This work was supported by the NIH grant GM120209 (GS). The authors thank the OSUCCC Genomics Shared Resources Core for their services (CCC Support Grant NCI P30 CA16058).

<ol>
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The authors have nothing to disclose.

We discuss here some key considerations to successfully perform RIPiT. Foremost, individual IPs must be optimized to achieve highest possible efficiency at each step. The amount of FLAG agarose beads for the input number of cells described here has proven to be robust for a wide range of proteins we have tested. As only a small fraction of partner proteins is co-immunoprecipitated with the FLAG protein, the amount of antibody needed for efficient second IP is usually low (less than 10 &#181;g). Small-scale RIPiT (from on...

Within cells, RNA exists in complex with proteins to form RNA:protein complexes (RNPs). RNPs are assembled around RNA binding proteins (RBPs, those that directly bind RNA) but also comprise of non-RBPs (those that bind RBPs but not RNA), and are often dynamic in nature. RBPs and their cofactors function collectively within RNPs to execute regulatory functions. For example, in the nonsense-mediated mRNA decay (NMD) pathway, the UPF proteins (UPF1, UPF2, and UPF3b) recognize the prematurely terminated ribosome. Each of the UPF proteins can bind to RNA, but it is only when they assemble together that an active NMD complex begins to form. Within this complex, UPF1 is further activated by phosphorylation by a non-RBP SMG1, and such UPF1 activation eventually leads to recruitment of mRNA decay inducing factors1,2. In this example, RBPs require non-RBP cofactors for recruitment and activation of the RNP complex that triggers NMD. Yet another property of RNPs is their compositional heterogeneity. Consider the spliceosome, which exists in distinct E, A, B or C complexes. Different spliceosome complexes have overlapping and distinct proteins3. To study RNP functions, it is important to elucidate which RNAs are bound by an RBP and its associated proteins. Many methods exist to accomplish this, with each approach having its distinct advantages and disadvantages4,5,6,7.
The widely popular methods to identify RBP binding sites &#8212; crosslinking followed by immunoprecipitation (CLIP) and its various variations -&#160;rely on ultraviolet (UV) light to crosslink an RBP to RNA8. However, this is not an effective approach for non-RBPs within RNPs, which do not contact the RNA directly. Here, we describe an alternative approach that is applicable to RBPs and non-RBPs alike, to isolate and identify their RNA binding sites. This approach termed RNA immunoprecipitation in tandem (RIPiT) consists of two immunoprecipitation steps, which help achieve higher specificity as compared to a single purification (Figure 1)9,10. As the individual immunoprecipitation (IP) steps can be carried out at a lower stringency as compared to CLIP, RIPiT does not depend on availability of antibodies that can withstand presence of strong detergents during immunoprecipitation. The most unique advantage of RIPiT is the ability to target two different proteins in two purification steps; this provides a powerful way to enrich a compositionally distinct RNP complex from other similar complexes11.
Small variations to the RIPiT procedure can further enhance RNP enrichment. For instance, some RNA-protein or protein-protein interactions within RNPs are transient and it may be difficult to efficiently purify footprints of such complexes. To stabilize such interactions, RNPs can be crosslinked within cells with formaldehyde prior to cell lysis and RIPiT. For example, we have observed that a weak interaction between the exon junction complex (EJC) core factor, EIF4AIII and the EJC disassembly factor, PYM12&#160;can be stabilized with formaldehyde treatment such that more RNA footprints are enriched (data not shown). Prior to cell harvesting and RIPiT, cells can also be treated with drugs to stabilize or enrich RNPs in a particular state. For example, when studying proteins that are removed from mRNA during translation (e.g., the EJC13, UPF114), treatment with translation inhibitors such as puromycin, cycloheximide or harringtonine can lead to increased occupancy of proteins on RNAs.
The amount of RNA recovered from RIPiT is usually low (0.5-10 pmoles, i.e., 10-250 ng RNA considering an average RNA length of 75 nt). The primary reason for this is that only a small fraction of a given protein is present in complex with other proteins within RNPs (any &#34;free&#34;&#160;protein IP&#39;ed in the first step is lost during the second IP). To generate RNA-Seq libraries from this RNA, we also outline here an adaptation of previously published protocol suitable for such low RNA inputs15,16&#160;(Figure 2), which yields high-throughput sequencing ready samples in 3 days.

