Functional characterization of pseudouridine (Ψ) in mammalian mRNA has been hampered by the lack of a quantitative method that maps Ψ in the whole transcriptome. We report bisulfite-induced deletion sequencing (BID-seq), which uses a bisulfite-mediated reaction to convert pseudouridine stoichiometrically into deletion upon reverse transcription without cytosine deamination. BID-seq enables detection of abundant Ψ sites with stoichiometry information in several human cell lines and 12 different mouse tissues using 10–20 ng input RNA. We uncover consensus sequences for Ψ in mammalian mRNA and assign different ‘writer’ proteins to individual Ψ deposition. Our results reveal a transcript stabilization role of Ψ sites installed by TRUB1 in human cancer cells. We also detect the presence of Ψ within stop codons of mammalian mRNA and confirm the role of Ψ in promoting stop codon readthrough in vivo. BID-seq will enable future investigations of the roles of Ψ in diverse biological processes. Pseudouridine sites in mRNA are detected at base resolution and functionally investigated. Ψ is a prevalent RNA modification that can impact diverse biological functions of different RNA species3. Ψ is also known to exist in mRNA in mammals6. Thirteen putative pseudouridine synthase (PUS) enzymes have been annotated in the human genome9, and mutations in these enzymes have been associated with a wide range of human diseases12. Specific PUS enzymes have been reported to catalyze Ψ deposition in mammalian mRNA13, which may impact mRNA processing, metabolism and translation. However, mechanistic studies have been hampered by an inability to comprehensively detect Ψ at base resolution and to quantify the modification level or stoichiometry at the modified sites.Posttranscriptional RNA modifications occur in all life forms and all types of RNAN-cyclohexyl-N′-(2-morpholinoethyl)carbodiimide methyl-p-toluenesulfonate (CMC) to produce CMC-modified Ψ, which generates a stop signature during reverse transcription (RT)14. This approach has been employed for transcriptome-wide Ψ mapping (named ‘Ψ-seq’ or ‘Pseudo-seq’)5, identifying a modest number of Ψ sites in human mRNA with only 13 sites overlapped between the two independent datasets, accounting for a small proportion of the Ψ sites in human mRNA based on liquid chromatography tandem mass spectrometry (LC-MS/MS)6. An azide-modified CMC has been used to enrich Ψ-containing RNA fragments for sequencing (CeU-seq)6, allowing the detection of many more Ψ sites; however, it lacks base resolution and stoichiometry information at the modified sites.Previous detection of Ψ within RNA has relied mostly on its reaction with 16, we report here BS-induced deletion sequencing (BID-seq) as a base-resolution method for quantitative and transcriptome-wide mapping of Ψ. We discovered a BS reaction condition that quantitatively converts Ψ to a Ψ-BS adduct without cytosine deamination, leading to unique deletion signatures at Ψ sites during reverse transcription. We used BID-seq to detect fraction-altered Ψ sites upon knockdown of individual PUS enzymes in HeLa cells, and identified ‘writer’ proteins for Ψ sites in mRNA. We observed more Ψ-modified mRNA sites in mouse tissues than in human cell lines, with highly Ψ-modified transcripts displaying higher abundance and tissue-specific features. We identified TRUB1 as a main mRNA Ψ ‘writer’ protein that regulates mRNA stability. We additionally uncovered a number of Ψ sites within stop codons of mammalian mRNA, and confirmed the role of Ψ in promoting stop codon readthrough19 in vivo.Taking advantage of a recently reported reactivity of bisulfite (BS) towards Ψ5C in RNA, Khoddami et al. made a surprising observation that BS treatment could lead to modest base deletions during RT at Ψ sites in RNAs (RBS-seq)  were detected in human 18S and 28S rRNA, respectively, using the RBS-seq protocol, but the signals on human mRNA were weak, with only 78 sites detected with a deletion rate of greater than 5%16. The conventional BS reaction condition in RBS-seq inevitably converted all the cytosines into uracils and thus reduced read complexity, resulting in a notable proportion of reads that could not be aligned to mRNA exons. Nevertheless, the discovery of Ψ-BS-adduct-induced deletion during RT provided a completely new principle for potential Ψ detection.In a recent effort to map meq) Fig. 15,16. Th16, we tested two commercial bisulfite kits (Zymo and Epigentek) used for conventional BS treatment on synthetic 5-mer RNA oligonucleotides AGXGA (X = C or Ψ). In both cases, we observed quantitative C-to-U conversion, but no formation of Ψ-BS adducts  MS showed quantitative C-to-U conversion, the efficiency of Ψ-BS adduct formation varied and was less than 30% among four replicates  is critical to BS-mediated deamination, whereas a neutral pH is more suitable for the BS reaction with uracil-BS Fig. . Indeed,-BS Fig. .2SO3 and 0.36 M NaHSO3) and screened commercial reverse transcriptases. We found that SuperScript IV generated a high deletion rate (~70%) at the fully modified Ψ site after the new bisulfite reaction followed by RT, amplification and sequencing, whereas the deletion ratio was almost undetectable (<1%) in the untreated ‘input’  with BS at neutral pH  low modification fraction: 18S rRNA Ψ1,136 and 28S rRNA Ψ4,463  as controls to plot calibration curves for these sequence contexts , USΨAG (S = C or G), poly-U (UUUUU or more), NGΨGG (N = A or C or G or U) and GSΨGA (S = C or G) Fig. . HEK293T9, with dyskerin pseudouridine synthase 1 (DKC1) known to rely on H/ACA snoRNAs to guide pseudouridine deposition27. Most other PUS enzymes are thought to be stand-alone enzymes that function without snoRNAs28. To identify PUS enzymes that catalyze Ψ deposition at individual sites in mRNA, we performed shRNA knockdown of eight known PUS enzymes in HeLa cells followed by BID-seq. We noticed substantially reduced Ψ modification in shControl versus wild-type HeLa cells, most probably due to either cellular stress or immune stimulation caused by lentivirus transfection. We were still able to detect 133 mRNA Ψ sites with Ψ fractions above 10% in shControl HeLa cells and used these 133 sites to study Ψ deposition by writers under the same lentivirus infection conditions . TRUB1 regulated 70 sites out of 133  and poly-U (UUUUU or more Us) , PUS1 (refs. 32), PUS3, PUS7L, PUSL1, TRUB2 and DKC1 (refs. 27) also deposited 40, 28, 30, 24, 28, 28 and 33 Ψ sites in HeLa transcripts, respectively , RPUSD1, RPUSD2, RPUSD3 and RPUSD4 (ref. 32). Note that more effective knockdown or knockout with deeper sequencing may help confidently assign ‘writer’ proteins for all 133 mRNA Ψ sites in shControl cells.PUS7  in mRNA from mouse B cell, bone marrow, CD4 T cell, CD8 T cell, cerebral cortex, cerebellum, heart, kidney, liver, small intestine, testis and thymus, respectively  from all 12 tissues Fig. . We nextRps8, which lacks detectable Ψ sites) to group all Ψ-modified genes, with Ψ-strength of 1.0 as the cutoff  per mRNA in a portion of pseudouridylated mRNAs in mouse tissues Fig. , especiaoff Fig. . We thenoff Fig. , suggestTo further study the features of Ψ-modified genes, we grouped tissue-specific genes in each tissue type , and analyzed how many of them are Ψ-modified in each tissue. Notably, 16%, 24%, 22%, 16%, 38% and 22% of tissue-specific transcripts are Ψ-modified in bone marrow, cerebellum, heart, kidney, liver and small intestine Fig. . CollectWe next investigated the potential functions of nontissue-specific genes in each tissue type. GO analysis of these genes in each tissue type showed common functional clusters on endoplasmic reticulum, ribosome, ATP binding, nucleotide/RNA binding, etc., which display similarity to those in human cell lines Fig.  and 6b. ND1, CO1 and ND4, with Ψ stoichiometry at around 20–60%, from cultured human cell lines  overexpression is known to increase cell size and proliferation35. We also found consistently that TRUB1 depletion could inhibit cell growth, arrest cells in G1 phase, and cause reduced cell size  remained unchanged Fig. . We inveime Fig. . Taken t17. More recently, it was demonstrated that Ψ can facilitate noncanonical base pairing in the ribosome decoding center to promote nonsense suppression19. Despite these important observations, whether Ψ naturally exists in stop codons of mRNA and promotes stop codon readthrough in vivo remains unclear. In HeLa, HEK293T and A549 cells, BID-seq revealed several pseudouridylation sites in stop codons  in NDUFS2, CTSC, PLP2, MDK, SMOX, CUL3 and C7orf50 mRNAs, with Ψ fraction ranging from 10% to 40%  for NDUFS2 substantially increased stop codon readthrough from around 3% up to ~14% , there are 13 annotated pseudouridine synthetases9 in the human genome that may install Ψ to different RNA species including mRNA. While studies of pseudouridylation of relatively abundant RNA species such as rRNA and tRNA have led to functional understandings, functional roles of Ψ in mRNA and other low abundant RNA species remain unclear, mostly because of the lack of a quantitative method that can not only map Ψ sites at the base resolution but also reveal the exact modification stoichiometry at individual sites.Pseudouridine (Ψ) is one of the most abundant RNA modifications in mammalian mRNA. The 0.1–0.25% of Ψ/U ratio on polyA-tailed RNA measured by LC-MS/MSTo resolve this challenge, we report BID-seq for quantitative and comprehensive Ψ mapping in different species of RNA. This method can be applied to 10–20 ng input RNA, allowing RNAs isolated from precious samples to be analyzed. Instead of 5–30% formation of Ψ-BS in RBS-seq, we can induce close to stoichiometric formation of Ψ-BS with the new protocol. The BID-seq bisulfite reaction condition afford high deletion ratios on most motif contexts Fig. . This ne6A methylation, in which a methyltransferase complex installs most methylation sites, pseudouridylation of mammalian mRNA seems to be more heterogeneous with multiple enzymes involved. While TRUB1 is responsible for a large portion of Ψ deposition in mRNA, other PUS enzymes we tested seem to contribute to mRNA pseudouridylation to various degrees, including DKC1, which uses snoRNA to guide pseudouridylation27.The quantitative nature of BID-seq allowed us to reveal sequence motifs of pseudouridylation and assign pseudouridine synthase to individual sites in knockdown experiments in cell lines. Different from mammalian mRNA m41.While we uncovered hundreds of confident Ψ sites in multiple human cell lines, we identified a lot more frequently modified Ψ sites in mouse tissues. We consistently detected a few thousand Ψ sites with higher than 10% modification stoichiometry in most of these tissue mRNAs Fig. . For exa6A and Ψ, one tends to destabilize mRNA43, and the other tends to stabilize mRNA. Finally, we reveal the presence of numerous Ψ sites at mRNA stop codons; some of these Ψ-modified stop codons could induce stop codon readthrough in specific tissues , with very low background deletion rate (<1%) at untreated Ψ sites Fig. . The unm2 in a Heracell VIOS 160i incubator (Thermo Scientific). All cell lines were grown in DMEM medium  supplemented with 10% v/v FBS and 1% penicillin/streptomycin (Gibco). The percentage of surviving cells after treatment was assessed by the SRB assay46. Cell cycle distribution and cell size determination were assessed by flow cytometry.HeLa, HEK293T and A549 cells were purchased from the American Type Culture Collection (ATCC). 