% solution)&amp;nbsp;</td><td>New England Biolabs</td><td>M0437M (component #: B1004SVIAL)</td><td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&amp;mu;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&amp;ordm;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&amp;nbsp;alcohol 25:24:1 pH = 5.2&amp;nbsp;</td><td>Thermo Fisher Scientific</td><td>J62336&amp;nbsp;</td><td></td></tr><tr><td>PrimeScript Buffer (5X)</td><td>TaKaRa</td><td>2680A&amp;nbsp;</td><td></td></tr><tr><td>PrimeScript Reverse Transcriptase</td><td>TaKaRa</td><td>2680A&amp;nbsp;</td><td></td></tr><tr><td>QIAquick&amp;nbsp;Gel Extraction Kit&amp;nbsp;</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&amp;nbsp;(30 U/&amp;mu;L)&amp;nbsp;</td><td>New England Biolabs</td><td>M0264L (component #: M0264LVIAL)</td><td>NEB M0264L contains RecJf (30 U/&amp;mu;L) and NEBuffer 2 (10X); store at -20 &amp;deg;C</td></tr><tr><td>RNase Inhibitor (murine; 40 U/&amp;mu;L)&amp;nbsp;</td><td>New England Biolabs</td><td>M0314L&amp;nbsp;(component #: M0314LVIAL)</td><td>Store at -20 &amp;deg;C</td></tr><tr><td>SeqAMP&amp;nbsp;DNA Polymerase&amp;nbsp;</td><td>TaKaRa</td><td>638509&amp;nbsp;</td><td>TaKaRa 638509 contains SeqAMP&amp;nbsp;DNA Polymerase&amp;nbsp;and SeqAMP&amp;nbsp;PCR Buffer (2X)</td></tr><tr><td>SeqAMP&amp;nbsp;PCR Buffer (2X)&amp;nbsp;</td><td>TaKaRa</td><td>638509&amp;nbsp;</td><td>TaKaRa 638509 contains SeqAMP&amp;nbsp;DNA Polymerase&amp;nbsp;and SeqAMP&amp;nbsp;PCR Buffer (2X)</td></tr><tr><td>Shrimp Alkaline Phosphatase (1 U/&amp;mu;L)&amp;nbsp;</td><td>New England Biolabs</td><td>M0371L&amp;nbsp;(component #: M0371LVIAL)</td><td></td></tr><tr><td>Sodium hydroxide (NaOH)</td><td>Sigma-Aldrich</td><td>&amp;nbsp;S5881</td><td>Prepare a working solution (5 M) and store it at ambient temperature</td></tr><tr><td>T4 DNA Ligase (400 U/&amp;mu;L)&amp;nbsp;</td><td>New England Biolabs</td><td>M0202L&amp;nbsp;(component #: M0202LVIAL)</td><td>NEB M0202L contains T4 DNA Ligase (400 U/&amp;mu;L) and T4 DNA Ligase Reaction Buffer (10X)</td></tr><tr><td>T4 DNA Ligase Reaction Buffer (10X)&amp;nbsp;</td><td>New England Biolabs</td><td>M0202L&amp;nbsp;(component #: B0202SVIAL)</td><td>NEB M0202L contains T4 DNA Ligase (400 U/&amp;mu;L) and T4 DNA Ligase Reaction Buffer (10X)</td></tr><tr><td>T4 RNA Ligase 1 (30 U/&amp;mu;L)&amp;nbsp;</td><td>New England Biolabs</td><td>M0437M (component #: M0437MVIAL)</td><td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&amp;mu;L), T4 RNA Ligase Reaction Buffer (10X), PEG 8000 (1X), and ATP (100 mM)</td></tr><tr><td>T4 RNA Ligase Reaction Buffer (10X)&amp;nbsp;</td><td>New England Biolabs</td><td>M0437M (component #: B0216SVIAL)</td><td>NEB M0437M contains T4 RNA Ligase 1 (30 U/&amp;mu;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.

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

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 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 ...

Universidad San Sebastian

Research

JoVE Journal

Genetics

644 Views.  Massachusetts Institute of Technology. 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.

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

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

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 ...

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 ...

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Quantitative real-time PCR (QPCR) has emerged as an accurate and valuable tool in profiling gene expression levels.  One of its many advantages is a lower detection limit compared to other methods of gene expression profiling while using smaller amounts of input for each assay.  Automated qPCR setup has improved this field by allowing for greater reproducibility.  Its convenient and rapid setup allows for high-throughput experiments, enabling the profiling of many different genes simultaneously in each experiment.  This method along with internal plate controls also reduces experimental variables common to other techniques.  
We recently developed a qPCR assay for profiling of pre-microRNAs  (pre-miRNAs) using a set of 186 primer pairs.  MicroRNAs have emerged as a novel class of small, non-coding RNAs with the ability to regulate many mRNA targets at the post-transcriptional level.  These small RNAs are first transcribed by RNA polymerase II as a primary miRNA (pri-miRNA) transcript, which is then cleaved into the precursor miRNA (pre-miRNA).  Pre-miRNAs are exported to the cytoplasm where Dicer cleaves the hairpin loop to yield mature miRNAs.  Increases in miRNA levels can be observed at both the precursor and mature miRNA levels and profiling of both of these forms can be useful.  There are several commercially available assays for mature miRNAs; however, their high cost may deter researchers from this profiling technique.  Here, we discuss a cost-effective, reliable, SYBR-based qPCR method of profiling pre-miRNAs.  Changes in pre-miRNA levels often reflect mature miRNA changes and can be a useful indicator of mature miRNA expression.  However, simultaneous profiling of both pre-miRNAs and mature miRNAs may be optimal as they can contribute nonredundant information and provide insight into microRNA processing.  Furthermore, the technique described here can be expanded to encompass the profiling of other library sets for specific pathways or pathogens.