<table border="0" cellpadding="0" cellspacing="0" style="width:1167px;" width="1166">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Anti-FLAG Affinity Gel</td>
			<td style="width:112px;">Sigma</td>
			<td style="width:101px;">A2220</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:363px;">ATP, [&#947;-32P]- 3000Ci/mmol 10mCi/ml EasyTide, 250&#181;Ci</td>
			<td style="width:112px;">PerkinElmer</td>
			<td style="width:101px;">BLU502A250UC</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BD Disposable Syringes with Luer-Lok Tips (200)</td>
			<td style="width:112px;">Fisher</td>
			<td>14-823-435</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Betaine 5M</td>
			<td style="width:112px;">Sigma</td>
			<td style="width:101px;">B0300</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">biotin-dATP</td>
			<td style="width:112px;">TriLink</td>
			<td style="width:101px;">N-5002</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">biotin-dCTP</td>
			<td style="width:112px;">Perkin Elmer</td>
			<td style="width:101px;">NEL540001EA</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Branson Sonifier, Model SSE-1</td>
			<td style="width:112px;">Branson</td>
			<td style="width:101px;"></td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">CircLigase I</td>
			<td style="width:112px;">VWR</td>
			<td style="width:101px;">76081-606</td>
			<td style="width:591px;">ssDNA ligase I</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">DMEM, High Glucose</td>
			<td style="width:112px;">ThermoFisher</td>
			<td style="width:101px;">11995-065</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">DNA load buffer NEB</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;"></td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Dynabeads Protein A</td>
			<td style="width:112px;">LifeTech</td>
			<td style="width:101px;">10002D</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Flp-In-T-REx 293 Cell Line</td>
			<td style="width:112px;">ThermoFisher</td>
			<td style="width:101px;">R78007</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:363px;">GeneRuler Low Range DNA Ladder</td>
			<td style="width:112px;">ThermoScientific</td>
			<td style="width:101px;">FERSM1203</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Hygromycin B</td>
			<td style="width:112px;">ThermoFisher</td>
			<td align="right" style="width:101px;">10687010</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Mini-PROTEAN TBE Gel 10 well</td>
			<td style="width:112px;">Bio-Rad</td>
			<td align="right" style="width:101px;">4565013</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Mini-PROTEAN TBE-Urea Gel</td>
			<td style="width:112px;">Bio-Rad</td>
			<td align="right" style="width:101px;">4566033</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">miRCAT-33 adapter 5&#8242;-TGGAATTCTCGGGTGCCAAGGddC-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Mirus transIT-X2 transfection reagent</td>
			<td style="width:112px;">Mirus</td>
			<td style="width:101px;">MIR 6004</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Mth RNA ligase</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;">E2610S</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">PE1.0 5&#8242;-AATGATACGGCGACCACCGAGATCTACACT 
			CTTTCCCTACACGACGCTCTTCCGATC*T-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">PE2.0 5&#8242;-CAAGCAGAAGACGGCATACGAGATCGGTCTC 
			GGCATTCCTGCTGAACCGCTCTTCCGATC*T-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:363px;">Phenol/Chloroform/Isoamyl Alcohol (25:24:1, pH 6.7, 100ml)</td>
			<td style="width:112px;">Fisher</td>
			<td style="width:101px;">BP1752I-100</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Purple Gel Loading Dye (6x)</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;">NEB #7025</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Q5 DNA Polymerase</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;">M0491S/L</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="22">
			<td height="22" style="height:23px;width:363px;">RNase I, E. coli, 1000 units</td>
			<td style="width:112px;">Eppicenter</td>
			<td style="width:101px;">N6901K</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">SPIN-X column</td>
			<td style="width:112px;">Corning</td>
			<td style="width:101px;">CLS8160-24EA</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">Streptavidin beads</td>
			<td style="width:112px;">ThermoFisher</td>
			<td style="width:101px;">60210</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:363px;">Superscript III (SSIII)</td>
			<td style="width:112px;">ThermoScientific</td>
			<td align="right" style="width:101px;">18080044</td>
			<td style="width:591px;">reverse transcriptase enzyme</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">SybrGold</td>
			<td style="width:112px;">ThermoFisher</td>
			<td style="width:101px;">S11494</td>
			<td style="width:591px;">gold nucleic acid gel stain</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:363px;">T4 Polynucleotide Kinase-2500U</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;">M0201L</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:363px;">T4RNL2 Tr. K227Q</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;">M0351S</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Tetracycline</td>
			<td style="width:112px;">Sigma</td>
			<td align="right" style="width:101px;">87128</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:363px;">Thermostable 5&#180; App DNA/RNA Ligase</td>
			<td style="width:112px;">NEB</td>
			<td style="width:101px;">M0319S</td>
			<td style="width:591px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE1 5&#8242;-pGGCACTANNNNNAGATCGGAAGA 
			GCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTC 
			TTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE10 5&#8242;-pGGTGTTCNNNNNAGATCGGAAG 
			AGCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCT 
			CTTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE11 5&#8242;-pGGTAAGTNNNNNAGATCGGAA 
			GAGCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTC 
			TTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE12 5&#8242;-pGGAGATGNNNNNAGATCGGAAGA 
			GCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTC 
			TTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE2 5&#8242;-pGGGTAGCNNNNNAGATCGGAAGAG 
			CGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCT 
			CTTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE35&#8242;-pGGTCGATNNNNNAGATCGGAAG 
			AGCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCT 
			CTTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE4 5&#8242;-pGGCCTCGNNNNNAGATCGGAAGA 
			GCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTC 
			TTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE5 5&#8242;-pGGTGACANNNNNAGATCGGAAGA 
			GCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTC 
			TTCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE6 5&#8242;-pGGTAGACNNNNNAGATCGGAAGAG 
			CGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTCTTC 
			CGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE7 5&#8242;-pGGGCCCTNNNNNAGATCGGAAG 
			AGCGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTCT 
			TCCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE8 5&#8242;-pGGATCGGNNNNNAGATCGGAAGAG 
			CGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTCTT 
			CCGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">TruSeq_SE9 5&#8242;-pGGACTGANNNNNAGATCGGAAGAG 
			CGTCGTGTAGGGAAAGAGTGT-SPACER 18-CTCGGCATTCCTGCTGAACCGCTCTTC 
			CGATCTCCTTGGCACCCGAGAATTCCA-3&#8242;</td>
			<td>Any</td>
			<td></td>
			<td style="width:591px;">this protocol is only compatible with the Illumina sequencing platform</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:363px;">Typhoon 5 Bimolecular Imager</td>
			<td style="width:112px;">GE Healthcare Life Science</td>
			<td align="right" style="width:101px;">29187191</td>
			<td style="width:591px;"></td>
		</tr>
	</tbody>
</table>