293TN cells were purchased from System Bioscience. Cells were cultured at 37 °C with 5.0% COThe following antibodies were used in this study: rabbit monoclonal anti-NDUFS2, clone EPR16266 , mouse monoclonal anti-GAPDH, clone 0411 , rabbit polyclonal anti-SELENOF , mouse monoclonal anti-UBE2E3, clone OTI7E8 , rabbit polyclonal anti-PPP1R2 , rabbit polyclonal anti-NT5C3 , rabbit polyclonal anti-SZRD1 , rabbit polyclonal anti-SNRPD1 , rabbit polyclonal anti-DNAJC19 , rabbit polyclonal anti-MAPKAP1 , rabbit monoclonal anti-CD52, clone EPR3153(2) , rabbit polyclonal anti-A2LD1 , anti-rabbit IgG, horseradish peroxidase (HRP)-linked antibody , anti-mouse IgG, HRP-linked antibody .47. Viruses were concentrated by the PEG-it Virus Precipitation Solution and used for infecting cells in the presence of TransDux (System Biosciences). Transfected cells were selected by 2 μg ml–1 puromycin. To generate the dcas13D-TRUB1 or PUS1 cells, HeLa cells were transfected with PB-Cuo–TRUB1 (or PUS1) -dCas13D-IRES-GFP-EF1α-CymR-Puro construct following commercial protocol (System Biosciences). Pools of stable transfectants were selected by antibiotics or sorted by flow cytometry. Doxycycline (1 μg ml–1) was used to induce shRNA while cumate (30 μg ml–1) was used to induce gRNA expression. The shRNA, siRNA and gRNA sequences are listed in Supplementary Table For transient transfection, cells were transfected with casRx  gRNA construct and pCMV-TRUB1-dCas13D by Lipofectamine 2000 Transfection Reagent (Invitrogen) according to manufacturer’s protocol, or with siRNA by Lipofectamine RNAiMAX Transfection Reagent (Invitrogen) following commercial protocols. For lentivirus production, a lentiviral construct  together with pMD2.G  and psPAX2  were cotransfected into 293TN cells (System Biosciences) as previously describedTotal RNA isolation: (1) mouse tissues were weighted and homogenized in TRIzol reagent (Invitrogen) until no visible chunks were left, while cultured cells could be smoothly suspended in TRIzol reagent; (2) cellular total RNA was isolated according to the TRIzol reagent manufacturer’s protocol, followed by isopropanol precipitation; (3) when we extracted rRNA-depleted total RNA for a typical RNA-seq or lifetime sequencing, RiboMinus Eukaryote System v.2 (Invitrogen) was further used for rRNA removal;+ RNA enrichment.polyA-tailed RNA isolation: Dynabeads mRNA DIRECT Purification Kit (Invitrogen) was used for polyA+ RNA from HeLa cells or mouse tissues, around 10–20 ng RNA was fragmented using RNA Fragmentation Reagents  and heated at 70 °C for 14 min, followed by purification with RNA Clean and Concentrator (Zymo Research). 3′-End repair and 5′-phosphorylation were conducted with T4 polynucleotide kinase (PNK) . RNA was mixed with 3 µL 10× T4 PNK reaction buffer  and 3 µl T4 PNK, diluted to a final volume of 30 µl, and incubated at 37 °C for 45 min; then 1.5 µl T4 PNK and 1.5 µl 10 mM ATP were added for another incubation at 37 °C for 45 min, followed by RNA Clean and Concentrator (Zymo Research) purification eluting with 10 µl RNase-free water. To perform 3′-adapter ligation, 10 µl 3′-repaired and 5′-phosphorylated RNA fragments were incubated with 1.0 µl 20 µM RNA 3′ SR Adapter  at 70 °C for 2 mins and placed immediately on ice. Then, 2.5 µl 10× T4 RNA Ligase Reaction Buffer , 7.5 µl PEG8000 (50%), 1 µl SUPERase•In RNase Inhibitor and 2 µl T4 RNA Ligase 2 truncated KQ  were added to the RNA–adapter mixture49. The reaction was incubated at 25 °C for 2 h followed by 16 °C for 10 h. The reaction was further diluted to 47 µl with nuclease-free water, and the excessive adapters were removed with 2 µl 5′-deadenylase  at 30 °C for 30 min followed by adding 1 µl RecJf  for ssDNA digestion at 37 °C for 30 min. The 3′-end-ligated RNA was extracted by RNA Clean and Concentrator (Zymo Research) and eluted with 9.3 µl RNase-free water.Wild-type HeLa, HEK293T or A549 cells ; HeLa shControl or PUS knockdown cells were prepared as described ; mouse tissues, except immune cells, were collected from two replicates (one wild-type mouse per replicate), age- and sex-matched. To harvest enough material, mouse immune cells such as B cells, CD4 T cells and CD8 T cells were collected and combined from two replicates (eight wild-type mice per replicate), again age- and sex-matched. After extracting the polyANNNNN-3′) at 70 °C for 2 mins and placed immediately on ice. Then 2.5 µl 10× T4 RNA ligase reaction buffer, 1.0 µl 25 mM ATP, 10 µl PEG8000 (50%) and 1 µl T4 RNA Ligase 1  were added to the RNA–adapter mixture. The reaction was mixed well and incubated at 25 °C for 8 h, followed by RNA Clean and Concentrator (Zymo Research) purification, eluting with 10 µl RNase-free water.The purified RNA was incubated with 1.2 µl 10 µM 5′ SR Adapter  and incubated at 70 °C for 3 h. Then, 75 µl RNase-free H2O, 270 µl RNA binding buffer (RNA Clean and Concentrator), and 400 µl ethanol were added to the reaction mixture, which was mixed well and loaded on a RNA Clean and Concentrator-5 column. After spinning and washing once with 200 µl RNA wash buffer (RNA Clean and Concentrator), 200 µl RNA Desulphonation Buffer  was added to the column and incubated at room temperature for 1 h. This was followed by spinning and washing twice with 700 µl RNA wash buffer (RNA Clean and Concentrator), followed by eluting RNA with 10.5 µl RNase-free water. Then the ‘Input’ was diluted to 10.5 µl with RNase-free water. Both ‘Input’ and ‘Treated’ samples were mixed with 1.0 µl 2.0 µM SR RT primer (5′-AGACGTGTGCTCTTCCGATCT -3′) at 65 °C for 2 min and moved immediately onto ice. To this was added 4 µl 5× SSIV Buffer, 2 µl 10 mM dNTP Solution Mix (NEB), 1 µl 100 mM dithiothreitol, 0.5 µl RNaseOUT Recombinant Ribonuclease Inhibitor  and 1 µl SuperScript IV Reverse Transcriptase . The reaction was mixed well and incubated at 50 °C for 1 h, followed by adding 1 µl RNase H  and incubating at 37 °C for 20 min. The reaction mixture was heated at 70 °C for 5 min and then cDNA was purified using DNA Clean and Concentrator (Zymo Research). The eluted cDNAs (20 µl) were stored at –80 °C; 4 µl was used for each 15-cycle PCR amplification reaction, which was performed with the SR Primer for Illumina (NEB) and indexed primers (from NEBNext Multiplex Oligos for Illumina). All libraries were purified on a 3.5% low melting point agarose gel and sequenced on Illumina Nova-seq 6000 with single-end 100 bp read length.A 1.5 µl aliquot of the purified RNA was saved for ‘Input’ library construction, 8.5 µl was subjected to BID-seq optimized bisulfite treatment, as the ‘Treated’ sample. The 8.5 µl RNA was mixed with 45 µl freshly prepared BID-seq BS reagent (2.4 M Na–1) was added, followed by mixing well via pipetting. The reaction mixture was incubated in PCR instrument at 70 °C for 4 h. After cooling to room temperature, 10 μl Tris-HCl buffer  was added and mixed well by pipetting. The mixture was incubated at 37 °C for 1 h for desulfonation. Then, 2 μl of the mixture was added to 40 μl resin (Bio-Rad) and allowed to stand at room temperature for 30 min. Then 1.8 μl supernatant was mixed with matrix 2′,4′,6′-trihydroxyacetophenone monohydrate and loaded onto a MALDI plate. The MALDI-TOF MS recorded the signals using negative reflector mode.To 9 μl of optimized BS reagent, 1 μl of synthetic RNA oligo AGXGA , 1 μl nuclease P1  and 10.5 μl water, followed by an incubation at 42 °C for 3 h. Then, 3 μl freshly prepared 1.0 M NH4HCO3, 1 μl Fast AP  and 1 μl water were added, and the reaction mixture was incubated at 37 °C for 3 h. Upon the completion of incubation, the reaction mixture was diluted to 50 μl and the samples filtered through 0.22 μm Millex-GV polyvinylidenedifluoride filters (Millipore). A 5 μl sample was then injected into a ZORBAX SB-Aq 4.6 × 50 mm column (Agilent) on UHPLC (Agilent) coupled to a SCIEX 6500+ Triple Quadrupole Mass Spectrometer in positive electrospray ionization mode. The nucleosides were quantified based on the nucleoside to base transitions: 268 to 136 (A), 282.1 to 150.1 (m6A), 245 to 113.1 (U), 245.1 to 125 (Ψ) and compared with calibration curves. Three biological independent replicates were used for Ψ level quantification, and each sample was injected three times.To measure Ψ in mRNA in HeLa and mouse tissues, mRNA was purified from total RNA by two rounds of polyA–1 for 6 days or transient knockdown cells for 3 days before harvest. For RNA lifetime study, cells were treated with actinomycin D with a final concentration of 5 μg ml–1 for 0, 3, 6 and 9 h. Cells were then harvested by trypsinization, and total RNA was isolated as described above. ERCC ExFold RNA Spike-In Mix (Invitrogen) was added to 2 μg total RNA and subjected to two rounds of RiboMinus. For comparing RNA expression responsive to TRUB1 knockdown, mRNA was isolated as described above. Three biological independent replicates per condition were sequenced; 10 ng rRNA-depleted RNA was used to construct libraries with SMARTer Stranded Total RNA-Set Kit v.2 (Takara Bio USA).Tet-On-shRNA cells were incubated with doxycycline with a final concentration of 1 μg ml−ΔCt method was used. Primers are listed in Supplementary Table RT was performed using PrimeScript RT Reagents Kit , according to the vendor’s protocol. A tenfold dilution of cDNA was used to measure relative transcript abundance by real-time PCR. FastStart Essential DNA Green Master (Roche) and QuantStudio 6 Pro Real-Time PCR System (Thermo Fisher Scientific) were used to conduct quantitative PCR. For each sample, technical triplicates were performed and normalized to the expression level of 18S rRNA and other internal standard genes. For mRNA lifetime study, 18S rRNA was used as internal control as its level is not affected by Actinomycin D treatment. To determine relative expression, the 250. In brief, protein samples were isolated by RIPA buffer  containing Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Scientific). BCA assay (Thermo Scientific) was used to determine the protein concentration. For IP, the antibody was conjugated to protein A/G magnetic beads by incubation at 4 °C for 2 h, followed by washing three times and incubating with cell lysates at 4 °C overnight. Equal amounts of purified protein were separated by SDS–PAGE followed by wet transfer to polyvinylidenedifluoride membranes. Blots were blocked with 5% nonfat milk or BSA and incubated with the primary antibody at 4 °C overnight. Signals were detected by HRP-linked secondary antibodies  together with SuperSignal West Pico Plus chemiluminescent substrate (Thermo Scientific) and imaged in a FluorChem R system (ProteinSimple). For the stop codon readthrough detection, the bands of interest were excised and submitted to MS bioworks using the MSB03 service.Immunoprecipitation (IP) and immunoblotting (IB) were performed as previously described+ RNA from HeLa cells was subjected to CMC-treatment and purified by ethanol precipitation. For each highly modified Ψ site in HeLa mRNA, two types of RT-qPCR primers were designed for (1) Ψ-region: the 250-nucleotide (nt) region centered by the target Ψ site; (2) control-region: the 250-nt region within this mRNA, without overlapping with the Ψ-containing 250-nt region. The same amount of untreated or CMC-treated5 HeLa RNA was used for two separate RT reactions, with the RT primer for the Ψ-region and control-region, respectively. For each RT reaction, polyA+ RNA was incubated with each gene-specific RT primer at 70 °C for 2 min and then moved quickly onto ice. Then 2 µl 5× first strand buffer, 0.5 µl dithiothreitol (100 mM), 1 µl dNTP (10 mM), 0.5 µl RNaseOut and 1 µl SuperScript III reverse transcriptase were added to the RNA-primer mixture and diluted to a final volume of 20 µl. FastStart Essential DNA Green Master (Roche) and QuantStudio 6 Pro Real-Time PCR System (Thermo Fisher Scientific) were used for RT-qPCR quantitation. The readthrough ratio was calculated by the Cq values on Ψ-region normalized to the control-region, in CMC-treated sample versus untreated samples. Primer sequences are listed in Supplementary Table PolyA36 with some modifications. HeLa polyA+ RNA was extracted from total RNA with Dynabeads mRNA DIRECT Purification Kit (Invitrogen). To 5.5 μg of polyA+ RNA (-CMC) in 12 μl RNase-free water, 28 μl 1× TEU Buffer (50 mM Tris-HCl (pH 8.3), 4 mM EDTA, 7 M urea) and 2 μl SUPERase•In RNase Inhibitor  were added. To 8.3 μg polyA+ RNA (+CMC) in 12 μl RNase-free water, 24 μl 1× TEU Buffer, 4 μl 1.0 M CMC in 1× TEU Buffer and 2 μl SUPERase•In were added and incubated at 30 °C for 16 h. Next, 140 μl RNase-free water, 20 μl 3.0 M NaOAc (pH 5.2), 550 μl 100% ethanol and 1 μl GlycoBlue coprecipitant  were added, and RNA was