Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Taken together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs. Circular RNAs (circRNAs), a class of covalently closed RNA molecules formed via back-splicing (BS) or lariat precursors, are involved in various biological processes and pathogenesis by fine-tuning the eukaryotic gene regulatory network . CircRNABy detecting the back-splicing junctions (BSJs) of circRNAs with deep sequencing, short-read RNA sequencing discriminates circRNAs with low expression   and nano6) by RCRT for circRNA and regular reverse transcription (RT) for linear residuals. After tailing poly(A) of the first strand and synthesizing the second strand with an anchor primer (P2-T24), the double-strand cDNA was amplified with P1 and P2 to construct the final circFL-seq library. To benchmark full-length circRNA sequences of the circFL-seq library, eight known circRNAs were selected for PCR validation in the HEK293T library. Rolling circles of target circRNA  mode, clean reads (qscore≥7) were aligned to the reference genome to identify potential back-spliced junctions (BSJs) by the sign of chiastic overlapping segments . Then, rTo assess the performance of circFL-seq, we applied circFL-seq to eight libraries of six human cell lines  and produced 30 M clean reads sequenced by using one PromethION Flow Cell . We dete–16; Based on BSJs and boundary exons identified by circFL-seq, we classified circRNAs into seven types: exonic, intronic, novel splicing site (NSS), intergenic, novel UTR, antisense, and read-through . For higCDR1as , we identified 23,267 internal AS events , includiCDR1as . We furtCDR1as –8.Based on read-spanning BSJs, short-read sequencing has long been used for genome-wide characterization of circRNAs. Thus, for comparison, eight RNA-seq libraries (150 bp×2) with the same circRNA enrichment method for the same six cell lines   followed by nanopore sequencing. With a single circFL-seq library of the HEK293 cell line sequenced by one MinION Flow Cell, we compared the features of the RCRT strategy and RCA strategy. circFL-seq produced more full-length circRNA reads per 10s. 5820)  but idens. 5820) . Howevers. 5820) . The difs. 5820)  or by ths. 5820) . Thus, ws. 5820) . Specifis. 5820) . We alsos. 5820) , suggests. 5820) , which mWe then compared circFL-seq to isoCirc in two normal human tissues (brain and testis) with high sequencing depth. isoCirc built multiple libraries for each sample and sequenced on multiple MinION Flow Cells to obtain a high sequencing depth, while circFL-seq built one library for each tissue and sequenced the two libraries on one PromethION Flow Cell to obtain a comparable sequencing depth. isoCirc and circFL-seq detected 79,312 and 34,046 known BSJs, respectively. Known BSJs detected by isoCirc were more likely  to be expressed at low levels (read counts=1), indicating that more libraries were more sensitive for detecting circRNAs, overcoming bias and artifacts related to sampling, RNase R treatment, cDNA synthesis, and amplification.Both circFL-seq and CIRI-long employed an RCRT strategy for library construction. Although the two methods were applied in different species (human vs. mouse), the computational pipelines were compatible and could be compared with each other. We employed the computational pipeline of circFL-seq and CIRI-long to analyze HEK293 cells library of circFL-seq and a mouse brain library of CIRI-long, respectively. circFL-seq detected 27,869 BSJs from HEK293 cells (75.3% known in the database) and 18,396 BSJs from the mouse brain (68.6% known in the database), while CIRI-long detected 15,242 BSJs from HEK293 cells (76.9% known in the database) and 9258 BSJs from the mouse brain (69.9% known in the database) . Thus, tWith the benefit of high read coverage, circFL-seq is able to quantify circRNA expression levels. For BSJs identified by circFL-seq and RNA-seq, the amounts between the two methods were significantly correlated  . A total of 90 transcripts had more than 0.1 ratio differences between HeLa and SKOV3 cell lines. For example, circ-to 0.45) . Althoug results , supportGBF1 and MACROD2 (GBF1 to MACROD2 (PRICKLE2-AS1 and PTPRT-AS1) was experimentally validated  and has been found to play important roles in cancer . Althoug MACROD2 , we sele MACROD2 , and the MACROD2 . For com MACROD2 , suggest MACROD2 . In addialidated . All theell line .In this study, we established a full-length circRNA sequencing method, circFL-seq, by nanopore sequencing. Compared to short-read sequencing, which is limited to reconstructing short circRNAs (<500 nt), long-read sequencing with circFL-seq has shown advances in comprehensively identifying full-length circRNAs of all sizes (64–2334 nt in our data and more than 40% identified isoforms >500 nt). Very recently, isoCirc  and CIRIPrevious short-read RNA sequencing was used to quantify circRNAs based on BSJ-spanning reads, while circFL-seq with full-length reads was employed to finely quantify circRNAs at the isoform level. Thus, circFL-seq is able to identify differential internal AS events, helping to unravel the function of sequence variants. In our comparisons, circFL-seq was more sensitive than isoCirc and CIRI-long for AS detection, although the performance required full evaluation by ground-truth circRNA isoforms. Another novel application of full-length circRNA sequencing is to identify f-circRNA, which has been found to play important roles in cancer pathogenesis  and has Overall, circFL-seq is able to identify and quantify circRNAs at the isoform level and is more sensitive in circRNA AS event detection and f-circRNA detection. In addition, circFL-seq has been applied in a large-scale sequencer, PromethION, with pooling multiple libraries, which will further decrease the sequencing cost to twice that of RNA-seq. Thus, our developed circFL-seq process is an effective and affordable high-throughput full-length circRNA sequencing method for screening functional circRNAs at the omics scale.2. Cell lines were collected at 80–90% confluency. All cell lines were authenticated with STR profiling and tested negative for mycoplasma contamination. Total RNA was extracted by the FastPure Cell/Tissue Total RNA Isolation Kit (Vazyme) according to the manufacturer’s instructions. Total RNA from the human brain (#636530) and testis (#636533) was purchased from Clontech.The human cell lines HeLa, SKOV3, MCF7, VCaP, SH-SY5Y, HEK293T, and HEK293 were used in this study. HeLa, SKOV3, MCF7, and HEK293T cell lines were obtained from Jiadong Wang Laboratory . SH-SY5YSimilar to a previous method , nonoverThe reaction above was then mixed with 4 μl ATP (10 mM), 4 μl 10× Poly(A) Polymerase Reaction Buffer and 1 μl Poly(A) Polymerase (NEB) for poly(A) tailing and incubated at 37°C for 10 min. Purified RNA was isolated by 2.5× RNA Clean Beads (Vazyme) according to the manufacturer’s instructions.For circFL-seq, RNA was incubated at 37°C for 30 min and 70°C for 10 min in a 10 μl reaction that contained 1 U RNase R (Lucigen) and 1 μl 10× RNase R buffer. For RNA-seq, RNA was incubated under the same conditions but in a 20 μl reaction containing 2 U RNase R and 2 μl 10× RNase R buffer.GTCGACGGCGCGCCGGATCCATANNNNNN-3′) with HiScript III reverse transcriptase (Vazyme) for 5 min at 25°C, 50 min at 50°C, 2 min at 70°C, and 5 s at 85°C, followed by purification with 0.75× DNA Clean Beads (Vazyme) according to the manufacturer’s instructions.After enrichment of circRNAs, collected RNAs were reverse transcribed into first cDNA strands in a 20 μl reaction by P1-N6  tails were added at the 3′ ends in a 20 μl reaction by terminal deoxynucleotidyl transferase (Invitrogen) with final dATP and ddATP concentrations of 2.5 mM and 25 μM, respectively, followed by purification with 0.75× DNA Clean Beads.ATATCTCGAGGGCGCGCCGGATCCTTTTTTTTTTTTTTTTTTTTTTTT-3′) by I-5 High-Fidelity DNA polymerase (MCLAB) at 98°C for 2 min, 50°C for 2 min, and 72°C for 5 min.Next, second-strand cDNAs were synthesized using P2-T24 (5′-GTCGACGGCGCGCCGGATCCATA-3′) and P2 (5′-ATATCTCGAGGGCGCGCCGGATCC-3′) and amplified by 20 cycles of 98°C for 10 s, 67°C for 15 s, and 72°C for 75 s, followed by 0.5× DNA Clean Bead purification. Approximately 10–50 ng purified cDNAs were further amplified by 8–10 cycles in a set of four 50 μl PCRs with P1 and P2, followed by purification with 0.5× DNA Clean Beads.Then, cDNAs were equally split into four 50 μl PCRs with primers P1  and NEBNext Ultra II End repair/dA-tailing Module (NEB), followed by purification with DNA Clean Beads. For multiplexing, repaired and end-prepped DNA was barcoded with Native Barcode by NEB Blunt/TA Ligase Master Mix (NEB), followed by purification with DNA Clean Beads. Barcoded samples were pooled together in equimolar amounts. Single samples without barcodes or pooled barcoded samples (700 ng) were ligated to an ONT Adapter with NEBNext Quick Ligation Module (NEB), followed by purification with DNA Clean Beads. The DNA library was mixed with sequencing buffer, and beads were loaded onto a PromethION or MinION R9.4 Flow Cell and run on a PromethION (performed by Grandomics) or MinION sequencer, respectively.After circRNA enrichment, RNA was isolated by 2.5× RNA Clean Beads (Vazyme). cDNA libraries were constructed and barcoded by the VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina (Vazyme) and VAHTS RNA Adapters set1/set2 for Illumina (Vazyme) according to the manufacturer’s instructions for the strand-specific rRNA depletion library. The cDNA library was fragmented, and an insert size of approximately 300 bp was selected. Eight libraries were pooled together and sequenced on the Illumina HiSeq X Ten platform of Annoroad Gene Technology with a paired-end read length of 150 bp.https://github.com/yangence/circfull, Raw sequenced data in fast5 format files were transformed to FASTQ format files (kept reads with qscore≥7.0) and were demultiplexed by guppy (4.2.2). We employed porechop (v0.2.4) to trim barcode and circFL-seq primers (P1 and P2) and split chimeric reads to obtain clean reads for each sample. Then, RG mode (Clean reads were mapped to the human genome (hg19) by minimap2  (v2.12) The CS of clean reads was detected by TideHunter (v1.0) with the parameters ‘-f 2 c 1.5 p 30 l’. Following evaluation by Tandem Repeats Finder (v4.09) with parameters ‘2 5 7 80 5 5 2000 h -ngs’, CS was removed if containing internal tandem repeats, defined by an alignment score >40 or length of internal tandem repeats longer than half of the CS.To locate the genomic region of CS, pseudo query sequences from three combined monomers of a CS were created. The position of full-length circRNA was detected with RG mode.CCRs with FSJs detected were first selected to determine the strand origin of sequenced reads, that is, first or second strand, based on the GT/AG motif of the FSJ and mapped strand. For each 100 nt flanking end of the raw read, the maximum identical number of bases to primers P1, P2, T24, and their reverse complementary sequences was calculated by the Smith-Waterman algorithm. With these numbers of identical bases as predictor variables and strand origin as the target variable, a random forest classifier was trained on 75% selected CCRs to predict the strand