Transposases exist in both prokaryotes and eukaryotes and catalyze the movement of defined DNA elements (transposon) to another part of the genome in a ‘cut and paste’ mechanism (Kleckner, 1981; Finnegan, 1989; Curcio and Derbyshire, 2003). Taking advantage of this catalytic activity, transposases are widely used in many biomedical applications: for instance, an engineered, hyperactive Tn5 transposase from E. coli can bind to synthetic 19 bp mosaic end-recognition sequences appended to Illumina sequencing adapters (termed ‘Tn5 transposome’) (Adey et al., 2010) and has been utilized in an in vitro double-stranded DNA (dsDNA) tagmentation reaction (namely simultaneously fragment and tag a target sequence with sequencing adaptors) to achieve rapid and low-input library construction for next-generation sequencing (Adey et al., 2010; Goryshin and Reznikoff, 1998; Picelli et al., 2014a; Caruccio, 2011; Ramsköld et al., 2012; Gertz et al., 2012). In addition, Tn5 was also used for in vivo transposition of native chromatin to profile open chromatin, DNA-binding proteins and nucleosome position (‘ATAC-seq’) (Buenrostro et al., 2013). While Tn5 has been broadly adopted in high-throughput sequencing, bioinformatic analysis and structural studies reveal that it belongs to the retroviral integrase superfamily that act on not only dsDNA but also RNA/DNA hybrids (for instance, RNase H). Despite the distinct substrates, these proteins all share a conserved catalytic RNase H-like domain (Figure 1a; Yang and Steitz, 1995; Savilahti et al., 1995; Nowotny, 2009; Rice and Baker, 2001). Given their structural and mechanistic similarity, we attempted to ask whether or not Tn5 is able to catalyze co-tagmentation reactions to both the RNA and DNA strands of RNA/DNA hybrids (Figure 1b), in addition to its canonical function of dsDNA transposition. In this study, we tested this hypothesis and found that indeed Tn5 possesses in vitro tagmentation activity towards both strands of RNA/DNA hybrids. As a proof of concept, we apply such Transposase-assisted RNA/DNA hybrids Co-tagmEntation (TRACE-seq) to achieve rapid and low-cost RNA sequencing starting from total RNA extracted from 10,000 to 100 cells. We find that TRACE-seq performs well when compared with conventional RNA-seq methods in terms of detected gene number, gene expression measurement, library complexity, GC content and differential expression analysis, although TRACE-seq shows bias in gene body coverage and is not strand-specific. At the same time, it avoids many laborious and time-consuming steps in traditional RNA-seq experiments. Such Tn5-assisted tagmentation of RNA/DNA hybrids could have broad applications in RNA biology and chromatin research.

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qPCR Ct values of tagmentation products of samples under different conditions in Figure 1d and e.

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To test whether Tn5 transposase has tagmentation activity on RNA/DNA hybrids, we prepared RNA/DNA duplexes by performing mRNA reverse transcription. We first validated the efficiency of reverse transcription and the presence of RNA/DNA duplexes using a model mRNA sequence (IRF9,~1000 nt) as template (Figure 1—figure supplement 1a). We then subjected the prepared RNA/DNA hybrids from 293T mRNA to Tn5 transposome, heat-inactivated Tn5 transposome and a blank control (without Tn5), respectively (see Methods). The hybrids were then recovered and their length distribution was analyzed by Fragment Analyzer (Figure 1c). Comparing with the heat-inactivated Tn5 sample or the blank control sample, the Tn5 transposome sample exhibited a modest but clear smear signal corresponding to small fragments ranging from ~30–650 base-pair (bp) (the blue patches in Figure 1c). Consistent with the fragmentation event, we also observed a down shift of large fragments ranging from ~700–4000 bp (the orange patches in Figure 1c). In addition, the fragmentation efficiency increased in a dose-dependent manner with the transposome, suggesting that fragmentation of RNA/DNA hybrids is dependent on Tn5 (Figure 1—figure supplement 1b).