precipitated overnight at –80 °C.CMC-RT and ligation-assisted PCR analysis of Ψ modification (CLAP) was performed as described previouslyg at 4 °C for 30 min, the supernatant was removed and 1 ml 70% ethanol was added and incubated at –80 °C for 2 h. After that, samples were centrifuged again, supernatant was removed and dried pellet was resuspended in 40 μl 50 mM Na2CO3, 2 mM EDTA (pH 10.4) and 1 μl SUPERase•In and incubated at 37 °C for 6 h. Next, samples were purified by RNA Clean and Concentrator (Zymo Research) as follows: 260 μl RNA binding buffer and 300 μl 100% ethanol were added to each sample and, subsequently, the vendor’s protocol was followed. Recovered RNA underwent 5′ phosphorylation as follows: to 1 μg of CMC ± polyA+ RNA in 20 μl RNase-free water were added 2.5 μl SUPERase•In, 5 μl 10× T4 PNK Buffer, 2.5 μl 10 mM ATP and 5 μl T4 PNK . The volume was adjusted to 50 μl with RNase-free water and the reaction run at 37 °C for 1 h followed by purification with RNA Clean and Concentrator (Zymo Research).Next, samples were centrifuged at 7,000+ RNA (10 μl) was mixed with 1 μl 100 μM RNA-5 Blocking Oligo (/5AmMC6/rArCrCrCrA) and denatured at 65 °C for 2 min, followed by moving onto ice immediately. Then 3 μl 10× T4 RNA ligase buffer, 3 μl 10 mM ATP, 10 μl 50% PEG8000, 1 μl RNaseOUT recombinant ribonuclease inhibitor and 1 μl T4 RNA ligase I  were added, mixed well and incubated at 25 °C for 2 h then at 16 °C for 12 h. After that, samples were purified by RNA Clean and Concentrator (Zymo Research) as follows: 270 μl binding buffer and 300 μl 100% ethanol were added to each sample and the vendor’s protocol was then followed. Samples of 40 ng (–CMC) and 60 ng (+CMC) ligated polyA+ RNA underwent reverse transcription as follows: 2 μl (–CMC) and 3 μl (+CMC) ligation mixtures were mixed with 1 μl 1.0 μM target-specific primer and incubated at 65 °C for 2 min then put on ice. Then, 2 μl 10× AMV RT Buffer, 2 μl 10 U μl–1 AMR RT , 2 μl 10 mM dNTP, 0.5 μl Murine RNase inhibitor and the final volume was adjusted with water to 20 μl. RT was run for 1 h at 42 °C followed by denaturation at 80 °C for 5 min. Next, 1 μl RNase H  was added and the mixture incubated at 37 °C for 20 min followed by denaturation at 80 °C for 5 min. To 10 μl RT reaction mixture, 1.5 μl adapter/split oligonucleotide mixture  was added and incubated at 75 °C for 3 min, followed by moving onto ice. Then, 4 μl 10× T4 DNA ligase buffer, 5 μl DMSO, 1 μl 40 U μl–1 T4 DNA Ligase  and 18.5 μl H2O and incubated at 16 °C for 16 h followed by denaturation at 65 °C for 10 min. For PCR, 5 μl of the ligation mixture was mixed with 8 μl 5× Q5 reaction buffer, 0.8 μl 10 mM dNTPs, 2.4 μl of the mixture gene-specific forward/reverse primers (5 μM) and 0.5 μl Q5 high-fidelity DNA polymerase , followed by adjusting the volume to 40 μl. PCR product was amplified for 35 cycles at the following annealing temperatures: 70 °C for ERH, 64 °C for CDC6 and 65 °C for TRIP6. Then, 10 μl PCR mixture was mixed with 2 μl 6× TriTrack DNA Loading Dye . Samples and Low Range DNA Ladder  were loaded at 4 °C onto pre-run 4–20% Novex TBE Gel (Invitrogen) and run at 10 V cm–1 at 4 °C. Gels were stained with SYBER gold nucleic acid gel stain  and imaged with a Bio-Rad Imaging System. Band intensity was quantified in Image Lab v.5.0 (Bio-Rad).All recovered polyAFor 18S Ψ822 as a positive control, total RNA was treated as described above. PCR product was amplified for 25 cycles at the annealing temperature of 65 °C. All primer sequences are listed in Supplementary Table y and Ψ fraction x can be expressed by the following equation:A is the dropout ratio of modified fragments, B is the background deletion rate (the deletion rate at unmodified U), and R is BID-seq induced deletion ratio. The parameters of A, B and R for each sequence context are provided in Supplementary Table The 30-mer RNA probes with –NNΨNN– were used as the ‘100% Ψ’ standard. RNA oligonucleotides containing –NNUNN– were used as ‘0% Ψ’. The ‘100% Ψ’ and ‘0% Ψ’ standards were combined to generate six oligonucleotide mixtures at different methylation levels . All oligonucleotide mixtures were subjected to BID-seq in parallel, the deletion rate patterns of each sequence context were analyzed and a fitting curve was plotted based on the relationship of observed deletion rate and Ψ fraction. The observed deletion rate The sequencing data were all trimmed with cutadapt tool to remove adapters and low-quality reads. PCR duplicates were removed with BBMap tool (v.38.73), 5-mer random barcodes at reads ends were trimmed and low-quality or short reads (less than 20 nt) were removed using cutadapt tool (v.1.15). Remaining reads were aligned to hg38 or mm10 genome using Tophat2 (v.2.1.1) and bowtie2 (v.2.4.0) allowing, at most, three mismatches. The generated.bam files were split into positive and negative strands and sorted using Samtools (v.1.9). Sequence variants were identified by measuring the base composition at each position using bam-readcount software (v.0.8.0). The generated bam-readcount results were parsed and analyzed by inhouse scripts. Internal deletion ratio at each Ψ candidate site suggested by Tophat2, was further calculated by data output from bam-readcount pipeline and confirmed by direct IGV visualization (v.2.8.0). In summary, one Ψ candidate site needs to satisfy the following criteria in its deletion profile: (1) deletion rate above 5% (with deletion count above five) in BID-seq libraries; (2) deletion rate below 1% in ‘Input’ libraries; (3) total reads coverage depth above 20 in both BID-seq and ‘Input’ libraries; (4) deletion rate above 1.5-fold over background in any given sequence motif .The ‘input’ samples of BID-seq, for both human cell lines and mouse tissues, are equivalent to regular RNA-seq; therefore, we quantified the gene-level read counts of input samples that aligned to hg38 or mm10 for gene expression analysis with Cufflinks software (v.2.2.1). GO analysis was performed using the online analysis software DAVID 2021 ; 7-week-old male and female mice were used. Mice were housed in a virus-free facility at 21 ± 1 °C with a controlled 12-h light cycle ). The animals had access to standard chow and water ad libitum. The relative humidity was controlled at 55% ± 10%. All mouse experiments were approved by the University of Chicago Institutional Animal Care and Use Committee.t-tests on the statistical significance of differences between groups. All statistical analysis and data graphing were done in Prism (v.9.2.0) software.For BID-seq libraries, two or three biologically independent replicates were used in each experiment with cultured cells. Immunoblots are representative images from at least three rounds of independent experiments. Data are presented as the mean ± s.d., with two-tailed Student’s No statistical methods were applied to pre-evaluate sample size. No data were excluded from analysis. Samples in this study were not randomized. Blinding was not used for this study because cell culture, sample preparation, reagents and experimental settings were kept consistent for each experiment.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41587-022-01505-w.Supplementary InformationSupplementary Figs. 1–8.Reporting SummarySupplementary Table 1Supplementary Tables 1–23.Supplementary Data 1Unprocessed gels for Supplementary Fig. 7h.Supplementary Data 2Unprocessed blots for Supplementary Fig. 8d,e.Supplementary Data 3Statistical source data for Supplementary Fig.1.Supplementary Data 4Statistical source data for Supplementary Fig.2.Supplementary Data 5Statistical source data for Supplementary Fig.3.Supplementary Data 6Statistical source data for Supplementary Fig.4.Supplementary Data 7Statistical source data for Supplementary Fig.5.Supplementary Data 8Statistical source data for Supplementary Fig.6.Supplementary Data 9Statistical source data for Supplementary Fig.7.Supplementary Data 10Statistical source data for Supplementary Fig.8.


The nucleocapsid protein (N) of SARS-CoV-2 plays a pivotal role during the viral life cycle. It is involved in RNA transcription and accounts for packaging of the large genome into virus particles. N manages the enigmatic balance of bulk RNA-coating versus precise RNA-binding to designated cis-regulatory elements. Numerous studies report the involvement of its disordered segments in non-selective RNA-recognition, but how N organizes the inevitable recognition of specific motifs remains unanswered. We here use NMR spectroscopy to systematically analyze the interactions of N’s N-terminal RNA-binding domain (NTD) with individual cis RNA elements clustering in the SARS-CoV-2 regulatory 5’-genomic end. Supported by broad solution-based biophysical data, we unravel the NTD RNA-binding preferences in the natural genome context. We show that the domain’s flexible regions read the intrinsic signature of preferred RNA elements for selective and stable complex formation within the large pool of available motifs. SARS-CoV-2 genome turnover is mediated by its N protein, but precise parameters driving the necessary RNA specificity have remained enigmatic. Here, Korn et al. reveal N’s N-terminal domain to distinguish regulatory viral RNA motifs with a preference for transiently folded elements of functional impact. As for other CoVs, it is subject to genomic and sub-genomic transcription2, replication3 and condensation into newly assembled particles with each round of viral propagation4.Despite impressive progress in vaccination, coronaviral infections will remain a severe threat to human health. This calls for efforts towards curing infections based on a detailed understanding of the most prevalent molecular interactions specific to the virus. The responsible pathogen of the Covid-19 pandemic, the severe acute respiratory syndrome coronavirus-2  carries a large single-stranded (ss) RNA genome of almost 30000 nucleotides (nt)4. N is more than 90% conserved to the SCoV homolog, appears most abundant on the general protein level5 and in RNA-protein complexes (RNPs)7. N integrates five distinct parts (often termed N1-N5), comprising two domains that are intertwined by intrinsically disordered regions (IDR), with IDR2 (N3) embedding the SR-region ion Fig. . While tion Fig. . A disti9 in genome processing, e.g. its role in RNA synthesis12, and immune evasion15. For its primary function, genome encapsidation, N combines protein oligomerization with RNA-binding18, which in combination accounts for the occurrence of RNA-protein condensates both in vitro and in cells. For this, a plethora of studies since the SCoV-2 outbreak has intensively addressed the influence of N phosphorylation16, the contributions of N domains19, the role of viral RNA20, the viral nsp321, and host proteins22. Very recent work ultimately claimed that phase separation in infected cells enhances viral transcription and assembly23.N gathers multiple functions24 indicating its interaction with literally all types and sequences of RNA, and its potency of melting structured RNA in a functional context25. The modular character and high degree of disorder in N might favor promiscuous RNA-binding28. Indisputably, full functionality requires a sensor for particular RNAs like packaging signals (PS), transcription-regulatory sequences (TRS) and other, still enigmatic regulatory elements. Literature extensively describes highly conserved, functional structures in the SCoV-2 genomic RNA (gRNA), specifically within the regulatory genomic ends32, both in vitro and in virions/infected cells36.N was early termed an RNA chaperone17. As shown in Fig. 37 as part of the 5’ge comprises a set of cis-regulatory RNA elements (5’ge RNA elements), termed 5’ stem-loops (SL) 1 to 5, with the start codon located at the end of SL5. Additional 5’ge RNA elements follow starting from SL6 and beyond, in parts resolved on the secondary structure level31  bound by N and claims those are targeted for packaging42. Thus, N target RNA selectivity has remained enigmatic seeing the large pool of RNAs in an infected cell, but likely involves multivalency as the