origin of all clean reads. The performance of the classifier was evaluated on the remaining 25% CCRs by accuracy and AUC value for each sample . CircRNAAfter RG detection supplemented with cRG detection, low-quality circRNAs with an unsplicing ratio of BSJ<0.1 were filtered out. CircRNA isoforms with reliable BSJs and FSJs were retained if more than half of the circRNA reads were perfectly aligned on ±4 bp of junctions. CircRNA BSJs were filtered out if both BSJs were located in ±30 bp of repeat elements.For PCR validation, sample RNA was reverse transcribed to cDNA products for 5 min at 25°C, 50 min at 50°C, 2 min at 70°C, and 5 s at 85°C with random hexamers by HiScript III. For RT-qPCR, sample RNA was reverse transcribed to cDNA products for 15 min at 37°C and 5 s at 85°C with random hexamers and oligo(T) according to the manufacturer’s instructions.A pair of divergent PCR primers was designed and included 6–8 additional G bases at the 5′ end  to reducTotal RNA of the HeLa cell line (1 μg) was treated with RNase R (4 U) in a 10 μl reaction and then reverse transcribed to the cDNA products. For each circRNA isoform from the same BSJ, divergent primers  targetinGAPDH as a reference gene were used. Thermal cycling was carried out on an Applied Biosystems 7500 Fast system at 95°C for 5 min, followed by 40 cycles of 95°C for 10 s and 60°C for 24 s.Total RNA of HeLa and SKOV3 cell lines (1 μg) w/wo RNase R treatment (4 U) was reverse transcribed for RT-qPCR. For specific circRNAs, primers  across aTotal RNA of the MCF7 cell line (1 μg) w/wo RNase R treatment (4 U) was reverse transcribed for PCR. We validated full-length f-circRNA with primers  for the RNA-seq data were aligned to the human reference genome (hg19) by BWA  v0.7.17, CIRI-fuhttps://github.com/Xinglab/isoCirc, https://github.com/bioinfo-biols/CIRI-long, Analysis of isoCirc and CIRI-long data, isoCirc nanopore sequencing data were downloaded from the Sequence Read Archive (SRA: SRP235284). CircRNAs were analyzed with the isoCirc computational pipeline . The computational software of circFL-seq can be accessed from https://github.com/yangence/circfull, (swh:1:rev:2e8be5c116e9c226661c63e35223f2120272ccfc. The circFL-seq and RNA-seq data produced by this study have been deposited in SRA (PRJNA722575). Information on circRNAs detected by circFL-seq is available in the figshare repository  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Acceptance summary:Yang and colleagues report circFL-seq a method for sequencing full length circular RNAs with rolling circle RT and nanopore sequencing. While two other methods have recently been published, this manuscript does add to a growing literature on long read sequencing of circular RNAs.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and James Manley as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Fangqing Zhao (Reviewer #3).Thank you for submitting your article "circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. Reviewers all agree that a major weakness of the present manuscript is with the comparison to existing methods. The authors should compare circFL-seq to CIRI-long, and reviewers agree that a three way comparison to isoCirc is informative. The authors should accompany their comparisons with a discussion about strengths and weaknesses of each of the methods.2. The current claims and conclusions that circFL-seq is the superior method is not well supported by their data in this version of the manuscript. The authors should provide fair critiques of the other method (see comments about ligation). The authors need to point out potential limitations. For example, Is CircFL-seq biased to detecting only highly expressed transcripts?3. In comparison to isoCirc, circFL-seq identified fewer circRNA isoforms with higher read coverage of the detected circRNAs, which may be a PCR artifact. The authors need to address this concern. In addition, in two published studies, isoCirc and CIRI-long have also used nanopore sequencing to characterize circRNA isoforms and alternative splicing events. However, both studies have reported a relatively higher percentage of retained introns  compared to the number of 3.5% of intron retention events in line 139. The authors should clarify the reason behind this difference.4. The authors found six f-circ derived from GBF1-MACROD2 fusion and validated their junctions using Sanger sequencing. Besides, the authors also used short-read RNA-seq to validate the linear fusion junctions. What's the ratio of linear and circular transcript derived from these gene fusion loci? Is there any possibility that these f-circRNAs are derived from trans-splicing events? Considering that short-read RNA-seq data cannot effectively distinguish circular and linear transcripts, the authors may try to search for nanopore reads spanning the fusion region, which can provide direct evidence for these gene fusion events.5. Because of the high error rate of nanopore sequencing, the authors should compare the error rate of CS sequence before and after cRG correction to elucidate the ability to correct sequencing errors with the cRG mode.6. The authors trained a random forest classifier to predict the strand origin of circular reads. How many CCRs were used as the training set, and how's the performance of the random forest classifier? The authors should provide more data about this step.General comments:1. The Figures and Supplemental Figures were not labeled with figure numbers, making it extremely difficult to read . The authors should label the figure number more clearly.2. Some figures are not clearly labeled. For example, in Figure 2j, what does each lane represent? Also, in Figure 3e, it is not clear what the y-axis is. The authors state that it is read coverage, but how come the value is from 0 to 1? In Figure 3d, the authors state that it is the correlation between HeLa and SKOV3, but it was not clear what axis is HeLa and SKOV3, respectively. Again, what does the shaded area mean in these figures ? The authors should check through the figure label more carefully and correct them accordingly.Reviewer #1 (Recommendations for the authors):Overall, this is a nice piece of work that can be published after revision. Below are my specific comments for the authors:1. As noted in my public review, the authors should carry out a two-way comparison between circFL-seq and CIRI-long, as well as a three-way comparison between circFL-seq, CIRI-long, and isoCirc. Given that circFL-seq and CIRI-long are both based on RCRT while isoCirc is based on RCA, it would be interesting to see if circFL-seq and CIRI-long produce more concordant results in terms of the discovery and quantitation of full-length circular RNAs, given the similarity in their experimental strategies.2. In both Introduction and Discussion, the authors noted that the use of ligation in isoCirc may lead to false discoveries of circular RNAs. While this statement is technically correct from an experimental standpoint, such false discoveries can be recognized and removed computationally – therefore this is not a fair critique of isoCirc. In fact, the isoCirc computational pipeline is designed to remove such artifacts, using stringent requirements for alignment quality and presence of canonical splice site motifs in all forward and back splice junctions within full-length circular RNA transcripts.3. Line 65-67: "Thus, an accurate but affordable method to detect full-length circRNA remains to be developed for wide application in screening functional circRNAs at the omics scale". This statement leaves the impression that such a method currently does not exist, which is not a fair representation of the current literature with the recent publications of isoCirc and CIRI-long.4. The authors reported that compared to isoCirc, circFL-seq produced more full-length circular RNA reads at the same library depth but identified fewer circular RNA isoforms. The authors appeared to present this finding as a positive feature of circFL-seq. For example, in line 265-267, the authors stated that "as a trade-off, isoCirc produced fewer reads with the same sequencing depth, which raised sequencing costs and weakened its ability to detect and accurately quantify high-quality circRNAs". There are multiple issues with this statement. In terms of the precision of circular RNA quantitation , the metrics presented for circFL-seq are in fact quite comparable to the metrics presented in the isoCirc paper, so there is no evidence that circFL-seq provides a better quantitation of circular RNAs. Moreover, given that the ground-truth is not known, an alternative interpretation to this observation is that circFL-seq may be biased towards highly expressed circular RNAs, and may lack the ability to discover moderately and lowly expressed circular RNAs. Overall, in comparing different methods, the authors should aim to provide an impartial discussion about the strengths and weaknesses of individual methods, and avoid over-interpreting the data in favor of their own method.5. The discussion about CDR1as is interesting. Can CIRI-long detect this circular RNA?6. The authors should also cite the BioRxiv preprint by Rahimi et al., (https://doi.org/10.1101/567164), which is another method for nanopore sequencing of circular RNAs.7. Overall, the manuscript is easy to read and follow, but it could benefit from a thorough editing by a language editor.Reviewer #2 (Recommendations for the authors):1. In comparison to isoCirc, circFL-seq identified fewer circRNA isoforms with higher read coverage of the detected circRNAs. This raises a concern that the outcome may result from that RCRT captures circRNA less efficiently than RCA, resulting in fewer circRNA molecules are captured in circFL-seq. The higher read coverage may simply come from sequencing the same circRNA molecule from the PCR amplification artifacts. This may also explain why circFL-seq cannot detect circRNAs with low read count or lowly expressed circRNAs. In this case, the authors cannot use back splice junction (BSJ) detection saturation as an indicator to compare the required read-depth between isoCirc and circFL-seq. Also, given the concern above, the "high read coverage" does not necessarily mean "high quality" nor "high accuracy" as claimed by the authors. The authors should address this concern before claiming on the benefits of high read coverage.2. The advantages of circFL-seq over other existing technologies are not well-supported. For example, the authors claim that RCRT has lower residual linear RNA contamination than RCA, but the authors do not provide any data or evidence supporting the claim. Also, the authors claim that the circFL-seq gives higher circRNA read coverage; hence it is beneficial for circRNA quantification. However, the real "benefits" over other technologies (RNA-seq and isoCirc) for circRNA quantification are not clear since the RNA-seq (and isoCirc) quantification is significantly correlated with circFL-seq as demonstrated by the authors.3. In the manuscript, the results are often comparable with known database and existing technologies when the authors focus on the "high quality" circRNAs only (circFL-seq read counts >= 5) which also have high expression