ADAR1 expression, interaction with ADAR1, and binding to Alu elements. We highlight that editing regulation by Ro60 is consistent with the global up-regulation of RNA editing in systemic lupus erythematosus. Additionally, most key editing regulators act in a cell type-specific manner. Together, our work provides insights for the regulatory mechanisms of RNA editing.Adenosine-to-inosine (A-to-I) editing, mediated by the ADAR enzymes, diversifies the transcriptome by altering RNA sequences. Recent studies reported global changes in RNA editing in disease and development. Such widespread editing variations necessitate an improved understanding of the regulatory mechanisms of RNA editing. Here, we study the roles of >200 RNA-binding proteins (RBPs) in mediating RNA editing in two human cell lines. Using RNA-sequencing and global protein-RNA binding data, we identify a number of RBPs as key regulators of A-to-I editing. These RBPs, such as TDP-43, DROSHA, NF45/90 and Ro60, mediate editing through various mechanisms including regulation of  Giovanni Quinones-Valdez et al. examined the role of over 200 RNA-binding proteins in mediating A-to-I RNA editing. They identified several RNA-binding proteins that regulate ADAR1 expression, interaction, or binding with Alu elements in a cell type-specific manner. In human cells, the most prevalent type of RNA editing is adenosine to inosine editing (A-to-I editing), catalyzed by the protein family adenosine deaminases acting on RNA (ADARs)3. A-to-I editing can have profound impacts on gene expression through a wide spectrum of mechanisms, including changing protein-coding sequences3, modifying splice sites5, affecting RNA nuclear export6 and altering microRNA sequences or their target sites3.RNA editing refers to the alteration of RNA sequences through insertion, deletion or substitution of nucleotides8. Although the function of most of these sites remains unknown, the involvement of RNA editing in various biological processes is increasingly appreciated9. Recent studies showed that the editing levels of numerous RNA editing sites vary significantly across tissues, developmental stages and disease status12. These findings prompted many outstanding questions, one of which relates to the regulatory mechanisms that underlie the widespread editing variations. ADAR proteins are the best-known regulators of the human editomes3. However, variations in the expression levels of the ADAR genes alone can only account for some of the observed editing variations14. Thus, there is a critical need for identifying and understanding additional regulatory mechanisms of RNA editing.Facilitated by high-throughput RNA sequencing (RNA-seq) methods, millions of A-to-I editing sites have been identified in human cells16. A number of RBPs have been reported to affect RNA editing14. For example, some RBPs, such as SRSF9 and RPS14, interact with ADAR2 and affect A-to-I editing of a number of substrates17. The protein FMRP (encoded by the FMR1 gene) was shown to affect RNA editing by interacting with the ADAR proteins in multiple organisms21. Another ADAR1-interacting protein, DICER, was reported to inhibit ADAR1 editing activity in vitro22. In addition to ADAR-interacting proteins, other RBPs may affect editing by influencing the double-stranded RNA (dsRNA) substrates of ADAR or the interaction between ADAR and dsRNAs. For example, several RNA helicases, including RNA helicase A (DHX9) and DDX15, were reported to repress RNA editing, presumably by disrupting dsRNA structures23. The catalytically inactive ADAR protein, ADAR3, represses RNA editing by competitively binding to dsRNA targets in both human cells and C. elegans25. Another dsRNA-binding protein, Staufen, binds to numerous inverted Alu repeats26, the most prevalent type of ADAR1 substrates in human cells. As a result, Staufen may also regulate RNA editing, although this topic needs further investigation.RNA-binding proteins (RBPs) are important regulators for all steps of RNA maturation. Post-transcriptional RNA processing, such as splicing and polyadenylation, is controlled by the formation of different ribonucleoprotein complexes with RBPs at their core27. Although the function of the majority of RBPs is unknown, it is now clear that many RBPs play a role in multiple steps of post-transcriptional RNA processing15. However, compared to other processes such as splicing, for which a large number of splicing factors have been cataloged, the number of proteins known to regulate RNA editing remains relatively small. Nevertheless, it is known that ADAR1 potentially interacts with numerous other proteins28 and many RBPs may recognize Alu elements or dsRNA targets30. Thus, it is very likely that additional editing regulators remain to be uncovered, which will help to explain the observed editome variability in diseases, between cell types and developmental stages. To this end, we carried out a systematic analysis of the potential involvement of a large panel of RBPs in regulating RNA editing in human cells. We report a number of RBPs as key regulators of RNA editing, most of which function in a cell type-specific manner. Our findings greatly expand the repertoire of known RBPs as RNA editing regulators.It was estimated that more than 3000 RBPs exist in human cells31. Specifically, data from two human cell lines, K562 (chronic myelogenous leukemia) and HepG2 (liver hepatocellular carcinoma) were included. Individual knockdown experiments were carried out for 222 and 225 RBPs in K562 and HepG2 cells, respectively. For each RBP, two biological replicates of knockdown were carried out, followed by polyA-selected RNA-seq, which were accompanied by two replicates of control experiments. An average of 33.5 million pairs of reads (2 × 100 or 2 × 101 nt) were obtained for each knockdown or control replicate. To identify RNA editing sites, the RNA-seq data were analyzed using our previously developed methods35, followed by batch-normalization (see Methods).To examine potential regulatory mechanisms of RNA editing, we analyzed a large number of RNA-seq datasets generated upon knockdown of hundreds of RBPs as part of the ENCODE project35. As expected, the majority of editing sites were located in Alu regions . We observed that different RBPs induced variable degrees of editing changes, ranging from being negligible to affecting nearly 50% of all testable sites (see Methods) Fig. . As a poA comparison of differentially edited sites associated with different RBPs revealed that ADAR1 shared many sites with other RBPs in both cell lines Fig. . Specifi36. Compared to RBPs that induced editing reduction upon knockdown, those associated with editing up-regulation upon their knockdown did not cluster strongly. Importantly, experimental batches did not confound the clusters in either hierarchical clustering or WGCNA  data of TDP-43 (HepG2 cells) from the ENCODE consortium31   data for the above proteins generated by the ENCODE projectle) Fig. . As a po39. In this study, we observed that DROSHA is one of the RBPs that induced the strongest reduction in RNA editing upon its knockdown in K562 cells, affecting ~30% of all testable editing sites  are likely direct regulators of RNA editing via ADAR1 interactions. Both proteins are well-known RNA-dependent interacting proteins of ADAR1tes Fig. . Its imptes Fig. . Similartes Fig. . ILF3 . We observed that a small number of RBPs are associated with high levels of Alu-binding, manifested as the high percentage of eCLIP peaks overlapping sense or antisense Alu elements  demonstrated a substantial bias for preference to either sense or antisense Alus, compared to the background sense/antisense Alu composition in expressed genes . In contrast, the sense/antisense Alu compositions of ILF3 and ADAR1 peaks are similar to the background, which may indicate that their binding specificity relies on RNA structures more than on the specific sequences.Alu sequences, especially inverted Alu pairs, form dsRNA structures, which are the main ADAR1 substrates for RNA editing in human cellsnts Fig. . As expeNext, we asked whether the extent of Alu-binding correlates with the level of impact of each RBP on RNA editing. Surprisingly, there is little correlation between these two variables Fig. . In addi45. Anti-Ro60 antibodies occur in many patients with systemic lupus erythematosus (SLE), an autoimmune disease characterized by interferon activation, autoantibodies and multi-organ tissue destruction46. We analyzed RNA editing patterns using RNA-seq data derived from the blood samples of 99 SLE patients and 18 controls47. Consistent with our findings in K562 cells  common to both cell lines for each of the 35 RBPs. Indeed, common testable editing sites for most RBPs only constitute <50% of all testable sites in each cell line . We observed that Ro60 binds to Alu elements and TROVE2 knockdown induced an increase in RNA editing for more than 1000 editing sites in K562 cells. This editing change was recapitulated in SLE patients with loss of Ro60 function. The patient samples showed a substantial change in RNA editing, to a higher extent than that observed in K562 cells, possibly due to the combined impact of ADAR1 p150 upregulation and Ro60 loss of function in SLE. Another disease-related protein with involvement in regulating RNA editing is TDP-43 (encoded by the gene TARDBP). TDP-43 is a key player in the pathogenesis of Amyotrophic Lateral Sclerosis (ALS)21. Thus, the role of FXR1 in RNA editing depends greatly on the cell type, as similarly observed for other proteins in this study , but not HepG2 cells . Data released between October 2014 and January 2017 is included in this study. These data were generated in 24 and 26 batches in the K562 and HepG2 cell lines, respectively.Fastq files of RNA-seq data generated following RBP knockdown or control shRNA transfection were downloaded from the ENCODE data portal34 against the human genome (hg19) and Ensembl transcriptome55 (Release 75), with the parameters m = 0.05 and b = 0.03. Only uniquely mapped reads were retained for further analysis. Duplicated reads  were removed from the alignment files.RNA-seq reads were aligned using RASER v0.5232. This step removes likely sequencing errors based on base call quality, mismatch nucleotide changes and mismatch position in the reads. We then filtered these mismatches by removing those located in homopolymers, splice sites, simple repeats, and those whose read coverage demonstrated a strand bias33. These sites were further processed using GIREMI35 to obtain high-confidence editing sites. GIREMI identifies editing sites based on the mutual information between editing sites and/or SNPs. Since the RNA-seq experiments were conducted in multiple batches, we designed a scheme to reduce the potential batch effects. Specifically, within each batch, multiple RBP knockdown experiments and one control shRNA experiment (2 replicates each) were carried out. We assume that only a minority of RBPs, if any, in a batch regulates the