basis for high affinity26. Much effort has been undertaken to establish that IDRs 1 and 2 increase affinity by non-specific interactions26, rather than exhibit specificity. Hence, it is plausible to attribute specificity – needed to distinguish regulatory sequences like PS or TRS – to the folded NTD.Surprisingly little is known about specific RNA-recognition by N. Studies have used intentionally short, random or bulk RNAs to derive determinants of preferences with very heterogeneous/contradictive outcome43. In a pioneering study, Dinesh et al. provide an atom-resolved model of TRS recognition by the NTD. They suggest the interaction as little-specific and preferential binding to rely on the ssRNA nature8. In line with this, a recently solved complex structure with a short, unrelated dsRNA reveals the NTD to interact exclusively – and thus non-specifically – with the phosphate backbone, giving no hint at drivers for specificity42. A study by Redzic et al. showed NTD binding to both ss and stem-looped RNA by NMR, indicating a preference for a GU-rich single strand, but taking into account the context of NTD-flanking IDRs40. Of note, TRS-derived ssRNA sequences in recent studies were examined outside their natural SL context40. Consequently, we lack a systematic comparative analysis of the NTD’s potential to differentially engage with the manifold types of regulatory viral RNA elements.Several studies support this assumption by claiming the NTD to express specificity for the TRS in CoVs17, suggesting them to encode unique features for NTD specificity. We use NMR spectroscopy to identify a particular atom-resolved NTD signature provoked by the binding of those RNAs. In addition, we use analytical SEC, SAXS, and CD spectroscopy to manifest unique, stable complexes between NTD and SL3/Ext, while we show that both regions have transient RNA-structural propensities. We suggest that the comparable sequence compositions encode a unique element with respect to its structural dynamics, which is used by N’s NTD for distinct recognition in the genome. By this, NTD exploits an additional level, given by complex stability that is key to selectivity within the broadly recognized landscape of RNAs.We here specifically focus on the role of the NTD in mediating target specificity, to approach the complex interactome of N with RNA. We provide insight into the differential interactions of N’s NTD with the 5’ge RNA cis-regulatory elements SL1-SL6. Going beyond its promiscuity, we particularly concentrate on binding to the previously described regulatory sequences in SL3 and a region extending SL4 in 3’  among individual SCoV-2 5’ge RNA elements in EMSAs  plots. Unexpectedly, the NTD CSP pattern was similar for all 5’ge elements, and significantly shifting amides clustered with the positively charged surface  derived from ITC experiments in both directions of titrating  and adjacent N-terminal loop (N-loop finger)  Fig. . SimilarWe next used analytical size-exclusion chromatography (aSEC) to define complex stabilities of the NTD with SL2 + 3 and elements of the SL4 hub Fig. . NTD forWe wondered if differential complex stabilities correlate with distinct NMR chemical shifts and in-depth-analyzed the reporter residues in HSQC-based titrations. We found a striking correlation of CSP trajectories with the type of complexed RNA Fig. . For exa1H CSPs of basic finger residues 90 to 104  and the N-loop (G60) fingers to bulky isoleucines Fig. . R107A –We compared the ability of NTD wildtype and NTD mutants to distinguish between SL4ext and the less preferred SL4 by means of CSP signatures Fig. . Indeed,41 or monomeric RNA models, respectively , a solution method combinable with SEC that yields low-resolution structural information, thus complementing NMR. SEC-SAXS allows analyzing individual fractions of NTD, RNA, and RNPs, and their contributions in mixed states. The quality of the SAXS data is seen from excellent fits of experimental scattering for free NTD and RNAs with calculated curves from the atomic coordinates of an NTD monomer46, independent of concentration, by SEC-MALS and aSEC in our physiological/NMR (phosphate) buffer system , as well as overall volumes and MWs caused by NTD/RNA association  profiles . This underlines the sole presence of non-bound RNA in the respective fractions even in the presence of NTD  compared to the Ext and SL4ext RNPs  profiles and subsequently Dmax when comparing Ext and SL4ext to SL2 + 3. Here, the latter clearly reflects the possible rearrangement compensated by the NTD-binding as expressed by similar profiles and maximum distances, while both Ext and SL4ext reveal significant increases in complex dimensions. In line with the Rg analysis, SL4 profiles show no differences for SEC-SAXS frames between RNA alone and in the presence of NTD, while SL2 + 3, Ext, and SL4ext show clear shifts of RNA-contained frames  R2 Fig. . RNA-med R2 Fig. . This is15N longitudinal (R1) and transversal (R2) relaxation rates of NTD alone and in complex with Ext. We found that NTD R1 values for flexible regions, such as the N-loop and the basic finger, are up to 26% elevated above the average of 1.5 s−1. This effect is largely obliterated in complex with Ext, where basic finger residues share the domain average R1 of 1 s−1 or lower  of 14.9 kDa, which perfectly fits the expected MW of monomeric NTD  of SL2 + 3 and the SL4 hub RNAs using CD-spectroscopy  and a labile 3’ element . Notably, TM values for isolated SL4 (67 °C) and Ext (38 °C) RNAs were higher compared to those in SL4ext, likely due to construct design for in vitro transcription , we chose a set of RNAs to examine the general role of ss vs. ds and stem-looped RNA. We used two 19mer RNA oligos (ss19T and ss19B) with a 7mer A/U core motif that can be hybridized into a duplex RNA (ds19). We also included SL_AUA with a stable stem of 7 GC base pairs capped by an AU-rich hexaloop   clearly revealed the extension as the key element for NTD recognition  revealed significantly increased RNP amounts for SL2 + 3 and Ext, and to a lower extent also SL4ext  cis elements. We hypothesize the superior recognition of TRS and Ext is favorably influenced by stable proximal structures like SL2 and SL4. This underlines the important role of genomic context in the recognition by N and may involve other types of RNA regions flanking the actual NTD target site.Our data unambiguously support the TRS as a preferred NTD target in line with previous studies57. Of note, Ext not only shows characteristics of a dynamic element in this work, but was previously also found as an integral part of a potential upstream ORF together with SL458. This sheds a new light on the regulatory hub SL4ext and its preferred recognition by NTD.Our SAXS and CD data show that both SL3 and Ext exist as transiently folded elements. We suggest their characteristic dynamic behavior adds to preferred targetability. The unique combination of RNA sequence and labile fold could be a particular feature for the virus to recognize its own RNA in the large pool of available motifs Fig. . Likewis59. This allows speculating whether IDRs and NTD act in concert to engage with RNAs. Although discussed earlier43 our data do not support an active role of NTD in RNA-unfolding. Instead, we find NTD to shift ratios of Ext conformers towards ssRNA. N might mask regulatory RNA elements from accessibility to other RBPs and thus e.g. prepare the RNA for packaging. The dynamic interaction of identical cis RNAs in different conformers with multiple RBPs was shown for eukaryotic 3’-UTRs recently61. For NTD - Ext, our data also stress the crucial role of temperature for viral fitness, which will shift equilibria of neuralgic RNAs to favored engagement with N.N’s IDRs are capable of interfering with structured RNAs to a much higher, but less specific degree62. In line with our findings, the study suggests the specificity of N is mediated via its N-terminus. Via truncations, N may disconnect specific from non-specific RNA-binding during the viral life cycle. ii) The NTD was shown to be modulated in RNA-binding through arginine methylation at positions 95 and 17739, both strongly affected in our NMR-based analysis. Selective methylation of arginines will steer RNA-binding at relevant infected-cell stages. iii) The modification of viral RNA will strongly impact specific recognition by NTD as it leads to RNA restructuring63. Burgess et al. found excessive m6A methylation of the SCoV-2 genome relevant for viral replication64. Interestingly, the 5’-UTR is majorly excluded indicating a role of this region for specific RNPs with unmodified sequences. This assumption is corroborated by our data on SL2 + 3 and Ext, exhibiting sequence-encoded intrinsic dynamics, which are less compatible with regulation via methylation. iv) Although the NTD does not invoke LLPS on its own65, it was shown to have a measurable influence on condensation20. The different facets of condensates in vitro are suggested to represent the discrimination of capsid assembly and genome processing via phosphorylation of the NTD-flanking SR-region16. Potentially, phosphorylation is used to regulate the degree of RNA specificity of NTD during the infectious cycle.We here established the NTD as to express RNA preferences, which is supported by current literature: i) N was found to appear in multiple proteoforms18. N forms structurally dynamic dimers66 that get compacted upon RNA-interaction and the NTD gets packed onto the CTD67. The protein will exhibit higher specificity by facilitating simultaneous interaction with e.g. SL3 and Ext. In that regard, the role of RNA structure for compactness in capsids was shown in a Model Icosahedral Virus68, possibly in order to position target motifs for N domains in an efficient spatial arrangement, and also suggested as similar requirement in SCoV-269.Finally, recent work shows a pivotal role for CTD-mediated dimers of N70. The possible repression of viral replication on the level of LLPS interference71 has already been addressed, including the identification of inhibitors, likely against the NTD. Similarly, the herein investigated 5’ge RNA elements were shown to have the principal potency as SCoV-2 RNA drug target sites72. Our HSQC-based analysis of the NTD binding to 5’-UTR elements now allows for a site-specific interrogation of RNP-druggability using the preferred RNAs.Given its high abundance and immunogenicity N is a prime target for vaccination and inhibitor search54. Our data provide insights into the subtle differences in RNA motifs that likely account for specific recognition by the NTD. A recent study has presented SCoV-2 virus-like particles that allow examining the precise role of N sequences and RNA motifs for quantifiable packaging54. The detailed meaning of particular NTD complexes with genomic RNA motifs in a natural context, e.g., of such studies, can now directly be correlated with atomic signatures.Finally, different from transcriptional regulation it appears unrealistic for N to use only a few specific sites for genome packaging, which is supported by multiple studies, albeit in parts controversiallyExact temperatures are given in Kelvin for each experiment, either in the Methods Section or – if required – in the corresponding Fig. legend. Whenever experiments were carried out without distinct temperature control, we refer to it as room temperature (RT). By our definition – and to our best knowledge – RT spans a range between 22 and 25 °C.1. Domain boundaries were defined in analogy to the available NMR structure 8, spanning amino acids 44 to 180. The NTD wildtype (WT) and point mutants  were cloned as described in the Supplementary Methods.The SCoV-2 N protein NTD coding sequence was based on NCBI reference genome entry NC_045512.2, identical to GenBank entry MN908948 and recently described in detail by us73. Briefly, uniformly (13C and) 15N-labeled (or unlabeled) NTD protein was purified via size exclusion chromatography on a Superdex™ 75 HiLoad 16/600 column (Cytiva) in NTD standard buffer . The concentrated NTD protein was further