RNA editing, particularly A-to-I RNA editing, has been shown to play an essential role in mammalian embryonic development and tissue homeostasis, and is implicated in the pathogenesis of many diseases including skin pigmentation disorder, autoimmune and inflammatory tissue injury, neuron degeneration, and various malignancies. A-to-I RNA editing is carried out by a small group of enzymes, the adenosine deaminase acting on RNAs (ADARs). Only three members of this protein family, ADAR1–3, exist in mammalian cells. ADAR3 is a catalytically null enzyme and the most significant function of ADAR2 was found to be in editing on the neuron receptor GluR-B mRNA. ADAR1, however, has been shown to play more significant roles in biological and pathological conditions. Although there remains much that is not known about how ADAR1 regulates cellular function, recent findings point to regulation of the innate immune response as an important function of ADAR1. Without appropriate RNA editing by ADAR1, endogenous RNA transcripts stimulate cytosolic RNA sensing receptors and therefore activate the IFN-inducing signaling pathways. Overactivation of innate immune pathways can lead to tissue injury and dysfunction. However, obvious gaps in our knowledge persist as to how ADAR1 regulates innate immune responses through RNA editing. Here, we review critical findings from ADAR1 mechanistic studies focusing on its regulatory function in innate immune responses and identify some of the important unanswered questions in the field. Following the pioneering discovery of Dr. Bass and colleagues that the conversion of adenosine to inosine was the basis underlying dsRNA-unwinding, and that this conversion was mediated by an adenosine deaminase ,2, mammaADAR1 was originally thought to be the enzyme responsible for GluR-B mRNA editing ,42 and tAlthough this critical function for ADAR1 was demonstrated in knockout animal models more than a decade ago, the delineation of the mechanism by which it functions turned out to be challenging. In addition to its role in editing mRNA and microRNA precursors ,38,47,49−/− embryos die around 11.5–12 days post coitum (d.p.c.) with substantial cell death in the liver [−/− embryonic liver hematopoietic stem cells revealed that ADAR1 gene deletion was strongly associated with a gene expression signature of type I and type II interferon-stimulated genes (ISGs) [−/− cells. Protein levels of IFN-α and IFN-β were also dramatically increased in the embryonic tissue, although type II IFN-γ was not detectable [ADAR1he liver ,48. A ges (ISGs) . At the s (ISGs) . Interestectable . These f−/− mouse model. In fact, we observed that inducible deletion of ADAR1 in newborn mice resulted in IFN-α and IFN-β gene expression in brain tissue [A link between ADAR1 and type I IFN production in human also came from observations in Aicardi–Goutières syndrome (AGS) , a severn tissue . It is kn tissue , such as−/− embryos could not survive beyond 12.5 d.p.c. [As an IFN-inducible protein, ADAR1 has long been believed to play critical roles in viral infection ,69,70. I5 d.p.c. ,48, but 5 d.p.c. ,83. As e−/− mice contributed to embryonic death. However, a recent study found that deletion of the Ifnar1 gene (encoding the receptor for type I IFN) or STAT 1 did not prevent the lethality of ADAR1 deficiency [−/− embryonic death. This is also consistent with work showing that increased IFN can be tolerated [Type I IFN is known to play critical roles in antiviral infection  and can ficiency . Therefoolerated . The detThe deamination activity of ADAR1 and ADAR2 converts adenosine to inosine in the double-stranded regions of RNA molecules, yielding inosine-containing RNA in cells ,19,20. V−/− embryonic cells. While both RIG-I−/− and MDA5−/− mice were highly susceptible to infection with respective RNA viruses, MDA5−/− mice did not show obvious abnormalities in noninfectious conditions. In contrast, most RIG-I−/− embryos died at embryonic days 12.5 to 14.0; a few mice survived to birth but died within three weeks [−/− and ADAR1−/− embryos occurred at very similar time points. A combination of deletion of both ADAR1 and MDA-5 genes may result in a complicated disruption of cellular function besides RNA sensing. Although RIG-I deletion did not rescue the lethality of ADAR1−/− embryos, it did not rule out the possibility that ADAR1 regulates RIG-I initiated RNA-sensing signaling in certain pathological conditions. In experiments with cell lines infected with virus or hypoxemia treated adult mouse hepatocytes, we observed that downregulation of RIG-I suppressed type I IFN production caused by ADAR1 deficiency [Both RIG-I and MDA5 are cytosolic RNA receptors that detect microbial RNAs and trigger an innate immune response through IFN signaling ,80,81. Hficiency ,95,96. IIdentification of the editing site(s) in potential RNA substrate is the goal of RNA editing studies. Unbiased screening methods, including chemical methods ,98,99, cUnder biological conditions, it appears that the activity of the innate immune system is kept in check through ADAR1 editing of selected RNA transcripts. In such a condition the cytosolic RNA-sensing pathway of the innate immune system does not respond to endogenous cellular RNAs. The silent RNA-sensing pathway might be maintained through two different mechanisms . First, There are many questions that need to be answered for a better understanding of the mechanism by which ADAR1 suppresses the innate immune response through edited RNAs. For example, why does only ADAR1 and not ADAR2 deficiency cause IFN production? Although ADAR1 and ADAR2 show some specificity for RNA substrates, many editing sites are catalyzed by both proteins . It has In summary, the RNA editing enzyme ADAR1 plays a critical role in the regulation of innate immune activation through the suppression of endogenous RNA sensing. ADAR1 deficiency causes sterile inflammation and tissue injury and is implicated in many diseases. The catalytic activity of ADAR1 that converts adenosine to inosine in the dsRNA region of RNA transcripts was shown to be necessary for its inhibitory function in INF production. Specific RNA editing by ADAR1, not ADAR2, suppresses the immune response to endogenous RNA. However, RNA editing efficiency is usually low on most editing sites. How edited RNA suppresses the RNA-sensing pathway is not yet fully understood.


Transcription in bacteria is controlled by multiple molecular mechanisms that precisely regulate gene expression. It has been recently shown that initial RNA synthesis by the bacterial RNA polymerase (RNAP) is interrupted by pauses; however, the pausing determinants and the relationship of pausing with productive and abortive RNA synthesis remain poorly understood. Using single-molecule FRET and biochemical analysis, here we show that the pause encountered by RNAP after the synthesis of a 6-nt RNA (ITC6) renders the promoter escape strongly dependent on the NTP concentration. Mechanistically, the paused ITC6 acts as a checkpoint that directs RNAP to one of three competing pathways: productive transcription, abortive RNA release, or a new unscrunching/scrunching pathway. The cyclic unscrunching/scrunching of the promoter generates a long-lived, RNA-bound paused state; the abortive RNA release and DNA unscrunching are thus not as tightly linked as previously thought. Finally, our new model couples the pausing with the abortive and productive outcomes of initial transcription. RNA synthesis by bacterial RNA polymerase is interrupted by pauses but their role in RNA synthesis is poorly understood. Here the authors use single-molecule FRET and biochemical analysis to show that pausing regulates branching between the abortive and productive outcomes of initial transcription. The regulatory mechanisms are either directly encoded in the transcribed DNA sequence or mediated by protein transcription factors or small-molecule signals. The target of transcription initiation regulators may be the function of RNAP itself, or the accessibility or affinity of promoters for RNAP. Further regulation occurs in the elongation and termination phases of transcription5.Transcription initiation by DNA-dependent RNA polymerase (RNAP) constitutes the first and often decisive step in gene expression in bacteria. To balance the output of transcription with environmental and cellular needs, an extensive set of molecular mechanisms has evolved to regulate the efficiency and specificity of transcription initiationσ70 initiation factor  to form an RNAP holoenzyme7. The holoenzyme employs sequence-specific interactions between the σ70 and the – 35 and – 10 promoter elements 9  to RPO18. Although conformational strain resulting from the DNA scrunching may promote abortive initiation11, multiple other factors such as the presence of the σ3.2 region (which obstructs the entry to the RNA-exit channel22), strong RNAP–promoter interactions23, and the initially transcribed sequence25 also contribute.To perform promoter-specific transcription initiation, the bacterial RNAP core associates with housekeeping nts Fig.  to form O)9 Fig. . During 23. In many σ70-dependent promoters, the rate-limiting step is attributed to the half-life of RPO or the rate of promoter escape. An extensively studied example of an escape-limited promoter is lacUV526, which produces substantial amounts of abortive products; further, transcriptional pausing was identified in the ITC formed on lac promoter after the synthesis of 6-nt RNA, in part due to the clash of the 5′-RNA end with the σ3.2 region28.The step that defines the overall rate of transcription initiation varies between promoters29, and initial RNA synthesis30. Complementing this structural insight with detailed functional analysis is hampered, however, by the multi-step, asynchronous nature of transcription initiation pathways. Single-molecule techniques, which can provide a direct readout for several mechanistic steps and resolve co-existing reaction pathways, are well positioned to overcome the complexity of transcription initiation34.Recent advances in structural characterization of bacterial transcription initiation complexes have created intriguing hypotheses on how specific molecular interactions and conformational changes drive holoenzyme formation, promoter recognition, open complex formationlac promoter, i.e., with consensus − 35 and − 10 elements, and a 17 bp spacer. We present evidence that the ITC6 pause represents a major control point where the ITCs branch to three competing downstream reaction pathways: pause exit by productive transcription; abortive-RNA release; and slow cycling between DNA conformations with different extents of scrunching but without RNA release. The partitioning between these three paths and their kinetics depends on distinct interactions and structural elements. The rate of productive pause exit is synergistically controlled by the initial transcribed sequence and the interaction of the 5′-RNA end with σ3.2 region, whereas perturbing RNAP interactions with the initially transcribed region favors the entry into the scrunching/unscrunching pathway.Here we combine single-molecule and biochemical analysis of initial transcription to explore the mechanistic basis of the pause encountered by ITC6 