identification of footprints of rna:protein complexes via rna immunoprecipitation in tandem followed by sequencing (ripit-seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT employs two purification steps. First, immunoprecipitation of a tagged RNP subunit is followed by mild RNase digestion and subsequent non-denaturing affinity elution. A second immunoprecipitation of another RNP subunit allows for enrichment of a defined complex. Following a denaturing elution of RNAs and proteins, the RNA footprints are converted into high-throughput DNA sequencing libraries. Unlike the more popular ultraviolet (UV) crosslinking followed by immunoprecipitation (CLIP) approach to enrich RBP binding sites, RIPiT is UV-crosslinking independent. Hence RIPiT can be applied to numerous proteins present in the RNA interactome and beyond that are essential to RNA regulation but do not directly contact the RNA or UV-crosslink poorly to RNA. The two purification steps in RIPiT provide an additional advantage of identifying binding sites where a protein of interest acts in partnership with another cofactor. The double purification strategy also serves to enhance signal by limiting background. Here, we provide a step-wise procedure to perform RIPiT and to generate high-throughput sequencing libraries from isolated RNA footprints. We also outline RIPiT&#39;s advantages and applications and discuss some of its limitations.

A successful RIPiT will result in the immunoprecipitation of both proteins of interest and other known interacting proteins, and the absence of non-interacting proteins. As seen in Figure 3A, both Magoh and EIF4AIII were detected in the RIPiT elution, but HNRNPA1 was not (lane 6). In parallel, RNA footprints that have co-purified with the RNP complexes was detected via autoradiography (Figure 3B) or bioanalyzer (<strong class="x...

Watch this Scientific Journal Video about Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq at JoVE.com

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Here, we present a protocol to enrich endogenous RNA binding sites or &#34;footprints&#34;&#160;of RNA:protein (RNP) complexes from mammalian cells. This approach involves two immunoprecipitations of RNP subunits and is therefore dubbed RNA immunoprecipitation in tandem (RIPiT).

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT ...

identification-footprints-rnaprotein-complexes-via-rna

Research

JoVE Journal

Genetics

10.4K Views.  The Ohio State University. Here, we present a protocol to enrich endogenous RNA binding sites or "footprints" of RNA:protein (RNP) complexes from mammalian cells. This approach involves two immunoprecipitations of RNP subunits and is therefore dubbed RNA immunoprecipitation in tandem (RIPiT).