subjected to ion exchange chromatography to remove possible traces of co-purified RNases in the sample. To this end, the salt concentration of the NTD protein sample was adjusted to 50 mM KCl before applying it to a 1 mL ENrich™ S (Bio-Rad) cation exchange column equilibrated with 25 mM KPi pH 6.5, and 50 mM KCl. A continuous KCl gradient from 50 mM to 500 mM was used to elute the NTD at an approximate KCl concentration of 100 mM. The salt concentration of the final sample was adjusted to 150 mM KCl prior to further use. 2 mM of TCEP or DTT was added for samples to be used in SAXS and MALS.Production and purification of the SCoV-2 N NTD and mutants thereof were guided by74. NMR measurements were carried out at the Frankfurt BMRZ using Bruker spectrometers of 600–950 MHz proton Larmor frequency, equipped with cryogenic probes and using Z-axis pulsed field gradients. All RNAs, NTD, and complexes were consistently measured in NTD buffer including 5% v/v D2O at 298 K or as otherwise indicated in figures or at relevant text sites. Data acquisition and processing were undertaken using Topspin versions 3 and 4. Cosine-squared window functions were applied for apodization in all dimensions. Spectra were referenced with respect to added DSS and for 13C/15N as suggested in75. Analysis of CSPs and hetNOE ratios was performed using the CCPNMR analysis 2.4 software suite76 and the program Sparky77. Details are given in the Supplementary information.All software applications related to structural biology, including structure visualization and image creation by PyMol (Schrödinger) have been run via the SBGrid platform1H}15N heteronuclear NOE experiments were performed as interleaved HSQC-based pseudo-3D versions including solvent suppression by WATERGATE sequence78 and a saturation delay of 6 s from samples of 160–340 µM (apo) and 220 µM in complex with Ext. Guided by a previous study on SARS-CoV NTD44 we recorded hetNOE experiments of the protein at various field strengths .The {1H-15N-HSQC experiments were acquired with a nitrogen offset at 117 ppm and a constant spectral width of 34 ppm using 96–256 indirect complex points. For titrations, we used 128 indirect points and complex forward linear prediction until 192 points. For RNA titrations, we added RNA to the apo NTD sample after running the reference HSQC to the final titration point and subsequently re-mixed sub-stoichiometric ratios with fresh apoprotein from the identical batch. For RNA-observed titrations, an increased number of scans (see Source Data) compensated marginal dilution. For salt titrations of NTD in complex with 1.2x molar excess of 5’ge RNA elements 1H-15N-HSQC were recorded after adding respective volumes of a 3.3 M KCl stock solution, with subsequent incubation for four hours to allow samples to reach equilibrium at 50, 140, 240, and 405 mM, respectively. Spectra of complexes and the NTD alone were obtained from measurements at 800 MHz, 298 K, using 32 scans per FID. Combined 1H-15N-chemical shift perturbations (CSP) were calculated in ppm according to eq 1Significance was defined as the mean plus standard deviation.79 applying a binomial water suppression delay optimized for the center of respective imino proton resonances.Imino proton measurement of RNAs was carried out via jump-return 1D spectraControl spectra of RNA alone for the salt titrations were acquired at 800 MHz, 298 K, 3072 direct FID points, using 256 scans, and a water recycle delay of 39.06 µs.15N SL4ext with NTD we used a sample of 120 µM in a 5-mm tube and acquired 64 scans with a recycle delay of 0.3 s. Unlabeled NTD was added at 298 K stepwise from a stock of 532 µM to a maximum of twofold excess, and the number of scans was adjusted to compensate for dilution. Complex spectra were sequentially measured at 298 K and 278 K.For the BEST-TROSY-based titration of 15N relaxation data of NTD alone and in complex with Ext RNA were acquired as pseudo 3D-experiments at 298 K and a field strength of 600 MHz including temperature compensation. We used the following T1-delays: 10 ms, 30 ms, 50 ms, 90 ms, 150 ms, 250 ms, 500 ms, 1000 ms, and 1500 ms (NTD-Ext) or 2000 ms (apoNTD). For T2, we used delays of 16.96 ms, 33.92 ms, 50.88 ms, 67.84 ms, 101.76 ms, 135.68 ms, 169.6 ms, 203.52 ms, and 271.36 ms for the apo NTD and 16.96 ms, 33.92 ms, 50.88 ms, 67.84 ms, 84.8 ms, 101.76 ms, 118.72 ms, 135.68 ms, and 169.6 ms for the NTD-Ext complex. All recycling/pre-scan delays were set to 2 s. Data were analyzed and fitted with the CCPNMR analysis 2.4 software suite76, with the given errors being a measure of the fit quality.80 equipped with a Pilatus 6 M 2D photon counting area detector. All samples were mixed from purified components, shock-frozen in liquid nitrogen, delivered to the beam line, defrozen and subjected to SEC-SAXS after extensive centrifugation. Prior testing revealed that shock-freezing and thawing does not negatively influence sample states. Samples were normally delivered 1-2 days before beamtime and locally stored at −80 °C. For SEC-SAXS runs, 90 µL of protein, RNA, and protein-RNA complex samples ( ~ 300 µM each) were loaded onto a Superdex75 Increase 10/300GL  in standard phosphate buffer using an Agilent 1260 Infinity Bio-Inert HPLC system at a flow rate of 0.6-0.7 mL/min. Between 2000–2520 successive 2D SAXS data frames of 0.995 s each were collected from the continuously flowing eluate spanning one column volume (24 mL) and delivered directly to the beam line.In-detail procedures on SAXS measurements and relevant information on the utilization of derived values are given in the Supplementary information. Briefly, SEC-SAXS measurements were performed at 293 K at the EMBL P12 BioSAXS beamline at PETRAIII (DESY synchrotron Hamburg)81 (detailed in the Supplementary Methods): After the subtraction of appropriate background/buffer scattering contributions from the SEC-peak sample frames, we used the CHROMIXS82 -derived Rg values calculated through the SEC-elution peaks, to guide the scaling and averaging of the SAXS data and produce the final 1D profiles as reported in the text  curves in Fig. I(0), using the BIOXTAS RAW suite83. All final curves were processed to derive Rg, Porod volumes, P(r) and molecular weight estimates using Bayesian inference84. Structural parameters and relevant quality of fit assessments are reported in Supplementary Table 85.The following data averaging and reduction was undertaken using the ATSAS 3.0 software suite51 via 50 iterative steps using CRYSOL86 as a model fit procedure. Secondary structure was manually given for the initial step, based on the models as described in Supplementary Fig. 87.We used the finally processed SEC-SAXS data to create structural models for SL2 + 3, SL4, Ext, and SL4ext with RNAmasonry31. For SL2 + 3 (5’ and 3’) and SL4ext (5’), we used slightly extended constructs to allow for native boundaries of stem-loops  transcription reactions (4 h at 37 °C) were terminated by the addition of ethylenediaminetetraacetic acid (EDTA) and the RNAs were precipitated with 2-propanol overnight at −20 °C. RNAs were separated on 12–16 % denaturing polyacrylamide gels and visualized by UV shadowing. Excised RNA-fragments were eluted into 0.3 M NaOAc overnight and subsequently washed, concentrated, and buffer-exchanged to NTD buffer. 15N-labeled RNA (SL4ext) was produced accordingly, with 15N-labeled uridine (rUTP) and guanosine (rGTP) (Cambridge Isotope Laboratories), and unlabeled adenosine (rATP) and cytidine (rCTP) (Sigma-Aldrich). Sequences of SCoV-2 5’ge RNA elements and of CoV-non-related RNAs are listed in Table Covid19-nmr consortium. SL2 + 3, SL4ext and Ext DNA templates were kind gifts of Julia Weigand’s lab (Marburg University), cloned by Stephen Peter. For P2, ss19T, ss19B, and SL_AUA, complementary oligonucleotides (Sigma Aldrich) were annealed and cloned into an HDV-containing vector using Gibson assembly . For P3, P3_A, P3_B, SL1_extSL1, SL1_extSL4, SL4_extSL1, complementary oligonucleotides (Sigma Aldrich) were annealed and used as templates for in vitro transcription. Final RNA samples were buffer-exchanged to NTD buffer and sample quality, homogeneity, and long-term stability were verified by native and denaturing PAGE as well as 1D NMR experiments by means of the characteristic imino proton pattern. Details are given in the Supplementary information.We used RNA secondary structure models of 5’ge RNAs SL1-SL6 taken from recently published dataAnalytical SEC runs at RT were performed by loading 100 µg of protein, protein-RNA complex (100 µg protein with a 1.2-fold molar excess of RNA), or RNA (varying RNA amounts corresponding to 1.2-fold molar excess as in the complex) samples, respectively, onto a Superdex75 Increase 10/300GL column  equilibrated with NTD buffer using an ÄKTA-explorer FPLC. Flow rates were set to 0.75 mL/min. The run was monitored with two UV absorbance (260 and 280 nm) traces. All SEC runs were analyzed using ChromLab_v6 (Bio-Rad) and the traces were plotted in OriginPro. Additional details are given in the Supplementary information.The UV-observed melting of various SCoV-2 5’ge RNAs used in this study was carried out on a JASCO J-810 spectropolarimeter equipped with a Peltier temperature control module (JASCO). 20 μM RNA sample in NTD standard buffer was used for measurements in a cuvette (Helma QS) with a sample length of 1 mm. Both CD and UV absorbance were monitored. RNA melting experiments were recorded at 260 nm from 5 to 95 °C with a heating rate of 1 °C/min. The bandwidth was set to 1 nm with a digital integration time of 1 s. Melting temperatures were obtained by fitting normalized raw UV absorbance data with a one- or two-transition model in OriginPro. The first derivative of the normalized UV absorbance at 260 nm over temperature was plotted to derive transition points.88 with the following modifications: 30 pmol RNA transcripts were dephosphorylated using Quick CIP (NEB) following the manufacturer’s protocol and resuspended in H2O. Subsequently, 5′ end-labeling of 15 pmol RNA transcripts with [γ-32P]-ATP was accomplished with T4 polynucleotide kinase (NEB). Labeled RNA was separated from unincorporated [γ-32P]-ATP by column purification (NucAway) and adjusted with NTD buffer  to 0.03 pmol/μl. Binding was performed for 10 min at RT in 20 μl reaction volume in the presence of 0.6 μg tRNA from baker’s yeast (Sigma), 3 nM 32P-labeled RNA, 1 mM MgCl2 and various dilutions of NTD in NTD buffer. After the addition of 3 μl loading buffer  the RNP complexes were resolved by PAGE  at 80 V for 75 min at 23 °C, with pre-cooled (4 °C) TB running buffer  for improved resolution. Gels were dried and subsequently exposed to a phosphor imager screen and visualized using a Typhoon laser scanner (GE). Images were exported using ImageQuant TL (v8.1).For the initial analysis of NTD RNA-binding preferences, radioactive EMSAs were performed according to referenceFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Studies of PRC2 in vitro indicate that RNA inhibits its histone methyltransferase (HMTase) activity through mutually antagonistic binding with nucleosomes, but some in vivo studies paradoxically suggest that RNA binding is necessary to facilitate its chromatin occupancy and HMTase activity. Our findings unveil a mechanism for direct exchange of RNA and DNA/nucleosome on the PRC2 protein complex, which reconciles these prior findings by allowing RNA regulation of PRC2 to be antagonistic or synergistic depending on RNA–nucleosome proximity. Furthermore, there is an increasing awareness that multiple chromatin-associated proteins exhibit regulatory RNA binding activity, and our findings indicate that this “direct transfer” mechanism may be generally required for RNA recruitment of proteins to chromatin. The chromatin-modifying enzyme, Polycomb Repressive Complex 2 (PRC2), deposits the H3K27me3 epigenetic mark to negatively regulate expression at numerous target genes, and this activity has been implicated in embryonic development, cell differentiation, and various cancers. A biological role for RNA binding in regulating