editing of a particular site. Based on this assumption, for each editing site identified in any dataset of a batch, we defined the control editing level as the average of its editing level in all RBP knockdown and control experiments in the same batch. This procedure was omitted for a small number of batches where only one RBP was included. Based on clustering results  or opposite (n2) directions in their changes of editing levels upon knockdown were obtained. Pairs of RBPs with less than 20 total shared differentially edited sites were not considered (thus with no links in the plot). Then, we obtained the number (t) of shared testable sites in the datasets of the two RBPs. The ratios n1/t and n2/t were then calculated, where n2/t was reported as -n2/t to represent the opposite directions in editing changes. The final overlap scores are defined as the Z-score of these ratios across all RBP pairs for each cell line.We calculated overlap scores to represent the degrees of overlap among differentially edited sites associated with a pair of RBPs. For each pair of RBPs, two overlap scores were defined, represented by two links in the CIRCOS plots Fig. . The scon) and the fraction of these differentially edited sites with increased editing level upon knockdown (r). We then randomly sampled n sites from all testable sites of the same RBP-knockdown dataset and calculated a similar fraction z-score of r was therefore defined as r*, respectively. Finally, the empirical p value of r was calculated by comparing to r*.We tested whether there exists a significant bias in the direction of editing changes (higher or lower relative to controls) caused by the knockdown of an RBP using a bootstrap sampling approach Figs. , and 5. For TARDBP Fig. , we addi58. This method finds networks (modules) of nodes based on their topological overlap. For each cell line, the nodes of the WGNCA network consisted of all the RBPs with knockdown data. The edge scores between the nodes  were calculated using pairwise correlation (bicorrelation as recommended by WGCNA) between their differential editing levels between knockdown and controls. We employed WGCNA to create signed networks, which required a soft threshold of 12 to satisfy scale-free topology36. Modules in the resulting dendrograms were then examined manually , where only uniquely-mapped reads were retained. All the alignments were performed by the STAR aligner60 with ENCODE standard parameters . All alignments were required to be end-to-end without soft-clipping. eCLIP peaks were called using a Poisson model38 by requiring a Bonferroni-corrected p value cutoff of 0.01.To accommodate potential Alu-binding proteins whose eCLIP reads may not align uniquely, the eCLIP data were analyzed using a step-wise mapping procedure7 that satisfy the following: ≥15 combined total read coverage from the two replicates; located in the same gene as the differentially edited site; not identified with edited reads in any dataset of our study. For ADAR1, we used non-REDIportal A’s as random controls, instead of known editing sites.Next, we examined whether the distance between differentially edited sites upon an RBP knockdown and the eCLIP peaks of the RBP is significantly closer than expected by chance. For each differentially edited site, we calculated its distance to the closest eCLIP peak within the same gene. differentially edited sites in genes that do not have an eCLIP peak were discarded. As control sites, we used known editing sites from the REDIportal databaseFor each RBP, we randomly selected the same number of control sites as that of differentially edited sites and calculated the distance between a control site and the closet eCLIP peak within the same gene. We repeated this process 200 times to generate 200 sets of controls. The distances of each set of controls to eCLIP peaks were visualized via empirical cumulated distribution function (eCDF), similarly for the distances of the actual differentially edited sites to eCLIP peaks. Next, we calculated the area under the curve (AUC) of each distance eCDF and compared the AUC corresponding to differentially edited sites and those resulted from the 200 sets of control sites. It is expected that smaller distances lead to larger AUCs. Thus, to determine whether the differentially edited sites were significantly closer to eCLIP peaks than expected by chance, we calculated the two-sided p-value by fitting the AUC values of the controls with a normal distribution. In addition, a fold-change was calculated as the ratio between the AUC associated with differentially edited sites and the mean AUC of the control eCDFs.For each pair of RBPs tested in the same cell line, we took the union of their associated differentially edited sites and further retained only those sites that are testable in both RBP knockdown datasets. Testable sites were defined as those with ≥5 total reads per replicate, and with ≥10% editing level in either knockdown or control. Using these editing sites, we asked whether the directions of editing changes upon knockdown of the two RBPs are concordant by calculating the directional agreement score. Specifically, for each of the above differentially edited site, we labeled it as + or − if its change in editing level upon RBP knockdown is a positive or a negative value, respectively. For sites with the same label for both RBPs, a +1 agreement score was assigned. Otherwise, a score of −1 was given. If the editing site is differentially edited in only one of the two RBP knockdowns, a score 0 was given. The final directional agreement score of a pair of RBPs is defined as the average value of the score of each included editing site in this analysis.The directional agreement score of the same RBP between K562 and HepG2 was calculated similarly.−1 penicillin/streptomycin at 37 °C and 5% CO2. Ten million cells were collected and lysed in 1 mL non-denaturing lysis buffer at pH 8.0, containing 20 mM Tris-HCl, 125 mM NaCl, 1% NP-40, and 2 mM EDTA supplemented with complete protease inhibitor cocktail. Extracted proteins were incubated overnight with DROSHA antibody  at 4 °C; precipitation of the immune complexes was performed with Dynabeads Protein G  for 4 h at 4 °C, according to the manufacturer’s instructions. After immunoprecipitation, the beads were washed three times with the lysis buffer at 4 °C and eluted from the Dynabeads using elute buffer . Twenty microliters were loaded onto the gel and the samples were processed by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and analyzed by Western blot. The following antibodies were used for the Western blots: ADAR1 antibody  and DROSHA antibody . The HRP-linked secondary antibodies were used and the blots were visualized with the ECL kit .Cells were maintained with DMEM supplemented with 10% FBS and 100 U mLRenilla reniformis) was used for transfection efficiency. Transfections were performed with the use of Lipofectamine 3000 (Invitrogen). HepG2 cells were seeded into 12 well plates at a density of 2.0 × 105 cells per well the day before transfection. For each well of cells 1.0 μg of the pGL3 constructs were co-transfected with 0.1 μg of the pSV40-Renilla vectors. The transfected cells were collected after 48 h. Luciferase activities were measured with the Dual-Luciferase Reporter Assay System . To normalize for transfection efficiency, the reporter activity was expressed as the ratio of firefly activity to renilla activity. For each construct, three independent experiments were performed in triplicate.TDP-43 ChIP peak regions were cloned into a firefly luciferase reporter pGL3 vectors (Promega). The pSV40-Renilla vector (Promega) encoding the Renilla luciferase reporter gene Rluc  are available at Supplementary InformationSupplementary Data 1Description of Additional Supplementary Files


Escherichia coli (E. coli) and approved as the first anabolic treatment of osteoporosis in the EU and the USA. Feasibility of expression and secretion of a tag- fused form of TPD into Bacillus subtilis (B. subtilis) was examined due to several advantages of B. subtilis over E. coli in production of recombinant proteins with pharmacological activities.Biosynthetic teriparatide (1–34) (TPD) is a N-terminally truncated version of human parathyroid hormone (hPTH). The recombinant form of this polypeptide has been expressed in B. subtilis. Expression of target protein was analyzed by SDS-PAGE and western blotting upon induction by IPTG.A codon optimized gene containing TPD open reading frame carrying enterokinase site in its upstream was fully synthesized. According to our cloning scheme, this synthetic polynucleotide was used as a template for PCR amplification using engineered primers in such a way that a polyhistidin tag was added in frame to the upstream of the amplicon as well as two restriction sites at its ends. The resulted amplicon, a cassette containing His-tag, enterokinase site and TPD, from 5′ to 3′, was cloned into pTZ57R/T vector and subjected to sequencing.The cassette was then subcloned into pHT43 shuttle vector and transformed into The accuracy of construction of pHT43-TPD was confirmed by sequencing and restriction map analyses. SDS-PAGE and western blotting results showed that the recombinant fusion form of hPTH was successfully expressed and secreted into cytoplasm and extracellular medium.B. subtilis; however, optimization of expression conditions is required for enhancing target protein yield.TPD may be successfully expressed and secreted in  Osteoporosis, as a persistent skeletal disorder involves decreased bone strength due to density reduction and increased fracture risk leading to an inequality in normal bone remodeling process where bone mass is resorbed by osteoclasts while failing to create adequate space. As a result, bone microarchitecture becomes porous and susceptible to fracture due to a compromise in its structural integrity Currently, recommened osteoporosis therapy involves reducing bone remodeling by inhibiting hone resorption. This treatment is not sufficient in decreasing osteoprosis rates of morbidity and mortality Parathyroid Hormone (PTH) and its analogue Teriparatide (TPD) provide the possibility of improving skeletal microarchitecture and represent a new class of anabolic therapies for the treatment of severe osteoporosis Anabolic properties of full-length PTH are incorporated in TPD as a recombinant human parathyroid hormone (1–34) corresponding to the first 34 amino acids of hPTH Bacillus subtilis (B. subtilis) has been widely adopted for protein production due to its “Generally Recognized As Safe” (GRAS) status in biotechnological processes g/L) without a significant codon bias B. subtilis has a naturally high secretory capacity and exports proteins directly into extracellular medium simplifying downstream purification and prevents formation of inclusion bodies Gram-positive bacterium B. subtilis over Escherichia coli (E. coli) in production of recombinant proteins with pharmacological activities, in the present study, a