Video: Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

Investigation of RNA Synthesis Using 5-Bromouridine Labelling and Immunoprecipitation

When steady state RNA levels are compared between two conditions, it is not possible to distinguish whether changes are caused by alterations in production or degradation of RNA. This protocol describes a method for measurement of RNA production, using 5-Bromouridine labelling of RNA followed by immunoprecipitation, which enables investigation of RNA synthesized within a short timeframe (e.g., 1 h). The advantage of 5-Bromouridine-labelling and immunoprecipitation over the use of toxic transcriptional inhibitors, such as &#945;-amanitin and actinomycin D, is that there are no or very low effects on cell viability during short-term use. However, because 5-Bromouridine-immunoprecipitation only captures RNA produced within the short labelling time, slowly produced as well as rapidly degraded RNA can be difficult to measure by this method. The 5-Bromouridine-labelled RNA captured by 5-Bromouridine-immunoprecipitation can be analyzed by reverse transcription, quantitative polymerase chain reaction, and next generation sequencing. All types of RNA can be investigated, and the method is not limited to measuring mRNA as is presented in this example.

This method can be used to measure RNA synthesis. 5-Bromouridine is added to cells and incorporated into synthesized RNA. RNA synthesis is measured by RNA extraction immediately after labelling, followed by 5-Bromouridine-targeted immunoprecipitation of labelled RNA and analysis by reverse transcription and quantitative polymerase chain reaction.

When steady state RNA levels are compared between two conditions, it is not possible to distinguish whether changes are caused by alterations in ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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

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

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

Artificial RNA Polymerase II Elongation Complexes for Dissecting Co-transcriptional RNA Processing Events

Eukaryotic mRNA synthesis is a complex biochemical process requiring transcription of a DNA template into a precursor RNA by the multi-subunit enzyme RNA polymerase II and co-transcriptional capping and splicing of the precursor RNA to form the mature mRNA. During mRNA synthesis, the RNA polymerase II elongation complex is a target for regulation by a large collection of transcription factors that control its catalytic activity, as well as the capping, splicing, and 3&#8217;-processing enzymes that create the mature mRNA. Because of the inherent complexity of mRNA synthesis, simpler experimental systems enabling isolation and investigation of its various co-transcriptional stages have great utility.
In this article, we describe one such simple experimental system suitable for investigating co-transcriptional RNA capping. This system relies on defined RNA polymerase II elongation complexes assembled from purified polymerase and artificial transcription bubbles. When immobilized via biotinylated DNA, these RNA polymerase II elongation complexes provide an easily manipulable tool for dissecting co-transcriptional RNA capping and mechanisms by which the elongation complex recruits and regulates capping enzyme during co-transcriptional RNA capping. We anticipate this system could be adapted for studying recruitment and/or assembly of proteins or protein complexes with roles in other stages of mRNA maturation coupled to the RNA polymerase II elongation complex.

Here, we describe the assembly of RNA polymerase II (Pol II) elongation complexes requiring only short synthetic DNA and RNA oligonucleotides and purified Pol II. These complexes are useful for studying mechanisms underlying co-transcriptional processing of transcripts associated with the Pol II elongation complex.

Eukaryotic mRNA synthesis is a complex biochemical process requiring transcription of a DNA template into a precursor RNA by the multi-subunit enzyme RNA ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

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 ...

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to agriculture. However, the Ralstonia model is considerably underexplored in comparison to other models involving bacterial plant pathogens, such as Pseudomonas syringae in Arabidopsis. Research targeted to understanding the interaction between Ralstonia and crop plants is essential to develop sustainable solutions to fight against bacterial wilt disease but is currently hindered by the lack of straightforward experimental assays to characterize the different components of the interaction in native host plants. In this scenario, we have developed a method to perform genetic analysis of Ralstonia infection of tomato, a natural host of Ralstonia. This method is based on Agrobacterium rhizogenes-mediated transformation of tomato roots, followed by Ralstonia soil-drenching inoculation of the resulting plants, containing transformed roots expressing the construct of interest. The versatility of the root transformation assay allows performing either gene overexpression or gene silencing mediated by RNAi. As a proof of concept, we used this method to show that RNAi-mediated silencing of SlCESA6 in tomato roots conferred resistance to Ralstonia. Here, we describe this method in detail, enabling genetic approaches to understand bacterial wilt disease in a relatively short time and with small requirements of equipment and plant growth space.

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Here, we present a versatile method for tomato root transformation followed by inoculation with Ralstonia solanacearum to perform straightforward genetic analysis for the study of bacterial wilt disease.

