Conclusions Smart-Seq2 is a single-cell RNA- seq method that generates cDNA libraries with longer average size and higher yield compared to SMARTer™. Smart-seq2 transcriptome libraries have improved detection, coverage and accuracy as well as lower technical bias. Finally, Smart-seq2 entirely relies on off-the-shelf reagents, which allows the generation of sequencing libraries in a cost- effective manner. Introduction Until few years ago it was extremely challenging to generate full-length cDNA libraries from single cells. Existing methods either use template-switching (TS) or rely on the 3´- end poly(A) tailing of the cDNA. Our group has recently shown 1 that TS provides more even coverage across transcripts than poly(A)-tailing methods, in agreement with previous observations 2,3. However, despite the popularity of single-cell transcriptome sequencing, so far no systematic efforts have been made to improve the cDNA library yield, average length or overall quality. Simone Picelli 1, Åsa K. Björklund 1,2, Omid R. Faridani 1, Sven Sagasser 1,2, Gösta Winberg 1,2 & Rickard Sandberg 1,2 1 Ludwig Institute for Cancer Research, Stockholm, Sweden; 2 Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden Simone PicelliLudwig Institute for Cancer ResearchNobels väg 3, Stockholm (Sweden) Smart-seq2 * for sensitive full-length transcriptome profiling in single cells Materials and methods We evaluated a large number of RT & PCR conditions as well as different template-switching oligos (TSOs) using 1 ng total RNA (n=457). Results were confirmed on human (HEK293T, n=159; DG-75, n=34) and mouse (C2C12, n= 30; MEF, n=39) single cells. To assess the performance we sequenced HEK293T single cells, comparing Smart-seq2 (n=35) and SMARTer (n=4). Reads were aligned with STAR and expression levels quantified as RPKM as described before 1. Results We observed a two-fold increase in yield compared to SMARTer IIA oligo when using a LNA-modified TSO and starting from 1 ng RNA (Fig. 2a). Yield also increased when adding betaine in combination with 9-12 mM MgCl 2 (Fig. 2b). Interestingly, average library length increased by 370 nt if dNTPs were added before RNA denaturation along with the oligo(dT) primers (Fig. 2c). The positive effects of LNA-modified TSO, betaine and MgCl 2 were confirmed on human and mouse single cells of different sizes and RNA contents (Fig. 2d and Fig. 2e). Moreover, skipping bead-based purification after first strand cDNA synthesis led to longer libraries (Fig. 2f). Figure 2. Improvements in cDNA library yield and length. In brackets the number of experiments for each condition. a) Median yield after pre-amplification when using different TSOs, relative to a TSO with 3rG at the 3´-end. b) Median yield after pre-amplification with (black) or without (grey) betaine, as a function of increasing MgCl 2 concentration. c) Average length of pre-amplified cDNA when adding dNTPs before (“early”) or after (“late”) RNA denaturation. d) Median yield after pre-amplification from HEK293T cells when using SMARTer TSO or LNA-based TSO (“rGrG+G”). e) Median yield after pre-amplification from DG-75 cells with or without betaine. f) Length of cDNA libraries from HEK293T cells in reactions with or without bead-based purification after first strand synthesis and before PCR pre-amplification. To assess the impact of our method on single-cell transcriptome profiling we sequenced and compared Smart-seq2 and SMARTer™ using HEK293T cells. Smart-seq2 can detect more genes (Fig. 3a) and has lower technical variability for low- and medium-abundance transcripts (Fig 3b). This led to the detection of 2,372 more genes on average (Fig. 3c). The replacement of the Advantage 2 DNA Polymerase (SMARTer™) with KAPA HiFi (Smart-seq2) allowed the detection of more genes at higher GC levels (Fig. 3d). Moreover, Smart-Seq2 reads display more even coverage at both the 3´- and 5´-end of the transcripts approaching the expected fractions (Fig. 3e). Finally, Smart-Seq2 allows the generation of high-quality libraries using only off-the- shelf reagents, which makes the method considerably cheaper than commercial sample preparation kits. Figure 3. Sensitive full-length transcriptome profiling in single cells. a) Percentage of genes reproducibly detected in replicate cells, binned according to expression level. b) s.d. in gene expression estimates within replicates in bins of genes sorted according to expression levels. c) Mean number of genes detected in HEK293T cells for SMARTer and Smart-seq2 at different RPKMs cutoffs. d) Mean fraction of genes detected (RPKM>1), sorted by GC content. “No preamplification” included as a control. e) Mean fraction of reads aligning to the 3´-most 20% of the genes, the 5´-most 20% and the middle 60% for single cells (n≥4). Figure 1. Flowchart of Smart-Seq2, tagmentation and enrichment PCR. * The results presented here are part of: “Smart-seq2 for sensitive full-length transcriptome profiling in single cells”, Nature Methods, in press