We next asked whether RNA/DNA hybrids are tagged by Tn5 and performed quantitative polymerase chain reaction (qPCR) quantification for the three samples. We observed that cycle threshold (Ct) value of the Tn5 transposome sample is about eight cycles smaller than the heat inactivated Tn5 sample or the control sample, indicating approximately 256 times more amplifiable products (Figure 1d). We also tested different buffer conditions and found that the performance of Tn5 remained similar, indicating the robustness of the Tn5 tagmentation activity (Figure 1—figure supplement 1c). Using Sanger sequencing, we validated that the adaptor sequences are indeed ligated to the insert sequences (Figure 1—figure supplement 1d).

To compare Tn5 tagmentation efficiency between RNA/DNA hybrids and dsDNA, we performed tagmentation and qPCR on equal amount of mRNA RT products and genomic DNA (gDNA). Average Ct value of the hybrids samples was about four cycles more than gDNA samples, indicating the efficiency of Tn5 toward hybrids is about 1/16 of that of dsDNA (Figure 1e). It is known that natural RNA/DNA hybrids favor A-form conformation. Interestingly, in the presence of PEG200, hybrids were found to favor B-form conformation (Pramanik et al., 2011), which we expected to make the hybrids a better substrate of Tn5. Indeed, addition of PEG200 diminishes this difference by greatly improving the Tn5 tagmentation efficiency towards hybrids (Figure 1e). This result indicates that the conformation of substrates certainly affects the preference of Tn5. We also ruled out the possibility of gDNA contamination in RT products (Figure 1—figure supplement 1e). Taken together, under optimized conditions, Tn5 shows significantly improved efficiency towards RNA/DNA hybrids.

As reverse transcriptase could produce dsDNA from RNA/DNA hybrids, we next designed an experiment by eliminating the RT component and directly assess tagmentation activity using annealed RNA/DNA hybrids where no dsDNA is possible. We annealed in vitro transcribed and purified ssRNA (CLuc, 150 nt, GC% = 51%) with chemically synthesized complementary ssDNA. We confirmed the successful production and purity of the RNA/DNA hybrids by dot-blot assay and native-PAGE (Figure 1—figure supplement 1f and g). A 8-cycle difference between Tn5 transposome sample (Ct = 22.68) and the control sample (Ct = 30.40) was reproducibly observed (Figure 1—figure supplement 1h). As a positive control, we also annealed two complementary ssDNA strands of the same 150bp-CLuc sequence to produce dsDNA and observed that a Ct value of 18.08 for the dsDNA sample (Figure 1—figure supplement 1h). While it is unclear this difference in tagmentation efficiency obtained from short oligos can be applied to long oligos, this result clearly demonstrates that Tn5 has a direct tagmentation activity towards RNA/DNA hybrids.

Having demonstrated the tagmentation activity of Tn5 on RNA/DNA hybrids, we then thought about its potential applications. RNA/DNA duplexes can be found in many in vivo scenarios, including but not limited to R-loop and chromatin-bound lncRNAs (Santos-Pereira and Aguilera, 2015; Li and Fu, 2019). Under in vitro conditions, RNA/DNA hybrids are also key intermediates in various molecular biology and genomics experiments. For instance, RNA has to be first reverse transcribed into cDNA in a traditional RNA-seq experiment so as to construct a library for sequencing. Because traditional RNA-seq library construction involves many laborious and time-consuming steps, including mRNA purification, fragmentation, reverse transcription, second-strand synthesis, end-repair and adaptor ligation, we attempted to replace the process using the tagmentation activity towards RNA/DNA duplexes. With the help of TRACE-seq, these steps are replaced with a ‘one-tube’ protocol (Figure 2a), which uses total RNA as input material and involves just three seamless steps (reverse transcription, tagmentation and strand extension and PCR), without the need for a second strand synthesis step. We first conducted TRACE-seq with 200 ng total RNA as input and tested several enzymes and conditions (Supplementary file 1); we observed very high correlation in gene-expression levels among three replicates, indicating TRACE-seq is highly reproducible (Figure 2b). To test the robustness of TRACE-seq, we performed the experiments with 20 ng and 2 ng total RNA. TRACE-seq results are again highly reproducible among replicates (Figure 2—figure supplement 1a,b). More importantly, gene expression levels measured using different amount of starting materials remain consistent with each other (Figure 2c).

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Distribution of reads across known genome features for NEBNext Ultra II RNA kit, Smart-seq2 and TRACE-seq with different amount of RNA as input.