PRC2 histone methyltransferase activity is generally accepted, but the nature and mechanism of this relationship remains an area of active investigation. Notably, many in vitro studies demonstrate that RNA inhibits PRC2 activity on nucleosomes through mutually antagonistic binding, while some in vivo studies indicate that PRC2’s RNA-binding activity is critical for facilitating its biological function(s). Here we use biochemical, biophysical, and computational approaches to interrogate PRC2’s RNA and DNA-binding kinetics. Our findings demonstrate that PRC2-polynucleotide dissociation rates are dependent on the concentration of free ligand, indicating the potential for direct transfer between nucleic acid ligands without a free-enzyme intermediate. Direct transfer explains the variation in previously reported dissociation kinetics, allows reconciliation of prior in vitro and in vivo studies, and expands the potential mechanisms of RNA-mediated PRC2 regulation. Moreover, simulations indicate that such a direct transfer mechanism could be obligatory for RNA to recruit proteins to chromatin. PRC2 is a histone methyltransferase (HMTase) that sequentially deposits three methyl groups onto lysine 27 of histone H3 ) plots  , 20, whir G4 RNA A. Linear]) plots B suggest]) plots , which i]) plots . It is p]) plots , 40. Thu1) and dissociation from (k−1) RNA (R) and nucleosome DNA (N), PRC2 mutually antagonistic binding to RNA and nucleosome, the catalytic deficiency of RNA-bound PRC2 (cat) methylation of nucleosomes (Nm). In addition, the model includes the direct transfer reactions (kθ) demonstrated by our present studies  indicate that RNA should be antagonistic to PRC2 HMTase activity in free solution (α = 1), as observed experimentally. However, RNA should eventually become synergistic as the RNA–nucleosome effective molarity in direct transfer events increases .We simulated reactions under this scheme using our empirically determined rate constants for association, dissociation, and direct transfer events  and usin−1R × 109) . Notably, the ability of RNA to boost PRC2 HMTase activity is completely ablated if direct transfer is ablated (kθ = 0) . Overall, these data suggest that PRC2’s mutually antagonistic RNA and nucleosome binding could be reconciled with RNA-mediated recruitment of PRC2 to chromatin under some conditions, but only if PRC2 can direct transfer from RNA to nucleosomes.As expected, all RNA effects on PRC2 HMTase activity are ablated if the PRC2-RNA complex is unstable (k1) and dissociation from (k−1) RNA (R) and nucleosome DNA (N) and the catalytic (kcat) methylation of nucleosomes (Nm). It also utilizes the α tuning parameter for effective molarity (with the same caveats as for 1) and dissociation (δ2).In the case of PRC2 and some other chromatin-associated proteins, RNA and nucleosomal DNA bind mutually antagonistically . However, other proteins can stably bind both RNA and chromatin simultaneously. For example, the transcription factor Yin Yang 1 (YY1) binds DNA and RNA independently, and Sigova et al.  proposedSI Appendix, Fig. S7). Importantly, this synergy is completely dependent on formation of the stable ternary complex, and preventing its formation (δ1 = 0) ablates RNA-dependent increases in activity . Thus, direct translocation between RNA and nucleosome DNA without a free-enzyme intermediate seems necessary to improve activity rate for the alternative situation of independent RNA and nucleosome binding.We simulated reactions under this scheme using kinetic constants for RNA and DNA binding that are consistent with those reported by Sigova et al. for YY1  and usincat = 0) from the prior reaction scheme . However, this is dependent on an RBTF’s ability to function on its nucleosome target with RNA co-bound  and on formation of the ternary complex . Consequently, translocation without a free protein intermediate seems necessary for RNA-mediated facilitation of activity for proteins with independent RNA and nucleosome binding, independent of whether the protein acts catalytically or simply by binding DNA.These data address chromatin binders with catalytic activity, but RNA-binding transcription factors (RBTFs) like YY1 have biological activity that is not catalytic in nature. To interrogate the relevance of our findings to such RBTFs, we eliminated catalytic activity  allows RNA facilitation of activity . Interestingly, non-catalytic independent binders  have their RNA-mediated effects ablated by relatively minor destabilization  of the ternary complex if there is no bias between ligands (δ2N = δ2R) . However, a million-fold less stable ternary complex still allows RNA-mediated recruitment  or inhibition  if the RBTF has a bias toward RNA  or nucleosome  dissociation from the ternary complex, respectively. Collectively these data suggest that the seemingly distinct PRC2-like and Sigova et al. models for RNA-mediated recruitment to chromatin both rely on translocation between RNA and nucleosome DNA through a ternary complex intermediate, and they differ only in the stability of their ternary complexes . Thus, some form of direct transfer may be generally necessary for RNA-binding chromatin-associated proteins to have their functions on chromatin facilitated by RNA.These findings indicate that competitive , though we acknowledge that our simulations only incorporate the α parameter in the presence of direct transfer. To test this question in a manner that avoids use of the α parameter, we employed single-molecule dynamics (SMD) simulations of mutually exclusive protein binding to RNA and nucleosomes tethered together. Our findings indicate that increasing RNA binding affinity increases RNA occupancy  as expected, but slightly decreases nucleosome occupancy . Similarly, the simulations show increased intermolecular distance between nucleosomes and nearby unbound protein  and a reduced concentration of unbound protein in nucleosome-adjacent solvent space , confirming an antagonistic relationship between RNA and nucleosome occupancy. We note that our SMD approach is a first approximation that does not, for example, account for Debye–Hückel effects on short range electrostatics, which could affect the assumption of isotropic diffusion around RNA/nucleosome. Thus, our collective findings suggest that RNA–nucleosome proximity alone is not sufficient for RNA-mediated recruitment of mutually exclusive binders to chromatin, with the limitation that more fine-grained interrogations may show otherwise under specific conditions.Our data suggest that direct transfer creates a synergistic relationship between RNA concentration and protein activity, but only if the α value  is high, implying that direct transfer alone is insufficient for RNA-mediated facilitation of protein function. We next consider whether RNA–nucleosome proximity could allow RNA-mediated protein recruitment to chromatin for strictly exclusive binders . It might seem that a reservoir of RNA-bound protein directly adjacent to chromatin DNA could increase chromatin occupancy by the protein. However, our results imply that RNA–nucleosome proximity without direct transfer should be insufficient for RNA-mediated facilitation of protein activity , yet the PRC2 does not act there . FurtherThese findings demonstrate that PRC2 can translocate directly between polynucleotide species that are biologically relevant. Previous studies of such direct transfer have concerned homo-multimeric proteins, where a ligand bound to one protomer is in position to displace a second ligand bound to a nearby protomer. Therefore, the occurrence of direct transfer with PRC2 was unexpected, and it raises compelling questions about the underlying biophysical mechanism. We consider a model B that inWe note that it is not yet clear how RNA and nucleosome DNA compete for binding on the surface(s) of PRC2, though some structural insights are emerging –47. ThisθD/k−1P ratios for direct transfer reactions in 5 M−1, suggesting that flux through a direct transfer pathway  has been proposed to increase its search efficiency for DNA target sites , 55; see and 56. 5me) or somatic (PRC25m) isoform of AEBP2 . For dsDNA constructs, complementary oligos ordered from IDT were mixed at 5 µM (ligand) or 300 µM (competitor) each in annealing buffer , subjected to a thermocycler program  for annealing, then annealing confirmed via Native-PAGE. Concentrations of all ligands were confirmed spectroscopically using manufacturer-provided extinction coefficients.All oligos were ordered from IDT, and their sequences in IDT syntax are provided . Subscript of each binding buffer indicates the concentration of KCl in milli-molarity .All binding buffers (BB) contained 50 mM TRIS (pH 7.5 at 25 °C), 2.5 mM MgClBinding Buffers), then dispensed in 36 µL volumes into the wells of a 384-well black microplate (Corning #3575). PRC2 was prepared at 10X the reported concentrations via serial dilution in binding buffer. Binding reactions were initiated by addition of 4 µL of PRC2 solution to the corresponding prereaction mix, then incubated 30 min at room temperature. Wells with binding buffer only were also included for blanking. Fluorescence polarization readings were then taken for 30 min in 30-s intervals with a TECAN Spark microplate reader . Each experiment had two or four technical replicates per protein concentration (as indicated), and at least three independent experiments were performed per protein–polynucleotide combination.Pre-reaction mix was prepared with 5 nM ligand molecule in respective binding buffer . Briefly, polarization versus time data were calculated for each reaction, the last 10 data points for each reaction averaged to generate an equilibrium polarization value, and then equilibrium polarization values were plotted as a function of protein concentration. Plot data were regressed with SI Appendix, Eq. S2 to calculate Kdapp for the interaction.Raw data were analyzed in R v4.1.1 with the FPalyze function  in binding buffer (see Binding Buffers), then dispensed in 36 µL volumes into the wells of a 384-well black microplate (Corning #3575). Decoy was prepared at 10X the reported concentrations via serial dilution in binding buffer or carrier polynucleotide  to determine koffobs , and koffobs values were plotted as a function of competitor concentration. Plotted data (with background koffobs subtracted to mitigate temperature effects on polarization) were regressed  via SI Appendix, Eq. S4.1 and then SI Appendix, Eq. S4.2 with tuning parameters constrained to the SI Appendix, Eq. S4.1 solutions, then the regression models compared with the Bayesian Information Criterion (−1P and/or kθD) were determined from the best-performing regression model. If minimum polarization was not reached during competition experiments , then it was manually defined with minimum polarization data from corresponding binding curve data (d DeterminationFP-Based K).Raw data were analyzed in R v4.1.1 with the FPalyze function . Briefly, polarization versus time data were calculated for each reaction, each reaction’s polarization data were normalized to the maximum and minimum polarization across all reactions, each normalized reaction was fit with an exponential dissociation function (riterion  (BIC). Rdapp (d DeterminationFP-based K). Then, log10{Kdapp} versus log10{[KCl]} plots were regressed in R via stats:lm (package:function). Regression indicated m = 5.7 × 10−4 ± 0.34 (slope) and b = −8.5 ± 0.49 (intercept) for the G4 RNA data, and m = 1.4 ± 0.68 and b = −5.2 ± 1.1 for the dsDNA data, where values are the regressions’ estimate ± SE.Binding curve data A were reData, Materials, and Software Availability). Briefly, [ET], [NT], [RT], Kd, k−1, kθ, kcat, and α were user-provided. Then, other rate constants and initial conditions were calculated via SI Appendix, Eq. S7, and the system of differential equations  was solved by numerical integration. Initial reaction rates (V0) were calculated as the average rate of change in [mT] during the first 5% of each reaction. By default, k−1N = 9.1 × 10−5 s−1, k−1R = 1.7 × 10−3 s−1, kθN = 91, kθR = 170, kθNN = 260, KdN = 5.1 nM, and KdR = 2.3 nM were taken directly from −1 are from self-competitions), kcat = 10−1 s−1 was taken from PRC2 literature . Briefly, [ET], [NT], [RT], Kd, k−1, kcat, α, β, and δ were user-provided. Then, other rate constants and initial conditions were calculated via SI Appendix, Eq. S7, and the system of differential equations  was solved by numerical integration. Initial reaction rates (V0) were calculated as the average rate of change in [mT] during the first 5% of each reaction. By default, KdR = 400 nM and KdN = 200 nM were taken from Sigova et al. , tables were prepared with Word (Microsoft), graphs were prepared with R v4.1.1, protein structures were prepared in PyMOL v2.5.2 (Schrodinger), and figures were assembled in PowerPoint (Microsoft).Appendix 01 (PDF)Click here for additional data file.


