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 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>
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			<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>
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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 ...

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Genetics

629 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

Quantifying Abdominal Pigmentation in Drosophila melanogaster

Pigmentation is a morphologically simple but highly variable trait that often has adaptive significance. It has served extensively as a model for understanding the development and evolution of morphological phenotypes. Abdominal pigmentation in Drosophila melanogaster has been particularly useful, allowing researchers to identify the loci that underlie inter- and intraspecific variations in morphology. Hitherto, however, D. melanogaster abdominal pigmentation has been largely assayed qualitatively, through scoring, rather than quantitatively, which limits the forms of statistical analysis that can be applied to pigmentation data. This work describes a new methodology that allows for the quantification of various aspects of the abdominal pigmentation pattern of adult D. melanogaster. The protocol includes specimen mounting, image capture, data extraction, and analysis. All the software used for image capture and analysis feature macros written for open-source image analysis. The advantage of this approach is the ability to precisely measure pigmentation traits using a methodology that is highly reproducible across different imaging systems. While the technique has been used to measure variation in the tergal pigmentation patterns of adult D. melanogaster, the methodology is flexible and broadly applicable to pigmentation patterns in myriad different organisms.

Quantifying Abdominal Pigmentation in Drosophila melanogaster

This work presents a method to quickly and precisely quantify the abdominal pigmentation of Drosophila melanogaster using digital image analysis. This method streamlines the procedures between phenotype acquisition and data analysis and includes specimen mounting, image acquisition, pixel value extraction, and trait measurement.

Pigmentation is a morphologically simple but highly variable trait that often has adaptive significance. It has served extensively as a model for ...

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 ...

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

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 ...

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near full-length (intact and defective), HIV-1 proviruses. FLIPS allows determination of the genetic composition of integrated HIV-1 within a cell population. Through identifying defects within HIV-1 proviral sequences that arise during reverse transcription, such as large internal deletions, deleterious stop codons/hypermutation, frameshift mutations, and mutations/deletions in cis acting elements required for virion maturation, FLIPS can identify integrated proviruses incapable of replication. The FLIPS assay can be utilized to identify HIV-1 proviruses that lack these defects and are&#160;therefore potentially replication-competent. The FLIPS protocol involves: lysis of HIV-1 infected cells, nested PCR of near full-length HIV-1 proviruses (using primers targeted to the HIV-1 5&#39; and 3&#39; LTR), DNA purification and quantification, library preparation for Next-generation Sequencing (NGS), NGS, de novo assembly of proviral contigs, and a simple process of elimination for identifying replication-competent proviruses. FLIPS provides advantages over traditional methods designed to sequence integrated HIV-1 proviruses, such as single-proviral sequencing. FLIPS amplifies and sequences near full-length proviruses enabling replication competency to be determined, and also uses fewer amplification primers, preventing the consequences of primer mismatches. FLIPS is a useful tool for understanding the genetic landscape of integrated HIV-1 proviruses, especially within the latent reservoir, however, its utilization can extend to any application in which the genetic composition of integrated HIV-1 is required.

Full-length individual proviral sequencing (FLIPS) provides an efficient and high-throughput method for the amplification and sequencing of single, near full-length (intact and defective) HIV-1 proviruses and allows for determination of their potential replication-competency. FLIPS overcomes limitations of previous assays designed to sequence the latent HIV-1 reservoir.

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near ...

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. ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences is desired. We herein provide a detailed in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) protocol to study the RNA structural change in the presence of an experimental drug for spinal muscular atrophy (SMA), survival of motor neuron (SMN)-C2, and in exon 7 of the pre-mRNA of the SMN2 gene. In in vitro SHAPE, an RNA sequence of 140 nucleotides containing SMN2 exon 7 is transcribed by T7 RNA polymerase, folded in the presence of SMN-C2, and subsequently modified by a mild 2&#39;-OH acylation reagent, 2-methylnicotinic acid imidazolide (NAI). This 2&#39;-OH-NAI adduct is further probed by a 32P-labeled primer extension and resolved by polyacrylamide gel electrophoresis (PAGE). Conversely, 2&#39;-OH acylation in in-cell SHAPE takes place in situ with SMN-C2 bound cellular RNA in living cells. The pre-mRNA sequence of exon 7 in the SMN2 gene, along with SHAPE-induced mutations in the primer extension, was then amplified by PCR and subject to next-generation sequencing. Comparing the two methodologies, in vitro SHAPE is a more cost-effective method and does not require computational power to visualize results. However, the in vitro SHAPE-derived RNA model sometimes deviates from the secondary structure in a cellular context, likely due to the loss of all interactions with RNA-binding proteins. In-cell SHAPE does not need a radioactive material workplace and yields a more accurate RNA secondary structure in the cellular context. Furthermore, in-cell SHAPE is usually applicable for a larger range of RNA sequences (~1,000 nucleotides) by utilizing next-generation sequencing, compared to in vitro SHAPE (~200 nucleotides) that usually relies on PAGE analysis. In case of exon 7 in SMN2 pre-mRNA, the in vitro and in-cell SHAPE derived RNA models are similar to each other.