level. The fact suggests that circFL-seq result is trust-worthy on "high quality" only. It also suggests that circFL-seq may fail to detect the lowly expressed circRNAs. The authors should note and discuss these limitations.General comments:1. The Figures and Supplemental Figures were not labeled with figure numbers, making it extremely difficult to read . The authors should label the figure number more clearly.2. Some figures are not clearly labeled. For example, in Figure 2j, what does each lane represent? Also, in Figure 3e, it is not clear what the y-axis is. The authors state that it is read coverage, but how come the value is from 0 to 1? In Figure 3d, the authors state that it is the correlation between HeLa and SKOV3, but it was not clear what axis is HeLa and SKOV3, respectively. Again, what does the shaded area mean in these figures ? The authors should check through the figure label more carefully and correct them accordingly.Specific comments:1. In the Abstract section, the authors claim that "… the detection of cancer-related fusion circRNAs…". However, the authors did not provide any data or literature suggesting that the fusion circRNAs they identified by circFL-seq are really "cancer-related" or biologically meaningful. The claim needs to be revised or proved.2. In Figure2—figure supplement 1a, the full-length circRNA reads contribute very little percentage of the total clean reads (~2-5%). How come the RCRT method generate so little full-length circRNA reads? The authors should comment on this and discuss it.3.In Figure 3—figure supplement 3, the author claim that more reads are required for ONT to confidently identify circRNAs. Doesn't this compromise the cost-efficient claim of circFL-seq made by the authors earlier? The authors should comment on this and discuss it.4. In Figure 3—figure supplement 6a-d, how the saturation curves are calculated? It seems like isoCirc has much lower sequencing depth (~0.3 M) than circFL-seq. How do the authors compare the BSJ saturation in this case?5. When comparing the validity of circFL-seq and isoCirc, why do the authors focus on top 100 expressed BSJ only? A better comparison should be the total BSJ in circFL-seq and isoCirc.6. The authors should not use the same absolute circFL-seq BSJ read counts to define high-quality BSJs in isoCirc for the following reasons: (i) the read counts >= 5 in circFL-seq is arbitrary, there is no evidence suggesting that the isoCirc should use the same read counts to define high-quality BSJs. (ii) Since isoCirc captures more circRNAs, a lower BSJ read counts per circRNA is expected given the same sequencing depth. In both cases, lower BSJ read counts in isoCirc does not necessarily mean the BSJ is not "high-quality". Thus, the authors should not use absolute circFL-seq BSJ read counts as an indicator for the BSJ quality in isoCirc.7. In the PLOD2 circFL-seq and RNA-seq example shown by the authors, the authors suggest that the circPLOD2 has lower exon skipping event than its parent linear RNA in HeLa cells. However, given that the BSJ is exactly the same between exon-skipped and non-exon-skipped circPLOD2, how does the back-splicing mechanism distinguish different parent linear RNA isoforms that selectively back-splices the non-exon-skipped linear RNA to generate specific circPLOD2 isoform in HeLa cells?8. Are the f-circ detected by circFL-seq generated by RNA fusion or genomic fusion? Although the RNA-seq suggests a genomic fusion, it does not completely eliminate the possibility of a genomic fusion. A genomic PCR followed by Sanger sequencing should be performed to validate the fusion junction of the genome.Reviewer #3 (Recommendations for the authors):Considering that there are two recently published circRNA reconstruction tools based on nanopore sequencing, the authors should comprehensively compare their method with these two tools, and carefully discuss the advantages and disadvantages of these methods.Specific comments:1. In the Discussion section (line 260-262), the authors compared circFL-seq with the recently published CIRI-long method. Both circFL-seq and CIRI-long use a similar rolling circle reverse transcription strategy to amplify circRNAs. The authors may discuss the difference and (dis)advantages between their method and previous methods (isoCirc and CIRI-long).2. In section "Comparison with RNA-seq and isoCirc for circRNA detection", the authors compared circFL-seq with the isoCirc method and found that circFL-seq produced more circular reads but identified fewer circRNA isoforms, which is an interesting result. Does it mean that isoCirc has a better sensitivity or higher false discovery rate in detecting lowly expressed circRNAs? The authors should include more comparison (e.g. venn diagram between three sets under different BSJ thresholds) between circFL-seq, isoCirc, and public circRNA database  to demonstrate the advantages of their method.3. The authors found six f-circ derived from GBF1-MACROD2 fusion and validated their junctions using Sanger sequencing. Besides, the authors also used short-read RNA-seq to validate the linear fusion junctions. What's the ratio of linear and circular transcript derived from these gene fusion loci? Is there any possibility that these f-circRNAs are derived from trans-splicing events? Considering that short-read RNA-seq data cannot effectively distinguish circular and linear transcripts, the authors may try to search for nanopore reads spanning the fusion region, which can provide direct evidence for these gene fusion events.4. Because of the high error rate of nanopore sequencing, the authors should compare the error rate of CS sequence before and after cRG correction to elucidate the ability to correct sequencing errors with the cRG mode.5. The authors trained a random forest classifier to predict the strand origin of circular reads. How many CCRs were used as the training set, and how's the performance of the random forest classifier? The authors should provide more data about this step.6. In section "Evaluation of quantification of full-length circRNAs", it would be nice if the authors could compare the quantification results between their method on nanopore reads and previous method (e.g. CIRIquant) on short-read RNA-seq data.7. The authors used different names  to denote their sequencing and data analysis methods. It would be better if they can unify the name, say, circFL-seq, which may avoid misunderstanding. Essential revisions:1. Reviewers all agree that a major weakness of the present manuscript is with the comparison to existing methods. The authors should compare circFL-seq to CIRI-long, and reviewers agree that a three way comparison to isoCirc is informative. The authors should accompany their comparisons with a discussion about strengths and weaknesses of each of the methods.We agree that comparison among the three methods will facilitate selecting proper method for different purposes. However, detection of full-length circRNAs is affected by various confounding factors  product, isoCirc produced longer reads (up to 50 kb) containing more rolling circles than RCRT based methods (ca. 1 kb), and thus had an advantage in error correction during consensus sequences identification. As a trade-off, the cost of each full-length read is much higher in isoCirc than circFL-seq and CIRI-long. In addition, the unique ligation of cDNA in isoCirc may introduce false positive for circRNA detection by ligating cDNA from residual linear RNA. In contrast to isoCirc, both of circFL-seq and CIRI-long based methods employed RCRT to produce rolling circles during first-strand synthesis. The difference was that circFL-seq synthesized second-strand cDNA with an anchor primer while CIRI-long used template switching. Thus, these two methods are more resistant to the interference of residual linear RNA and generate shorter full-length reads of circRNA than isoCirc. Although decreasing sequencing cost of nanopore, shorter full-length reads have to perform error correction among reads besides intra-reads. Thus, RCRT based methods are not as sensitive as isoCirc with the same amount of full-length reads.Along with detection of circRNA full-length sequences, all the three methods were also able to detect alternative splicing events of circRNAs. Both circFL-seq and isoCirc detected AS events of circRNA in human samples. For HEK293 cell line sequenced by one MinION, circFL-seq could be more precise than isoCirc for BSJ detection (75.3% vs 43.8%) with known circRNAs annotated in databases for comparison, and identified more than two times of ES, A3SS, and A5SS but similar amount of IR events than isoCirc. (Supplementary file 4). Thus, benefiting from more full-length circRNA reads, circFL-seq is more sensitive for AS events. However, circFL-seq may fail to detect some IR events. We found that the reads of isoCirc-only IR showed high error rate in the intron region  which coAlthough circFL-seq and CIRI-long were applied in different species (human vs. mouse), the computational pipeline was compatible and could be compared with each other for the very similar experimental protocols. We employed the computational pipeline of circFL-seq and CIRI-long to analyze HEK293 cell line of circFL-seq and mouse brain data of CIRI-long, respectively. We found the computational pipeline of circFL-seq is much more sensitive with similar precision for BSJ detection. For AS events, circFL-seq is also more sensitivity, especially for ES, A3SS and A5SS events. (Supplementary file 5).We added above results and discussions in our revised manuscript and Supplementary file 4-6.2. The current claims and conclusions that circFL-seq is the superior method is not well supported by their data in this version of the manuscript. The authors should provide fair critiques of the other method (see comments about ligation). The authors need to point out potential limitations. For example, Is CircFL-seq biased to detecting only highly expressed transcripts?In the comparison with isoCirc on one MinION data of HEK293 cell line, circFL-seq is more precise with all read counts (both highly and lowly expressed transcripts). Thus, circFL-seq was not biased to only highly expressed transcripts . The sho3. In comparison to isoCirc, circFL-seq identified fewer circRNA isoforms with higher read coverage of the detected circRNAs, which may be a PCR artifact. The authors need to address this concern. In addition, in two published studies, isoCirc and CIRI-long have also used nanopore sequencing to characterize circRNA isoforms and alternative splicing events. However, both studies have reported a relatively higher percentage of retained introns  compared to the number of 3.5% of intron retention events in line 139. The authors should clarify the reason behind this difference.Sorry for the misunderstanding. Compared to isoCirc, circFL-seq detected fewer circRNA BSJs. However, less than half of isoCirc detected BSJs were not annotated in database, while 75.3% of circFL-seq detected BSJs were annotated in databse . Thus, cThank you for the inspiring question. We conducted a comprehensive investigation about AS detection among the three methods. Actually, the total amounts of IR events are comparable