designed tag fused TPD gene was cloned and used to transform B. subtilis for the first time. The obtained recombinant B. subtilis is capable of expression and particularly secretion of TPD into medium.Due to the above-mentioned advantages of A cassette encoding recombinant human parathyroid hormone favouring high-yield protein production was constructed based on prokaryotic codon usage. This sequence was synthesized by Bioneer Corporation  and was cloned into pET32a plasmid named pET32a-TPD. From 5′ to 3′, the cassette consisted of enterokinase site to cleave the fusion moiety and the open reading frame encodes for TPD.XbaI cut site and a His-tag overhang (5′ATTATCT AGACACCACCACCACCACCATGATGATGATGA TAAGAGCGTGAG3′) and reverse primer with a Aa-tII cut site and stop codon overhang (5′GCGCGACG TCTTAGAAGTTATGCACATCCTG3′) were designed in accordance with the cloning strategy and were used to amplify TPD gene from pET32a-TPD vector. Thirty rounds of amplification were performed per following cycle: pre-denaturing step at 94°C for 3 min proceeded by 30 cycles of denaturation at 94°C for 30 s, anannealing step at 55°C for 30 s, and an extension step at 72°C for 30 s. Final extension step at 72°C for 5 min was performed. bp.Full-length sequence of TPD was amplified from pET32a-TPD through PCR reaction using Taq polymerase . The forward primer with a E. coli XL1blue competent cells. Plasmids were purified from insert positive clones and then digested with XbaI/AatII after colony PCR screening from white colonies. The released inserts were re-cloned into expression vector pHT43 to construct pHT43-TPD (B. subtilis (WB600) by Eppendorf Eporator  according to the B. subtilis electro-transformation method B. subtilis colonies were cultured on LB agar medium containing 5 μg/ml chloramphenicol as a selective marker and were verified by colony PCR. Plasmid DNA preparation, restriction enzyme digestion and agarose gel electrophoresis processes were performed in accordance with the published methods Amplified fragments were purified and cloned into pTZ57R/T vector using the InsTAclone PCR Cloning Kit . Purified amplified fragments were then transformed to HT43-TPD . The fidB. subtilis (WB600) cells, harbouring the recombinant vector pHT43 were grown on a LB-agar plate containing 5 μg/ml chlromphenicol at 37°C. A single colony was selected from the plate and cultured for 4 hr in 5 ml LB broth containing 5 μg/ml chloramphenicol at 37°C and 200 rpm. One millilitre of this culture was used to inoculate 15 ml of the same medium which was incubated under similar conditions. 0.1 mM of Isopropyl-D-thio-galactoside (IPTG) was added to induce expression when the Optical Density (OD) of the culture medium at a wavelength of 600 nm reached about 0.7.ml/hr. Cell pellet of samples was then sedimented by centrifugation at 10000 rpm for 10 min. Protein preparation process was carried out for each section separately. The procedure of protein precipitation from the supernatant liquid was performed by saturation with Polyethylene Glycol (PEG) 8000 followed by centrifugation at 10000 rpm for 20 min at 4°C. Subsequently, fidelity of the TPD protein samples prepared from the cultivation broth was checked by sodium dodecyl Sulphate-Poly-acrylamide gel (SDS-PAGE) and Western Blotting (WB). “ImageJ” software was used to estimate TPD expression level.Culture samples were withdrawn at different time intervals at 1 bp band. Analysis of sequencing results also confirmed the fidelity of TPD sequence fused with His-tag. Accuracy of pHT43 vector carrying TPD gene was rechecked by conducting colony PCR and enzymatic digestion analysis  is an appealing host for production of heterologous secretory proteins Existence of stable expression systems is a necessity for economical production of recombinant proteins. In comparison to B. subtilis over E. coli in production of recombinant proteins with pharmacological activities, feasibility of expression and secretion of TPD in B. subtilis have been examined in this study for the first time, even though the efficiency of expression was not high enough. It is accepted that every recombinant protein is unique and needs some levels of adaptation for production in different systems. Expression conditions must be optimized to enhance the yield of soluble protein. These optimizations must be carried out at both level of molecular regulations and fermentation conditions.Owing to above-mentioned advantages of B. subtilis was taken as a candidate for TPD production through a new simplified and cost-effective approach in this research. Further studies are needed to optimize, purify and asses the biological activity of TPD expressed into B. subtilis.


Combining single-cell RNA-seq of mouse ESCs and NPCs, lincRNA gene deletions, conditional RNA depletion, and nuclear exosome profiling distinguishes RNA-dependent and RNA-independent lincRNA gene activities. trans from those modulating local gene expression. Some genes express stable and/or abundant lincRNAs in single cells, but many prematurely terminate transcription and produce lincRNAs rapidly degraded by the nuclear exosome. This suggests that besides RNA-dependent functions, lincRNA loci act as DNA elements or through transcription. Our integrative approach helps distinguish these mechanisms.Eukaryotic genomes produce RNAs lacking protein-coding potential, with enigmatic roles. We integrated three approaches to study large intervening noncoding RNA (lincRNA) gene functions. First, we profiled mouse embryonic stem cells and neural precursor cells at single-cell resolution, revealing lincRNAs expressed in specific cell types, cell subpopulations, or cell cycle stages. Second, we assembled a transcriptome-wide atlas of nuclear lincRNA degradation by identifying targets of the exosome cofactor Mtr4. Third, we developed a reversible depletion system to separate the role of a lincRNA gene from that of its RNA. Our approach distinguished lincRNA loci functioning in  Eukaryotic genomes are pervasively transcribed by RNA polymerase II (Pol II), producing many long non-protein-coding RNAs (lncRNAs) in addition to mRNAs . LncRNAsAirn [Bendr or Lockd [The biological significance of lncRNAs is strongly debated , with keAirn ) or the or Lockd ) affectscis (involving adjacent genomic loci) or in trans . LincRNAs are highly differentially expressed between cell types  tail and regulation by key transcription factors . As lincll types  and manyll types . On the ll types . In suppll types . Furtherll types . New appll types .Two broad strategies are currently used to search for functional lincRNA genes. The first makes predictions based on the properties of the gene or the RNA product, including tissue- or cell type–specific expression, co-expression with other genes, evolutionary conservation, subcellular localisation, or RNA processing and stability . The secApproaches to identify functional lincRNA genes are rapidly improving in potency and ease of use. For example, direct RNA-targeting approaches using antisense oligonucleotides (ASOs)  or CRISPcis or in trans and whether the RNA product is required for this, and (iii) to more broadly assess the properties of lincRNA transcripts, to help understand how many lincRNA genes produce a functional RNA, versus how many might instead function as DNA elements or via the act of transcription.With the above considerations in mind, we sought to address three key challenges to understanding lincRNA gene functions : (i) to trans, we predict that up to a third of lincRNA loci act in cis to modulate the expression of neighbouring genes. Second, the RNA product may not be required for these local lincRNA gene activities. To investigate this further, we examined lincRNA properties. We profiled the transcriptome-wide direct targets of the nuclear exosome, finding that in contrast to mRNAs, most lincRNAs prematurely terminate transcription (within a few hundred nucleotides of their promoter) and are rapidly targeted for degradation. These properties support recent suggestions that functional lincRNA genes might commonly act as DNA elements or through transcription, without requiring the RNA  and derived neural precursor cells (NPCs) using both single-cell and bulk RNA sequencing. We identified lincRNAs with notable cell type– or subpopulation-specific expression and predicted functionally relevant lincRNA genes. Focusing on 16 of the most promising lincRNA genes, we generated cell lines in which these loci were deleted. In parallel, we developed a self-cleaving ribozyme approach to deplete the expression of some of these lincRNA transcripts, while minimising disruption to the lincRNA gene . Overall, our in-depth analyses of 140 cell lines highlight two key aspects. First, although many lincRNA genes function in  the RNA  and undeIn summary, our study unravels key differences between lincRNA and mRNA behaviours and provides a comprehensive resource to predict and experimentally test lincRNA gene functions and in particular the role of the RNA product.To identify lincRNA genes that might contribute to cell identity, we generated an atlas of lincRNA expression across a differentiation time course from mESCs to NPCs using both single-cell and bulk RNA sequencing. We selected this model system because many lincRNAs are expressed and a robust in vitro differentiation protocol exists . FurtherWe performed strand-specific, rRNA-depleted total RNA-seq for two biological replicates each of mESCs grown in 2i + LIF medium, mESCs grown in serum + LIF (leukemia-inhibitory factor) medium, and during days 3, 6, and 8 of the in vitro NPC differentiation. To obtain a single, comprehensive catalogue of lincRNA genes, we combined a de novo transcript assembly from our data with published lincRNA coordinates  and EnseLincRNA and mRNA combined annotations and properties. (A) Set of annotations used in this study. (B) Highly variable lincRNAs used for Fig 2A. Related to Figs 1, 2, and 6. This file contains a key describing each column.Table S1 linc1503) was modulated during neuronal differentiation  are differentially expressed between these three (and other) subpopulations  was sufficient to resolve cell types  and confPvt1, linc1333, Pvt1 that accumulate to high levels in “jackpot cells” may function in specific subpopulations of cells.We anticipated lincRNAs to be more heterogeneously expressed than mRNAs across single cells  or G2/M   between 113 lincRNA and ∼3,600 protein-coding genes  . Notablygulation . TherefoSignificant correlations with lincRNA expression, across single cells. Related to Fig 2F.Table S3 In summary, our integrated bulk and single-cell RNA-seq analysis provides a comprehensive catalogue of 1721 lincRNAs detected during mESC to NPC differentiation (Tables S1 and S2). We classify lincRNAs expressed specifically in mESCs, NPCs, and/or across subpopulations of cells or during specific cell cycle stages and explore the potential of these lincRNA genes to participate in gene regulatory networks (Table S3). Ultimately, however, such