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR qPCR Arrays

We will demonstrate the setup and analysis of pre-microRNA 96-well arrays for QPCR using a robot as well as by hand with a Thermo Scientific Matrix multichannel pipette.

Quantitative real-time PCR (QPCR) has emerged as an accurate and valuable tool in profiling gene expression levels.  One of its many advantages is a lower ...

Biology

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3&#8217; and 5&#8217; oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3&#8217; and 5&#8242; ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.

MicroRNAs (miRNAs) are a widely conserved class of regulatory molecules. Here we describe a miRNA cloning method that relies upon two potent ligation steps followed by high-throughput sequencing. Our method permits accurate genome-wide quantitation of miRNAs.

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide ...

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs

Probe-based quantitative PCR (qPCR) is a favoured method for measuring transcript abundance, since it is one of the most sensitive detection methods that provides an accurate and reproducible analysis. Probe-based chemistry offers the least background fluorescence as compared to other (dye-based) chemistries. Presently, there are several platforms available that use probe-based chemistry to quantitate transcript abundance. qPCR in a 96 well plate is the most routinely used method, however only a maximum of 96 samples or miRNAs can be tested in a single run. This is time-consuming and tedious if a large number of samples/miRNAs are to be analyzed. High-throughput probe-based platforms such as microfluidics (e.g. TaqMan Array Card) and nanofluidics arrays (e.g. OpenArray) offer ease to reproducibly and efficiently detect the abundance of multiple microRNAs in a large number of samples in a short time. Here, we demonstrate the experimental setup and protocol for miRNA quantitation from serum or plasma-EDTA samples, using probe-based chemistry and three different platforms (96 well plate, microfluidics and nanofluidics arrays) offering increasing levels of throughput.

Circulating microRNAs have recently emerged as promising and novel biomarkers for various cancers and other diseases. The goal of this article is to discuss three different probe-based real-time PCR platforms and methods that are available to quantify and determine the abundance of circulating microRNAs.

Probe-based quantitative PCR (qPCR) is a favoured method for measuring transcript abundance, since it is one of the most sensitive detection methods that ...

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

The cardiac autonomic nervous system is crucial in controlling cardiac function, such as heart rate and cardiac contractility, and is divided into sympathetic and parasympathetic branches. Normally, there is a balance between these two branches to maintain homeostasis. However, cardiac disease states such as myocardial infarction, heart failure, and hypertension can induce the remodeling of cells involved in cardiac innervation, which is associated with an adverse clinical outcome.
Although there are vast amounts of data for the histological structure and function of the cardiac autonomic nervous system, its molecular biological architecture in health and disease is still enigmatic in many aspects. Novel technologies such as single-cell RNA sequencing (scRNA-seq) hold promise for the genetic characterization of tissues at single-cell resolution. However, the relatively large size of neurons may impede the standardized use of these techniques. Here, this protocol exploits droplet-based single-nucleus RNA sequencing (snRNA-seq), a method to characterize the biological architecture of cardiac sympathetic neurons in health and disease. A stepwise approach is demonstrated to perform snRNA-seq of the bilateral superior cervical (SCG) and stellate ganglia (StG) dissected from adult mice.
This method enables long-term sample preservation, maintaining an adequate RNA quality when samples from multiple individuals/experiments cannot be collected all at once within a short period of time. Barcoding the nuclei with hashtag oligos (HTOs) enables demultiplexing and the trace-back of distinct ganglionic samples post sequencing. Subsequent analyses revealed successful nuclei capture of neuronal, satellite glial, and endothelial cells of the sympathetic ganglia, as validated by snRNA-seq. In summary, this protocol provides a stepwise approach for snRNA-seq of sympathetic extrinsic cardiac ganglia, a method that has the potential for broader application in studies of the innervation of other organs and tissues.

This protocol describes the detailed, low-input sample preparation for single-nucleus sequencing, including the dissection of mouse superior cervical and stellate ganglia, cell dissociation, cryopreservation, nucleus isolation, and hashtag barcoding.

The cardiac autonomic nervous system is crucial in controlling cardiac function, such as heart rate and cardiac contractility, and is divided into ...

Medicine

3' End Sequencing Library Preparation with A-seq2

Studies in the last decade have revealed a complex and dynamic variety of pre-mRNA cleavage and polyadenylation reactions. mRNAs with long 3&#39; untranslated regions (UTRs) are generated in differentiated cells whereas proliferating cells preferentially express transcripts with short 3&#39;UTRs. We describe the A-seq protocol, now at its second version, which was developed to map polyadenylation sites genome-wide and study the regulation of pre-mRNA 3&#39; end processing. Also this current protocol takes advantage of the polyadenylate (poly(A)) tails that are added during the biogenesis of most mammalian mRNAs to enrich for fully processed mRNAs. A DNA adaptor with deoxyuracil at its fourth position allows the precise processing of mRNA 3&#39; end fragments for sequencing. Not including the cell culture and the overnight ligations, the protocol requires about 8 h hands-on time. Along with it, an easy-to-use software package for the analysis of the derived sequencing data is provided. A-seq2 and the associated analysis software provide an efficient and reliable solution to the mapping of pre-mRNA 3&#39; ends in a wide range of conditions, from 106 or fewer cells.

This protocol describes a method for mapping pre-mRNA 3&#39; end processing sites.

Studies in the last decade have revealed a complex and dynamic variety of pre-mRNA cleavage and polyadenylation reactions. mRNAs with long 3&#39; ...

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 ...