The term “RNA-seq” refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data. While multiple steps are necessary to process input consisting of FASTQ sequencing files, a reference genome FASTA, and a GTF annotation28, to an output of quantifications using kallisto and bustools, these steps are greatly facilitated by the wrapper tool, kb-python. kb-python can extract reference transcriptomes from reference genomes and run kallisto and bustools in workflows optimal for each assay type. The kb-python tool simplifies the running of kallisto and bustools to the extent that all of this can be done in two steps: `kb ref` for generating a kallisto index from an annotated reference genome and `kb count` for mapping and quantification. Thus, kallisto, bustools, and kb-python make RNA-seq preprocessing efficient, modular, flexible, and simple.1The preprocessingindex from a set of sequences, referred to as targets, representing the set of sequences that RNA-seq reads can be mapped to. In a standard analysis, these targets are usually transcript sequences . However, more generally, users can define targets from any sets of sequences they wish to map their sequencing reads against. Since kallisto is a tool that leverages pseudoalignment, the mapping procedure relies on read assignment, such that each read is deemed to be compatible with a certain set of targets, rather than standard alignment. The kallisto index is based on the Bifrost29 implementation of the colored de Bruijn graph30, which enables memory-efficient and rapid read assignment.For RNA-seq read mapping, kallisto builds an kb ref command . The index created by --workflow=nac (nac: nascent and cDNA) contains both the cDNA and the nascent transcripts. The nascent transcript sequences consist of the full gene (both exons and introns). This nac index is suitable for single-nucleus RNA-seq as there exists a high abundance of non-mature transcripts captured in nucleus-based sequencing assays.32 Additionally, this nac index should be used for analyses that require jointly modeling nascent and mature RNA species.38 For both the standard and nac workflows, a user supplies a genome FASTA and GTF annotation, which kb-python uses to extract the relevant sequences. Finally, if one wishes to index a custom set of targets or of k-mers  allows specific biotypes to be selected from the GTF file, making possible filtering of entries such as pseudogenes, which can improve read mapping accuracyry usage . It is rkb count command within kb-python enables mapping and quantification of bulk, single-cell, and single-nucleus RNA-seq reads (The eq reads . As diffThe specifications for sequencing assay technology within kb-python are as follows:Technology string: A technology string for a particular type of assay can be supplied via the -x option. The technology string can be used in one of three ways:kb --list) so one can name one of those directly (e.g. one can specify -x 10xv3).Option 1: Several assays are predefined within the software , and the biological sequence that is to be mapped .Strandedness: If a read (or the first read in the case of paired-end reads) is to be mapped in forward orientation, one should specify --strand=forward. If it is to be mapped in reverse orientation, one should specify --strand=reverse. If one does not want to map reads with strand-specificity, then one should specify --strand=unstranded. If a predefined name is used in the technology string -x option (option 1), then kb-python uses a default stranded option for that technology ; otherwise, the default is unstranded. Setting the --strand option explicitly will overrule the default option.Parity: If the technology involves two biological read files that are derived from paired-end sequencing (as is the case with Smartseq243 and Smartseq344 and many bulk RNA sequencing kits), one should specify --parity=paired to perform mapping that takes into account the fact that the reads are paired-end. Otherwise, one can specify --parity=single. If a predefined name is used in the -x technology string option (option 1), then kb-python uses the default parity option for that technology .On list: For single-cell and single-nucleus sequencing assays, barcodes are used to identify each cell or nucleus. The “on list” of barcodes represents the known barcode sequences that are included in the assay. Barcodes extracted from the sequencing reads will be error-tolerantly mapped to this list in a process known as barcode error correction. The on list filename can be specified with the -w option in kb count. It can also be obtained by seqspec.40 If an on list is not provided or cannot be found for the given technology, then an on list is created by bustools via the bustools allowlist command which identifies repeating barcodes in sequencing reads. If the technology does not include cell barcodes (as is the case in bulk RNA-seq), the “on list” option is irrelevant and no barcode processing occurs which should be the case for assays that don’t include cell/nuclei barcodes . If a predefined name is used in the -x technology string option (option 1), then kb-python uses the default on list option for that technology.--workflow=nac should be used in kb count so that the nascent and mature RNA species are quantified accurately; otherwise that option should be omitted or --workflow=standard (which is the default) can be explicitly specified. For the nac workflow, one obtains three count matrices: 1) nascent, 2) mature, and 3) ambiguous. In most experiments, the plurality of reads will be “ambiguous” since they originate from exons, which are present in both nascent RNA and mature RNA. Therefore, it is desirable to generate additional matrices by adding the counts from those three matrices, which users can either do themselves or by using the --sum option.24--sum=total adds all three matrices, --sum=cell adds the mature and ambiguous matrices, and --sum=nucleus adds the nascent and ambiguous matrices. Different matrices may be used for different types of analyses. For example, in single-cell RNA-seq analysis (where most “ambiguous” counts are likely of mature RNA origin), jointly modeling the mature+ambiguous count matrix (--sum=cell) with the nascent count matrix permits biophysical modeling of RNA processing.38 In single-nucleus RNA-seq quantification, one might want to use --sum=nucleus to add up the nascent+ambiguous counts. The kb-python, kallisto, and bustools commands for the standard workflow and the nac workflow are shown in If a nac index was generated by kb ref, 48, sleuth49, limma-voom51, and other differential gene expression programs.In addition to single-cell and single-nucleus RNA-seq, kb count can be used for bulk RNA-seq. Bulk RNA-seq generally does not have UMIs or cell barcodes  and relies on cDNA mapping. With -x BULK as the technology string, a workflow specific for bulk RNA-seq quantification is executed . This wiTo facilitate multi-sample analysis, artificial unique sample-specific barcodes can be created and stored in the BUS file and the resulting mapping between the artificially generated barcode and the sample ID is outputted. These sample-specific barcodes are 16-bp in length and are also stored in the BUS file. Where there exists both a cell barcode (like in single-cell RNA-seq) and a sample-specific barcode, both sets of barcodes will be outputted so that each entry in the resulting output count matrix can be associated with a particular cell and a particular sample. To utilize the multi-sample workflow, a batch file containing the file names of the FASTQ files must be provided .--dry-run option in kb count outputs the kallisto and bustools commands that will be run without actually running the programs. Also, the --verbose option in kb count is helpful for examining the kallisto and bustools commands that are being run as well as their output.The technical details of how kb count utilizes kallisto and bustools are detailed in the following paragraph. Note that the kallisto bus command within kallisto to produce a BUS file, which stores the read mapping information, and then uses bustools23 commands to process the BUS file. The kallisto bus command maps RNA-seq reads to a kallisto index, and the resultant BUS file stores the mapping information, including the barcode, unique molecular identifier (UMI), and the equivalence class representing the set of transcripts the read is compatible with.23 In certain RNA-seq assays, barcodes and/or UMIs may not be present, and are therefore not considered when processing the BUS file. After the mapping step is complete, the BUS file is sorted via the bustools sort command to facilitate further processing. For single-cell and single-nucleus experiments with multiplexed barcodes in the sequencing reads, an “on list” of barcodes, representing the known barcode sequences that are included in the assay, needs to be provided. If an “on list” is unavailable, the bustools allowlist command can be used to construct one from a sorted BUS file. The barcodes in the sorted BUS file are error-corrected to the “on list” via bustools correct, then the BUS file is sorted again with bustools sort. The final sorted, on list-corrected BUS file is then used to generate quantifications via count matrices through the bustools count command. At any point, a sorted BUS file can be inputted into bustools compress to create a compressed BUS file (a BUSZ file), which can be subsequently decompressed via bustools decompress.52 Other bustools features enable more specialized workflows beyond what is provided by kb-python , one can supply the --cm option for quantification.Gene-level count matrices: In single-cell and single-nucleus RNA-seq, typically a gene-level count matrix is produced by collapsing UMIs to the gene level. Here, the bustools count command is run with the --genecounts option is not provided, and --multimappingis provided to avoid discarding reads or collapsed UMIs that are assigned to multiple genes. If UMIs are not present in the sequencing technology, the --cm option is supplied to perform counting without UMI collapsing. While downstream analyses can be performed on TCCs55, it is more often useful to produce transcript-level abundances from the TCCs for technologies where sequencing reads span the full length of transcripts, such as bulk RNA-seq. In such cases, an expectation-maximization algorithm is typically performed to probabilistically estimate transcript abundances.56 The procedure to generate transcript-level abundance matrices is performed by running the kallisto quant-tcc command on the TCC matrices.Transcript-level count matrices: Transcript-compatibility counts (TCCs) are counts assigned to equivalence classes where each equivalence class is defined by a unique set of transcripts. For producing a matrix of transcript-compatibility counts (TCCs), the 40 provides a specification for the structure of genomic sequencing assays, formatted in a machine-readable YAML file. The specification can be readily inputted into the kallisto bustools workflow for preprocessing reads from a given assay  being the matrix columns.Here, the quantification output of the kb count command is described. While the initial step of kb count uses kallisto to