on a consensus variant of lac promoter for real-time imaging of individual transcription-engaged RNAP–promoter complexes. Earlier studies of this consensus lac promoter derivative identified a strong pause at ITC635. We modified the original promoter design in two ways to allow in-depth biophysical analysis of the rate-limiting ITC6 pause  FRET readouts for three structural states through downstream DNA scrunching10: an unscrunched (US) open complex RPO (EFRET = 0.49 ± 0.003), a partly scrunched (PS) paused complex ITC6 (EFRET = 0.37 ± 0.001), and a fully scrunched (FS) pause-cleared complex at ITC11 (EFRET = 0.80 ± 0.002)  sensor with fluorophore-labeled consensus use Fig. . First, O complexes , the FRET signal showed an almost instant transition from the US to the PS state  a specific sequence motif (a non-template Y+6G+7 in the transcribed DNA strand35) akin to that causing sequence-specific pausing in elongation39. We dissected the contributions of these two elements to the ITC6 pause using our single-molecule FRET assay.Two elements appear to contribute to RNAP pausing at ITC6: (i) the clash of 5′-RNA end with the ion Fig. , which bσ3.2) by initiating transcription either using a synthetic dinucleotide (ApA) or using ATP, which adds a 5′-triphosphate tail to the 5′-RNA end. To evaluate the effect of the pause sequence motif on the detailed dynamics of initial transcription, we replaced the sequence T+6G+7 (on non-template DNA) with G+6T+7, creating a “ΔP promoter” .To explore the steric-clash hypothesis, we modified the 5′-RNA end of the nascent transcript (and thus its interaction with tITC6) by focusing on the subpopulation of molecules displaying the US→PS→FS scrunching sequence  promoter compared with the ΔP promoter using ApA  for the WT promoter with either ApA or ATP as the starting substrate, or for the ΔP promoter with ApA as the starting substrate  to exit the ITC6 pause on the first attempt with a descriptive model similar to a binding isotherm , whereas ATP-initiated complexes had a lower KNTP compared with ApA-initiated ones . The probability Pmax,esc was relatively constant, with 79 ± 5% of the molecules reaching the FS FRET level on the first attempt at saturating NTP concentration. These results suggest that ITCs can exit a weak ITC6 pause (ΔP promoter) efficiently even at low NTP concentration, while overcoming a strong ITC6 pause (WT promoter + ApA at the 5′-RNA end) requires higher NTP concentration , indicating that T+6G+7 motif enforces pausing at ITC6  from the ITC6 pause  complexes managed to escape the pause on the first attempt  of RNAPmpt Fig. . In bulkmpt Fig. 35.σ region 2 and the – 10 promoter element, we replaced the consensus – 7 thymine in the non-template DNA by an adenine  and US (ΔtUS) states  to exit a state with rate k1  probabilities of PS and US states, remained fairly constant in all used NTP concentrations  at higher NTP concentrations  ~ 0.6. Notably, these values were also independent of the NTP subset used , the nature of the RNA 5′-end, or any of the tested RNAP-promoter variations  = 0.57 ± 0.05  = 0.79 ± 0.07 for US, Fig. Analysis of the complexes pulsed with ITC11 NTPs identified two types of unscrunching/scrunching molecules: the first cycled between US and PS FRET levels only Fig. , whereas.05 Fig. . The US/n and dtn + 1), independently of the scrunching state they originate from , pulled down, washed, and immersed into NTP-free reaction buffer; beads and supernatant were then analyzed at specified times to obtain the time-dependent profile of retained and released RNAs . We observed that the 6-nt RNA became converted quantitatively to 7-nt RNA  and 20–30% of ITCs that retain 6–7-nt RNA products and catalytic competence for tens of min after NTP depletion. These results clearly show that the nascent RNA can be stably trapped within the cyclically unscrunching/scrunching RP complex, until being eventually elongated.lac promoter. We specifically examined the role of the σ3.2 region, the nature of pausing, and pausing-related conformational changes such as scrunching/unscrunching in the presence and absence of RNA release.In this study, we employed a refined, high contrast single-molecule FRET assay to quantitatively dissect the reaction pathway and kinetics of the ITCs on the consensus σ3.2 region has been described as a good candidate for causing pausing, and structural, biochemical and single-molecule biophysical studies have confirmed that by occluding the RNA-exit channel of RNAP the σ3.2 region forms a barrier for the elongation of the nascent transcript past 5–6 nt27 . Our new finding that the triphosphate moiety at the 5′-RNA end, which specifically interacts with the σ3.220, both shortens the half-life of ITC6 pause and increases the probability of productive pause exit further suggests that σ3.2 is a major pause determinant in initial transcription. We also demonstrated that substitution of the σ3.2 residue F522, which contacts template DNA upstream of the active site and represents a barrier for initial RNA synthesis29, decreases the probability of successful RNA extension beyond the PS state. Thus, competition between the RNA 5′-end and residues from the σ3.2 region is required for efficient promoter escape, likely by promoting σ3.2 extrusion.The 27 Figs.  and 6. Cσ3.2 conformations present in different ITC6 complexes. Based on the structural considerations20 and the stage of initial transcription , the clash between σ3.2 and RNA 5′-end may hamper the movement of the template DNA and/or RNA to the post-translocated register, and therefore stabilize the pre-translocated state35. We provide an additional evidence in favor of this hypothesis by showing that βD446A RNAP (which de-stabilizes the post-translocated37) displayed twofold decreased ITC6 pause exit rate. However, because the pause exit rate did not strongly depend on the NTP concentration, the pause is not directly controlled by the thermodynamic equilibrium between the pre- and post-translocated states of ITC6. By similar reasoning, the pause is also not controlled by the catalytic rate of post-translocated ITC6. We thus postulate that the pause-controlling step is kinetic and involves relatively slow repositioning of the σ3.2 tip in a way that the barrier to forward translocation is removed. The highest observed pause exit rate on the promoter variant lacking the consensus pause motif . Importantly, several studies of pausing during transcription elongation have shown the predisposition of the pre-translocated RNAP to isomerize into a catalytically inactive off-pathway state, known as the elemental pause45. The σ3.2-dependent translocation barrier encountered during initial transcription may thus act, by accumulating the pre-translocated ITC6, to increase the probability to isomerize into an elemental pause-like state  and permissive (non-pausing RPs) 38, also affects pausing during transcription initiation (ref. 35 and this study). According to our current data the pause exit rate during initiation (~ 0.3 s−1) is similar to that during elongation (~ 0.5 s−1)38. The substitution of the motif increased both the pause exit rate and the probability to exit the pause. In contrast, substitution D446A in the CRE-pocket, which was previously shown to increase consensus pausing37, also impaired RNA extension in the ITC6. Overall, it appears that the first events leading to a pause during initiation and elongation phases of transcription are similar: an energetic (transcribed sequence in elongation) or physical  barrier to translocation delays RNAP in the pre-translocated register35 from where the protein can, with sequence-dependent efficiency, branch-off to a catalytically inactive elemental pause state /G] motif, first identified in elongation consensus pauses28 (see Supplementary Discussion). The probability to enter the unscrunching/scrunching pathway inversely correlated with NTP concentration  interaction with the production of longer abortive RNAs, while producing a higher yield of promoter escape.Although the entry of ITC6 into the elemental pause was nearly obligatory  that rapidly (within 10 sec) synthesized long RNA (ITC > 10) and a second population that was stalled in early transcription (ITC < 10), and that released RNA slowly, similarly to moribund complexes48. We propose that these two populations, i.e., the population producing quickly long RNAs and the moribund complexes, are consistent with the two populations we described here, i.e., the RP complexes that exited the ITC6 pause on the first attempt  were sonicated for 30 min in a 2% (V/V) solution of Hellmanex III /deionized water. After being thoroughly rinsed with deionized water, the coverslips were dried, disposed into a plasma cleaner , and exposed to a nitrogen plasma for 30 min. The coverslips are subsequently immerged into a 1% (V/V) solution of Vectabond /acetone for 10 min. The coverslips were then rinsed in deionized water and dried with a stream of nitrogen gas. After disposing a silicone gasket  on each coverslip, each well was filled with 20 µl of a 100 mg/ml solution of methoxy-PEG (5 kDa)-SVA/ biotin-PEG (5 kDa)-SC (2.5% (w/w)  in 50 mM MOPS-KOH buffer, pH 7.5 The wells were incubated for ~ 1.5 h and thoroughly rinsed with a 1 × phosphate-buffered saline  solution. The coverslips were stored at 4 °C up to 2 weeks before use.2, 1 mM dithiothreitol (DTT), 1 mM cysteamine hydrochloride, 5% glycerol (V/V), 0.5 g/l bovine serum albumin) and subsequently rinsed with reaction buffer. After adjusting the coverslips on the microscope, 100 pM of His-tagged protein–DNA complex, e.g., RPo, was incubated in the observed well until the desired density of molecules on the coverslips surface was reached, followed by one-step rinsing with reaction buffer.The pegylated coverslips were incubated for ~ 10 min with a solution 0.5 mg/ml of Neutravidin  in 0.5 × PBS and subsequently rinsed with 1 × PBS. Preceding observation on the microscope, the coverslips were incubated for ten min with a 3% (V/V) solution of Penta•His biotin conjugate antibody  in reaction buffer , 100 mM potassium glutamate, 10 mM MgCl54. The expression and purification of the WT σ70 have been previously described in ref. 21.The expression and purification of the core bacterial RNAP have previously been described in ref. σ70 in 20 mM Tris-HCl pH 7.9, 150 mM NaCl, 0.1 mM EDTA, 50% (V/V) Glycerol, and 0.1 mM DTT, and incubated at 30 °C for 30 min. The resulting holo-complex was stored at – 20 °C.Core RNAP (0.5 µM) was mixed with 0.6 µM of Holo-complex (5 nM) was mixed with 2.5 nM of DNA promoter in reaction buffer and incubated for 10 min at 37 °C to form the RPo complex.The DNA constructs preparation is described in detail in the Supplementary Methods.55. Shortly, the 532 nm and the 642 nm wavelength laser beams  were focused in the back focal plane of an oil immersion objective  and illuminate alternatively the field of view, i.e., ALEX mode56. The TIRF-reflected beams were directed toward a position sensor to control the objective focal plane distance to the sample at a fixed position . The photons resulting from the de-excitation of the dyes molecules, i.e., fluorescence, were separated from the excitation laser beams with a dichroic mirror and spectrally splitted