among the three methods. However, circFL-seq identified more ES, A3SS, and A5SS, resulting in the lower IR proportion. We added these data in the manuscript and Supplementary file 4,5.4. The authors found six f-circ derived from GBF1-MACROD2 fusion and validated their junctions using Sanger sequencing. Besides, the authors also used short-read RNA-seq to validate the linear fusion junctions. What's the ratio of linear and circular transcript derived from these gene fusion loci? Is there any possibility that these f-circRNAs are derived from trans-splicing events? Considering that short-read RNA-seq data cannot effectively distinguish circular and linear transcripts, the authors may try to search for nanopore reads spanning the fusion region, which can provide direct evidence for these gene fusion events.bona fide fusion circRNAs from GBF1-MACROD2.Based on the nanopore reads counts of f-circ and RT-qPCR results, we inferred that the ratio of circular and linear transcript of gene fusion loci was 9.9%. To verify these f-circ were circular transcripts rather than linear transcripts, we detected these f-circ in RNase R treated samples and validated by Sanger sequencing . In addition, we also found nanopore reads that contained more than 4 full circles of f-circ. Together, these evidences supported the GBF1 and MACROD2 or trans-splicing events between GBF1 and MACROD2. Given that there were at least five f-circ isoforms of GBF1-MACROD2, we speculated that these f-circ were derived from genomic fusion rather than trans-splicing. However, length of the adjacent introns  of the fusion site were too large to detect genomic fusion by PCR. Long-read sequencing of genomic DNA could be helpful to identify the fusion junction and confirm the origin in the future.We thank the reviewers for the interesting question that the f-circ were derived from genomic fusion of GBF1-MACROD2 E1-E2-E4A nanopore read with four circles of f-circ from TCAAACGAAATGCCCGATGGAGCACCACTTTCCACTGGATGAAGAACGGGATCACAAACGCATAGTTTCGGTCATCTAAAGGAGGTTTTAAACAGTATAACAGTGGATGGCTGTATTCATAGAGCAGCCGGCCCCTGTTTGCTAGCTGAATGTCGTAACCTGAATGAAAACAGTGATACTGGACATGCAAAAATCACATGTGGCTATGACCTTCCTGCGAAATGTTTGCCAAGATGGTGGATAAGAATATTTACATCATTCAGGGGAGATTAACATTGTGGTTGGGGCCATCAAACGAAATGCCCGATGGAGCACCCATACACCACTGGATGAGAACGCCCCGGGATCCTCTGCTGCATAGTTTCGGTCATCTAAAGGAGGTTTTAAACAGTATAACAGTGGATGGCTGTATTCATAGAGCAGCCGGCCCCTGTTTGCTAGCTGAATGTCGTAACCTGAATGGCTGTGATACTGGACATGCAAAAATCACATGTGGCTATGACCTTCCTGCAAAATGTTTGCCAAGATGGTGGATAAGAATATTTACACATCATTCAAGGGGAGATTGCTGTGTGGTTGGGGCCATCAAACGAAATGCCCGATGGAGCACCCGCACACACTGGATGAAGAACGGGATCCTGCTGCATAGTTTCGGTCATCTAAAGGAGGTTTAAACAGTATAACAGTGGATGGCTGTATTCATAGAGCAGCCGGCCCCTGTTTGCTAGCTGAATGTCGTAACCTGAATGGCTGTGATACTGGACATGCAAAAATCACATGTGGCTATGACCTTCCTGCAAAATGTTTGCCAAGATGGTGGATAAGAATATTTACATCATTCAAGGAGATTAACATTGTGGTTGGGGCCATCAAACGAAATGCTTGACGGAACACCCATACACCACTGGATGAAGAACGCAGGATCCTCTGCTGCATAGTTTCGGTCATCTAAAGGAGGTTTTAAACAGTATAACAGTGGATGGCTGTATTCATAGAGCAGCCGGCCCCCTGTTGCTAGCTGAATGTCGTAACCTGAATGGCTGTGATACTGGACATGCAAAAATCACATGTGGCTATGACCTTCCTGCAAAATGTTTGCCAAGATTGGTGGATAAGAATATTTACATCATTCCGGGAGATTAACATTGTGGTTGGGGCCATCAAACGAAATGCCCGATGGAGCACCCGCGCCTACTGGATGAGCAAGGATCCTCTGCTGCATAGTTTCGGTCATCTAAAGGAGGTTTTAAACAGTATAACAGTGGATGGCTGTATTCATAGAGCAGCCGGCCCCTGTTTGCTAGCTGAATGTCGTAACCTGAATGGCTGTGATACTGGACATGCAAAAATCACATGTGTATGACCTTCCTGCAAAATGTTTCTGCCAGATGGTATGGATC5. Because of the high error rate of nanopore sequencing, the authors should compare the error rate of CS sequence before and after cRG correction to elucidate the ability to correct sequencing errors with the cRG mode.Thanks for the advice. With the suggested analysis, we found that cRG correction significantly reduced the error rate of both mismatch and indels. We have supplemented the information in our manuscript and Figure 2—figure supplement 2C, D.6. The authors trained a random forest classifier to predict the strand origin of circular reads. How many CCRs were used as the training set, and how's the performance of the random forest classifier? The authors should provide more data about this step.Thanks for the suggestion. We have supplemented details of the random forest classifier including the sample size for training and performance for predication . We have supplemented the information in our manuscript and Supplementary file 8.General comments:1. The Figures and Supplemental Figures were not labeled with figure numbers, making it extremely difficult to read . The authors should label the figure number more clearly.Sorry for the inconvenience. We have revised and labeled all figures and supplemental figures.2. Some figures are not clearly labeled. For example, in Figure 2j, what does each lane represent? Also, in Figure 3e, it is not clear what the y-axis is. The authors state that it is read coverage, but how come the value is from 0 to 1? In Figure 3d, the authors state that it is the correlation between HeLa and SKOV3, but it was not clear what axis is HeLa and SKOV3, respectively. Again, what does the shaded area mean in these figures ? The authors should check through the figure label more carefully and correct them accordingly.Thank you for your careful review. We have labeled the lane name in Figure 2j. We have revised ‘read coverage’ to ‘adjusted coverage’ in Figure 3e, which was a ratio value normalized by max read coverage. In Figure 3d, the x-axis is the fold change of expression level between HeLa and SKOV3, the y-axis is the -∆∆CT of RT-qPCR between HeLa and SKOV3. The shaded areas in Figure 3c, 3d, 3f, 3g were 95% confidence intervals. Besides revisions in figures, we have added more details in figure legend (marked as G2).Reviewer #1 (Recommendations for the authors):Overall, this is a nice piece of work that can be published after revision. Below are my specific comments for the authors:1. As noted in my public review, the authors should carry out a two-way comparison between circFL-seq and CIRI-long, as well as a three-way comparison between circFL-seq, CIRI-long, and isoCirc. Given that circFL-seq and CIRI-long are both based on RCRT while isoCirc is based on RCA, it would be interesting to see if circFL-seq and CIRI-long produce more concordant results in terms of the discovery and quantitation of full-length circular RNAs, given the similarity in their experimental strategies.Thank you for your suggestion. We have performed the three-way comparison.2. In both Introduction and Discussion, the authors noted that the use of ligation in isoCirc may lead to false discoveries of circular RNAs. While this statement is technically correct from an experimental standpoint, such false discoveries can be recognized and removed computationally – therefore this is not a fair critique of isoCirc. In fact, the isoCirc computational pipeline is designed to remove such artifacts, using stringent requirements for alignment quality and presence of canonical splice site motifs in all forward and back splice junctions within full-length circular RNA transcripts.We agreed that false discoveries can be recognized and removed computationally. However, not all of those false discoveries were identified by the computational pipeline of isoCirc. We focused on the false discoveries of top expressed circRNAs to eliminate the effect of high noise of nanopore reads and disturbance of low expressed circRNAs. As clarified in our manuscript, when focusing on the top 100 expressed circRNA BSJs identified by each method, BSJs from both circFL-seq and RNA-seq (collected from isoCirc study) were annotated in databases, while 22 of the top 100 BSJs identified by isoCirc lacked evidence support from the database, RNA-seq, or circFL-seq results. Specifically, two products were from histone genes whose linear RNAs are resistant to RNase R degradation. In addition, we also failed to experimentally validate the two potential circular products with divergent primers. Thus, there are abundant false discoveries which were not removed by the computational pipeline of isoCirc. We have made this description clearer in our manuscript (marked as Q1.2).3. Line 65-67: "Thus, an accurate but affordable method to detect full-length circRNA remains to be developed for wide application in screening functional circRNAs at the omics scale". This statement leaves the impression that such a method currently does not exist, which is not a fair representation of the current literature with the recent publications of isoCirc and CIRI-long.We have revised this statement.4. The authors reported that compared to isoCirc, circFL-seq produced more full-length circular RNA reads at the same library depth but identified fewer circular RNA isoforms. The authors appeared to present this finding as a positive feature of circFL-seq. For example, in line 265-267, the authors stated that "as a trade-off, isoCirc produced fewer reads with the same sequencing depth, which raised sequencing costs and weakened its ability to detect and accurately quantify high-quality circRNAs". There are multiple issues with this statement. In terms of the precision of circular RNA quantitation , the metrics presented for circFL-seq are in fact quite comparable to the metrics presented in the isoCirc paper, so there is no evidence that circFL-seq provides a better quantitation of circular RNAs. Moreover, given that the ground-truth is not known, an alternative interpretation to this observation is that circFL-seq may be biased towards highly expressed circular RNAs, and may lack the ability to discover moderately and lowly expressed circular RNAs. Overall, in comparing different methods, the authors should aim to provide an impartial discussion about the strengths and weaknesses of individual methods, and avoid over-interpreting the data in favor of their own method.Because circFL-seq is more sensitive and more precise for BSJ detection, circFL-seq could also provide better quantitation. However, due to the advantage of quantification performance is not obvious, we have removed the advantage descriptions of circFL-seq for circRNA quantification.4. The discussion about CDR1as is interesting. Can CIRI-long detect this circular RNA?CDR1as. We also employed the computational pipeline of circFL-seq on isoCirc data but failed to detect CDR1as. Thus, we speculated that isoCirc data lack the sequenced reads of CDR1as, the reason of which could due to the experimental protocol. Because some circRNAs, including CDR1as, are sensitivity to RNase R , we hypothesized that digestion of linear transcripts by RNase R might require to be optimized in the protocol of isoCirc. We have removed the discussions about CDR1as in Discussion section.Thank you for your suggestion. Because CIRI-long only published mouse brain data, we employed the computational pipeline of CIRI-long on our circFL-seq data of HEK293 cell line. CIRI-long also successfully identified 5. The authors should also cite the BioRxiv preprint by Rahimi et al., (https://doi.org/10.1101/567164), which is another method for nanopore sequencing of circular RNAs.We have cited the preprint paper in the introduction section as ref 13.6. Overall, the manuscript is easy to read and follow, but it could benefit from a thorough editing by a language editor.We have made a language editing by American Journal Experts (AJE).Reviewer #2 (Recommendations for the authors):Major concerns:1. In comparison to isoCirc, circFL-seq identified fewer circRNA isoforms with higher read coverage of the detected circRNAs. This raises a concern that the outcome may result from that RCRT captures circRNA less efficiently than RCA, resulting in fewer circRNA molecules are captured in circFL-seq. The higher read coverage may simply come from sequencing the same circRNA molecule from the PCR amplification artifacts. This may also explain why circFL-seq cannot detect circRNAs with low read count or lowly expressed circRNAs. In this case, the authors cannot use back splice junction (BSJ) detection saturation as an indicator to compare the required read-depth between isoCirc and circFL-seq. Also, given the concern above, the "high read coverage" does not necessarily mean "high quality" nor "high accuracy" as claimed by the authors. The authors should address this concern before claiming on the benefits of high read coverage.Thank you for the suggestion. We agreed that saturation curve was not appropriate and thus deleted related data from our revised manuscript (marked as Q2.1).Comprehensive comparison between RCA and RCRT has been discussed above. And the worry about bias of high expressed circRNA has also been discussed above.2. The advantages of circFL-seq over other existing technologies are not well-supported. For example, the authors claim that RCRT has lower residual linear RNA contamination than RCA, but the authors do not provide any data or evidence supporting the claim. Also, the authors claim that the circFL-seq gives higher circRNA read coverage; hence it is beneficial for circRNA quantification. However, the real "benefits" over other technologies (RNA-seq and isoCirc) for circRNA quantification are not clear since the RNA-seq (and isoCirc) quantification is significantly correlated with circFL-seq as demonstrated by the authors.Sorry for the misunderstanding. We have performed the three-way comparison.3. In the manuscript, the results are often comparable with known database and existing technologies when the authors focus on the "high quality" circRNAs only (circFL-seq read counts >= 5) which also have high expression level. The fact suggests that circFL-seq result is trust-worthy on "high quality" only. It also suggests that circFL-seq may fail to detect the lowly expressed circRNAs. The authors should note and discuss these limitations.Sorry for confusing. The term “high quality” we used here was not equivalent to high expression level. With higher sequencing depth, lowly expressed circRNAs would become “high quality”. For lowly expressed circRNAs, circFL-seq was not more biased than isoCirc .General comments:1. The Figures and Supplemental Figures were not labeled with figure numbers, making it extremely difficult to read . The authors should label the figure number more clearly.2. Some figures are not clearly labeled. For example, in Figure 2j, what does each lane represent? Also, in Figure 3e, it is not clear what the y-axis is. The authors state that it is read coverage, but how come the value is from 0 to 1? In Figure 3d, the authors state that it is the correlation between HeLa and SKOV3, but it was not clear what axis is HeLa and SKOV3, respectively. Again, what does the shaded area mean in these figures ? The authors should check through the figure label more carefully and correct them accordingly.Sorry for the inconvenience. We have revised and labeled all figures and supplemental figures.Specific comments:1. In the Abstract section, the authors claim that "… the detection of cancer-related fusion circRNAs…". However, the authors did not provide any data or literature suggesting that the fusion circRNAs they identified by circFL-seq are really "cancer-related" or biologically meaningful. The claim needs to be revised or proved.We have removed the phrase “cancer-related” in our revised manuscript.2. In Figure2—figure supplement 1a, the full-length circRNA reads contribute very little percentage of the total clean reads (~2-5%). How come the RCRT method generate so little full-length circRNA reads? The authors should comment on this and discuss it.In our manuscript, we have shown the percentage of BSJs reads from RNA-seq is only 0.15%–0.32%, an amount ten times lower than that of full-length circRNA reads with circFL-seq. In addition, the percentage isoCirc ranged between 3.5% and 4.0%, which is comparable with circFL-seq.3.In Figure 3—figure supplement 3, the author claim that more reads are required for ONT to confidently identify circRNAs. Doesn't this compromise the cost-efficient claim of circFL-seq made by the authors earlier? The authors should comment on this and discuss it.Sorry for the misunderstanding. circFL-seq is developed for full-length circRNA sequencing. In the detection of BSJs, the cost of circFL-seq is much higher than RNA-seq. For the cost-efficient question in the full-length level, we want to compare circFL-seq to isoCirc. Due to the high noise of ONT reads, both circFL-seq and isoCirc have shown circRNAs with more read counts is more reliable. Because circFL-seq have the advantage to produce more reads. Thus, circFL-seq is a cost-efficient method for full-length circRNA sequencing.4. In Figure 3—figure supplement 6a-d, how the saturation curves are calculated? It seems like isoCirc has much lower sequencing depth (~0.3 M) than circFL-seq. How do the authors compare the BSJ saturation in this case?We have revised our discussion about the figure.5. When comparing the validity of circFL-seq and isoCirc, why do the authors focus on top 100 expressed BSJ only? A better comparison should be the total BSJ in circFL-seq and isoCirc.In this section, we want to find evidence of false discoveries from cDNA ligation of isoCirc. We focused on the false discoveries of top expressed circRNAs to eliminate the effect of high noise of nanopore reads and disturbance of low expressed circRNAs.6. The authors should not use the same absolute circFL-seq BSJ read counts to define high-quality BSJs in isoCirc for the following reasons: (i) the read counts >= 5 in circFL-seq is arbitrary, there is no evidence suggesting that the isoCirc should use the same read counts to define high-quality BSJs. (ii) Since isoCirc captures more circRNAs, a lower BSJ read counts per circRNA is expected given the same sequencing depth. In both cases, lower BSJ read counts in isoCirc does not necessarily mean the BSJ is not "high-quality". Thus, the authors should not use absolute circFL-seq BSJ read counts as an indicator for the BSJ quality in isoCirc.Sorry for confusing. We agreed that an absolute cutoff value was not fair for methods comparison. Actually, we collected all full-length circRNA (read counts >= 1) for comparison. The details of updated results were described above.7. In the PLOD2 circFL-seq and RNA-seq example shown by the authors, the authors suggest that the circPLOD2 has lower exon skipping event than its parent linear RNA in HeLa cells. However, given that the BSJ is exactly the same between exon-skipped and non-exon-skipped circPLOD2, how does the back-splicing mechanism distinguish different parent linear RNA isoforms that selectively back-splices the non-exon-skipped linear RNA to generate specific circPLOD2 isoform in HeLa cells?Sorry for the misunderstanding. We actually compared the exon skipping between two circular isoforms instead of between circRNA and its linear cognate.8. Are the f-circ detected by circFL-seq generated by RNA fusion or genomic fusion? Although the RNA-seq suggests a genomic fusion, it does not completely eliminate the possibility of a genomic fusion. A genomic PCR followed by Sanger sequencing should be performed to validate the fusion junction of the genome.Thank you for your suggestion. Details was described above.Reviewer #3 (Recommendations for the authors):Considering that there are two recently published circRNA reconstruction tools based on nanopore sequencing, the authors should comprehensively compare their method with these two tools, and carefully discuss the advantages and disadvantages of these methods.Specific comments:1. In the Discussion section (line 260-262), the authors compared circFL-seq with the recently published CIRI-long method. Both circFL-seq and CIRI-long use a similar rolling circle reverse transcription strategy to amplify circRNAs. The authors may discuss the difference and (dis)advantages between their method and previous methods (isoCirc and CIRI-long).We have discussed the differences of three methods and the limitation of circFL-seq.2. In section "Comparison with RNA-seq and isoCirc for circRNA detection", the authors compared circFL-seq with the isoCirc method and found that circFL-seq produced more circular reads but identified fewer circRNA isoforms, which is an interesting result. Does it mean that isoCirc has a better sensitivity or higher false discovery rate in detecting lowly expressed circRNAs? The authors should include more comparison (e.g. venn diagram between three sets under different BSJ thresholds) between circFL-seq, isoCirc, and public circRNA database  to demonstrate the advantages of their method.A more detailed comparison was conducted.3. The authors found six f-circ derived from GBF1-MACROD2 fusion and validated their junctions using Sanger sequencing. Besides, the authors also used short-read RNA-seq to validate the linear fusion junctions. What's the ratio of linear and circular transcript derived from these gene fusion loci? Is there any possibility that these f-circRNAs are derived from trans-splicing events? Considering that short-read RNA-seq data cannot effectively distinguish circular and linear transcripts, the authors may try to search for nanopore reads spanning the fusion region, which can provide direct evidence for these gene fusion events.We have discussed the question in above.4. Because of the high error rate of nanopore sequencing, the authors should compare the error rate of CS sequence before and after cRG correction to elucidate the ability to correct sequencing errors with the cRG mode.We have discussed the question above.5. The authors trained a random forest classifier to predict the strand origin of circular reads. How many CCRs were used as the training set, and how's the performance of the random forest classifier? The authors should provide more data about this step.We have discussed the question above.6. In section "Evaluation of quantification of full-length circRNAs", it would be nice if the authors could compare the quantification results between their method on nanopore reads and previous method (e.g. CIRIquant) on short-read RNA-seq data.Thank you for suggestion. In the section, we have compared the quantification results between circFL-seq and RNA-seq (quantified by CIRI2), including Pearson correlation of expression level and DEC detection, which have shown comparable ability of BSJs quantification.7. The authors used different names  to denote their sequencing and data analysis methods. It would be better if they can unify the name, say, circFL-seq, which may avoid misunderstanding.Thank you for suggestion. We have unified the name to circFL-seq in our manuscript.