produce a BUS file located at output_dir/output.bus, the actual quantification results are located in matrices in subdirectories of output_dir/. All matrices have the extension .mtx and will be in a sparse matrix (Matrix Market) file format with the barcodes (i.e. the cells or samples) being the matrix rows and the genes :The standard workflowcells_x_genes.mtx: The count matrix (in Matrix Market file format); only exonic reads are countedcells_x_genes.barcodes.txt: The barcodes representing the matrix row namescells_x_genes.genes.txt: The gene IDs representing the matrix column namescells_x_genes.genes.names.txt: Same as cells_x_genes.mtx except with gene names instead of gene IDs for the matrix columnscells_x_genes.barcodes.prefix.txt: If sample-specific barcodes are generated in addition to cell barcodes being recorded, then this file will be created and the sample-specific barcodes will be stored here. The lines of this file correspond to the lines in the cells_x_genes.barcodes.txt which contains the cell barcodes (both files will have the same number of lines). The sample-specific barcodes and cell barcodes can be joined together as a unique identifier for downstream analysis.nac workflow: same as the standard workflow except the .mtx files produced are differentcells_x_genes.mature.mtx: The mature RNA count matrixcells_x_genes.ambiguous.mtx: The nascent RNA count matrixcells_x_genes.nascent.mtx: The ambiguous RNA count matrixcells_x_genes.cell.mtx: The mature+ambiguous RNA count matrix (note: this is what is quantified into the count matrix in the standard workflow)cells_x_genes.nucleus.mtx: The nascent+ambiguous RNA count matrixcells_x_genes.total.mtx: The mature+nascent+ambiguous RNA count matrix--tcc option is used.For RNA-seq assays (e.g. bulk RNA-seq or Smartseq2) that profile the full length of transcripts in which case it is desirable to perform transcript-level quantification, the output_dir/counts_unfiltered/ which contains the following files for the standard workflow:The first step to doing transcript-level quantification is to obtain transcript-compatibility counts (TCCs) over equivalence classes (ECs). The TCCs will be outputted into cells_x_tcc.mtx: The count matrix containing the TCCscells_x_tcc.barcodes.txt: The barcodes representing the matrix row namescells_x_tcc.ec.txt: The equivalence classes representing the matrix column names  for all transcripts within the equivalence class)output_dir/quant_unfiltered/ directory which contains the following:The --tcc option will additionally produce transcript-level estimated counts which will be placed in the matrix.abundance.mtx: The matrix containing the transcript-level estimated countsmatrix.abundance.tpm.mtx: The matrix containing the TPM-normalized transcript-level abundancesmatrix.efflens.mtx: A matrix that contains the transcript effective lengthsmatrix.fld.tsv: A file with two columns, containing the mean and standard deviation, respectively, of the fragment length distribution used to produce transcript-level abundances and effective lengths for each row of the matrix.matrix.abundance.gene.mtx: A matrix that is the same as the matrix.abundance.mtx matrix except counts are aggregated to gene-levelmatrix.abundance.gene.tpm.mtx: A matrix that is the same as the matrix.abundance.tpm.mtx matrix except TPMs are aggregated to gene-leveltranscripts.txt: The transcript names representing the matrix column names for the transcript-level quantification matricesgenes.txt: The gene IDs representing the matrix column names for the gene-level aggregation quantification matricestranscript_lengths.txt: The transcript names along with their lengthsrow names are the individual samples and will be the same as those in output_dir/counts_unfiltered/cells_x_tcc.barcodes.txt - The output_dir/matrix.cells and output_dir/matrix.sample.barcodes files provide a mapping between the sample name and the sample barcode.*Note: The *Note: The --matrix-to-directories option will output each row of the matrix into a separate subdirectory. In other words, using this option will produce multiple new directories within output_dir/quant_unfiltered/. Each one will be named abundance_{n} . Within each subdirectory, an abundance.tsv text file and abundance.h5 HDF5 file will be created containing the quantifications for that particular sample. These abundance files are identical to the abundance files produced by the original version of kallisto for bulk RNA-seq.60 for downstream processing in python, an anndata61 object needs to be created , the matrices that are loaded in will become much smaller and more efficient to process.A 64-bit computer running either macOS, Windows, or a Linux/Unix operating system.kallisto version 0.50.1 or later (which comes packaged with kb-python)bustools version 0.43.1 or later (which comes packaged with kb-python)kb-python version 0.28.0 or laterPython 3.7 or later (for kb-python version 0.28.0)Bulk, single-cell, or single-nucleus RNA sequencing reads in (possibly gzip) FASTQ format.unfiltered GTF file) and an hour to generate the nac index. For the preprocessing of 800 million Illumina sequencing reads (stored in a single pair of fastq.gz files) produced by single-cell RNA-seq from 10x Genomics, kb count with the nac workflow can take under an hour on 8 threads and under 40 minutes on 16 threads, with an even lower runtime for the standard workflow.The runtime depends on the size of the reference being indexed, the number and length of the sequencing reads being processed, other properties of the dataset being quantified, system hardware, and the number of threads allotted. The kb ref command only needs to be run once to create the index against which reads will be mapped. With 8 threads on a server with x86-64 architecture and 32 Intel Xeon CPUs (E5-2667 v3 @ 3.20GHz), kb ref takes approximately 15 minutes to generate a standard index from the GRCm39 mouse genome  file created by kb ref should be the exact file used by kb count when running kb count on that index. All the transcripts in the t2g file must be exactly the same as the transcripts present in the kallisto index. Incompatibilities can lead to unpredictable behavior in the bustools quantification step.31 .dna.toplevel.fa.gz files or the GENCODE67 .primary_assembly.genome.fa.gz files should be used as the reference genome. Use of FASTA files incompatible with the supplied GTF will lead to errors.When using kb ref to generate a kallisto index, a genome FASTA file (not a transcriptome FASTA file) should be supplied along with the genome annotation GTF file. A transcriptome file will automatically be generated by kb ref and be indexed by kallisto. In general, the EnsemblWhen performing multiple kb-python runs simultaneously, a different temporary directory must be specified via the --tmp option for each run. The temporary directory also must not exist beforehand.Finally, one should make sure that the value supplied to the -x technology string option matches the assay from which the sequencing reads were generated.Note: If the technology string begins with a −, for example: −1,0,0:0,0,5:0,5,0, one would need to write −x “ −1,0,0:0,0,5:0,5,0” to avoid the string being misinterpreted as a command-line flag.Here, we describe the procedures to use for mouse samples of paired-end bulk RNA-seq, 10x (version 3) single-cell RNA-seq, and 10x (version 3) single-nucleus RNA-seq.Input:Paired-end unstranded mouse RNA-seq reads (3 samples):sample1_R1.fastq.gz sample1_R2.fastq.gzsample2_R1.fastq.gz sample2_R2.fastq.gzsample3_R1.fastq.gz sample3_R2.fastq.gz1.pip install kb_python2.ftp.ensembl.org/pub/release-108/fasta/mus_musculus/dna/Mus_musculus.GRCm39.dna.primary_assembly.fa.gzwget ftp.ensembl.org/pub/release-108/gtf/mus_musculus/Mus_musculus.GRCm39.108.gtf.gzwget 3.kb ref -i index.idx -g t2g.txt -f1 cdna.fasta \    Mus_musculus.GRCm39.dna.primary_assembly.fa.gz \    Mus_musculus.GRCm39.108.gtf.gz4.kb count -x BULK -o output_dir -i index.idx -g t2g.txt \    --parity=paired --strand=unstranded \    --tcc --matrix-to-directories \    sample1_R1.fastq.gz sample1_R2.fastq.gz \    sample2_R1.fastq.gz sample2_R2.fastq.gz \    sample3_R1.fastq.gz sample3_R2.fastq.gz5.Output for sample 1:output_dir/quant_unfiltered/abundance_1/abundance.tsvoutput_dir/quant_unfiltered/abundance_1/abundance.gene.tsvoutput_dir/quant_unfiltered/abundance_1/abundance.h5Output for sample 2:output_dir/quant_unfiltered/abundance_2/abundance.tsvoutput_dir/quant_unfiltered/abundance_2/abundance.gene.tsvoutput_dir/quant_unfiltered/abundance_2/abundance.h5Output for sample 3:output_dir/quant_unfiltered/abundance_3/abundance.tsvoutput_dir/quant_unfiltered/abundance_3/abundance.gene.tsvoutput_dir/quant_unfiltered/abundance_3/abundance.h5The abundance.tsv files contain the transcript-level abundances. The abundance.h5 file contains the same information as the abundance.tsv files except in HDF5 format. The abundance.gene.tsv files contain the gene-level abundances (taken by summing up the transcript-level abundances for each gene). These files can be used in downstream differential gene expression programs.Input:10x version 3 single-cell RNA-seq reads: R1.fastq.gz and R2.fastq.gz1.pip install kb_python2.ftp.ensembl.org/pub/release-108/fasta/mus_musculus/dna/Mus_musculus.GRCm39.dna.primary_assembly.fa.gzwget ftp.ensembl.org/pub/release-108/gtf/mus_musculus/Mus_musculus.GRCm39.108.gtf.gzwget 3.kb ref -i index.idx -g t2g.txt -f1 cdna.fasta \    Mus_musculus.GRCm39.dna.primary_assembly.fa.gz \    Mus_musculus.GRCm39.108.gtf.gz4.kb count -x 10xv3 -o output_dir -i index.idx -g t2g.txt \    R1.fastq.gz R2.fastq.gz5.Output:output_dir/counts_unfiltered/cells_x_genes.mtxoutput_dir/counts_unfiltered/cells_x_genes.barcodes.txtoutput_dir/counts_unfiltered/cells_x_genes.genes.txtoutput_dir/counts_unfiltered/cells_x_genes.genes.names.txtThe cells_x_genes.mtx is the count matrix file with the barcodes (the row names) listed in cells_x_genes.barcodes.txt and the gene names (the column names) listed in cells_x_genes.genes.names.txt .Input:10x version 3 single-nucleus RNA-seq reads: R1.fastq.gz and R2.fastq.gz1.pip install kb_python2.ftp.ensembl.org/pub/release-108/fasta/mus_musculus/dna/Mus_musculus.GRCm39.dna.primary_assembly.fa.gzwget ftp.ensembl.org/pub/release-108/gtf/mus_musculus/Mus_musculus.GRCm39.108.gtf.gzwget 3.kb ref --workflow=nac -i index.idx -g t2g.txt \    -c1 cdna.txt -c2 nascent.txt -f1 cdna.fasta -f2 nascent.fasta \    Mus_musculus.GRCm39.dna.primary_assembly.fa.gz \    Mus_musculus.GRCm39.108.gtf.gz4.kb count -x 10xv3 --workflow=nac -o output_dir \    -i index.idx -g t2g.txt -c1 cdna.txt -c2 nascent.txt \    --sum=total R1.fastq.gz R2.fastq.gz5.Output:output_dir/counts_unfiltered/cells_x_genes.mature.mtxoutput_dir/counts_unfiltered/cells_x_genes.nascent.mtxoutput_dir/counts_unfiltered/cells_x_genes.ambiguous.mtxoutput_dir/counts_unfiltered/cells_x_genes.cell.mtxoutput_dir/counts_unfiltered/cells_x_genes.nucleus.mtxoutput_dir/counts_unfiltered/cells_x_genes.total.mtxoutput_dir/counts_unfiltered/cells_x_genes.barcodes.txtoutput_dir/counts_unfiltered/cells_x_genes.genes.txtoutput_dir/counts_unfiltered/cells_x_genes.genes.names.txtThis workflow can be used for both single-cell RNA-seq and single-nucleus RNA-seq. Many count matrix files (.mtx files) are generated. For quantification of total RNA present in each cell or nucleus, one would want to use the cells_x_genes.total.mtx. For biophysical models that jointly consider spliced and unspliced transcripts, one may want to use cells_x_genes.cell.mtx (for the “spliced” transcripts) and cells_x_genes.nascent.mtx (for the “unspliced” transcripts).The barcodes (the matrix row names) are listed in cells_x_genes.barcodes.txt and the gene names (the matrix column names) are listed in cells_x_genes.genes.names.txt .There are many ways to extend the standard workflows beyond bulk RNA-seq, 10x single-cell RNA-seq, and 10x single-nucleus RNA-seq. For an additional, extended example that involves preprocessing mouse multiplexed single-nucleus SPLiT-seq RNA-seq data with a filtered mouse genome annotation, see Supplement 1