computational predictions must be tested. Therefore, based on these analyses, we prioritised 16 lincRNA genes expressed in mESCs that we now sought to investigate experimentally .We wanted to rigorously test the functions of our shortlisted lincRNA genes  and to iOur method is simple and relies only on the stable integration of a 52-nt self-cleaving ribozyme sequence into a lincRNA gene using CRISPR-Cas9. Following transcription, the lincRNA transcript is cleaved and ultimately degraded. Our approach has several advantages: (i) it targets lincRNA transcripts with minimal perturbation to the DNA locus, which can additionally be controlled with an inactive point mutant ribozyme, (ii) it knocks down nuclear targets, (iii) it can be used to study long-duration processes such as cell differentiation, (iv) it does not suffer from nonspecific and off-target effects, and (v) it can be reversed with a blocking ASO to rescue lincRNA expression.G9a, by homozygous integration of the HHRz (G9a expression by up to 81% across four different cell lines (versus five controls), resulting in widespread gene expression changes of known G9a targets  and Hepatitis Delta virus (HDVRz), for their ability to cleave different reporter constructs  and S2A the HHRz . The HHR targets , as meas targets . This colinc1405, linc1509, and linc1611)  could be due to the sequence context of the tested site impeding ribozyme folding. Inclusion of a flanking insulator sequence  might help for these genes, or an alternative strategy  could be tested. Importantly, our method can destabilise nuclear transcripts such as linc1405 and linc1509 (linc1509 intron (= pre-lincRNA) levels , obtaining between two and eight biological replicates for each gene, and assessed knockdown efficiency by RNA-seq  and in sinc1611) . The inelinc1509 , support) levels .A stable knockdown can highlight the impact of lincRNA transcripts on steady-state gene expression and/or across temporal processes . However, lincRNAs might perform transient roles affecting cell state and transitions. Furthermore, some lincRNAs exhibit high variability in expression between clonal cell lines (in part due to their low expression levels), which make comparisons of wild-type and knockdown cell lines challenging and demands many biological replicates. We therefore sought to inhibit HHRz cleavage in our cell lines to rapidly rescue lincRNA levels and thus enable short-lived or subtle lincRNA functions to be studied in an isogenic background.linc1405. Cells treated with 1.5 μM toyocamycin for 12 h partially recovered linc1405 levels (to ∼30% of wild-type), but this was accompanied by massive cell death and perturbation of housekeeping genes such as TBP (data not shown). The drastic effect on cellular viability is consistent with toyocamycin effects when used in Toxoplasma gondii , the depletion is stable over many cell divisions, and RNA levels can be rescued using a blocking ASO. Therefore, as we now began to examine the functions of our shortlisted lincRNA genes in more detail, where possible, we included lincRNA HHRz knockdown cell lines alongside lincRNA gene deletions.In summary, the HHRz approach is a useful addition to the expanding range of methods  for dissecting the functions of lincRNA genes, and in particular, for testing the role of the RNA product . Using tcis (adjacent genes) or in trans (distant genes). To examine the functions of our shortlisted lincRNA genes  List of cell lines used. The groups used for DESeq2 analysis  are indicated. The control group includes wild-type cell lines and cell lines where targeting a locus with for deletion or ribozyme integration was not successful. (B) Guide RNA sequences used to delete lincRNA genomic loci. Related to the Materials and Methods section and Fig S3A.Table S4 Oligonucleotides used in this study. Cas9 guide RNA sequences are highlighted in red. Related to the Materials and Methods section.Table S5 Miat deletion, genes involved in cell cycle progression  or cardiac muscle development (Sox6) were misexpressed. This is consistent with the reported roles of Miat in myocardial infarction and cell cycle regulation , or the conservative nature of our approach whereby we included many negative control cell lines. Nonetheless, for 5/10 lincRNA loci we tested, we observed some evidence of functions in trans.We first looked for global transcriptional changes in the lincRNA gene deletion cell lines, using wild-type cell lines and cell lines with neutral integrations or genome edits as controls  site insertions and a recent study which identified an intronic region of Firre that can function as a DNA enhancer element in a reporter assay , we compared our HHRz and deletion cell lines . We obtalincRNA_2485 neighbourhood, cis-regulation involving lincRNA genes could be widespread, although this can also reflect co-regulation of nearby mRNA and lincRNA pairs. To distinguish between these possibilities on a gene-by-gene basis, further lincRNA knockdown and deletion experiments will be required.We re-examined all our RNA-seq data and found that numerous lincRNAs were co-expressed with adjacent genes  and overcis or in trans, and in an RNA-dependent or RNA-independent manner). This underscores the importance of testing the role of the RNA product with knockdown approaches, such as the HHRz method we introduce, ASOs, or CRISPR-Cas13. These methods can easily show when an RNA product is functional  , and thiTo resolve this key question, we obtained our own set of transcriptome-wide RNA half-life measurements for mESCs, by monitoring global RNA levels following transcription shut-off by actinomycin D. Compared with mRNAs, lincRNAs had a slightly shorter median half-life when expression levels were considered . RepeatiIn yeast, lncRNA classes are distinguished by differential targeting by the nuclear exosome and its cofactor complexes , and based on exosome knockdown studies, the same appears to be true in mammals . We reasThis approach offers unique advantages over RNA half-life measurements. First, the cells are not perturbed, and so we accurately capture steady-state exosome substrates. Second, this approach provides a direct readout of nuclear degradation, and so the results are not complicated by the high redundancy between RNA decay pathways. Third, we can capture transient interactions, enabling us to monitor processes such as co-transcriptional decay. Fourth, as Mtr4 is present in various nuclear exosome cofactor complexes , we simWe obtained four CRAC datasets for Mtr4, and one for an untagged cell line. As Mtr4 targets specific regions of ribosomal RNA precursors , we valiExons: Mtr4 binding here reflects mature mRNA turnover .Introns: Mtr4 binding here implicates it in intron removal. Notably, we observed a prominent peak of oligo(A)-tailed reads mapping to intron 3′ ends see also , suggestPromoter-proximal regions: Mtr4 bound abundantly to RNA fragments mapping to the 1-kb region downstream of the transcription start site (TSS) see also . This reUpstream antisense regions: Mtr4 also bound abundantly to antisense transcripts produced upstream of the TSS . This reWe next analysed mRNAs, where Mtr4 acts in multiple decay pathways. We were able to distinguish these decay pathways based on RNA fragments mapping to the following mRNA regions .Exons: MSubsequent analyses revealed that ptRNAs and PROMPTs behave identically and differed only in their orientation relative to the mRNA promoter. For clarity, we therefore refer to these transcripts as sense PROMPTs and antisense PROMPTs  and nuclear stability of the mature transcript. Next, we used our data to quantitatively compare these processes for mRNAs and lincRNAs.linc1494 has previously been suggested to function as a cis-regulatory element, based on its conservation with a human enhancer  and normalising to Pol II NET-seq as a measure of transcription  tails, reasoning that as these reveal initial sites of Mtr4 recruitment to excised introns , they woPlotting the average nucleotide distribution around these genomic sites revealed an enrichment of Ts on both sides and an enrichment of G further upstream . This palinc1405 and several other lincRNA loci  which are functionally relevant, and for those that are, (ii) is the RNA itself functional? The greatest challenges in answering these questions are the overwhelming number of lincRNA genes, which must somehow be prioritised for in depth analysis, and the current lack of methods to distinguish RNA-dependent from RNA-independent functions . By comptrans functions , which was recently shown to play a role during mesendoderm differentiation . These knockdowns did not affect neighbouring gene expression, in contrast to the lincRNA locus deletions, and we speculate that these lincRNA loci act independently of the RNA product. As some RNA remains, we cannot rule out the possibility that the remaining RNA was sufficient for function (a caveat of any knockdown method). Parallel approaches can help resolve this ambiguity, such as looking at the properties of the RNA. For example, in the case of the linc1405 locus, which enhances expression of the neighbouring gene Eomes, our CRAC analysis revealed the transcript to be highly unstable and therefore most likely a dispensable by-product. Furthermore, a previous study (linc1405 and Eomes loci and found that blocking linc1405 transcription with a poly(A) signal does not prevent linc1405 activating Eomes expression. Therefore, the DNA element or promoter-proximal transcriptional activity of the linc1405 locus appears to be sufficient for contacting and activating expression of Eomes. We suggest that a strategy combining locus deletion, poly(A) integration, HHRz/ASO/CRISPR-Cas13 knockdown, and inspection of RNA properties is the most informative approach for investigating new lincRNA genes. Notably, the Pvt1 locus was also shown to function via its promoter element, which competes for contacts with enhancers  function despite having a low abundance , and low in NPCs , and it  in NPCs . As neurIn summary, it is now clear that when studying a lincRNA gene, it is critical to consider the roles of transcription, the DNA element, and the RNA product . This wiFor the mESC to NPC differentiation, we used male mESCs from C57BL/6J female × Mus musculus castaneus male (BC8) and Mus musculus castaneus female × C57BL/6J male (CB9) crosses . For alll-glutamine, 15% (vol/vol) FBS, 0.1 mM β-mercaptoethanol, 50 μg/ml penicillin, 80 μg/ml streptomycin, and home-made LIF conditioned medium) on gelatin-coated dishes. Where indicated, mESCs were grown in 2i medium  on gelatin-coated dishes. All cell cultures were grown at 37°C with 5% CO2.mESCs were mostly cultured in serum + LIF medium  on bacteriological Greiner Petri dishes in 15-ml cell aggregate (CA) medium . The plates were gently shaken twice per day to avoid very large CAs forming. CA medium was replaced after 2 d, by gently transferring the CAs into a 50-ml Falcon tube using a 25-ml pipette, allowing the CAs to settle for 