in two channels, e.g., donor and acceptor that are imaged on the same electron-modifying charge-coupled device camera . For 100 ms ALEX illumination, i.e., 200 ms frame time acquisition, the laser power measured preceding the dichroic mirror is ~ 0.4 mW for donor excitation laser and ~ 0.09 mW for the acceptor excitation laser. For 40 ms ALEX illumination , i.e., 80 ms frame time, the laser power measured preceding the dichroic mirror is ~ 1 mW for donor excitation laser and ~ 0.25 mW for the acceptor excitation laser.The single-molecule TIRF microscope for FRET experiments has been previously described in ref. 58.The imaging buffer contained the reaction buffer completed with 1 mM Trolox, 1 mM COT, 1% (w/V) glucose, 0.4 µg/ml of catalase, and 1 mg/ml of glucose oxidase (Sigma Aldrich). The catalase and the glucose oxidase were pre-mixed together in a solution of 50 mM KCl and 50 mM Tris-OAc buffer pH 7.3 at 100 × concentrationThe data were acquired after immobilization of the RPo complex to the surface. After ~ 200 frames (~ 20 s), the imaging buffer is spiked with a 12.5 × NTP solution and the reaction is observed for the remaining ~ 5800 frames .O was incubated with NTPs in the reaction buffer for 10 s before the reaction buffer was exchanged twice and finally replaced with imaging buffer, followed by the start of the acquisition. The buffer exchange procedure takes ~40 sec to be completed before the start of the acquisition.For the post-RNA synthesis rinsing experiments, the RPAll single-molecule FRET experiments were performed at 22 °C.59 to extract the intensities of co-localized donor and acceptor, i.e., FRET-pair. The following parameters from Twotone-ALEX were used to select only the FRET pairs formed by a single ATTO647N acceptor dye and a single Cy3b donor dye: channel filter as DexDem&&AexAem&&DexAem , a width limit between the donor and the acceptor between 1 and 2 pixels, a nearest-neighbor limit of 6 pixels, and a maximal ellipticity of 0.6 (ellipticity is defined as the ratio of the minor and the major axis of the ellipse). The traces extracted from the Twotone-ALEX analysis were then sorted to remove all the traces that displayed extensive blinking or multisteps photobleaching, i.e., that contain more than one donor or acceptor dye in the same diffraction limited intensity spot.FRET pair localization and detection: The movies recorded on the camera were offline analyzed using the home-built Matlab routine Twotone-ALEXEFRET for initial transcribing complexes in the presence of different subsets of nucleotides that allowed maximal transcript lengths of 6, 7, or 11 nucleotides, and compared their FRET profiles with RPO. These experiments allowed unambiguous assignment of the RPO and ITC11 FRET levels (EFRET) as ~ 0.5 and ~ 0.76, respectively  accumulates in significant amounts in conditions that allow continuous transcription past the 6–7-mer RNA and that ITCs do not pause again until reaching the position + 12 of the DNA template used here27.We calibrated the FRET sensor by measuring ipt Fig.  is consiIDA and IDD being respectively the intensity of the acceptor and of the donor upon donor excitation62. The traces were analyzed through a modified version of the hidden Markov model ebFRET software from ref. 63 (the modified code is available from the corresponding authors on reasonable request), where only steps longer than two frames and separated from the subsequent step by more than twice the Allan deviation estimated at five frames were conserved64 to be assembled into dwell time. The first dwell time, i.e., preceding NTP addition, and the last dwell time, i.e., preceding photobleaching or transition to FS FRET state, of each trace were removed from the dwell time distribution.The FRET efficiency dynamics for each FRET-pair was calculated with the standard formula 65. Shortly, the distribution of m exponentials:kn and pn are the characteristic rate of the mth exponential and its probability, respectively. The minimum number of exponential to fit the distributions was determined for each distribution by using the Bayes Schwarz Information Criterion66. We calculate the maximum likelihood estimate of the parameters (MLE)67 by maximizing:N is the number of collected dwell times 68. The ebFRET software63 was also used to extract the peak positions of each FRET level, subsequently fitted with a Gaussian function, with the peak center and the SD as free parameters (Supplementary Figs. A detailed analysis of the dwell time distributions is provided in ref. The data sets generated and analyzed during the current study are available from the corresponding author on reasonable request.Supplementary Information


From a total of 78 samples evaluated, 30 were positive by qRT-PCR and ISH-RNA, including vesicular lesions of affected sows, ulcerative lesions in the tongue of piglets and various other tissues with no evidence of histological lesions. Nineteen samples were negative for SVV by qRT-PCR and ISH-RNA. The Ct values of the qRT-PCR from ISH-RNA positive tissues varied from 12.0 to 32.6 . The ISH-RNA technique is an important tool in diagnosing and investigating the pathogenesis of SVV and other emerging pathogens.Seneca Valley virus (SVV) is the causative agent of an emerging vesicular disease in swine, which is clinically indistinguishable from other vesicular diseases such as foot-and-mouth disease. In addition, SVV has been associated with neonatal mortality in piglets. While a commercial SVV qRT-PCR is available, commercial antibodies are lacking to diagnose SVV infections by immunohistochemistry (IHC). Thus, a novel  Seneca Valley virus, which belongs to the Senecavirus A species, has been isolated from pigs since 1988 and was reported as picornavirus-like particle . Historiin situ hybridization (ISH) are the main methodologies used for this purpose. While IHC identifies a specific protein associated with the pathogen, ISH detects the pathogen’s nucleic acid within the tissue sections. Immunohistochemistry is limited on availability of species-specific antibodies and performance variation among suppliers and batches  as enumerated in stratum spinosum of the epidermis (All the cutaneous tissues from the vesicular lesions (n = 6) on the snout of SVV affected sows were positive by ISH-RNA. SVV mRNA was more frequently observed in the pidermis . Interespidermis . Tissue Splenic samples with qRT-PCR positive results from affected piglets  were ISH-RNA positive either clustered in small foci or diffusely distributed throughout the tissue section but they did not show any evidence of histological lesions . Lymph nMyocardial samples had low number of positive ISH-RNA results (n = 2/7), but the distribution of SVV was characterized by small red dots diffusely distributed in the cytoplasm of cardiomyocytes. In the small and large intestines, virus was detected mainly in enterocytes’ cytoplasm . In the in situ hybridization technique identified replication of SVV in tissue fragments of the tongue, tonsil, lung, heart, spleen, liver, small intestine, and colon, which has not been previously reported. In addition, this novel ISH-RNA platform is helpful in the diagnosis of emerging pathogens in the absence of a commercial antibodies for IHC. This ISH-RNA technique has been applied in research and diagnostics in human medicine [The novel RNA based medicine ,14 whilemedicine –19 in thClinical signs of SVV are indistinguishable from other important vesicular diseases, such as foot-and-mouth disease, which is considered by the World Organization for Animal Health (OIE) as one of the most contagious disease of animal, with the potential to result in devastating economic losses. Consequently, SVV outbreaks would have significant impacts on the US pork industry, such as temporary closures of pork processing plants, resource re-allocation for disease investigations, increased pre-weaning mortality and increased sow culling rates. Based on the recent US outbreak, the SVV ISH-RNA played a critical role as a rapid diagnostic response by supporting the SVV qRT-PCR.The ISH-RNA technique for SVV established the association between histological lesions and the presence of the virus, with minimal background and non-specific staining.Three lymph node samples  were analyzed by ISH-RNA. The samples with relatively high Ct value  was ISH-RNA positive, while the sample with low Ct values  was ISH-RNA negative. We speculate that qRT-PCR detected virus being processed by professional antigen-presenting cells, but not replicating actively virus particles. Conversely, the two lymph nodes showing positive staining detected by ISH-RNA would represent actively replicating SVV characterized by expression of virus mRNA.As expected, SVV ISH-RNA positive cells were observed in all snout vesicles . Interestingly, positive SVV cells were found in tissues of tongue from piglets showing erosive glossitis while negative SVV results were obtained in tissue sections of tongue without lesions, similarly to other reports  that useThe lack of histological lesions associated with high levels of SVV in tissues from affected piglets was a critical feature, which needs further clarification. The pathogenesis associated with mortality in neonatal piglets has not been reported to date. Vesicular disease was recently demonstrated in 9- and 15-week old pigs experimentally infected with SVV ,22. A reThe present study detected SVV using ISH in various tissues in affected piglets. Our findings corroborate with previous study of high SVV titer detected by qRT-PCR ,6. DiarrFurthermore, although we have observed SVV positive signals in lungs (5/8), no histological lesions were found. Opposite situation has been reported in piglets from different regions in Brazil . Althougin situ hybridization. The ISH-RNA technique developed for SVV detection greatly removed this constraint, based on the lack of non-specific staining in sections negative by PCR, and there was no difficulty in interpretation of specific SVV staining in positive samples.False positive staining was considered an important limitation of classical SVV-ISH-RNA showed high specificity, but the sensitivity was not equivalent to the qRT-PCR assay. Nevertheless, while qRT-PCR detects genomic RNA from viable or non-viable virus particles in lysed homogenate of fresh tissues, ISH allows the identification of metabolically active virus through detection of viral mRNA within morphologically preserved tissues sections. The combination of qRT-PCR and ISH will result in a high sensitive and specific approach to diagnosis SVV in pig samples with a morphological context.in situ hybridization platform allows the association of SVV and histological lesions, which complements qRT-PCR as a diagnostic technique to investigate potential emerging pathogens. The ISH-RNA offers a simple and effective early diagnostic technique for SVV in sows with vesicular disease and in acute neonatal mortality cases. The ISH-RNA technique is also a potential methodology to improve the knowledge on the pathogenesis of SVV, especially in ETNL-affected animals.In conclusion, an ISH-RNA technique for SVV was established in this study and demonstrated high level of specificity. This novel S1 Table(DOCX)Click here for additional data file.