Detailed protocols for both in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) experiments to determine the secondary structure of pre-mRNA sequences of interest in the presence of an RNA-targeting small molecule are presented in this article.

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

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 ...

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Chromosomal aneuploidy, one of the main causes leading to embryonic development arrest, implantation failure, or pregnancy loss, has been well documented in human embryos. Preimplantation genetic testing for aneuploidy (PGT-A) is a genetic test that significantly improves reproductive outcomes by detecting chromosomal abnormalities of embryos. Next-generation sequencing (NGS) provides a high-throughput and cost-effective approach for genetic analysis and has shown clinical applicability in PGT-A. Here, we present a rapid and low-cost semiconductor sequencing-based NGS method for screening of aneuploidy in embryos. The first step of the workflow is whole genome amplification (WGA) of the biopsied embryo specimen, followed by construction of sequencing library, and subsequent sequencing on the semiconductor sequencing system. Generally, for a PGT-A application, 24 samples can be loaded and sequenced on each chip generating 60&#8722;80 million reads at an average read length of 150 base pairs. The method provides a refined protocol for performing template amplification and enrichment of sequencing library, making the PGT-A detection reproducible, high-throughput, cost-efficient, and timesaving. The running time of this semiconductor sequencer is only 2&#8722;4 hours, shortening the turnaround time from receiving samples to issuing reports into 5 days. All these advantages make this assay an ideal method to detect chromosomal aneuploidies from embryos and thus, facilitate its wide application in PGT-A.

Here, we introduce a semiconductor sequencing method for preimplantation genetic testing for aneuploidy (PGT-A) with the advantages of short turnaround time, low cost, and high throughput.

Chromosomal aneuploidy, one of the main causes leading to embryonic development arrest, implantation failure, or pregnancy loss, has been well documented ...

An Assay for Quantifying Protein-RNA Binding in Bacteria

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding of an RNA binding protein (RBP) to the initiation region of the mRNA, which interferes with ribosome binding. In the presented method, we utilize this blocking phenomenon to quantify the binding affinity of RBPs to their cognate and non-cognate binding sites. To do this, we insert a test binding site in the initiation region of a reporter mRNA and induce the expression of the test RBP. In the case of RBP-RNA binding, we observed a sigmoidal repression of the reporter expression as a function of RBP concentration. In the case of no-affinity or very low affinity between binding site and RBP, no significant repression was observed. The method is carried out in live bacterial cells, and does not require expensive or sophisticated machinery. It is useful for quantifying and comparing between the binding affinities of different RBPs that are functional in bacteria to a set of designed binding sites. This method may be inappropriate for binding sites with high structural complexity. This is due to the possibility of repression of ribosomal initiation by complex mRNA structure in the absence of RBP, which would result in lower basal reporter gene expression, and thus less-observable reporter repression upon RBP binding.

In this method, we quantify the binding affinity of RNA binding proteins (RBPs) to cognate and non-cognate binding sites using a simple, live, reporter assay in bacterial cells. The assay is based on repression of a reporter gene.

In the initiation step of protein translation, the ribosome binds to the initiation region of the mRNA. Translation initiation can be blocked by binding ...

AQRNA-seq for Quantifying Small RNAs

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Quantifying Abdominal Pigmentation in Drosophila melanogaster

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

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

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

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Overexpressing Long Noncoding RNAs Using Gene-activating CRISPR

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

An Assay for Quantifying Protein-RNA Binding in Bacteria