5 min, resuspending the CAs in fresh CA media using a 25 ml pipette, and transferring the CAs into a fresh dish. CA medium was replaced again after a further two days and retinoic acid added . The medium was again replaced after a further two days, with CA medium + 5 μM retinoic acid.mESCs were cultured for two passages on gelatin-coated plates before dissociating with trypsin, washing twice in PBS, and seeding  were transfected into mESCs using the Lipofectamine 3000 Transfection kit, and after 24 h of incubation, the medium was replaced with fresh medium containing either guanine (100 μM with 400 μM NaOH) or 400 μM NaOH (control treatment). Luciferase assays were performed 48 h post-transfection using the Dual-Luciferase Reporter Assay System according to the manufacturer’s protocol. Luciferase activities were measured with a microplate luminometer . Renilla was normalised to firefly and all values represent the mean ± SD from three biological replicates.To test the HDV ribozyme and aptazymes, the psiCHECK-2 vector was used, with the various sequences cloned downstream of the Active:CGUAGAUCUCCCCCCCACCCCUAAAGAAUACCACGCCUGUCACCGGAUGUGUUUUCCGGUCUGAUGAGUCCGUGAGGACGAAACAGGAACACAAAUCACCCGCUCUCUAAUACACACAUUCACCUAAUAGAAInactive:CGUAGAUCUCCCCCCCACCCCUAAAGAAUACCACGCCUGUCACCGGAUGUGUUUUCCGGUCUAAUGAGUCCGUGAGGACGAGACAGGAACACAAAUCACCCGCUCUCUAAUACACACAUUCACCUAAUAGAATo test the hammerhead ribozyme, the wild-type or mutant ribozyme sequences were inserted into the 3′-RNA end accumulation during turnover (TREAT) reporter  downstreO-(2-methoxyethyl) chemistry (2′-MOE) and C nucleotides were further modified with a methyl at the 5 position (5-methyl-2′MOE-C).Transfections of 15 pmol fully modified 2′-O-methoxy ethyl/phosphorothioate oligonucleotides were performed using 1.5 μl Lipofectamine 3000 reagent and 100 μl OptiMEM, which was added to 250,000 mESCs in 250 μl mESC medium. For lower concentrations, less of the oligo/Lipofectamine/OptiMem mix was added (20 μl or 4 μl). The medium was replaced after 6-8 h with fresh mESC medium, and RNA extracted after a total of 24 h using the Norgen Single Cell RNA purification kit. The oligos GAAAACACATCCGGTGACAG (HHRz#2) and CGTTCTCGAGCCGACTCACA (negative control) were used. The antisense oligonucleotides were fully modified with 2′-For qRT–PCR of mESC ribozyme cell lines, RNA was extracted using the Agilent Absolutely RNA Miniprep kit and 500 ng used for reverse transcription with the Primescript RT Kit. qRT–PCR was performed using the SsoAdvanced SYBR Green Supermix on a CFX96 Real-Time PCR System. Relative RNA abundance was calculated from CT values using the ΔCT method and normalising to TBP mRNA levels.For single-cell RNA sequencing, 4,000 cells were loaded onto a 10- to 17-μm Fluidigm C1 Single Cell mRNA Seq IFC, and the cells were captured according to the manufacturer’s instructions. The captured cells were manually annotated under a microscope, to identify capture chambers containing cell debris, dead cells, no cells, or multiple cells. Reverse transcription and cDNA preamplification was then performed in the 10- to 17-μm Fluidigm C1 Single Cell mRNA Seq IFC using the SMARTer Ultra Low RNA kit for the Fluidigm C1 System and Advantage 2 PCR kit. cDNA was harvested and diluted to 0.1–0.3 ng/μl. Sequencing libraries were then prepared using the Nextera XT DNA Sample Preparation kit and Nextera XT DNA Sample Preparation Index kit according to the Fluidigm instructions, “Using the C1 Single-Cell Auto Prep System to Generate mRNA from Single Cells and Libraries for Sequencing.” The 96 libraries from one IFC were pooled and 150-bp paired-end sequencing performed on the Illumina HiSeq2000.For bulk RNA sequencing of mESCs and NPCs from the differentiation time course, RNA was extracted and libraries prepared using the Wellcome Trust Sanger Institute sample preparation pipeline with Illumina’s TruSeq stranded RNA Sample Preparation Kit including ribosomal depletion step (RiboMinus). RNA and library quality control was performed by the WTSI sequencing facility. For bulk RNA sequencing of mESC ribozyme cell lines, RNA was extracted using the Agilent Absolutely RNA Miniprep kit, ribosomal RNA depleted using the ribo-zero rRNA removal kit, and libraries prepared using the ScriptSeq V2 RNA-Seq Library Preparation kit.−2). The cells were lysed by incubating with 10 ml RIPA buffer in the dish on ice for 30 min . The cells were further disrupted using a cell scraper and then lysates collected and centrifuged at 6,500 g for 20 min at 4°C. The supernatant was snap-frozen in liquid nitrogen until required. Note that milder extraction conditions  can be used for cytoplasmic or non-chromatin-associated proteins, which will greatly improve subsequent binding to the FLAG beads.mESCs were grown in 2 × 15-cm dishes to ∼80% confluency, the dishes washed twice with PBS, the PBS removed, and cells cross-linked on ice (with the dishes facing up) in a Stratagene Stratalinker 2400 .Lysates were thawed on ice, and incubated with 100 µl anti-FLAG M2 magnetic beads overnight. The supernatant was then removed, and beads washed three times with 1 ml TN150 . Protein–RNA complexes were eluted by incubating the beads in 1.5 ml TN150 supplemented with 5 mM β-mercaptoethanol and 0.3 mg/ml 3× FLAG peptide, rotating at 4°C for 2 h. The eluate was then incubated with 50 µl Dynabeads M-280 Streptavidin, rotating at 4°C overnight. The beads were washed twice in TN600  and twice in TN150 supplemented with 5 mM β-mercaptoethanol. RNA was then fragmented by incubating the beads in 500 μl TN150 supplemented with 5 mM β-mercaptoethanol and 1 μl of 0.1 U diluted RNace-IT. After 4 min at 37°C, the reaction was quenched by replacing the solution with 400 μl WBI . The beads were washed twice more in WBI, then three times in 400 μl 1× PNK  Alkaline phosphatase treatment : 8 U TSAP and 80 U RNasIN.(ii) 3′ linker ligation : 0.1 nmol miRCat-33 DNA linker, 40 U T4 RNA Ligase 1, 80 U RNasIN, and 12.5% (vol/vol) PEG8000.(iii) 5′ phosphorylation : 40 U T4 PNK and 2 μl γ32P-ATP .(iv) 5′ linker ligation : 0.2 nmol 5′ linker, 40 U T4 RNA Ligase 1, 1.25 mM rATP, 80 U RNasIN, and 12.5% (vol/vol) PEG8000.The following four enzymatic reactions were then performed in 80 μl 1× PNK buffer (omitting the Nonidet P40 substitute), to ligate 3′ and 5′ adapters onto the RNA fragments. After each enzymatic reaction, the beads were washed once in WBI and thrice in 1× PNK.After the final reaction, the beads were washed three times in WBII , resuspended in 30 µl 1× NuPAGE LDS sample buffer, heated at 95°C for 2 min, and the eluate quickly removed and loaded onto a NuPAGE 4–12% polyacrylamide gel. The gel was run at 100 V for ∼1 h, and then protein–RNA complexes transferred to Hybond-C extra nitrocellulose membrane (Amersham) at 150 V for 1.5 h using a wet transfer system and NuPAGE transfer buffer with 15% methanol. The membrane was then briefly dried, exposed to BioMax MS film (4 h to overnight), and the region corresponding to the protein–RNA complex cut out.g for 20 min. The top phase was transferred into a new tube and 1 ml ethanol and 20 μg glycogen added. The solution was stored at −20°C overnight to precipitate RNA and then centrifuged at 14,000 g for 30 min. The RNA pellet was washed once with 70% ethanol and allowed to briefly air-dry, before resuspending in 11 μl water + 1 μl 10 μM miRCat-33 RT oligo + 1 μl 10 mM dNTP mix. The solution was heated to 80°C for 3 min, snap-cooled on ice for 5 min, then the following mix added: 4 μl 5× first-strand buffer (SSIII kit) + 1 μl 100 mM DTT (SSIII kit) + 1 μl recombinant RNasIN. After incubating for 3 min at 50°C, 200 U of SuperScript III was added and the reverse transcription allowed to proceed for 1 h at 50°C. The reaction was stopped by heating to 65°C for 15 min, then RNA digested with 10 U RNase H at 37°C for 30 min. PCR reactions (80 μl) were then prepared, each with 2 μl cDNA, 10 pmol P5, 10 pmol PE, 12.5 nmol each dNTP, and 2.5 U LA Taq. Typically, we ran five PCR reactions and then concentrated the products by ethanol precipitation before resolving on a 3% metaphor agarose gel in 0.5× TBE. A smear corresponding to the size of the two adapters plus inserts  was then cut out and DNA extracted using the MinElute Gel Extraction kit, eluting in 20 μl water. If the experiment was successful, we repeated the PCRs with the remaining half of the cDNA.The membrane slice was then incubated in 400 μl WBII with 1% (wt/vol) SDS, 5 mM EDTA, and 100 μg Proteinase K at 55°C for 2 h. The solution was then removed to another tube, 50 µl 3M NaAc, pH 5.2, and 500 µl of 1:1 phenol:chloroform mix added, and the mixture vortexed and centrifuged at 14,000 To measure transcriptome-wide RNA half-lives, 300,000 mESCs were seeded per well of 2 six-well dishes and grown for 48 h in serum + LIF medium. The medium was replaced by fresh medium containing 5 μM actinomycin D (from a 5 mg/ml stock in DMSO) and the cells incubated for 30, 90, or 240 min. A mock treatment (240 min) was included, using medium with the same amount of DMSO but no actinomycin D. After the indicated times, the wells were washed twice with 37°C PBS and RNA extracted using the Agilent Absolutely RNA Miniprep kit. ERCC RNA spike-ins were added to the lysis buffer (1.7 μl of a 1:10 dilution per sample) before it was added to the cells. Ribosomal RNA was then depleted with the ribo-zero rRNA removal kit and high-throughput sequencing libraries prepared using the ScriptSeq V2 RNA-Seq Library Preparation kit.(i) (ii) (iii) Ensembl GRCm38.75 lincRNAs, miscRNAs, processed_transcripts, and antisense features.To compile a reference set of lincRNA annotations, features from the following sources were combined:Transcripts were excluded if they overlapped in either orientation with protein-coding Ensembl transcripts or a variety of “classical” noncoding RNAs defined by Ensembl . In cases where the remaining lincRNA transcripts overlapped, they were grouped into single clusters (“genes”). A small number of the resulting clusters were very long (>100 kb), corresponding to regions where individual genes could not easily be distinguished, and were excluded from subsequent analyses. The lincRNA annotations were then combined with all Ensembl non-lincRNA annotations to make a single reference file.(i) exonic sequence >1 kb,(ii) <50% of the exonic sequence overlaps with RepeatMasker features,(iii) putative lincRNAs are >5 kb away from mRNAs or “classical” noncoding RNAs, and(iv) expressed at ≥5 DESeq2 normalised counts per kb in at least one mESC or NPC bulk RNA-seq sample.For some analyses, this reference file was also supplemented with our own set of lincRNAs assembled from bulk RNA-seq data using Cufflinks. First, reads from 10 samples  were mapped to the mm10 genome with Tophat2, including splice sites from the reference file defined in “LincRNA annotations.” The 10 alignment files were then provided to Cufflinks along with the Ensembl/Necsulea/Guttman reference file for de novo transcript assembly, resulting in a merged set of transcript annotations. Any novel transcripts that did not overlap with Ensembl genes, or overlapped with a lincRNA gene, were provisionally classed as lincRNAs. This file contains many de novo–defined unannotated transcripts and was used whenever we wanted to quantify expression of the most complete set of noncoding RNAs. However, for analyses where we wanted to focus on a higher quality set of lincRNAs, we applied the following additional filters:LincRNAs and mRNAs that pass these filters are indicated in the “high.quality.biotype” column of Table S1.