The spatial organization of RNA within cells is a crucial factor influencing a wide range of biological functions throughout all kingdoms of life. However, a general understanding of RNA localization has been hindered by a lack of simple, high-throughput methods for mapping the transcriptomes of subcellular compartments. Here, we develop such a method, termed APEX-RIP, which combines peroxidase-catalyzed, spatially restricted in situ protein biotinylation with RNA-protein chemical crosslinking. We demonstrate that, using a single protocol, APEX-RIP can isolate RNAs from a variety of subcellular compartments, including the mitochondrial matrix, nucleus, cytosol, and endoplasmic reticulum (ER), with specificity and sensitivity that rival or exceed those of conventional approaches. We further identify candidate RNAs localized to mitochondria-ER junctions and nuclear lamina, two compartments that are recalcitrant to classical biochemical purification. Since APEX-RIP is simple, versatile, and does not require special instrumentation, we envision its broad application in a variety of biological contexts. Drosophila and Xenopus, and signaling at mammalian neuronal synapses  and RNA reporter systems—are powerful tools for elucidating the positions of a small number of target RNAs at low-to-moderate throughput . Alternaneighborhoods, with high spatial specificity, and within cellular structures that can be difficult to purify biochemically.Despite this progress, some technological gaps exist among current methods for studying RNA localization. Imaging approaches are of limited throughput, and may require specialized reagents, constructs, or microscopes that are only accessible to a handful of laboratories . The effHere we introduce such a technology—termed APEX-RIP—that enables unbiased discovery of endogenous RNAs in specific cellular locales. APEX-RIP merges two existing technologies: APEX (engineered ascorbate peroxidase)-catalyzed proximity biotinylation of endogenous proteins , and RNA2O2)—to live cells, APEX catalyzes the formation of biotin-phenoxyl radicals that then diffuse outward and covalently biotinylate nearby endogenous proteins. More distal proteins are not significantly labeled because the biotin-phenoxyl radical has a half-life of less than one millisecond  as more incisive negative controls. We furthermore employed HEK293T cells that stably expressed mitochondrially-localized APEX2  or in the cytosol  . The spetop), while endoplasmic reticulum-proximal mRNAs (middle). As a further test, we directly compared the enrichments from APEX2-NLS and APEX2-NES to one another, confirming that they had effectively parsed known nuclear- and cytosol-localized RNAs into the expected compartments (bottom and C). We used Receiver Operating Characteristic (ROC) analysis to obtain final transcript lists of 5740 nuclear RNAs and 5367 cytosolic RNAs, with observed contamination frequencies  of <1.6% and <1.5%, respectively —appeared preferentially nuclear. Many of these species have been proposed to play a role in dampening gene expression noise (bottom left); these include the known cytoplasmic lncRNA SNHG5, a modulator of staufen-mediated decay that influences colorectal tumor growth (bottom right). We were concerned that this atypical RNA localization might be artifactual, since diffusion of proteins between subcellular compartments during a ten-minute formaldehyde treatment might allow aberrant RNA-protein interactions to be chemically crosslinked. To rule out this possibility, we monitored the localization of APEX-labeled species during the course of a BP-first/crosslink-second NLS-APEX2-RIP experiment, and failed to observe significant migration of biotinylated proteins from the nucleus into the cytosol .Our APEX-RIP nuclear and cytosolic RNA lists provide an opportunity for a head-to-head comparison with the traditional Fractionation-Seq method for mapping subcellular RNA localization. ROC analysis of HEK 293T fractionation-Seq data obtained using library synthesis and sequencing protocols very similar to our own . We conclude that APEX-RIP rivals or outperforms Fractionation-Seq in terms of both specificity and coverage, for analysis of endogenous RNA subcellular localization.To compare the coverage, or sensitivity, of each method , we examined the enrichment in each dataset of lncRNAs, which are thought to be predominantly nuclear . We asseHaving established that APEX-RIP can enrich RNAs in membrane-enclosed cellular compartments, we next sought to address whether the technique could successfully capture the transcriptomes of ‘open’ subcellular regions. Previous proteomic work has shown that APEX tagging exhibits sufficient spatial specificity for such open compartments, since this technology has produced highly specific proteomic maps of, for example, the mammalian neuronal synaptic cleft , outer mAs a test case for the generality of APEX-RIP at such open compartments, we selected the Endoplasmic Reticulum (ER). The ER is an appealing target for several reasons. First, it is host to a known set of characteristic RNAs that we can use as positive controls—the so-called ‘secretome’—which comprises mRNAs encoding secreted, glycosylated, and/or transmembrane proteins that are translated on the rough ER. Second, the ER provides the opportunity to compare the efficacy of APEX-RIP to alternative approaches, since RNAs in this subcellular locale have been previously characterized both by Fractionation-Seq, and by a newer methodology termed proximity-dependent ribosome profiling . This lasee alsoSince it was initially unclear which face of the ER membrane  would be most amenable to the APEX-RIP method, we generated fusion constructs that localized the peroxidase catalytic center to each . ERM-APEp-value = 0.00009), parallel experiments in ERM-APEX2 cells exhibited only modest, qualitative enrichment of target species . Indeed, results from ERM-APEX2 cells were nearly indistinguishable from those acquired from APEX2-NES control cells . This is surprising since proteomic experiments in HEK 293T cells expressing the identical ERM-APEX2 construct yielded highly specific enrichment of ER-localized proteins  were obtained, authenticated by STR profiling from ATCC, and cultured in growth media consisting of 1:1 DMEM:MEM , supplemented with 10% Fetal Bovine Serum (FBS), 50 units/mL penicillin, and 50 μg/mL streptomycin, at 37°C and under 5% CO2. Cells were discarded at 25 passages. Cell lines were not tested for Mycoplasma contamination. For fluorescence microscopy imaging experiments (2.Human embryonic kidney (HEK) 293 T cells .Fluorescence confocal microscopy was performed with a Zeiss AxioObserver microscope with 63 × oil immersion objectives, outfitted with a Yokogawa spinning disk confocal head, a Cascade II:512 camera, a Quad-band notch dichroic mirror (405/488/568/647), 405 (diode), 491 (DPSS), 561 (DPSS) and 640 nm (diode) lasers . Alexa Fluor488 , Alexa Fluor 568 , and AlexaFluor647  and differential interference contrast (DIC) images were acquired through a 63x oil-immersion lens. Acquisition times ranged from 100 to 1,000 ms. For image acquisition and analysis, we used the SlideBook 6.0 software . At the indicated time points  FBS, per well. At ~60% confluency, cells were transfected with a GFP expression plasmid  using polyethyleneimine (PEI). Briefly, 150 ng plasmid was diluted into a 1:1 MEM:DMEM solution and incubated with 1 uL of PEI in a final reaction volume of 20 μL, for 15 min at room temperature, and added dropwise to cells. After 16 hr, cells were labeled and crosslinked according to BP–quench–then–crosslinking protocol , the cells were harvested by scraping, pelleted by centrifugation at 3,000 × g for 10 min, and stored at –80°C prior to use. Thawed pellets were lysed by gentle pipetting in RIPA lysis buffer , supplemented with 1 × protease cocktail , 1 mM PMSF (phenylmethylsulfonyl fluoride), for 5 min at 4°C. Lysates were then clarified by centrifugation at 15,000 × g for 10 min at 4°C before separation on homemade 8% SDS-PAGE gels. Gels were transferred to nitrocellulose membranes, stained by Ponceau S (0.1% (w/v) Ponceau S, 5% (v/v) acetic acid, in water) for 10 min at room temperature, and imaged. The blots were then blocked with blocking buffer (3% (w/v) BSA, 0.1% (v/v) Tween-20 in Tris-buffered saline) for 1 hr at room temperature, and incubated with primary antibodies in blocking buffer for 1 hr more. The dilutions of the antibodies are as followed: Mouse anti-V5 antibody (ThermoFisher Scientific RRID: AB_2556564) 1:1000 dilution and Mouse anti-FLAG antibody (ThermoFisher Scientific) 1:800 dilution. Blots were rinsed four times for 5 min with wash buffer , and then immersed in blocking buffer supplemented with Goat anti-Mouse IgG H + L HRP Conjugate , for 1 hr at room temperature. Blots were rinsed four times for 5 min with wash buffer, and developed with the Clarity reagent (Bio-Rad Laboratories) and imaged on an Alpha Innotech gel imaging system. Processing of streptavidin blots was similar. Following Ponceau imaging, blots were blocked in blocking buffer for 30 min at room temperature, immersed in blocking buffer supplemented with streptavidin-HRP  at room temperature for 15 min, rinsed with blocking buffer five times for 5 min each, developed and imaged using the Clarity reagent and an Alpha Innotech gel imaging system.For blotting experiments , stable The data in these experiments  were alst, calculated per well) and efficiencies , were calculated from ‘clipped’ data, using Real time qPCR Miner , were calculated using the first derivative approximation:The uncertainties of other functions, Sample sizes were determined in accordance with standard practices used in similar experiments in the literature; no sample-size estimates were performed to ensure adequate power to detect a prespecified effect size. Experiments were neither randomized nor blinded to experimental conditions. Each samples contained four technical replicates and no samples were excluded from analysis. Significance testing: Student’s