P = 0.004) in responder  than those in non-responder  NSCLC patients in a discovery cohort. Moreover, the percentages of baseline IgM+ memory B cells were higher (P < 0.001) in R group than those in NonR group, and associated with a longer progression free survival (PFS) (P = 0.003). By logistic regression analysis peripheral baseline IgM+ memory B cells were identified as an independent prognostic factor (P = 0.002) for the prediction of the responses to anti-PD-1 monotherapy with the AUC value of 0.791, which was further validated in another anti-PD-1 monotherapy cohort  whereas no significance was observed in patients receiving anti-PD-L1 monotherapy . Therefore, our data suggest the roles of peripheral IgM+ memory B cells in predicting the responses to anti-PD-1 treatment in Chinese advanced NSCLC patients.Tumor-infiltrating B cells and tertiary lymphoid structures have been identified to predict the responses to immune checkpoint inhibitors (ICIs) in cancer immunotherapy. Considering the feasibility of sample collection, whether peripheral B cell signatures are associated with the responses to ICI therapy remains unclear. Herein, we have defined peripheral B cell signatures in advanced non-small cell lung cancer (NSCLC) patients receiving anti-PD-1 monotherapy and investigated their associations with clinical efficacy. It was found that the percentages of B cells before the treatment (baseline) were significantly higher ( Immune checkpoint inhibitors (ICIs) targeting programmed cell death1 (PD1) and its ligand (PD-L1) have greatly improved therapeutic outcomes in multiple carcinomas including advanced non-small cell lung cancer (NSCLC). However, the limited benefit population of ICI monotherapy makes it necessay to screen predictive biomarkers for stratifying the patients. Currently, PD-L1 expression on tumor cells has been used in clinic as an indicative biomarker , 3, intr+CD8+ T cells and the expansion of neoantigen-specific T cell clones after anti-PD-1 treatment, which suggests the restoration of systematic anti-tumor immunity of CD8+ T cells after receiving ICI immunotherapy  and Guangdong Provincial People’s Hospital (n = 35) from September 2017 to October 2020. Among them, 50 patients receiving nivolumab monotherapy after the failure of one to two prior systemic chemotherapies in Shanghai Chest Hospital were designated as a discovery cohort   and incubated with the mixtures of fluorochrome-conjugated mAbs targeting multiple cell surface markers including human CD3, CD8, CD19, CD16, CD27, CD38, CD24, PD-1, PD-L1, IgG and IgM  were isolated by density gradient centrifugation using Lymphoprep™ reagent  according to the manufacturer’s instructions. Cells were stained with fluorochrome-conjugated monoclonal antibodies (mAbs) for multiplex flow cytometry analysis. Briefly, 1×10P-values were calculated by the log-rank statistics in the Kaplan-Meier analyses. Multivariate analyses of the signatures associated with the responses to anti-PD-1 therapy were performed by using the logistic regression. The receiver operating characteristic (ROC) curves were constructed by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity). P values were two-sided and P < 0.05 was considered statistically significant.All the data were represented by mean ± standard error of mean (S.E.M). Statistical analyses were conducted by using SPSS 19.0 software  or GraphPad Prism 6.0 . Tests for the differences between two groups were performed using a Wilcoxon test. Survival curves were performed by using the Kaplan-Meier method. The median frequencies were chosen as the cutoff to define the high and low group. etc. among R and NonR patients in cohort 1  were comparable with those of CD4+ T cells , but significantly higher than those of CD8+ T cells  and NK cells   than those in NonR patients (n = 33) (P = 0.004)  showed a significantly longer PFS   , memory B cells (CD27+CD19+), transitional regulatory B cells (CD24++CD38+CD19+) (tBreg) and plasmablasts (CD24-CD38+CD19+)  , 16 (Fi+ memory B cells were the most significant in cohort 1 (P = 0.002)  (+ memory B cells displayed a longer PFS (median PFS: high vs. low = 121 vs.47 days) (P = 0.020) , but significantly higher than those in IgM+ mature B cells (P < 0.001) (+ memory B cells were significantly higher than those in IgM- memory B cells (P < 0.001) and IgM+ mature B cells (P < 0.001) (Since baseline IgM+ memory B cell percentages at the baseline and 8 weeks after anti-PD-1 treatment. There were no significant changes of IgM+ memory B cell percentages from the baseline to 8 weeks after the treatment in R and NonR patients (+IgM+ memory B cells were reduced in R patients (P = 0.028) at 8 weeks whereas no statistical change was observed in NonR patients (P = 0.310) (+IgM+ memory B cells were increased in R patients (P = 0.030) at 8 weeks but stable in NonR patients (P = 0.476)  memory B cells and IgM+ mature B cells (+IgM+ memory B cells in R group than in NonR group whereas no similar trend was observed in PD-1+IgM- memory B cells (data not shown). This might be explained that PD-1+IgM+ memory B cells might be one of the targets of anti-PD-1 antibody, which is similar to PD-1+CD8+ T cells reported previously . All patients have provided written consent prior to blood collection.YW, JY, and SL conceived and designed the study. LX, LG, WX, RS, and SZ performed the flow cytometric experiments. JK, YY, YXY, WL, and YG were working on the clinical sample collection. LX, LG, JK, and YW performed data analysis. HC and ZL offered assistance and helpful discussions for data analysis. YW, SL, JY, and LX interpreted the results and drafted the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by the National Key R&D Program of China (2016YFC1303300), National Natural Science Foundation of China , Technology Innovation Program of Shanghai (19411950500), Talent Training Program of Shanghai Chest Hospital in 2019, Incubation Project Plan for Research in Shanghai Chest Hospital (2019YNJCM07), and Shanghai Chest Hospital Project of Collaborative Innovative Grant (YJT20191015).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


WT and HIV-1 JR-CSFK65R_M184V. Replication capacity was determined by measuring the viral growth on PBMCs over 10 days by longitudinally transferring cell culture supernatant to TZM-bl reporter cells. By utilizing the TZM-bl luciferase reporter assay, we determined replication capacity by measuring viral infectivity. The simplicity of the experimental setup allowed for all 346 primary HIV-1 isolates to be replicated at one time. Although the infectious input dose for each virus was normalized, a broad range of replication capacity values over 4 logs was observed. The approach was confirmed by two repeated experiments and we demonstrated that the reproducibility of the replication capacity values is statistically comparable between the two separate experiments. In summary, these results endorse our high throughput replication capacity assay as reproducible and robust and can be utilized for large scale HIV-1 replication capacity experiments in primary cells.HIV-1 replication capacity is an important characteristic to understand the replication competence of single variants or virus populations. It can further aid in the understanding of HIV-1 pathogenicity, disease progression, and drug resistance mutations. To effectively study RC, many assays have been established. However, there is still demand for a high throughput replication capacity assay using primary cells which is robust and reproducible. In this study, we established such an assay and validated it using 346 primary HIV-1 isolates from patients enrolled in the Zurich Primary HIV Infection study (ZPHI) and two control viruses, HIV-1 JR-CSF In order to understand viral behavior in regard to viral evolution ,2, varyiViral replication capacity is determined by replicating the virus of interest  on a specific cell type (primary cells or cell lines) and quantified using various established protocols , heteroduplex tracking assay (HTA)). Due to varying opinions, it is debated which assay is the best experimental structure for determining replication capacity. The two main setups that are discussed are dual competition infection and parallel infection.Dual competition infection allows the viruses to be competed against one another in the same experimental environment, which is thought to mimic the in vivo competition a virus undergoes within the host ,24. This50) [Various quantification protocols can be used to determine the viral growth over time, but assays quantifying the amount of viral antigen (p24 ELISA) and enzyme activity (RT activity) are the most common ,12,13,3050) ,31,32. Fgag, pol) cloned into a control backbone  [There are established assays that can determine replication capacities of large sample populations of recombinant viruses that consist of a specific part of the patients’ viral genome  ,34,35,36WT and HIV-1 JR-CSFK65R_M184V. The replication capacities were determined by quantifying viral growth on primary peripheral blood mononuclear cells (PBMCs) over a period of 10 days using the TZM-bl luciferase assay and calculating the area under the curve (AUC) for the replication curves. The experimental assay was completed twice, and we compared those AUC values from the two experiments to determine the robustness and reproducibility of the assay. Furthermore, the AUC values were used to analyze the differences in replication capacities among primary HIV-1 isolates and assess the potential correlations between replication capacity and other viral characteristics. Taken together, these results show that we established a high throughput replication capacity assay based on primary cells that is reproducible and robust.Therefore, the aim of this study was to establish a parallel infection, replication capacity assay for high throughput use in primary cells. The assay was validated using 346 primary HIV-1 isolates from the Zurich Primary HIV-1 Infection Study (ZPHI) and two control viruses, HIV-1 JR-CSFwww.clinicaltrials.gov, ID NCT00537966; approved by the ethical committee on September 2007 (KEK-ZH-Nr. EK-1452) [The ZPHI is an open label, non-randomized, observational, monocenter study at the University Hospital Zurich, Switzerland (EK-1452) ,39. All + T cells and plasma were isolated from patients’ blood and co-cultured with 3-way stimulated, CD8-depleted, HIV-1 negative donor peripheral blood mononuclear cells (PBMCs) [50) was determined by serial diluting the viruses on TZM-bl cells [Primary HIV-1 isolates were derived using patients’ blood from the first visit during acute/recent HIV-1 infection. Briefly, CD4 (PBMCs) . Primarybl cells . Brieflybl cells .WT, was obtained through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH: HIV-1 JR-CSF Infectious Molecular Clone (pYK-JRCSF) (Cat# 2708) from Dr. Irvin SY Chen and Dr. Yoshio Koyanagi [WT plasmid using the QuikChange XL Site-Directed Mutagenesis Kit  with sdm_M184V_fw (5′-CCAGACATAATTATCTATCAATACGTGGATGATTTGTATGTAGGATCTG-3′), sdm_M184V_rv (5′-CAGATCCTACATACAAATCATCCACGTATTGATAGATAATTATGTCTGG-3′), sdm_K65R_fw (5′-CTCCAGTATTTGCCATAAAGAGAAAAGACAGTACTAAATGGAG-3′) and sdm_K65R_rv (5′-CTCCATTTAGTACTGTCTTTTCTCTTTATGGCAAATACTGGAG-3′), following the manufacturer’s protocol. The primers were designed using the QuikChange Primer Design Tool offered by Agilent Technologies, Inc., accessed at https://www.agilent.com/store/primerDesignProgram.jsp [For control viruses, the HIV-1 full-length plasmid pJR-CSFKoyanagi ,44,45. Tgram.jsp  (accesseWT or pJR-CSFK65R_M184V) and 2 µg of polyethylenimine (PEI) per µg of DNA. After 48 h, the virus supernatants were harvested, filtered and the TCID50 was determined on TZM-bl cells as described above.HEK293T cells were transfected with mixtures containing serum-free DMEM , 25 µg of plasmid  was performed to determine the viral titer of the virus stocks as previously described . BrieflyWT and HIV-1 JR-CSFK65R_M184V and were used to calculate the amount of virus stock required for a multiplicity of infection (MOI) of 0.001. For the parallel infections, HIV-1 JR-CSFWT and HIV-1 JR-CSFK65R_M184V, were added separately to tubes containing 2 × 106 mixed donor, 3-way stimulated, CD8-depleted PBMCs. For the dual-competition infections, HIV-1 JR-CSFWT was combined with HIV-1 JR-CSFK65R_M184V at a 1:1 ratio for a combined MOI of 0.001 and added to 2 × 106 mixed donor, 3-way stimulated, CD8-depleted PBMCs. The cells were infected by spinoculation for two hours at 1′200 g, washed with PBS, resuspended in RPMI 1640 with 10% heat inactivated FBS, 10 U/mL IL-2, and 1% P/S, and cultured on 12-well plates in duplicates [To evaluate parallel and dual competition infections, the viral titers were determined for HIV-1 JR-CSFplicates . On daysWT and HIV-1 JR-CSFK65R_M184V in the mixtures were separately measured with allele-specific polymerase chain reaction (AS-PCR). The validity of the K65R_M184V AS-PCR protocol was evaluated and confirmed as previously described [WT and HIV-1 JR-CSFK65R_M184V.For both parallel and dual-competition infection data acquisition, p24 ELISA and RT-qPCR were performed. For p24 ELISA, 50 µl cell-free culture supernatant was inactivated with 1% Empigen and p24 antigen was quantified using an in-house p24 ELISA assay ,50. The escribed ,51. AS-PHEK293T cells were obtained through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH: HEK-293 Cells from Dr. Andrew Rice . The TZM6 per ml in RPMI 1640 containing 10% heat inactivated fetal bovine serum (FBS), 10 U/mL IL-2 and 1% penicillin streptomycin (P/S), split into 3 parts and stimulated with either 0.5 µg/mL phytohemagglutinin (PHA), 5.0 µg/mL PHA or OKT-3. After 48 h, the three stimulated parts were combined and cultured in RPMI 1640 with 10% heat inactivated FBS, 50 U/mL IL-2 or 10 U/mL IL-2, and 1% P/S [Buffy coats from three HIV-1 negative donors were CD8-depleted  and PBMCs were isolated by density gradient centrifugation. The cells were adjusted to 4 × 1050/mL  of the quadruplicates. This resulted in one replication capacity value for each virus.p-value ≤ 0.05 was considered statistically significant.The statistical analysis was performed using both Prism and R Studio. Comparisons between two variables were performed using the Wilcoxon matched pairs signed rank test. If more than two variables were compared, the Kruskal–Wallis test was performed. The correlations were determined using linear regression analysis. WT was used to produce the high replication competent virus, as well as, utilized to generate the less replication competent virus, pJR-CSFK65R_M184V. M184V and K65R are drug resistance mutations located in the reverse transcriptase of the viral pol gene. It has been previously shown that the K65R mutation reduces replication capacity up to 55% compared to the wildtype virus, but when paired with the M184V mutation replication capacity was reduced up to 70% compared to the wildtype in in vitro experiments [WT, while pJR-CSFK65R_M184V contained both mutations. Virus stocks were generated by transfection of HEK293T cells and harvesting filtered cell-free culture supernatant 48 h post transfection.To achieve control viruses that represent both high and low replication competence, the HIV-1 full-length wildtype plasmid pJR-CSFeriments . The twoWT replicating more efficient than HIV-1 JR-CSFK65R_M184V , which indicates that the HIV-1 p24 antigen produced during replication is proportional to the amount of infectious virus. The TZM-bl luciferase reporter gene assay can be easily scaled up for processing more samples, when compared to p24 ELISA. Furthermore, it measures viral infectivity. Therefore, the TZM-bl assay was used for all further experiments.Second, we examined whether viral replication should be quantified using either p24 ELISA or TZM-bl luciferase expression due to the requirement for the final assay to be high-throughput. To assess the similarities of the two quantification assays, cell-free culture supernatant was taken at days 3, 4, 5, 6, 7 and 10 and then split for quantification using either p24 ELISA or the TZM-bl reporter assay. The area under the curve (AUC) was calculated for the replication curves of the input of 200 TCIDch virus D. The coThird, we optimized the TZM-bl assay regarding the virus input on the TZM-bl cells. First, we assessed the incubation time regarding viral replication on the TZM-bl cells. For this experiment, cell-free culture supernatant was transferred from the plate containing HIV-1 infected PBMCs to the TZM-bl cells on days 3, 4, 5, 6, 7 and 10, and the luciferase expression was measured after 24 h and 48 h post infection. The replication curves for 48-h read out were approximately 1-log higher when compared to the 24-h replication curves E,F. This4–105 relative light units (RLU). Furthermore, the lowest input of 25 µL of 1:20 dilution  depicted a decrease of luciferase expression at day 3 by at most 1-log (103–104 RLU)  at 10104 RLU) G,H. ThesWT replicates at a higher efficiency when compared to the replication kinetics of HIV-1 JR-CSFK65R_M184V  and low (HIV-1 JR-CSFK65R_M184V) replication capacities  with a R2 of 0.72. This shows that for each virus, the replication capacity is highly reproducible in repeated experiments. Given this result, all the following analyses are based on the median AUC values obtained in the second experiment.To test the reproducibility, the large-scale replication capacity assay was performed twice. The replication kinetics of the control viruses were similar for both experiments . HIV-1 Jeriments A. In addn = 265, 76.6%). The primary HIV-1 isolates were predominantly derived from patient CD4+ T cells . During derivation of the primary HIV-1 isolates, the average number of days in culture was 20 days with the minimum and maximum at 5 and 48 days, respectively. The average titer of the primary HIV-1 isolate stocks was 5.28 logTCID50/mL . The majority of primary HIV-1 isolates are HIV-1 subtype B . The circulating recombinant HIV-1 forms (CRF) make up 13.4% (n = 46) of primary HIV-1 isolates subtypes with CRF01_AE and CRF02_AG being the most dominant at 8.1% and 2.9%, respectively.Characteristic summary of the primary HIV-1 isolates used in the experiments are shown in p-value = 0.763). In addition, all three pair-wise comparisons similarly show that the differences are statistically not significant .Next, we compared the distribution of the replication capacities for the different stages of the HIV-1 infection at the time of sampling A. The co+ T cells were higher compared to the median AUC values for plasma derived primary HIV-1 isolates. Furthermore, a Wilcoxon rank sum test confirmed that the differences between CD4+ T cells and plasma are statistically significant .Furthermore, we compared the distribution of the replication capacities for the two different sample types that were utilized to generate the primary HIV-1 isolates B. The co50/mL) and the number of days the patient CD4+ T cells or plasma was co-cultured with PBMCs when deriving the primary HIV-1 isolates, against the median AUC values of the replication capacity of the primary HIV-1 isolates . It is notable that HIV-1 subtype B presents a high replication capacity, as does HIV-1 subtype F.Finally, we compared the distribution of 13 different HIV-1 subtypes E. The boIn this study, we established a high-throughput parallel replication capacity assay using primary cells to study the replication capacities of 346 primary HIV-1 isolates of the Zurich Primary HIV-1 Infection Study. Although the assay was established to study primary HIV-1 isolates, we show that our assay is versatile and can be utilized to determine the HIV-1 replication capacity regarding many other topics such as fitness costs due to drug resistance mutations.It was important for the assay to determine the replication capacity using the whole virus because of the potential influence that multiple protein interactions could have on replication capacity . FurtherFor this high-throughput parallel infection assay, we infected primary cells. It can be argued that using primary cells for infection assays from multiple healthy donors can increase the likelihood of donor variability ,59,60. HThere are many different methods for quantifying HIV-1 viral growth over time. In this study, we compared the p24 ELISA method to the TZM-bl luciferase expression method and a correlation was observed between the median AUC values obtained from each assay. Our findings coincide with Etemad et al., 2014, who previously found that p24 antigen measurements were highly correlated with TZM-bl infectivity values, suggesting that either method could be used to quantify viral growth . By usin3–107) when the infectious input dose for each virus was normalized to 200 TCID50/well. Furthermore, we saw no significant differences among the replication capacities of primary HIV-1 isolates classified by their infection stage of acute, recent, or chronic. Other researchers have found similar results regarding replication capacities among primary HIV-1 isolates obtained in different infection stages [n = 69) and chronic (n = 12) were smaller than acute (n = 265), which could make the comparisons less robust. In addition, we found significant differences between the median AUC values of primary HIV-1 isolates derived from CD4+ T cells or plasma . Although it has been previously observed in our lab that the generation of primary HIV-1 isolates using patient plasma is more challenging than using CD4+ T cells, we currently have no explanation as to what could instigate these differences in the replication capacities. We intend to further explore that in the future. Furthermore, we compared the viral titers of the virus stocks to the RCs of the primary HIV-1 isolates. We did not observe a correlation between the viral titers of the virus stocks and the RC values, which was expected because all viruses were normalized to the same input amount of 200 TCID50/well.Although quite a lot has been published about primary HIV-1 isolate replication capacities, the labor-intensive nature of the replication assays produces limitations regarding the sample sizes of the studies that compare replication fitness to viral characteristics . By usinn stages ,11,12,31p-value < 0.0001). Due to the numerous HIV-1 subtypes circulating the globe, it was important for our assay to be able to determine the replication capacities of HIV-1 subtype B, as well as HIV-1 non-B subtypes. We demonstrated that our assay was viable for 13 HIV-1 subtypes, including 7 circulating recombinant forms (CRFs). Furthermore, we compared the replication capacities of the primary HIV-1 isolates of the 13 different HIV-1 subtypes and exposed significant differences among the replication capacities between the HIV-1 subtypes (In summary, our high throughput parallel replication capacity assay can determine the replication capacities of extensive sample sizes of primary HIV-1 isolates in the same primary cells in one large experiment. Furthermore, it yields robust and reproducible results that can be exploited for data analysis.


Transcriptional profiling analysis by RNA sequencing revealed the targets of miR155 and LSD1 in anti-CD19 CAR-T cells. Our experiments indicated that introduction of miRNA or shRNA expression into anti-CD19 CAR T-cells might be an effective strategy to improve the anti-tumor effects of CAR-T cell therapy.Chimeric antigen receptor (CAR) T cells targeting CD19 antigen have produced remarkable clinical outcomes for cancer patients. However, identifying measures to enhance effector function remains one of the most challenging issues in CD19-targeted immunotherapy. Here, we report a novel approach in which a microRNA (miRNA) or short-hairpin RNA (shRNA) cassette was integrated into CAR-expressing retroviral vectors. Using this system, we generated anti-CD19 CAR-T cells co-expressing miR155 or LSD1 shRNA and found that anti-CD19 CAR-T cells with miR155 upregulation or LSD1 downregulation exhibited increased anti-tumor functions  CD19-specific chimeric antigen receptor (CAR)-T cell therapy has achieved impressive progress in the treatment of hematopoietic malignancies, especially relapsed/refractory diffuse large B-cell lymphoma and relapsed/refractory B acute lymphoblastic leukemia since the US Food and Drug Administration approved the first two CD19 CAR-T cell treatments (axicabtagene ciloleucel and tisagenlecleucel) . HoweverOne of the major obstacles of CAR-T cell therapy is the limited expansion and persistence of CAR-T cells, which potentially hinder the long-term anti-tumor response in patients . Many stMammalian microRNAs (miRs) are small (~22 nucleotides) noncoding RNA oligonucleotides that regulate gene expression at the posttranscriptional level by targeting the corresponding mRNAs . miRNAs Lysine-specific demethylase 1  is a histone lysine-specific demethylase that removes mono- or dimethylation of H3K4 or H3K9 in the presence of flavine-adenine dinucleotide. LSD1 plays an important role in tumorigenesis and is a potential target in anti-tumor immunity . LSD1 inMiR155 and LSD1 were important epigenetic regulators with anti-tumor functions. MiR155 targets and negatively regulates LSD1 in both HFLS and MH7A cells, and upregulation of miR155 decreases the expression of LSD1 in rheumatoid arthritis . This isIn this study, we engineered CAR-T cells expressing either miR155 or short-hairpin RNA (shRNA) targeting LSD1 and explored the effects of miR155 expression or LSD1 knockdown on the efficacy of CAR-T cell therapy. Our results revealed that miR155 upregulation or LSD1 downregulation enhanced the anti-tumor functions of CD19-specific CAR-T cells. Our approach takes advantage of epigenetic regulators to modulate CAR-T cell activity and has significant implications for cancer immunotherapy.Phoenix-ECO cells and PG13 cells were obtained from the American Type Culture Collection (ATCC) and cultured in Dulbecco’s Modified Eagle Medium  with 10% FBS. The Raji cell line, which are lymphoblast-like cells derived from human Burkitt’s lymphoma, was obtained from ATCC. Raji cells stably expressing firefly luciferase (Raji-luc) were obtained from Beijing Vitalstar Biotechnology Company. Both cell lines were cultured in RPMI 1640 medium (Gibco) with 10% FBS. hCD19-SW620 cells expressing the human CD19 antigen were obtained from Tsinghua University  and cultured in DMEM with 10% FBS.Anti-CD19 CAR molecule consisted of anti-CD19 single-chain variable fragments (scFv) with variable heavy and light chains (FMC63) separated by a (G4S)3 linker, followed by a CD8 hinge and transmembrane domain and CD28 and CD3ζ intracellular signaling domains. The construct contained a signal peptide and myc tag upstream of the anti-CD19 CAR sequence. The myc tag was used to detect the transduction efficiency.http://www.mirbase.org/cgi-bin/mirna_entry.pl?acc=MI0000681), the LSD1 shRNA-1 sequence was 5’-CCGGGCCTAGACATTAAACTGAATACTCGAGTATTCAGTTTAATGTCTAGGCTTTTTG-3’ , and the LSD1 shRNA-2 sequence was 5’- CCGGGCTCCAATACTGTTGGCACTACTCGAGTAGTGCCAACAGTATTGGAGCTTTTTG-3’ .We generated three constructs with MFG retroviral vector: a construct expressing U6 RNA and the anti-CD19 CAR sequence, a construct expressing miR155 and the anti-CD19 CAR sequence and a construct expressing LSD1 shRNA and the anti-CD19 CAR sequence. The miR155 sequence was 5’-CTGTTAATGCTAATCGTGATAGGGGTTCTTGCCTCCAACTGACTCCTACATATTAGCATTAACAGTTTTT-3’ , followed by cDNA synthesis . RT-qPCR analysis was performed using the QuantStudio6 Flex system  and SYBR Green Mix . The primer sequences were provided in Vector titers were determined by two-step quantitative reverse transcription-polymerase chain reaction (RT-qPCR) analysis. First, RNA was purified from cell supernatants  Serum-free Medium (Gibco) supplemented with 10% fetal bovine serum  for 48 hours at 37°C in a 5% CO2 incubator. T cells were expanded for 10-20 days at a concentration of 1×106 cells/mL in the presence of continuous IL-2 stimulation.Human peripheral blood mononuclear cells were isolated and purified from peripheral blood of healthy human donors using Lymphoprep . Cells were then activated with 100 ng/mL human anti-CD3  and 100 U/mL IL-2  in AIM-V2 incubator at 37°C for 48 hours. The transduction efficiency was detected by flow cytometry analysis . The retroviral vector copy number integration per T cell was detected by qPCR; the primer sequences were provided in After activation for 48 hours, T cells were transduced with CAR retroviral vector and cultured in a 5% COFor the detection of miR155 expression, miRs were isolated from cells using the miRcute miRNA Kit . Reverse transcription was performed using the TaqMan MicroRNA Reverse Transcription Kit  and specific primers for miR155 and U6 RNA provided by Applied Biosystems. The expression of miRNAs was measured by RT-qPCR using the TaqMan Universal Master Mix II with UNG .® Green PCR Kit, QIAGEN). The primer sequences are provided in For the detection of LSD1 mRNA expression, total RNA was isolated from cells using the RNeasy Mini Kit (QIAGEN). cDNA was then synthesized from RNA  and subjected to RT-qPCR using SYBR qPCR mix . The miR155 expression levels were normalized to U6 small nucleolar RNA levels, and the LSD1 mRNA expression levels were normalized to A total of 80,000 CAR-T cells  and tumor cells  were co-cultured at a 1:1 effector/target (E/T) ratio for 12 hours in a 96-well plate (0.2 mL/well). Cell culture supernatants were analyzed by enzyme-linked immunosorbent assays with a Human IFN-γ ELISA Kit, Human TNF-α ELISA Kit and Human IL-2 ELISA Kit . Samples were measured on a SpectraMax Series Multi-Mode Microplate Reader  at a wavelength of 450 nm with a correction wavelength of 630 nm. Data were analyzed using SoftMax Pro 6.4.2 software (Molecular Devices).CAR-T cells were pre-labeled with the intracellular fluorescent label carboxyfluorescein diacetate succinimidyl ester. Covalently bound carboxyfluorescein diacetate succinimidyl ester is divided equally between daughter cells, enabling the discrimination of successive cell division rounds. After culturing labeled CAR-T cells with Raji cells at an E/T ratio of 1:2 for 24 hours, samples were analyzed by flow cytometry analysis by staining with APC labeled anti-human CD3 antibody . The proliferation assays and carboxyfluorescein diacetate succinimidyl ester-staining were performed in the continuous presence of IL-2.FITC-labeled anti-human CD4 antibody  and PerCP-Cyanine5.5-labeled anti-human CD8 antibody  were used to detect CD4+/CD8+ T cells by FLOW CYTOMETRY ANALYSIS.CM) cells were defined as cells expressing CD45RO and CD62L.PE/Cyanine7-labeled anti-human CD45RO antibody  and PE-labeled CD62L antibody  were used to differentiate memory and effector T cell populations; central memory T , and data were analyzed using CytoFLEX Software (Beckman Coulter).4) at E/T ratios of 1:1, 1:2, 1:4, 1:8 and 1:16 for 12 hours in a 96-well plate (0.2 mL/well), and then cells were stained with APC-labeled anti-human CD3 antibody  and FITC-labeled Annexin V  for 30 minutes. Data were collected on a CytoFLEX Cell Analyzer (Beckman Coulter) and analyzed with CytoFLEX Software (Beckman Coulter). The cytolytic activity of CAR-T cells was calculated as follows: percent cytolysis = (percentage of CD3-/Annexin V+ co-cultured cells) − (percentage of Annexin V+ monocultured tumor cells).For the apoptosis assay, CAR-T cells or non-transduced T cells (PanT) were co-cultured with Raji cells (4×10in vitro was performed. Raji and CAR-T cells were co-cultured at an E/T ratio of 1:1 in the presence of IL-2 (100 U/mL). After 48 hours, some CAR-T cells were harvested for the apoptosis assay and analyzed by flow cytometry analysis as described above. The remaining CAR-T cells were then transferred to a fresh culture of Raji cells. After each round, the apoptosis assay was performed, and three rounds of transfer over 6 days were conducted.For the stress test, repetitive antigen stimulation 4) were co-cultured with CAR-T cells or PanT cells at E/T ratios of 1:1, 1:2, 1:4, 1:8 or 1:16 for 12 hours in a 96-well white assay plate  (0.1 mL/well). Next, 100 µl of Luciferase Assay Reagent  were added, and cells were incubated at room temperature for 5 minutes. The fluorescence was measured on a SpectraMax Series Multi-Mode Microplate Reader , and data were analyzed using SoftMax Pro 6.4.2 software (Molecular Devices). Lysis was calculated according to the following formula: % lysis =  × 100/(maximum lysis − spontaneous lysis).For the luciferase assay, Raji-luc cells  xCELLigence system (Acea Biosciences). After 48 hours, CAR-T or PanT cells (5×103 cells in 50 μl of medium) were added to each well. The cells in the E-plates were monitored with the RTCA system over time.For the real-time cytotoxicity assay, 50 μl of AIM-V medium were added to each well of a 96-well E-plate  for the baseline measurement. Adherent target SW620 cells were then seeded at a density of 2×10scid Il2rgnull family, obtained from Beijing Vitalstar Biotechnology) were used for the mouse xenograft model. NPG/Vst female mice (6-8 weeks old) were inoculated intravenously (i.v.) with 1×106 Raji-luc cells. After 4 days, the mice were randomized to seven groups of six mice each: control, PanT, anti-CD19 CAR-T, RNAU6 anti-CD19 CAR-T, miR155 anti-CD19 CAR-T, LSD1 shRNA-1 anti-CD19 CAR-T, and LSD1 shRNA-2 anti-CD19 CAR-T. Animals then received a dose of 1×108/kg CAR-T cells or PanT cells by i.v. injection (that was about 2×106 CAR-T cells or PanT cells per mice), followed by a second dose at 7 days after the first injection. And the experiments were terminated at the 52nd day.Severe combined immune-deficient NPG/Vst mice . Before imaging, the mice were administered 150 mg/kg VivoGlo Luciferin In Vivo Grade (Promega) suspended in 200 μl PBS by intraperitoneal injection. After 3 minutes, the mice were placed in the anesthesia box of the Matrx Animal Anesthesia Ventilator System  with 1.5% isoflurane for 3 minutes. Bioluminescent imaging acquisition was then performed at the medium binning level with a 5 minutes exposure time. MetaMorph software (Molecular Devices) was used to acquire and quantify the bioluminescence imaging datasets. Tumor bulk was measured by MetaMorph imaging.Tumors were monitored twice a week by For the measurement of serum cytokine concentration, blood samples were taken from the tail veins of NPG mice on the 7th day after the second CAR-T cell injection. Serum IFN-γ concentration was measured using the Human IFN-γ ELISA Kit (Proteintech).For the assessment of T cell persistence, blood samples were taken from the tail veins of NPG mice at multiple time points during the experiment, including the 52nd day. CD3+T cells were detected by flow cytometry analysis using Brilliant Violet 785™-labeled anti-human CD3 antibody .The animal experiment protocol was approved by the Biomedical Research Ethics Committee of the Beijing University of Chinese Medicine (No. BUCM-4-2021010703-1004). Animal care and experiments strictly adhered to the American Physiological Society’s Guiding Principles for the Care and Use of Vertebrate Animals in Research and Training.http://www.targetscan.org/). The protein-protein interaction was analyzed by STRING (https://string-db.org/cgi/input.pl). The RNA sequencing data were deposited in NCBI Trace Archive NCBI Sequence Read Archive .Total RNA was isolated from CAR-T cells using TRIZOL reagent  following standard protocols. The RNA-seq and analysis was performed by CapitalBio Technology Inc . Predicted miR155 targets were acquired using the TargetScan database , a CD8 hinge and transmembrane domain, a CD28 co-stimulation domain and a CD3ζ activation domain . Among the 754 transcripts that were significantly downregulated in miR155 over-expressing anti-CD19 CAR-T cells, 15 transcripts were identified as predicted targets of miR155 by TargetScan  were randomly selected and examined in miR155 over-expressing anti-CD19 CAR-T cells by RT-qPCR. The results were consistent with the RNA-seq findings   were randomly selected and tested by RT-qPCR to verify the reliability of the results of RNA-seq. The results were consistent with the results of RNA-seq .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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