(i) low translational efficiency defined using riboSeqR analysis of mouse ribosome profiling data (Array Express study E-MTAB-2934),(ii) noncoding prediction by lncRScan-SVM , and(iii) expressed at >30 DESeq2 normalised counts per kb in at least one mESC or NPC sample.To define a set of 15 representative lincRNA genes for functional studies, we additionally considered the following filters, as well as extensive visual inspection of our RNA-seq datasets for ∼200 possible candidates:Genomic regions with 20 or more genes per 100 kb were also excluded as these were often incompletely assembled genes in regions of lower sequencing coverage.To quantify lincRNA versus mRNA stability and transcription termination , we filtered the “high.quality.biotype” lincRNAs/mRNAs further to obtain transcripts for which there was good supporting evidence that the annotated 5′ end was accurate —see “Calculating Mtr4:PolII ratios for PROMPTs, exons and introns.” For these analyses, we did not always require the lincRNA to be 5 kb away from a protein-coding gene, so long as its TSS was the “stronger” one .Reads for the 10 samples were mapped to the mouse genome (mm10) with Tophat2 and counted for features in the combined reference file (including de novo lincRNAs) using ht-seq count (mode = union). Feature counts were normalised using the rlog function in DESeq2 and the 1,000 genes with the highest variance plotted as a heat map, clustered based on pairwise Euclidean distances between genes.Paired-end reads were preprocessed using Trimmomatic, with the following settings: ILLUMINACLIP:Nextera_PE_adapters.fa:2:30:10:1:true LEADING:28 TRAILING:28 SLIDINGWINDOW:1:28 MINLEN:50. The reads were then aligned to mm10 using gsnap version 2015-09-29 with the following settings: --suboptimal-levels = 5 -n 1 -Q –nofails, and providing splice sites from the combined reference file (excluding de novo lincRNAs). Reads were counted for features using htseq-count (mode = union), and using the Ensembl/Necsulea/Guttman reference file (but not including cufflinks-defined novel noncoding RNAs). In total, 672 datasets were analysed, corresponding to 2 × 96 mESCs in 2i, 2 × 96 mESCs in serum + LIF, 96 NPCs (day 6), and 2 × 96 NPCs (day 8).(i) Visual inspection of the IFCs under the microscope .(ii) Low-quality cells defined by the cellity package.(iii) Cells with <0.5 million mapped reads.(iv) Cells with >10% of reads from mitochondrial genes.A number of features were then used to filter out low-quality cells.cis. To compare single-cell and bulk RNA-seq data in the same PCA, single cell versus bulk batch effects were first removed using the ComBat package in R.For principle component analysis (PCA), only mRNAs and lincRNAs were retained. The cells were normalised using the estimateSizeFactorsForMatrix function from DESeq2, and then PCA performed using the prcomp function in R. The 300 genes making the greatest contribution to each of the first seven principle components were then recorded as the “most variable genes,” and these were used for subsequent clustering analyses based on pairwise Euclidean distances. These distances were also used for examining correlated expression of genes in To measure gene expression heterogeneity between mESCs or between NPCs, we calculated the distance to the median, based on the method described in . For thi(i) The 3′ adapter was removed (if present) using fastx_clipper.(ii) Low-quality sequence was removed using the FASTX-Toolkit, specifically fastq_quality_trimmer -t 25, fastq_quality_filter -q 20 -p 90, and fastx_artifacts_filter.(iii) Reads with identical sequence and 5′ inline unique molecule identifier, UMI, were collapsed, as they are likely to arise from duplication during PCR.(iv) For CRAC, low-complexity regions were trimmed from the 3′ end of reads . This is because many Mtr4-bound RNA fragments contain non-genome-encoded oligo(A) tails that can lead to apparently unique mapping of repeat RNAs.(v) Homopolymers and dinucleotide-rich sequences were removed using prinseq-lite, with the following settings -min_len 18 -lc_threshold 20 -lc_method dust.Sequencing reads were preprocessed for standard analyses as follows.To obtain a set of reads that do not map to repetitive regions of the genome or small noncoding RNAs , we then filtered reads as follows. The reads were mapped separately, using NovoAlign, to three indexes: (i) the mm10 genome, (ii) mm10 protein-coding transcripts (cDNAs), and (iii) pseudochromosomes assembled from RepeatMasker repeats, the ribosomal DNA repeat  and gencTo examine reads arising from Mtr4-bound RNA fragments that contain non-genome-encoded oligonucleotide tails, only those reads for which a 3′ adapter (7 nt or longer) could be detected using Trimmomatic were retained. The reads were then filtered to retain only those for which each position has a FASTQ quality score of at least 30, and PRINSEQ was used to filter out homopolymers or dinucleotide-rich sequences. Identical sequences (including the inline 5′ UMI) were then collapsed to remove PCR duplicates.Reads for which 3′ adapters were detected and removed (described in the CRAC and NET-seq sequencing read preprocessing and filtering section) were mapped to mm10 using BLASTn (version: ncbi-blast-2.5.0). Any non-mapping region at the 3′ end of the read was recorded as a non-genome-encoded tail, removed from the 3′ end of the read, and annotated at the end of the read name. To exclude reads mapping to repeats, reads were processed as described above, resulting in a set of “nonrepeat” reads. As some of the non-genome-encoded tails might in fact arise from spliced RNA fragments, any reads mapping better to spliced mRNAs than to the genome were also excluded.≥2 tails were selected, the genome encoded portion mapped to the genome using bowtie2, and the 3′ end position recorded. Reads were then filtered depending on whether they mapped to introns, PROMPT regions , or antisense PROMPT regions . For genes where more than one A-tailed read mapped to a region, one read was selected at random. The genomic sequence was then extracted for 201-nt windows centred on each of the selected 3′ end positions, and the average nucleotide composition calculated at each position along the PROMPT, antisense PROMPT, and intronic windows. To distinguish intronic A-tailed reads aligning precisely to the 3′ end of the intron from those mapping elsewhere in the intron, we split the intronic reads into those ending with AG versus those that do not.To examine the genomic sequence composition around the 3′ end of RNA fragments with non-genome-encoded oligo(A) tails, reads with AFor pre-mRNA metaplots, “nonrepeat” reads were mapped to the genome (mm10) using bowtie2 (mode: -sensitive). The midpoint of each mapped read was taken and counted for 5-nt bins flanking each end of exons from protein-coding genes. For each gene, the Appris principal 1 isoform was used (selecting one at random where >1 existed for a single gene). For each transcript, bin totals were divided by the maximum bin total, and then bin averages calculated for each bin across all transcripts. For metaplots of 2-kb regions centred on protein-coding gene TSSes, the same procedure was used, except the bins were 10-nt wide.(i) The TSS is within 0.5 kb of an H3K4me3 ChIP-seq peak defined by MACS2.(ii) The sense PROMPT region for the TSS has a moderate NET-seq signal (20 counts).(iii) The sense PROMPT region has higher NET-seq signal than other TSSes of the same gene.(iv) There are no other 1-kb regions in the same gene that are not annotated as a TSS but that have > twofold more NET-seq signal than the 1-kb PROMPT region .(v) The RNA-seq signal in the 2-kb sense direction from the TSS must be at least 1.5× that of the RNA-seq signal in the upstream 2-kb antisense region (to remove transcripts that are upstream antisense PROMPTs).(vi) The transcript must not overlap snoRNAs.(vii) The transcript must be >2 kb, with at least 1 kb of exonic sequence.For this analysis, the set of lincRNA and mRNA annotations including de novo defined Cufflinks features was used. For each transcript, the primary TSS was identified using the following criteria.(i) sense PROMPT region: 1-kb downstream of the TSS, sense direction,(ii) antisense PROMPT region: 1-kb upstream of the TSS, antisense direction,(iii) exons: all exonic regions outside of the sense PROMPT region, and(iv) introns.For the transcripts corresponding to the selected TSSes, the transcript region was divided into the following categories:CRAC and NET-seq reads that do not map to repeats (see the CRAC and NET-seq sequencing read preprocessing and filtering section) were then counted for each of these features, for each selected lincRNA and mRNA transcript, and the Mtr4:PolII ratio calculated.G9a knock out dataset, G9a knock-out and wild-type RNA-seq data were aligned with gsnap, reads counted for Ensembl features, datasets normalised using the estimateSizeFactorsForMatrix function in DESeq2, and the fold change calculated (adding a pseudocount of eight to each measurement). Genes with a fold change of >1.5 were recorded as up-regulated. The significance of the overlap between up-regulated genes in our dataset and the Macfarlan dataset was assessed using a chi-squared test.Sequencing reads were aligned to the genome using gsnap, providing splice sites from the combined reference file (excluding de novo lincRNAs) and only retaining uniquely mapping reads. The reads were then counted either for Ensembl features or for the combined reference file (including de novo lincRNAs), using htseq-count (mode = union). For PCA, datasets were normalised using the estimateSizeFactorsForMatrix function in DESeq2, and batches of samples sequenced at different times corrected by centring the means in log space. For analysis of gene expression adjacent to lincRNA loci, the datasets were similarly normalised and batch-corrected by centring the median in log space. Expression changes were evaluated for significance using a Monte Carlo simulation. For transcriptome-wide differential expression analysis, the Wald test in DESeq2 was used (padj < 0.01), including the sequencing data as a factor in the design matrix. To identify genes changing in expression in the Macfarlan 1/2 = (240 × log(2))/log(med.ncounts.0/med.ncounts.240).For RNA half-life analysis, counts for lincRNAs and mRNAs in the combined reference file (including de novo lincRNAs) were used. Datasets were normalised using size factors calculated for the ERCC RNA spikes (mean count > 50) using the estimateSizeFactorsForMatrix method in DESeq2. To calculate half-lives, the following formula based on median counts at time = 0 and time = 240 was used: tGSE107493.All sequencing data have been deposited in the Gene Expression Omnibus, accession code: https://doi.org/10.26508/lsa.201800124.Supplementary Information is available at