two-tailed t-test.2O2 was added to a final concentration of 1 mM. After 1 min, media was aspirated, and the APEX labeling reaction was quenched by addition of 2 mL azide-free quenching solution , and further incubation at room temperature for 1 min. Thereafter, the liquid phase was aspirated, and cells were crosslinked by addition of 5 mL crosslink-quench solution (0.1% (v/v) formaldehyde, 10 mM sodium ascorbate, and 5 mM Trolox, in PBS). After 1 min, media were aspirated, and cells were again incubated in 5 mL fresh crosslink-quench solution, for 9 min at room temperature, with gentle agitation. The crosslinking reaction was terminated by addition of glycine  to a final concentration of 125 mM, and gentle agitation for 5 min at room temperature. Cells were washed twice with 2 mL room-temperature PBS, harvested by scraping, pelleted by centrifugation, and either processed immediately or flash frozen in liquid nitrogen and stored at –80°C before further analysis.Stable-expression HEK 293T cells were grown to 90% confluency in 6-well plates, as described above. Cells were incubated in fresh growth media supplemented with 500 μM Biotin Phenol (BP) ; also avg for 5 min at 4°C, moved to fresh tubes and each diluted with 1 mL Native lysis buffer , supplemented with ascorbate and trolox. For each sample, 20% was removed as ‘input;’ to the remainder was added 50 μL of streptavidin-coated magnetic bead slurry (ThermoFisher Scientific ) that had been equilibrated by two washes in 1:1 RIPA:NLB. Samples were incubated for 2 hr at 4°C with end-over-end agitation. Beads were subsequently washed with the following series of buffers : (1) RIPA buffer, supplemented with trolox and ascorbate, (2) RIPA buffer without radical quenchers, (3) high salt buffer , (4) urea buffer , (5) RIPA Buffer, (6) 1:1 RIPA: NLB, (7) NLB, and (8) TE .Unless otherwise noted, all buffers used during RNA isolation were supplemented to 0.1 U/ μL RNaseOUT (ThermoFisher Scientific), 1 × EDTA free proteinase inhibitor cocktail (ThermoFisher Scientific) and 0.5 mM DTT, final. Labeled, crosslinked cell pellets were thawed on ice (when necessary), and lysed by incubation in 1 mL ice-cold RIPA buffer, supplemented with 10 mM ascorbate and 5 mM Trolox, for 5 min at 4°C with end-over-end agitation. Samples were then sheared as described previously  using a 2O. Thereafter, contaminating DNA was removed by digestion with 5 U RQ1 RNase-free DNase I  in 100 μL of the manufacturer’s supplied buffer  at 37°C for 30 min. Purified RNAs were again cleaned up using Agencourt RNAClean XP magnetic beads, as above, and eluted into 30 μL H2O. The concentration and integrity of all samples was measured using an Agilent 2100 Bioanalyzer, following the ‘RNA Nano’ or ‘RNA Pico’ protocols, where appropriate. Samples were not heat-cooled prior to loading Bioanalyzer chips.Enriched RNAs were released from the beads by proteolysis in 100 μL of Elution Buffer  and 4 U RNaseOUT) at 42°C for 1 hr, followed by 55°C for 1 hr, as previously described . Eluted bottom). Briefly, cells were grown and APEX-labeled as described above. Following the 1 min incubation in H2O2, cells were immediately treated with 5 mL crosslink-quench solution for one minute at room temperature, to simultaneously quench the APEX2 BP labeling reaction and initiate formaldehyde crosslinking. The liquid phase was aspirated, and cells were incubated in 5 mL of fresh crosslink-quench for two additional 1 min incubation steps, followed by a third, 8 min incubation at room temperature with gentle agitation.For Mito-APEX2 experiments , we follThereafter, crosslinking was terminated by the addition of glycine, and cells were harvested as described above. All subsequent steps  proceeded as described above.top), cells were washed once with 5 mL PBS, and crosslinked in 5 mL 0.1% (v/v) formaldehyde in PBS for 10 min at room temperature, with gentle agitation. The crosslinking reaction was quenched by addition of glycine  to final concentration 125 mM, and gentle agitation for 5 min at room temperature. Crosslinked cells were then washed three times with PBS and incubated with 500 μM biotin-phenol (BP) in PBS at room temperature, for 30 min. Thereafter, H2O2 was added to a final concentration 1 mM, for 1 min. The liquid phase was then removed by aspiration, and cells were washed twice with 2 mL quenching solution . Crosslinked, labeled cells were harvested by scraping, and processed as described above.For the crosslinking–then–BP biotinylation protocol , top, cePurified RNA samples were depleted of ribosomal RNA using the Ribo-Zero Gold rRNA removal kit , generally in accordance with the manufacturer’s protocol. Briefly, RNA concentration and integrity were quantified on an Agilent 2100 Bioanalyzer, using ‘RNA Pico’ and, where appropriate, ‘RNA Nano’ kits. Samples were not concentrated prior to rRNA depletion, which can accommodate a maximum input volume of 17 μL. Therefore, samples with total input masses of ≤ 20 ng or 20–100 ng were mixed with 1 μL or 2 μL of Ribo-Zero rRNA Removal Solution, respectively, in 1x RiboZero Reaction Buffer, at a final volume of 20 μL. Reaction mixes were incubated at 68°C for 10 min, followed by 25°C for 5 min more, and thereafter added to 32.5 μL magnetic beads  by extensive pipetting. Binding reactions were incubated at room temperature for 5 min, gently vortexed for 5 s, and incubated for 5 min at 50°C, in a thermocycler. The supernatant, containing rRNA-depleted RNA, was diluted in water to 50 μL final volume, cleaned up with 50 μL Agencourt RNAClean XP beads and eluted with 19.5 μL of Elute, Prime, Fragment mix from the TruSeq RNA sample preparation kit, v2 (Illumina). Thereafter, libraries were prepared using the TruSeq RNA sample preparation kit, according to the manufacturer’s instructions, starting from ‘Incubate RFP’ step. Each library was given a unique index during synthesis. Library concentration was measured, and quality confirmed, on an Agilent 2100 Bioanalyzer, using ‘DNA High Sensitivity’ kits.While we did not explicitly include an RNA size-selection step in our library syntheses, we anticipate that smaller RNAs  would be relatively undersampled during our workflow. The mixing ratios used at all Agencourt bead-based cleanup steps  disfavor the binding of such smaller species. For tRNAs, compact structure and post-transcriptional modifications can hinder amplification, making absolute quantification difficult . FinallyIndexed libraries were pooled in equimolar concentrations, with no more than ten libraries per pool, and subjected to 50 cycles of paired end sequencing, followed indexing, on two lanes of Illumina HiSeq 2500 flow cells, run in rapid mode .In general, three biological replicates for each construct were performed. Two biological replicates were performed for the mito-APEX experiment in As a basis of comparison, we here summarize the salient differences between our library preparation method, and those used in the alternative subcellular transcriptomics papers cited.see below).For the HEK 293T nuclear-cytoplasmic transcriptome datasets , RNA isoDatasets for both ER Fractionation-Sequencing  and proxSCR_013035), with the flags, ‘--no-coverage-search’ and ‘--GTF gencode.v19.annotation.gtf’. Gene expression was quantified against the Gencode v19 reference transcriptome  with Cufflinks v2.2.1. (SCR_014597), assessing the statistical significance of differential expression via CuffDiff2 (RRID:SCR_001647), with the flags, ‘--dispersion-method per-condition’ and ‘--seed 42’.Deep sequencing reads were mapped to human genome assembly hg19 using TopHat v2.1.1 , RRID:SC v2.2.1. , RRID:SCNo explicit filtering was imposed to mask the quantification of any RNA species: although nuclear-encoded tRNA, 5.8S, 18S, and 28S rRNA genes are absent from the Gencode reference transcriptome, and are hence opaque to our analysis, all other transcripts were quantified in an unbiased manner. Each RIP experiment was quantified independently. All Seq data will be made available through GEO under accession GSE106493.Fold enrichments were calculated as follows:p-values greater than 0.05  were removed from analysis. For ER-fractionation dataset ≥0.904  combined with all other HEK293T-expressed genes that were predicted by Phobius as having secretory signals, but which were absent from MitoCarta  and specificity of APEX-RIP at each subcellular compartment, we assembled lists of established target and off-target genes tailored for that compartment.For analysis of the nuclear–cytosolic datasets , our ref2(fold enrichment) cutoff of ≥0.904 . The reference off-target list used in this analysis comprised 8855 mRNAs lacking secretory annotation, as assessed using Phobius, TMHMM, and SignalP, and which lacked the GOCC terms ‘Endoplasmic reticulum,’ ‘Golgi,’ ‘membrane,’ and ‘extracellular’ . Only Gene Ontology terms that were enriched with p-values less than 0.05 —relative to the human background—are shown.For analysis of contaminants in ER datasets , the mRNp-value analysis—see above—without further modification; To obtain an initial list of potential laminar RNAs, we identified transcripts that were significantly enriched both within the nucleus and near the ER membrane . We manusee above) the apparent abundance of each gene stems from its mature and all immature  transcripts. Hence, the 337 secretory mRNAs in our overlapping set might be regarded as potential false positives, corresponding to cases where we have measured mature mRNAs near the ER surface, and partially processed precursor species in the nucleus, and not discrete species that reside at the interface of the nucleus and ER . For this reason, we encourage omitting these genes in subsequent analysis of potential laminar RNAs  and Fractionation-Sequencing (‘Frac-Seq’)—depend upon specialized reagents and protocols that can limit throughput and general applicability. To address this fundamental need, we have developed a new strategy—APEX-RIP—which uses a simple toolkit and workflow to map the transcriptomes of discrete subcellular compartments at