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We then compared the library quality between TRACE-seq and NEBNext Ultra II RNA library prep kit, a commonly used kit for RNA-seq library construction. In addition, we conducted a comparison to Smart-seq2, which is a similar method in its use of oligo(dT) primed cDNA synthesis and Tn5 tagmentation. The HEK293T RNA used in these libraries was all from the same batch of cells. We found that TRACE-seq libraries exhibited similar percentage of reads mapped to annotated transcripts, rRNA contamination and gene numbers to NEBNext data when mRNA was used as input, but a higher rRNA contamination when total RNA was used as input (~9%, Supplementary file 2). In addition, a similar percentage of rRNA contamination was also observed in Smart-seq2 libraries (Supplementary file 3). Most of the genes detected by TRACE-seq overlap with that of NEBNext and Smart-seq2 (Figure 2d). In addition, TRACE-seq showed comparable performance in terms of gene expression measurement, using either a set of housekeeping genes (Figure 2e) or all genes (Figure 2—figure supplement 1c). The insert size of TRACE-seq library was moderately shorter (Figure 2—figure supplement 1d); in the meanwhile, TRACE-seq shows a higher coefficient of variation of gene coverage (0.54–0.70 vs 0.42–0.44, Figure 2—figure supplement 1e). We further found that TRACE-seq showed a slight tendency to 3’ end of the gene body (Figure 2f, Figure 2—figure supplement 1f). When transcripts were grouped according to annotated lengths, we found comparable gene body coverage for transcripts shorter than 1 kb among TRACE-seq, NEBNext kit and Smart-seq2 libraries. For transcripts with length between 1 and 4 kb, a slight 3’ end bias was observed in TRACE-seq library, while for transcripts longer than 4 kb, the central regions of transcripts were less covered by both TRACE-seq and Smart-seq2. We also performed TRACE-seq by using rRNA depletion together with random-primed cDNA synthesis. While this solved the 3’ end bias, a 5’ end bias appeared, which is a common phenomenon when using random primers during reverse transcription (Figure 2—figure supplement 1f). In spite of the gene body coverage bias, GC content (Figure 2g) and library complexity (Figure 2—figure supplement 1g) are unnoticeably affected. In addition, the gene expression measurement (Figure 2e) is also unaffected here because of the use of RNA with high quality (RIN: 9.5, Figure 2—figure supplement 1h); yet, cautions might be taken when the quality of RNA is compromised. Further inspection of reads distribution of TRACE-seq over genome features revealed similar pattern for that of NEBNext and Smart-seq2 (Figure 2h). Coverages of some representative transcripts are shown in Figure 2i.

One of the most important goals of RNA-seq is to detect differentially expressed genes among different samples. Having assessed the library quality of TRACE-seq, we next compared the performance of TRACE-seq in detecting differentially expressed genes between undifferentiated and differentiated mESCs to NEBNext Ultra II RNA library prep kit. As shown in Figure 3a, TRACE-seq successfully detected 4577 differentially expressed genes (3264 up-regulated genes and 1313 down-regulated genes), while NEBNext detected 4452 differentially expressed genes (3157 up-regulated genes and 1295 down-regulated genes). The overlapping gene number is 4,071, showing very high consistency between methods (Figure 3b). Besides, the fold change of the 4071 overlapping genes is highly correlated between the two methods (R > 0.99, Figure 3c). Therefore, TRACE-seq shows excellent performance in differential gene expression analysis.

Figure 3

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Previous studies found that Tn5 exhibits a slight insertion bias on dsDNA substrates (Goryshin et al., 1998; Green et al., 2012; Lodge et al., 1988). To further investigate whether potential bias exists for TRACE-seq, we thus characterized sites of Tn5-catalyzed adaptor insertion by calculating nucleotide composition of the first 30 bp of each sequence read per library. Similar to dsDNA substrates, we also observed an apparent insertion signature on RNA/DNA hybrids (Figure 2—figure supplement 1i). Nevertheless, per-position information contents were extremely low, suggesting such insertion bias is less likely to affect gene body coverage (Figure 2—figure supplement 1j). Overall, in spite of gene body coverage bias, TRACE-seq allows construction of high complexity RNA libraries and demonstrates similar performance as traditional RNA library preparation methods in terms of detected genes (97% and 93% overlapped with NEBNext and Smart-seq2 library respectively), gene expression measurement (R > 0.90) and differential expression analysis (R > 0.99), but outcompetes the traditional methods in terms of speed, convenience and cost.

Based on substrate diversity and the conserved catalytic domain of the retroviral integrase superfamily including the Tn5 transposase, we envision in this study that Tn5 may be able to directly tagment RNA/DNA hybrid duplexes, in addition to its canonical dsDNA substrates. Having validated such in vitro tagmentation activity, we developed TRACE-seq, which enables one-tube, low-input and low-cost library construction for RNA-seq experiments and demonstrates excellent performance in DE analysis. Compared to conventional RNA-seq methods, TRACE-seq does not need to pre-extract mRNA and synthesize a second DNA chain after mRNA reverse transcription. Therefore, TRACE-seq bypasses laborious and time-consuming processes, is compatible with low input, and reduces reagent cost (Supplementary file 4). During the preparation of this paper, an independent study also reported the similar finding and developed a RNA-seq method named SHERRY (Di et al., 2020). The major conclusions are very consistent between the two studies.

Despite its unique advantages, there is room to further improve TRACE-seq. For instance, the libraries generated by TRACE-seq in its current form are not strand-specific, which is a significant drawback for RNA-Seq experiments. Yet, TRACE-seq should be able to be converted to 5’ RNA-seq or 3’ RNA-seq, which can directionally preserve the 5’ or 3’ end information of transcripts (Cole et al., 2018; Pallares et al., 2020). In addition, TRACE-seq could be used in multiplex profiling when utilizing Tn5 transposase containing barcoded adaptors (Cusanovich et al., 2015; Zhu et al., 2019). Besides, if home-made Tn5 can be used (as have done in Picelli et al., 2014a; Kia et al., 2017; Kaya-Okur et al., 2019), the costs will be further cut down. The in vitro tagmentation efficiency of Tn5 on RNA/DNA hybrids could also be further improved. We have shown that the addition of PEG200 substantially enhanced the tagmentation efficiency of hybrids. It is also tempting to speculate that hyperactive mutants towards RNA/DNA hybrids could also be obtained through screening and protein engineering, as wild-type Tn5 transposase has been engineered to obtain hyperactive forms (Goryshin and Reznikoff, 1998; Wiegand and Reznikoff, 1992; Weinreich et al., 1994; Zhou and Reznikoff, 1997). Such hyperactive mutants are expected to have immediate utility in single-cell RNA-seq experiments, for instance. Moreover, Tn5 transposition in vivo has been harnessed to profile chromatin accessibility in ATAC-seq (Buenrostro et al., 2013); it remains to be seen whether or not an equivalent version may exist to enable in vivo detection of R-loop, chromatin bound long non-coding RNA and epitranscriptome analysis (Santos-Pereira and Aguilera, 2015; Li and Fu, 2019; Li et al., 2016; Song et al., 2020). To summarize, TRACE-seq manifests a ‘cryptic’ activity of the Tn5 transposase as a powerful tool, which may have broad biomedical applications in the future.

High-throughput sequence data has been deposited in GEO under accession code GSE143422.

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Author details

1. Bo Lu

   1. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

   Contribution

   Conceptualization, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Contributed equally with

   Liting Dong, Danyang Yi and Meiling Zhang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5852-0477

2. Liting Dong

   1. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

   Contribution

   Validation, Investigation, Visualization, Methodology, Writing - original draft

   Contributed equally with

   Bo Lu, Danyang Yi and Meiling Zhang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8396-374X

3. Danyang Yi

   State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

   Contribution

   Validation, Investigation, Visualization, Methodology, Writing - original draft

   Contributed equally with

   Bo Lu, Liting Dong and Meiling Zhang

   Competing interests

   No competing interests declared

4. Meiling Zhang

   State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

   Contribution

   Validation, Investigation, Methodology, Writing - review and editing

   Contributed equally with

   Bo Lu, Liting Dong and Danyang Yi

   Competing interests

   No competing interests declared

5. Chenxu Zhu

   State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

   Contribution

   Conceptualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4216-6562

6. Xiaoyu Li

   State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Chengqi Yi

   1. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   3. Department of Chemical Biology and Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing

   For correspondence

   chengqi.yi@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2540-9729

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