Cellular protein-RNA complexes assemble on nascent transcripts, but methods to observe transcription and protein binding in real time and at physiological concentrations are not available. Here, we report a single-molecule approach based on zero-mode waveguides that simultaneously tracks transcription progress and the binding of ribosomal protein S15 to nascent RNA transcripts during early ribosome biogenesis. We observe stable binding of S15 to single RNAs immediately after transcription for the majority of the transcripts at 35 °C but for less than half at 20 °C. The remaining transcripts exhibit either rapid and transient binding or are unable to bind S15, likely due to RNA misfolding. Our work establishes the foundation for studying transcription and its coupled co-transcriptional processes, including RNA folding, ligand binding, and enzymatic activity such as in coupling of transcription to splicing, ribosome assembly or translation. The early steps of ribosome assembly occur co-transcriptionally on the nascent ribosomal RNA. Here the authors demonstrate an approach that allows simultaneous monitoring of transcription and ribosomal protein assembly at the single-molecule level in real time. Many cellular functions rely on the formation of large protein-RNA complexes (RNPs), which is often coupled to fundamental processes such as transcription or translation. The assembly of compositionally heterogeneous RNPs begins with the transcription of the RNA and can occur through multiple parallel pathways. Transcription kinetics can influence the RNA folding pathway, which in turn affects both co- and post-transcriptional assembly of proteins or other ligands on the nascent RNA. Therefore, approaches are needed to observe directly the coupling between RNA synthesis and assembly process representing the physiological context for RNA folding.3. They have been extended beyond binary ligand-macromolecule interactions to monitor enzyme movement during transcription, translation, and replication and at the same time providing compositional and conformational information on those macromolecular machines in real time18. While these approaches have provided unprecedented insight into the structure-activity relationship of specific multicomponent systems, they suffer from one or more drawbacks: the lack of high-throughput measurements to capture rare events, limitation to low nM concentrations of fluorescently-labeled macromolecules, and limits on the number of components or observables that can be simultaneously monitored. Critically, the ability to work at higher, physiological ligand concentrations (>100 nM) is needed to define the kinetics of complex multistep processes such as the competition between RNA folding and protein binding that occur during co-transcriptional ribosome assembly or splicing. Data from sufficient numbers of molecules are crucial for statistically-robust analysis of these complex mechanisms.Delineating the coupling between assembly processes and biopolymer synthesis presents a significant experimental challenge. Single-molecule experiments are real-time approaches that allow simultaneous monitoring of multiple compositional and conformational parameters for complex systems with high temporal (ms) resolution20. This technology has been exploited for DNA sequencing21, for studying translation by the ribosome16 and for other applications22. By allowing high-throughput single-molecule analysis at high ligand concentrations at high temporal (10 ms) resolution, processive reactions such as transcription and translation can occur efficiently, and different reaction pathways can be observed directly.Zero-mode waveguide (ZMW) fluorescence microscope technology allows single-molecule real-time dynamics of complex biological systems to be delineated at physiological ligand concentrations for thousands of single biomolecules simultaneously through four spectral channelsHere we have developed a general method to track both transcription and the simultaneous assembly of proteins on the nascent transcript using ZMW technology. Stalled transcription complexes, comprising a DNA template, RNA polymerase (RNAP), and a short leader transcript, were immobilized in ZMWs, to allow observation of protein binding to single nascent RNAs Fig. . TranscrE. coli ribosomal protein S15 to nascent transcripts of the central domain of 16 S rRNA. Our results show that many transcripts do not bind to S15 at 20 °C. For other nascent RNA molecules, S15 binding occurs immediately after termination of transcription, however not co-transcriptionally. We find two distinct modes of S15 binding, which are rapid and transient association and dissociation of S15, and kinetically stable binding of S15. Overall, this approach provides an unprecedented view of the individual molecular events that occur during protein binding to a nascent RNA transcript, providing a powerful platform to understand the mechanism of co-transcriptional ribosome assembly, as well as other complex macromolecular processes.The complex process of ribosome assembly involves co-transcriptional binding of over fifty ribosomal proteins (r-protein) to the ribosomal RNA (rRNA). As a model system, we monitor here the binding of E. coli P1 promoter at the 5’-end, and a transcription terminator at the 3’-end  was initiated with nucleotide triphosphates (NTPs), omitting GTP, resulting in a complex with RNAP stalled at the first guanine nucleotide. This complex bears a nascent transcript of 50 nucleotides and is sufficiently stable to be visualized on a native gel . Real-time single-molecule experiments were initiated by delivering all four NTPs to the immobilized, stalled transcription elongation complex Fig. . To moniConsistent with a transcription elongation-dependent movement of the template 3’-end into the excitation volume close to the ZMW surface Fig. , we obse23. Consequently, the mean absolute fluorescence intensity change during transcription was poorly correlated with transcript length  ensured rapid detection of full-length RNA .To determine the NTP-dependence of transcription, transcription rates for a 171 nt RNA were measured at NTP concentrations ranging from 25–1000 μM. The mean rates varied between 0.4–16.3 nt suct Fig. . The rat–1 to 56.2 nt s–1 upon increasing the temperature from 20 °C to 35 °C , and with the reported activation energy for the rate of RNA elongation in vivo of ~10.9 kcal mol–1(ref. 27).Next, we investigated the temperature dependence of the transcription rate using the 517 nt construct. The rate increased from an average of 25.6 nt s °C Fig. . We fit –1), by controlling NTP concentration or temperature.These results show that we can monitor real-time transcription of single RNAs from DNA templates ranging from ~100 to 500 nucleotides, at transcription rates spanning more than two orders of magnitude  followed by two Cy3 dyes at the 3’ terminus  changes between different helical arms41. However, the binding kinetics of single S15 r-proteins have not yet been observed. More importantly, previous experiments for S15 binding were performed on a pre-transcribed and pre-folded rRNA and not a nascent RNA transcript, as would be the case during co-transcriptional ribosome assembly in a living cell.To compare protein binding to a pre-transcribed and pre-folded RNA with binding to a nascent RNA emerging from the RNAP, we investigated the interaction of the r-protein S15 with the 3-helix junction composed of helices 20, 21, and 22 in the central domain of the RNA Fig. . S15 is 43. We hybridized a Cy3-labeled DNA oligonucleotide to the 3′-end of the RNA, which allowed S15 binding to be monitored by FRET between the Cy3-DNA and Cy5-S15. The distance between residue 79 on S15 and the 3′-end of helix 20 in the three-helix junction RNA construct is ~43 Å based on the structure of the intact 30 S ribosomal subunit . The S15(T79C) variant was labeled with Cy5-maleimide, retaining full RNA binding activitynit Fig. . ImportaNatively folded” RNA molecules), while other molecules show bursts of transient S15 binding  or no protein binding at all   Fig. . We defi S15 binding events, along with rare short events of a few seconds Fig. . Of thos37. Using real-time monitoring of the RNA conformational changes upon S15 binding, we show that the RNA is in the closed native conformation during the stable binding events ,  by fitting the distribution of arrival times from end of transcription until the first stable binding event  obtained co-transcriptionally at 20 °C Fig. , exhibit40. Using this labeling scheme, we observed two types of FRET dynamics. The first type of trace, which represents the majority (>80%) of the RNA molecules, showed a transition from a low-FRET state to a high-FRET state shortly after S15 protein delivery and that persisted for several minutes  in real time is possible by following a FRET efficiency change between a 3’-end-labeled DNA transcription template and a labeled RNAP18. We readily observed S15 binding to RNA in the ZMW by monitoring a FRET signal between the Cy5-labeled protein and a Cy3-labeled DNA oligonucleotide that hybridizes to the transcript 3’-end or 5’-end. Since some nonspecific association of S15 to the surface of the zero-mode waveguides occurs, the FRET assay allows specific binding to be monitored even in the presence of a nonspecific background signal. In absence of non-specific protein binding, transcription and the binding of up to four different20 labeled proteins to the nascent RNA could be monitored in the ZMW with the current setup by directly detecting protein binding47  using a 30 kDa molecular weight cutoff Centricon centrifugal filter (Amicon), which improved transcription efficiency. The template sequence was verified by Sanger sequencing. The DNA template was effectively labeled by duplex formation with a 20 % molar excess of labeled DNA oligonucleotide, heating to 68 °C for 5 min and slow cooling to room temperature. The labeled template DNA duplex was not further purified, and the labeling efficiency (~100%) was quantified on a 4% agarose gel.All the constructs are presented in E. coli RNAP (NEB) in a buffer containing 50 mM Tris (pH 8.0), 14 mM MgCl2, 20 mM NaCl, 0.04 mM EDTA, 40 μg ml−1 BSA, 0.01% Triton X-100, for 20 min at 37 °C. Then, to prevent transcription reinitiation and to disrupt non-specific RNAP/DNA interactions that could lead to the loading of more than one RNAP molecule per DNA template, 1 mg ml−1 heparin (or 20 μg ml−1 rifampicin) was added, followed by incubation for a further 20 min at 37 °C. We verified the presence of a single RNAP molecule per DNA template in the