high depth and spatiotemporal resolution. APEX-RIP uses the engineered ascorbate peroxidase APEX to biotinylate proteins within a target subcellular compartment in live cells; these affinity-tagged proteins are then chemically crosslinked in situ to nearby RNAs. When applied to a variety of membrane-enclosed and membrane-adjacent compartments, the APEX-RIP strategy exhibited target specificity and coverage rivaling or exceeding those attained by conventional fractionation-sequencing-based approaches, at a depth far exceeding those attainable by imaging-based methods. Furthermore, APEX-RIP can be applied to compartments that are recalcitrant to conventional biochemical purification. Given the superior precision, flexibility, and ease of this approach, we anticipate that APEX-RIP will provide a powerful tool for dissecting RNA subcellular localization in a broad range of biological contexts.RNA subcellular localization is a critical factor that influences a wide array of biological processes, ranging from  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Vivek Malhotra (Senior Editor) and three reviewers, one of whom served as Guest Reviewing Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Live-cell mapping of organelle-associated RNAs via proximity biotinylation combined with protein-RNA crosslinking" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.This manuscript presents a new method, APEX-RIP, for detecting RNAs enriched in subcellular compartments. The method is an extension of this lab's previous method for isolation of protein complexes following in vivo proximity biotinylation using the engineered APEX2 enzyme. Here, APEX2 or HRP biotin labeling of proteins is combined with cross-linking and immunoprecipitation of RNA to probe the transcriptomes of specific subcellular locales. The method is not restricted to mRNAs, but can identify non-coding RNAs as well. The authors apply the method to map the RNA neighborhood compositions for cytosolic, nuclear, mitochondrial and ER cellular compartments in human HEK293 cells. The authors argue that the method advances the field of RNA localization and provides better temporal resolution, recall and specificity than current techniques including proximity-dependent ribosome profiling and subcellular fractionation-seq. To demonstrate the utility of the data, the authors offer interesting hypotheses about mitochondrial-ER junction and nuclear lamina RNAs, but these are highly speculative and not adequately supported by the data currently provided. There are also concerns about the spatial specificity and resolution of the method and the ability to discriminate contaminants from bona fide localization as well as the applicability to non-membranous compartments. Overall, the paper is well written, and data are presented that generally support the potential of APEX-RIP as a useful method to characterize subcellular transcriptomes. We have the following suggestions to better validate the method and improve the manuscript.Essential revisions:1) A major concern relates to the spatial specificity in membrane-free subcellular regions. The APEX-RIP methodology appears to work well for tightly bounded compartments , but its specificity declines rapidly as compartments become less well defined. While the authors do mention this limitation in the Discussion, the message of the paper should be rebalanced towards clarifying these drawbacks. In all approaches the authors detect significantly more RNAs than previous methods and explain this solely with higher sensitivity of the approach without adequately validating this claim. In particular, they fail to address the possibility that a certain proportion of the hits may be false positives resulting from diffusion of biotinylated proteins prior to crosslinking. Contrary to what the authors state that capture of RNA does not happen "during a one-minute reaction". Instead, those proteins that are biotinylated during a one-minute reaction are in principle free to diffuse and interact with additional RNA molecules for several minutes until crosslinking is complete. For example, many nuclear RNA-binding proteins that are presumably targets of APEX2-NLS and between the nucleus and cytoplasm. Given that nuclear shuttling occurs on the msec time scale but there is a minute between treatment with peroxide and crosslinking, and that these relatively abundant proteins directly contact RNA, it seems likely that a significant fraction of the processed mRNAs found in the APEX2-NLS experiment may not be nuclear RNAs at all, but cytoplasmic RNAs crosslinked to RNA-binding proteins that were biotinylated in the nucleus but transited to the cytoplasm. Similarly, the authors find that targeting APEX/HRP to the lumen of the ER results in much higher specificity of RNA recovery than when it is targeted to the ER surface. Presumably this is connected to the more limited diffusion of proteins that reside in the ER lumen vs. the surface in the period immediately following biotin labeling.Additionally, given the relatively mild and slow crosslinking conditions used, it seems inconsistent to assert that other RNA localization methods "fundamentally lack temporal precision [because] each requires extensively fixing and permeabilizing cells prior to data collection, during which time diffusion or the loss of cellular integrity can perturb endogenous RNA localization." The authors then state that "APEX-RIP is not encumbered by any of these constraints", but one could argue that during a 10 minute formaldehyde crosslinking procedure at room temperature, many biotinylated proteins are likely to keep diffusing and interacting with additional RNA molecules for some time. Have the authors considered alternatives to formaldehyde crosslinking e.g. UV crosslinking, which could be performed on ice in a shorter time, thus potentially limiting diffusion of biotinylated proteins?2) Another potential limitation of the method that should at least be discussed is applicability of the method to animal level analysis, for example eggs, embryos, or the nervous system of an intact animal, where mRNA localization is a crucial regulatory mechanism.3) Another major concern is discriminating contaminants as opposed to identifying novel subcellular RNA localization. Regarding the result  that 141 of the 2635 mRNAs in the ERM dataset code for mitochondrial proteins, it isn't clear why these were not identified as conspicuous "mitochondrion" contaminants when ontology analysis was done in the fractionation comparison. This analysis identified only 13 mitochondrion by ER fractionation-seq. The same might be said for the overlap of ER enriched and APEX-NLS enriched RNAs to arrive at the nuclear lamina localized RNA candidates. Why are these candidates not detected as contaminants in either individual set?4) The novel hypotheses generated from the APEX-RIP data (mito-ER junction and nuclear lamina RNAs) are potentially interesting but in both cases highly speculative. Both ideas would be greatly strengthened if the authors could validate that some APEX-RIP candidate RNAs indeed localize to the suggested sites (e.g. by FISH or live RNA tracking).5) The analysis of RNAs potentially associated with the nuclear lamina seems problematic. Whereas 14% of the set of nuclear RNAs detected overlap with the ER-associated RNAs, only 6% of the lncRNAs overlap. Thus, the lncRNAs are under-represented in the population of nuclear RNAs that could be associated with the lamina. The caveat that RNAs identified as nuclear may not actually be nuclear further weakens this analysis.6) In the third paragraph of the Discussion, and at several other places of the manuscript, the authors make claims such as: "Compared to fractionation-based technologies, APEX-RIP offers superior accuracy". This seems like an over-interpretation based on the evidence presented, especially for the analyses of nucleo-cytoplasmic RNA distribution profiles. Firstly, the authors appear to draw this conclusion by comparing APEX-RIP and Frac-seq data from different cell lines e.g. . While i7) The authors provide evidence that APEX-RIP captures RNAs with lower abundances than alternative approaches do, but it would be nice to have some sort of estimate of the limit of detection. Furthermore, there is no discussion for any of the experiments about false negatives.8) Regarding RNAseq library preparation and data analysis, it would be useful to include more detail in both the Materials and methods section and the main text to demonstrate that their sequencing datasets and the different reference datasets are indeed directly comparable. Specifically, in the Materials and methods section the authors should include the dataset IDs for the specific ENCODE datasets and explain whether they as well as the other mentioned datasets were generated using comparable library preparation and sequencing methods. While the authors specifically mention lncRNAs, it is unclear how other, often highly abundant, non-protein coding RNAs were treated in the analysis. While this should be explained in detail in the Materials and methods, it would be useful to also include a brief description in the main text.9) For IF assays to confirm proper APEX/HRP fusion subcellular targeting, co-labeling with organelle specific markers should be shown. Essential revisions:1) A major concern relates to the spatial specificity in membrane-free subcellular regions. The APEX-RIP methodology appears to work well for tightly bounded compartments , but its specificity declines rapidly as compartments become less well defined. While the authors do mention this limitation in the Discussion, the message of the paper should be rebalanced towards clarifying these drawbacks.was able to successfully map the transcriptome of an “open” compartment (the cytosolic face of the ER), though doing so entailed biotinylating that organelle’s lumen. Given that many biological processes occur in membrane-proximal regions, we feel that this approach should still have broad utility and be helpful to a wide range of biologists.Inspired by this critique, we have edited the manuscript to rebalance the message of the paper. We now make clear at multiple points within the revised text  that APEX-RIP has drawbacks, most notably lower spatial specificity in compartments that are less well defined. However, we would like to note that APEX-RIP In all approaches the authors detect significantly more RNAs than previous methods and explain this solely with higher sensitivity of the approach without adequately validating this claim.There are two possible reasons for why we detect more RNAs than previous approaches. One is that we have higher sensitivity. Another is that we recover more non-specific hits . We believe that the data presented in this study support the first explanation rather than the second. We note:1) New 2) For each compartment, we analyze the specificity of the resulting dataset in the best way that we know how. 3) We also directly calculate sensitivity for each dataset, which we term “coverage”, or recall of expected RNAs. In 4) When we take a closer look at the RNAs that we recover that other methods do not  Comparing to ribosome profiling in particular, it makes sense that we should capture more, since we can enrich non-coding or silent mRNAs whereas their method cannot.In particular, they fail to address the possibility that a certain proportion of the hits may be false positives resulting from diffusion of biotinylated proteins prior to crosslinking. Contrary to what the authors state that capture of RNA does not happen "during a one-minute reaction". Instead, those proteins that are biotinylated during a one-minute reaction are in principle free to diffuse and interact with additional RNA molecules for several minutes until crosslinking is complete. For example, many nuclear RNA-binding proteins that are presumably targets of APEX2-NLS and between the nucleus and cytoplasm. Given that nuclear shuttling occurs on the msec time scale but there is a minute between treatment with peroxide and crosslinking, and that these relatively abundant proteins directly contact RNA, it seems likely that a significant fraction of the processed mRNAs found in the APEX2-NLS experiment may not be nuclear RNAs at all, but cytoplasmic RNAs crosslinked to RNA-binding proteins that were biotinylated in the nucleus but transited to the cytoplasm.p-value, comparing time points = 0.374). From these data, we believe that it is unlikely that proteins biotinylated in the nucleus shuttle into the cytosol and give non-specific RNA capture to a significant degree. This claim is also supported by the RNA-Seq data itself, which shows minimal enrichment of cytosolic contaminants  in our nuclear APEX-RIP dataset.We thank the reviewer for pointing out this very legitimate concern, which has prompted us to perform a new set of experiments, shown in new Similarly, the authors find that targeting APEX/HRP to the lumen of the ER results in much higher specificity of RNA recovery than when it is targeted to the ER surface. Presumably this is connected to the more limited diffusion of proteins that reside in the ER lumen vs. the surface in the period immediately following biotin labeling.We have edited the text to add this point. We also believe that the increased specificity results from the difference in protein biotinylation patterns resulting from HRP-KDEL versus APEX2-ERM labeling. Because the biotin-phenoxyl radical does not cross cellular membranes, HRP-KDEL biotinylation stops abruptly at the ER membrane. In contrast, APEX-ERM biotinylation forms a “contour map” in which protein biotinylation is strongest at the ER membrane, and falls off nanometer by nanometer into the cytosol, but is still detectable even tens of nanometers away. When performing mass spec proteomics, we address this by ratioing biotinylation extent against a cytosolic APEX2-NES reference construct. Here, for APEX-RIP, we believe the formaldehyde crosslinking step captures distal RNAs in the case of APEX2-ERM biotinylation, but captures strictly ERM-proximal RNAs in the case of HRP-KDEL, whose biotinylation products are restricted to the ER lumen.Additionally, given the relatively mild and slow crosslinking conditions used, it seems inconsistent to assert that other RNA localization methods "fundamentally lack temporal precision [because] each requires extensively fixing and permeabilizing cells prior to data collection, during which time diffusion or the loss of cellular integrity can perturb endogenous RNA localization." The authors then state that "APEX-RIP is not encumbered by any of these constraints", but one could argue that during a 10 minute formaldehyde crosslinking procedure at room temperature, many biotinylated proteins are likely to keep diffusing and interacting with additional RNA molecules for some time.This is a valid point and we have edited the text to make the comparison to other methods more accurate. We now state that, compared to imaging-based approaches, the main advantages of APEX-RIP are (1) no use of detergent/methanol to permeabilize membranes, which can disrupt spatial relationships, and (2) full sequence data instead of just gene IDs. Hence more information can be gleaned about RNAs in specific locales.Have the authors considered alternatives to formaldehyde crosslinking e.g. UV crosslinking, which could be performed on ice in a shorter time, thus potentially limiting diffusion of biotinylated proteins?We agree that APEX-CLIP is a great idea, and are interested in pursuing it in future studies. However, we have disfavored its use here for several reasons. Since APEX-catalyzed biotinylation preferentially labels the outer surface of RNPs, and since UV-induced crosslinks only form at the very cores of these complexes, we feared (though have not tested ourselves) that this disconnection between the points of labeling and contact might result in a substantial loss of signal in multi-subunit complexes. We were also concerned that various RNA•protein interactions might exhibit a range of different crosslinking efficiencies, thereby introducing bias into our sampling of the organelle-wide transcriptome . Although the power of CLIP represents an attractive alternative, we believe that exploring this alternative falls beyond the scope of the current study.2) Another potential limitation of the method that should at least be discussed is applicability of the method to animal level analysis, for example eggs, embryos, or the nervous system of an intact animal, where mRNA localization is a crucial regulatory mechanism.This is an excellent point. We agree that a limitation of using APEX-RIP in animals or tissue is that one must contend with BP/H2O2/formaldehyde perfusion into tissue. FISH and fractionation-Seq are more tissue-ready in this respect. We have edited the Discussion to point this out.3) Another major concern is discriminating contaminants as opposed to identifying novel subcellular RNA localization. Regarding the result  that 141 of the 2635 mRNAs in the ERM dataset code for mitochondrial proteins, it isn't clear why these were not identified as conspicuous "mitochondrion" contaminants when ontology analysis was done in the fractionation comparison. This analysis identified only 13 mitochondrion by ER fractionation-seq.The jury is still out on where nuclear-encoded, mitochondrial-resident proteins are translated. They may be translated in the open cytosol, near the outer mitochondrial membrane, at the ER membrane, or even at mito-ER contacts. Thus, for evaluating the specificity of our ERM dataset, we did not think it fair to automatically classify anything with mitochondrial annotation as a “contaminant”. Instead, for the specificity analysis in Specifically for the 135 mRNAs  in our ER dataset that code for mitochondrial proteins, 132 of these have transmembrane annotation. So we do not count them as “non-specific”. The remaining three mitochondrial genes comprise 3.4% of the total set of non-secretory/non-TM mRNAs enriched by KDEL-RIP , which is why they aren’t evident in The same might be said for the overlap of ER enriched and APEX-NLS enriched RNAs to arrive at the nuclear lamina localized RNA candidates. Why are these candidates not detected as contaminants in either individual set?To address the valid critique, we will divide it into two parts: identifying contaminants in the nuclear dataset, and identifying contaminants in the ER KDEL-RIP dataset.all mature mRNAs as potential contaminants–to systematically address this issue. However, this analysis would require discrete quantification of the pre-processed and mature transcripts, for which we lack adequate sequencing depth. Given these considerations, we believe our approach – while imperfect – is reasonable.Finding contaminants in the Nuclear RNA experiment is difficult, largely because it’s hard to know, a priori, what those contaminants should be. All RNAs start off life in the nucleus, and so all expressed genes should be represented there, to some extent. Knowing this, we have approached the issue of specificity in our nuclear APEX-RIP dataset by two means. First, we looked for enrichment of lncRNAs in the nuclear dataset, and depletion of these transcripts in the cytosolic APEX-RIP data . Second,The second part of the question pertains to why nuclear-localized RNAs are not classified as contaminants in the ER KDEL-RIP dataset. We have taken care to approach this issue as conservatively as possible in our analysis. For example, of the 441 RNAs that are enriched both in our nuclear and KDEL-RIP datasets, 385 are mRNAs, 337 (87.5%) of which have secretory/TM annotation. The skeptical interpretation is that the KDEL-RIP experiment has enriched mature secretory/TM mRNAs localized at the ER surface, while the NLS-RIP experiment has enriched their corresponding immature species within the nucleus. Hence, we have masked them from being potential laminar candidates.Looking more broadly at the KDEL dataset: of the 2494 KDEL-enriched mRNAs, only 87 (3.5%) lack secretory/TM annotation – one might initially consider these few RNAs as potential contaminants. However, 48 of these transcripts (55%) are also enriched in the NLS dataset. By comparison, only 14% of KDEL-enriched secretome mRNAs  are enriched in the NLS-APEX-RIP, indicating that the putative KDEL “contaminant” list is highly enriched in nuclearlocalized RNA. Given the generally high specificity of our ER-associated RNA list, and the fact that these 48 are also nuclear-enriched, we have flagged them as being candidate laminar RNAs.In the revised manuscript, we have attempted to clarify our writing so as to emphasize these points. We furthermore explicitly discuss the possibility that some of our novel hits may be ascribed to experimental noise.4) The novel hypotheses generated from the APEX-RIP data (mito-ER junction and nuclear lamina RNAs) are potentially interesting but in both cases highly speculative. Both ideas would be greatly strengthened if the authors could validate that some APEX-RIP candidate RNAs indeed localize to the suggested sites (e.g. by FISH or live RNA tracking).We believe that this request is beyond the scope of this work, which was submitted as a Tools and Resources article, and accordingly presents new methodology for studying RNA localization in living cells, and multiple high quality datasets that contain thousands of novel RNAs not previously associated with specific organelles . We parse the data to identify mito-ER and lamina candidates only to illustrate how the new datasets that we contribute can be mined in straightforward ways for novel hypotheses–hypotheses that are excellent starting points for new bodies of work.Furthermore, we note that follow-up validation is not a simple matter that can be completed in 1-2 months. There are major technical considerations that suggest such follow up would be more suitable for a new study rather than as an addition to this work. First, the live RNA tracking methods suggested by the reviewer  require either expressing the target RNA as a chimeric transgene – which might not necessarily recapitulate the expression regime and subcellular localization of the endogenous gene – or editing the genomic locus itself, which is beyond the technical scope of this work. Likewise, such systems can themselves perturb the localization of the target RNA. Another suggested approach, FISH, is also rife with technical caveats . Fixation conditions can perturb localization, probe selection and validation can be technically cumbersome, and poorly vetted probes can lead to artefactual signal. Confounding this is that many of the novel RNAs we’ve identified are expressed at extremely low levels, making FISH quite challenging. Moreover, this analysis is complicated by a pervasive issue in the RNA localization field: the difficulty in comparing imaging and sequencing data . Certainly, if we had identified abundant RNAs that are massively enriched in both the NLS and KDEL data sets, we would expect a FISH signal that would be highly localized near the lamina. But, it is difficult a priori to predict what the FISH signal of a lowly-expressed species that is modestly–though significantly–enriched at the lamina might appear, especially if this RNA is among the first of its class to be identified. We therefore believe that addressing this question in a robust way would require an extensive analysis that exceeds the scope of the current work. Finally, given the longstanding difficulties in identifying laminar RNAs, the mere generation of a speculative candidate list represents a significant achievement. We present this list as a compelling starting point for future analysis.5) The analysis of RNAs potentially associated with the nuclear lamina seems problematic. Whereas 14% of the set of nuclear RNAs detected overlap with the ER-associated RNAs, only 6% of the lncRNAs overlap. Thus, the lncRNAs are under-represented in the population of nuclear RNAs that could be associated with the lamina. The caveat that RNAs identified as nuclear may not actually be nuclear further weakens this analysis.NEAT1 (PMID: 25630241), is enriched in our KDEL RIP experiment.While the reviewer’s comments are insightful, we offer the converse perspective on the data presented in Conversely, it may be more instructive to ask, of the KDEL-enriched RNA population, what proportions are also enriched from the nucleus? Looking at the data this way, we observe that, of the 2494 mRNAs enriched in the KDEL dataset, 385 (15%) are also enriched in the nucleus. For many of these RNAs we can further justify nuclear enrichment as a potential byproduct of regulated premRNA processing, as discussed above. However, of the 28 significantly enriched lncRNAs in the KDEL dataset, 11 (39%) are also enriched in the nucleus, a 2.5– fold higher proportion than that of their mRNA counterparts.Finally, given the general agreement between the APEX-RIP-derived nuclearcytoplasmic data and those acquired by other methods , we rema6) In the third paragraph of the Discussion, and at several other places of the manuscript, the authors make claims such as: "Compared to fractionation-based technologies, APEX-RIP offers superior accuracy". This seems like an over-interpretation based on the evidence presented, especially for the analyses of nucleo-cytoplasmic RNA distribution profiles. Firstly, the authors appear to draw this conclusion by comparing APEX-RIP and Frac-seq data from different cell lines e.g. . While iWe agree with the reviewer’s critique, and regret the oversight in our original analysis, which relied on nuclear-cytoplasmic RNA-seq data from the NHEK cell line. To address this concern, we have repeated this analysis using data derived from HEK 293T cells , the same line used in our experiments. While this is a much more reasonable basis for comparison, as the reviewer correctly anticipated, it has not substantially altered the experimental results. Our revised draft now focuses on these analyses.Second, the detection of ER mRNAs within the nucleus in Frac-seq data, but not APEX2-NLS samples, could simply underline the ability of the Frac-seq approach to purify and detect pre-mRNAs. Do the authors detect such species within APEX2-NLS RNA-seq dataset? Is read coverage of intronic regions comparable to that observed with nuclear Frac-seq?p-value ~0, We agree with the reviewer and have addressed with further analysis. Specifically, by quantifying intronic reads in the Frac-Seq , NLS- and NES-APEX-RIP datasets, we observed that the nuclear-cytoplasmic distribution of pre-mRNAs was nearly indistinguishable between the two methods: relative to whole-cell RNA, intronic reads were enriched in the nuclear compartment, and de-enriched in the cytosol, to similar extents . Further7) The authors provide evidence that APEX-RIP captures RNAs with lower abundances than alternative approaches do, but it would be nice to have some sort of estimate of the limit of detection.We have addressed this insightful point more robustly in the revised manuscript. Assigning a lower detection limit is often empirical, and absolutely measuring transcript abundances is technically challenging. However, we have tried to approach this problem with some degree of rigor in the revised Furthermore, there is no discussion for any of the experiments about false negatives.For each experiment, we characterize the coverage, or the recall of expected RNAs. The inverse of coverage is the false negative rate. Hence, if our coverage is 97.1% (for the “gold list” or “true positive list” of 71 mRNAs encoding established ER-resident proteins – 8) Regarding RNAseq library preparation and data analysis, it would be useful to include more detail in both the Materials and methods section and the main text to demonstrate that their sequencing datasets and the different reference datasets are indeed directly comparable. Specifically, in the Materials and methods section the authors should include the dataset IDs for the specific ENCODE datasets and explain whether they as well as the other mentioned datasets were generated using comparable library preparation and sequencing methods.We agree and apologize for the oversight. We have revised the text to include the information in both the Materials and methods section and main text of the manuscript. We furthermore explicitly discuss the differences between this workflow and those used to generate the Frac-Seq and Ribosome Profiling data to which we compare our results, which no longer includes any ENCODE dataset. While the Frac-Seq libraries to which we compare our work were generated through methods comparable to our own, ribosome profiling is substantially different. These differences ultimately mean that ribosome profiling samples a different subset of the transcriptome (i.e.–ORFs within mRNAs). As discussed in the text, we believe that, by analyzing the data at the gene level , and by examining fold enrichments  rather than absolute abundances, we have accounted for these differences. Moreover, the fact that ribosome profiling and APEX-RIP sample different subsets of the transcriptome– namely, that APEX-RIP can analyze ncRNAs while ribosome profiling cannot–is part of the message of our work.While the authors specifically mention lncRNAs, it is unclear how other, often highly abundant, non-protein coding RNAs were treated in the analysis. While this should be explained in detail in the Materials and methods, it would be useful to also include a brief description in the main text.As per the reviewer’s suggestion, we have expanded the description of our library preparation and analysis workflows, and discuss in both the Materials and methods and main text the caveats to our approach. We enumerate the classes of RNA that may be opaque to our analysis, and why. Furthermore, the data presented in column C of 9) For IF assays to confirm proper APEX/HRP fusion subcellular targeting, co-labeling with organelle specific markers should be shown.Based on the reviewer’s suggestion, we have now included organelle-specific markers in the revised immunofluorescence