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to ...

Screening and Identification of RNA Silencing Suppressors from Secreted Effectors of Plant Pathogens

RNA silencing is an evolutionarily conserved, sequence-specific gene regulation mechanism in eukaryotes. Several plant pathogens have evolved proteins with the ability to inhibit the host plant RNA silencing pathway. Unlike virus effector proteins, only several secreted effector proteins have showed the ability to suppress RNA silencing in bacterial, oomycete, and fungal pathogens, and the molecular functions of most effectors remain largely unknown. Here, we describe in detail a slightly modified version of the co-infiltration assay that could serve as a general method for observing RNA silencing and for characterizing effector proteins secreted by plant pathogens. The key steps of the approach are choosing the healthy and fully developed leaves, adjusting the bacteria culture to the appropriate optical density (OD) at 600 nm, and observing green fluorescent protein (GFP) fluorescence at the optimum time on the infiltrated leaves in order to avoid omitting effectors with weak suppression activity. This improved protocol will contribute to rapid, accurate, and extensive screening of RNA silencing suppressors and serve as an excellent starting point for investigating the molecular functions of these proteins.

Here, we present a modified screening method that can be extensively used to quickly screen RNA silencing suppressors in plant pathogens.

RNA silencing is an evolutionarily conserved, sequence-specific gene regulation mechanism in eukaryotes. Several plant pathogens have evolved proteins ...

Transcriptome-Wide Profiling of Protein-RNA Interactions by Cross-Linking and Immunoprecipitation Mediated by FLAG-Biotin Tandem Purification

RNA and RNA-binding proteins (RBPs) control multiple biological processes. The spatial and temporal arrangement of RNAs and RBPs underlies the delicate regulation of these processes. A strategy called CLIP-seq (cross-linking and immunoprecipitation) has been developed to capture endogenous protein-RNA interactions with UV cross-linking followed by immunoprecipitation. Despite the wide use of conventional CLIP-seq method in RBP study, the CLIP method is limited by the availability of high-quality antibodies, potential contaminants from the copurified RBPs, requirement of isotope manipulation, and potential loss of information during a tedious experimental procedure. Here we describe a modified CLIP-seq method called FbioCLIP-seq using the FLAG-biotin tag tandem purification. Through tandem purification and stringent wash conditions, almost all the interacting RNA-binding proteins are removed. Thus, the RNAs interacting indirectly mediated by these copurified RBPs are also reduced. Our FbioCLIP-seq method allows efficient detection of direct protein-bound RNAs without SDS-PAGE and membrane transfer procedures in an isotope-free and protein-specific antibody-free manner.

Here we present a modified CLIP-seq protocol called FbioCLIP-seq with FLAG-biotin tandem purification to determine the RNA targets of RNA-binding proteins (RBPs) in mammalian cells.

RNA and RNA-binding proteins (RBPs) control multiple biological processes. The spatial and temporal arrangement of RNAs and RBPs underlies the delicate ...

Discovering CsgD Regulatory Targets in Salmonella Biofilm Using Chromatin Immunoprecipitation and High-Throughput Sequencing (ChIP-seq)

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique that can be used to discover the regulatory targets of transcription factors, histone modifications, and other DNA-associated proteins. ChIP-seq data can also be used to find differential binding of transcription factors in different environmental conditions or cell types. Initially, ChIP was performed through hybridization on a microarray (ChIP-chip); however, ChIP-seq has become the preferred method through technological advancements, decreasing financial barriers to sequencing, and massive amounts of high-quality data output. Techniques of performing ChIP-seq with bacterial biofilms, a major source of persistent and chronic infections, are described in this protocol. ChIP-seq is performed on Salmonella enterica serovar Typhimurium biofilm and planktonic cells, targeting the master biofilm regulator, CsgD, to determine differential binding in the two cell types. Here, we demonstrate the appropriate amount of biofilm to harvest, normalizing to a planktonic control sample, homogenizing biofilm for cross-linker access, and performing routine ChIP-seq steps to obtain high quality sequencing results.

Discovering CsgD Regulatory Targets in Salmonella Biofilm Using Chromatin Immunoprecipitation and High-Throughput Sequencing ChIP-seq

Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) is a method used to establish interactions between transcription factors and the genomic sequences they control. This protocol outlines techniques for performing ChIP-seq with bacterial biofilms, using Salmonella enterica serovar Typhimurium bacterial biofilm as an example.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique that can be used to discover the regulatory targets of transcription ...