stalled transcription-elongation complex  final concentration and the samples were immediately loaded on a 2–4% agarose gel. The fluorescent bands were visualized and quantified using a VersaDocTM MP 4000 Imager (Bio-Rad).Transcription elongation was initiated by simultaneous addition of 1 mM NTPs, 40 nM labeled DNA oligonucleotide complementary to the 5’-end or 3’-end of the nascent RNA and 150 mM KCl. After 20 min, sucrose was added to a 6% .For denaturing PAGE experiments, transcriptions were performed identically except that during stalled complex formation, 150 nM 2, where ID(max) is the intensity of the Cy3-oligo in absence of FRET. This metric allows us to assign automatically a bound state using a single threshold per trace even for traces with very strong non-specific Cy5-protein binding . The emission rate of the dye is proportional to the electric field strength |E|2 of the excitation. The laser source in the PacBio instrument is modeled as a single wavelength Gaussian beam with wavelength 532 nm and waist radius 1 μm. The fluorescence emission is modeled as the incoherent sum of three orthogonal dipole sources with wavelength range 590-630 nm. The optical collection path is modeled as a simple microscope with numerical aperture of 0.923. The fluorescence detected is then the product of the excitation field strength and the relative efficiency of the incoherent sum of dipoles. We calculated this value for radial and azimuthal positions within a metal aperture with a radius of 70 nm and a thickness of 90 nm. We fitted the simulation results to the following exponential function: I = I0 × exp(−cz), where c is a function of r and z.In order to model the relative fluorescence detected by the system as a function of the position in the metal aperture, we needed to determine the electric field strength generated by the excitation source as well as the emission efficiency of the fluorescence. These electromagnetic fields were modeled using finite-difference time-domain (FDTD) simulation tools (Measurements from single-molecule fluorescence assays resulted from a specified number (n) of molecules from a single experiment.Supplementary InformationPeer Review FileDescription of Additional Supplementary InformationReporting SummarySource Data


RNA interference (RNAi), which is mediated by small interfering RNAs (siRNAs) derived from viral genome or its replicative intermediates, is a natural antiviral defense in plants, fungi, and invertebrates. Whether RNAi naturally protects humans from viral invasion is still a matter of debate. Nevertheless, exogenous siRNAs are able to halt viral infection in mammals. The current review critically evaluates the production of antiviral siRNAs, delivery techniques to the infection sites, as well as provides an overview of antiviral siRNAs in clinical trials. The term RNA interference (RNAi) is used to describe gene silencing at the mRNA level guided by small complementary non-coding RNA species. There are several classes of RNAi mediators, one of which, namely small interfering RNAs (siRNAs), facilitates antiviral immunity in plants, fungi, and invertebrates  chemical synthesis; (2) enzymatic production; and (3) Molecules of ssRNA are produced by automated solid-phase synthesis employing 2′-hydroxyl protecting groups that provide ribonucleoside phosphoramitides  to synthesize a complementary RNA strand starting from the very 3′ end  transcribed in a cell from an expression cassette containing a polymerase III promoter (U6 or H1), a DNA template of desired shRNA sequence, and transcription stop signal  vector is the first choice since it has been proved to be non-toxic, non-pathogenic, easy to produce, and it does not integrate into human genome  and RSV in animal models  thymidine overhangs on both 3′ ends to prevent its nuclease degradation  polymerase, viral proteins 24, and 35 completely protected rhesus macaques from lethal infection (Geisbert et al., Two clinical trials have been initiated to assess the efficacy and safety of shRNA-based TT-034 therapeutics, which was created for chronic hepatitis C treatment and delivered to hepatocytes by AAV vector. TT-034 was shown to be safe and well-tolerated. However, in February 2016 Benitec Biopharma decided to discontinue hepatitis C program due to low commercial opportunitiesIn conclusion, the development of RNAi-based therapeutics is still in its early stage and has experienced numerous pitfalls. Nonetheless, it has already been demonstrated that siRNAs can effectively inhibit the replication of various viruses despite different mechanisms they evolved to resist the pressure imposed by immune system and antiviral drugs. However, the possibility of generation of escape mutants, recently discovered inhibitors of RNAi in human viruses (Fabozzi et al., AL and MMP conceptualization, AL manuscript draft, AL and MMP manuscript revision.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To promote sustainable development, the Chinese government launched a new municipal solid waste (MSW) classification strategy in 2017. Shanghai was selected as one of the first pilot cities for MSW classification. The Shanghai municipal government first established the new MSW classification policy in 2017. The Shanghai Municipal Solid Waste Management Regulation was published in 2019 and came into effect on 1 July 2019. This short communication reports on Shanghai’s new MSW classification policy and its implementation. The main content and measures adopted by Shanghai’s government to ensure the effective implementation of the new MSW classification policy are introduced. Besides, a SWOT  analysis on the present policy and measures is conducted, and based on the results, some discussions and suggestions regarding the implementation of MSW classification in Shanghai and the whole of China are presented. Owing to rapid growth in urbanization and economic development, municipal solid waste (MSW) has become a serious problem in China, especially in mega cities ,2. MSW rMSW classification includes the classified throwing, collection, transportation, and disposal of MSW based on certain standards . HistoriShanghai produces nearly 9 million tons of MSW per year, and this amount is continuingly increasing ,19. ShanThis article reports on Shanghai’s new MSW classification policy and its implementation. First, the main contents of the new MSW classification policy of Shanghai are summarized. Then, some measures adopted by the government to ensure the effective implementation of the policy are introduced. Finally, discussions and suggestions regarding how to improve and promote the MSW classification policy in China are presented. According to the Shanghai Municipal Solid Waste Management Regulation , ShanghaThe classified waste will then be collected by qualified enterprises and transported to different sites for appropriate disposal . RecyclaDifferent types of waste will be transported by different kinds of specified waste transportation vehicles . ThousanEfforts are also made to decrease the waste production from the source. Governmental agencies and institutes are required to use products and equipment compatible with the environment ,22. RecyTo ensure effective implementation of the above new policy, some particular measures have been undertaken by the Shanghai government:(1) MSW classification publicity. Publicity on MSW classification started in early 2017. Promotional slogans, posters, and videos on MSW classification are presented in newspapers, magazines, televisions, and internet. Brochures are sent to citizens, and promotional activities have been held in the past two years in Shanghai ,24,26. N(2) School education. Teachers in every school are required to educate students on MSW classification, and the students are required to return home and teach their parents what they learned in school ,27. Figu(3) Specialist guidance. Volunteers or temporarily hired people, most of whom are enthusiastic retirees, are trained and assigned to some waste collection points to guide citizens to correctly classify waste ,24,28. F(4) Incentive methods. Green Accounts, which function as an incentive mechanism, have been widely distributed to Shanghai’s citizens ,24,29,30(5) Penalties. Laws and regulations have been introduced for the whole process of throwing, collection, transportation, and disposal of waste. According to the Shanghai Municipal Solid Waste Management Regulation, for example, those who fail to properly classify and/or throw waste will be fined CNY 50 to CNY 200, and waste transportation enterprises that mix the classified waste will be fined CNY 5000 to CNY 50,000 . Figure It may take a long time to establish a well-developed MSW classification system in Shanghai and the whole of China. Based on the descriptions above, a strengths, weaknesses, opportunities, and threats (SWOT) analysis on the present MSW classification policy in Shanghai was performed, which is tabulated in Existing research shows that leadership and financial support from government are important to MSW management and environmental impact assessments, especially in the initial stage ,36,37,38Lu et al. (2006) states that laws and regulations contribute a lot to the success of MSW classification policy of Taiwan (China) . In 2019People’s willingness, knowledge, and habit on MSW classification play key roles in MSW classification ,41,42,43In recent years, China has changed its development strategy from one based on rapid development to another focusing on environmental protection, and MSW classification is thus reiterated. Shanghai launched China’s first MSW classification policy to adhere to the new national development strategy.Shanghai classifies MSW into four categories: recyclable, hazardous, wet, and dry waste. Various forms of publicity and school education on MSW classification are performed to increase the people’s willingness and public knowledge of MSW classification. Volunteer, incentive, and punitive mechanisms are also adopted to ensure the effective implementation of the new policy.To build a well-developed MSW classification system in Shanghai and the whole of China will require a long period of time. Four suggestions for future development of the MSW classification system in Shanghai and the whole China are presented. First, the leading role of government is encouraged in the initial stage, and good coordination among different departments should be highlighted. Second, WCFS and private capital and enterprises should be involved to promote a sustainable and marketized MSW-relevant industry in the future. Third, laws and regulations should be further refined and optimized, and rigorous and continuous enforcement of the laws and regulations is strongly needed. Fourth, further publicity and school education on MSW classification are also necessary to make MSW classification a habit and a moral principle among citizens.This paper reported the new policy on MSW classification in Shanghai, China. The following conclusions are drawn: