Main

Many single-cell RNA sequencing (scRNA-seq) methods have been described and their respective strengths and weaknesses characterized1. However, researchers still face a compromise between methods with high cell throughput (that is, droplet or combinatorial indexing methods) but low transcript coverage and methods with high sensitivity and full-length transcript coverage (that is, plate-based methods). Based on Smart-seq3 (ref. 2)—the method currently offering the highest information content per profiled cell—we systematically evaluated the feasibility of reducing volumes, reagents and experimental steps, without sacrificing data quality. This resulted in Smart-seq3xpress, a scalable nanoliter implementation of Smart-seq3 with throughput limited only by the available equipment (for example, polymerase chain reaction (PCR) instruments), and sequencing-ready libraries can be generated in a single workday.

We hypothesized that the Smart-seq3 chemistry would work in substantially lower volumes when covered in an inert hydrophobic substance (‘overlay’) (Fig. 1a). Using accurate non-contact nanoliter dispensers, we scaled the reaction volumes of the lysis, reverse transcription (RT) and pre-amplification PCR steps down to 1:2, 1:5 and 1:10 of the established volumes and tested these conditions on K562 and HEK293FT cells (Fig. 1b). For the lowest volumes, cells were sorted by fluorescence-activated cell sorting (FACS) into 300 nl of lysis buffer covered with 3 µl of Vapor-Lock, with subsequent additions of 100 nl for RT and 600 nl for PCR. After shallow sequencing, alignment and error correction of reads, we observed similar numbers of detected genes and molecules per cell at a sequencing depth of 100,000 reads per cell (Fig. 1c and Extended Data Fig. 1a,b), confirming that reaction scaling is possible without compromising data quality or introducing unwanted variability. Further reduction of reaction volumes beyond 1:10 was also possible, although not further pursued as savings in reagents are diminishing and reactions may become vulnerable to variations in cell sorting fluid (~5 nl). We hypothesized that the overlays would both protect the low reaction volumes from evaporation and provide a ‘landing cushion’ for the FACS-sorted cells. Indeed, many overlays with varying chemical properties could be used with low-volume Smart-seq3 (Fig. 1d), including silicone oils with high viscosities and hydrocarbons with higher freeze points. As expected, overlays did not interfere with the cDNA synthesis reaction when tested in larger volumes (Extended Data Fig. 1c,d). Performing low-volume Smart-seq3 without an overlay resulted in drastic losses (Fig. 1d), explaining why earlier efforts to miniaturize plate-based scRNA-seq without overlay resulted in substantially decreased complexity3,4,5. Next, we investigated whether the time-consuming and plastics-consuming cDNA clean-up step could be omitted by instead diluting the cDNA now obtained in lower volumes. At equal sequencing depths, single-cell libraries generated with cDNA dilution and clean-up had no detectable differences (P = 0.89 and P = 0.71, respectively, t-test; Fig. 1e and Extended Data Fig. 1e,f). The lowered reaction volumes enabled better control of downstream reaction conditions, and we, therefore, investigated the relationship between RT and pre-amplification PCR volumes. Dilution of the RT reaction was found beneficial for optimal PCR performance, with the established 1.5× ratio of PCR to RT volume (Extended Data Fig. 1g). However, PCR extension times could be reduced from 6 minutes to 4 minutes without complexity losses for longer transcripts (Extended Data Fig. 1h–j).

Fig. 1: Scalable full-transcript coverage scRNA-seq with Smart-seq3xpress.
figure 1

a, Schematic of nanoliter cDNA synthesis reactions performed in wells of 384-well PCR plates with 3 µl of hydrophobic overlay. b, Illustration of reduced-volume experiments with the lysis, RT and PCR volumes used. c, The number of genes detected per HEK293TF cell at each reaction volume, when sampling 100,000 sequencing reads (n = 100, 19, 32 and 28 cells, respectively). P value represents a two-sided t-test between the 10-µl and 1-µl conditions. d, Influence of hydrophobic overlays on miniaturized cDNA synthesis (1 µl total volume). For each compound, boxes depict the number of genes detected per HEK293FT cell (n = 17, 34, 39, 28, 25, 24, 28, 38 and 70, respectively), subsampled at 200,000 sequencing reads per cell. e, Replacement of the bead-based cDNA cleanup by dilution in single HEK293FT (n = 58 and 52, respectively) cells. Box plots show the number of genes detected per cell and condition (at 100,000 reads) with P value for a two-sided t-test across conditions. f, Tagmentation complexity using 0.1 µl of ATM Tn5 enzyme per HEK293FT cell in relation to input cDNA. The median number of detected genes as a function of raw sequencing reads (n = 51, 53, 54, 53, 53 and 52 cells for 25, 50, 75, 100, 200 and 500 pg, respectively). g, Tagmentation complexity for varying amounts of cDNA input. Complexity was summarized as unique aligned and gene-assigned UMI-containing read pairs per 400,000 raw reads and HEK293FT cell (n = 49, 51, 51, 50, 51 and 44). h, Schematic outline of the Smart-seq3 and Smartseq3xpress workflows. i, The number of genes detected with Smart-seq3xpress after variable amounts of pre-amplification PCR cycles. Median number of genes is reported as a function of raw sequencing reads in HEK293FT cells (n = 93, 98, 108, 113, 102, 114 and 118 cells for 10, 12, 13, 14, 15, 16 or 20 cycles, respectively). j, Fraction of UMI-containing reads to internal reads for HEK293FT cells prepared with Smartseq3xpress (KAPA HiFi; 12 PCR cycles), at a variable range of TDE1 Tn5 amounts (n = 64 cells each). k, Fraction of UMI-containing reads to internal reads for HEK293FT cells prepared with Smartseq3xpress (SeqAmp; 12 PCR cycles), at a variable range of TDE1 Tn5 amounts (n = 60 cells each). l,m, Optimization of RT and PCR conditions across 376 experimental conditions on HEK293FT cells. Colors indicate particular experimental conditions: Smart-seq3xpress with Smart-seq3 TSO (purple; n = 912), 52 °C RT/alternate TSO implementation (yellow; n = 74), fixed spacer TSO variant (blue; n = 45), FLASH-seq TSO variant (green; n = 55), Smart-seq3xpress with improved TSO (pink; n = 63) and all other conditions (gray; n = 21,707). Scatter plots denote the level of artifactual TSO-UMI reads and RNA counting errors (l) as well as a percentage of ribosomal RNA (rRNA) mapped reads and number of detected genes in 100,000 reads after removal of strand invasion reads (m). n, Benchmarking of Smart-seq3 variants. Box plots show the number of genes detected per HEK293FT cell in full-volume Smart-seq3 (ref. 2), low-volume Smart-seq3 and Smart-seq3xpress implementations, at the indicated read depths (n = 109–110, 18–27, 9–170, 20–55 and 9–63 cells, depending on the cells available at the given sequencing depths). The box plots (in c, d, e, j, k and n) show the median and first and third quartiles as a box, and the whiskers indicate the most extreme data points within 1.5 lengths of the box. cSt, centistoke.

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A major cost in plate-based scRNA-seq is tagmentation, which needs to be performed individually on each cell, and, although reducing the amounts of commercial Tn5 has been suggested to cut reaction costs6,7, it is currently unclear to what degree Tn5 can be reduced without losing library complexity. We investigated how the cDNA input amounts influence library complexity, which revealed that 20-fold variations in cDNA amounts could be tolerated with minor effects on gene and unique fragment detection (Fig. 1f,g). Library complexity was mainly a product of absolute Tn5 and cDNA amounts with little effect from varying reaction volumes (Extended Data Fig. 2a,b). The slightly reduced complexity at the highest cDNA amounts (Fig. 1f) likely resulted from insufficient tagmentation due to the limited Tn5 amounts used. Similar results were observed when varying the Tn5 amounts on a fixed amount of cDNA input (Extended Data Fig. 2c,d), and high-quality libraries were obtained using both commercial amplicon tagmentation mix (ATM) Tn5 and in-house8 Tn5 (Extended Data Fig. 2e). Altogether, these results demonstrated that tagmentation reactions were robust over large variations in cDNA input and Tn5 amounts and that considerable savings in resources can be made in reducing Tn5 amounts with only minimal effect on library complexity (Extended Data Fig. 2d).

Having demonstrated robust tagmentation over large ranges of cDNA, Tn5 and reaction volumes, we realized an opportunity to exclude several time-consuming and resource-intensive experimental steps, including excessive cDNA pre-amplification, concentration measurements, fragment length quality control (QC) traces and cDNA amount normalization (Fig. 1h). Instead, cDNA products from fewer pre-amplification cycles could be directly tagmented without the above-mentioned steps—this strategy we termed Smart-seq3xpress. To explore this potential, we first generated libraries with low-volume RT (300 nl lysis volume) from HEK293FT cells and using a range of PCR pre-amplification cycles (10–20), which revealed very similar gene detection (Fig. 1i) without any need for additional enzymatic reaction clean-ups (Extended Data Fig. 2f,g). However, the resulting libraries were heavily biased toward 5′ reads that contain the unique molecular identifier (UMI) at the expense of the internal reads important for full-transcript coverage scRNA-seq2. This resulted from inefficient tagmentation and the inability to modulate the ratio of UMI-containing and internal reads by Tn5 amounts (Fig. 1j). Specifically, the high salt concentration in the KAPA PCR buffer likely resulted in template blocking during tagmentation9, whereas other PCR polymerases were compatible with direct tagmentation (Extended Data Fig. 3a,b). Notably, tagmentation of SeqAmp and Platinum II amplified cDNA had considerably lowered fraction of 5′ UMI reads, indicative of improved tagmentation compatibility (Extended Data Fig. 3c). Additional experiments identified SeqAmp as the preferred PCR polymerase, as it consistently performed well with improved gene and molecule detection over a range of template-switching oligonucleotide (TSO) and PCR primer amounts (Extended Data Fig. 3a–d).

To directly assess the accuracy of RNA counting in Smart-seq3xpress, we used the newly developed molecular spikes10 (UMIcountR R package). Whereas Platinum II had higher error rates, which resulted in inflated RNA counts (Extended Data Fig. 3e,f), SeqAmp with 2 µM TSO and 1 µM of each PCR primer had high sensitivity and accuracy, and the fraction 5′ UMI reads could be modulated as expected by varying the cDNA or Tn5 amounts (Fig. 1k).

Next, we realized that the Smart-seq3 TSO could mis-prime during RT to induce a strand invasion artifact11,12 (Extended Data Fig. 4a,b). We screened TSO sequences of various designs and higher RT temperatures (Supplementary Note 1) for their effect on mis-priming, accuracy in UMI-based counting, gene and RNA molecule sensitivity and general quality (Fig. 1l,m and Extended Data Fig. 4c,d). Although no TSO showed excellent performance across all metrics, new TSO sequences with improved performance compared to Smart-seq3 and other TSOs12 were identified. Next, we validated candidate TSOs in primary cells to confirm sensitivity (Extended Data Fig. 5a) and fine-tuned oligonucleotide primer concentrations for optimal performance (Extended Data Fig. 5b). Thus, Smart-seq3xpress with the improved TSO has substantially reduced strand invasion in both peripheral blood mononuclear cells (PBMCs) (Extended Data Fig. 5c) and HEK293FT cells (Extended Data Fig. 5d,e) while maintaining accurate RNA counting (Fig. 1l,m and Extended Data Fig. 4d).

Finally, we benchmarked both low-volume Smart-seq3 (1 µl, KAPA) and Smart-seq3xpress (12 PCR cycles, using either KAPA or SeqAmp with new and old TSO) against standard-volume Smart-seq3 (ref. 2). Notably, we observed an improved gene and molecule detection with SeqAmp-based Smart-seq3xpress (Fig. 1n and Extended Data Fig. 5f–h). Crucially, the material and resources needed to construct Smart-seq3xpress singe-cell libraries were ten-fold reduced, allowing researchers to substantially increase the cell numbers analyzed. Further streamlining and reduction of plastics consumables was achieved by collecting final libraries by centrifugation using a simple 3D-printed adapter (Extended Data Fig. 6a) and through contact-less combinatorial index dispensing or relying on tagmentation plates containing already dispensed desiccated index primers. Thus, the miniaturization and streamlining of Smart-seq3 was feasible at improved gene and molecular detection so that sequencing-ready Smart-seq3xpress libraries can be reached within a single workday (Extended Data Fig. 6b).

To showcase Smart-seq3xpress, we profiled 26,260 human peripheral blood mononuclear cells (hPBMCs) at an average depth of 258,000 read pairs per cell. Sequence data were demultiplexed, processed and quality controlled with zUMIs13, and, after stringent QC, Seurat14 was used for downstream analysis (Methods). The single-cell transcriptomes were visualized with uniform manifold approximation and projection (UMAP) and separated into 27 clusters (Fig. 2a) that were supported by all donors (Extended Data Fig. 7a) and protein staining from the index sorting (Extended Data Fig. 7b). Reconstruction of T cell receptor (TCR) sequences from the internal reads matched the identified T cell clusters (Fig. 2b), and the bulk of reconstructed TCR sequences corresponded to expected single-chain pairing (Fig. 2c). Consistently higher gene detection was observed across cell types for Smart-seq3xpress in comparison to Smart-seq2 (ref. 15) and Smart-seq3 (ref. 2) (Fig. 2d). A recent study14 showed that scRNA-seq alone was not capable of separating certain cell types and states in hPBMCs without additional single-cell protein measurements. In a dataset with 200,000 hPBMC transcriptomes generated with 10x Genomics, 228 protein markers (CITE-seq) were used to distinguish unconventional T cell populations (mucosal-associated invariant T (MAIT) cells), gamma-delta T cells and effector memory T cells. All these cell types separated by the Smart-seq3xpress transcriptome data alone (Fig. 2a), exemplified with marker gene expression for MAIT cells, gamma-delta T cells and a CD4+ T cell population characterized only by their clonal expression of specific TCRs (Fig. 2e,f). Notably, this highly granular de novo cell type and state characterization was obtained from only 26,260 Smart-seq3xpress transcriptomes, and similar granularity remained at lower sequence depths (Extended Data Fig. 8).

Fig. 2: Application of Smart-seq3xpress to hPBMCs.
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a, Dimensional reduction (UMAP) of 26,260 hPBMC transcriptomes produced with Smartseq3xpress (KAPA, four donors; SeqAmp with improved TSO, three donors) colored and annotated by cell type. EM, effector memory; CM, central memory; NK, natural killer; ILC, innate lymphoid cell; HSPC, hematopoietic stem and progenitor cell; MAIT, mucosal-associated invariant T cell. b, Smartseq3xpress-based TCR reconstruction (TRaCeR) overlayed onto UMAP. c, QC of TCR reconstructions obtained with Scirpy, enumerating the number of T cells with certain types of TCR reconstructions. d, Benchmarking of Smart-seq2, Smart-seq3 and Smart-seq3xpress (SeqAmp, improved TSO) in primary hPBMCs. Each cell was downsampled to 100,000 reads, and the number of detected genes from exon-mapping reads is shown for representative cell types: B cells (n = 73, 366 and 859, respectively), CD4+ T cells (n = 261, 1,270 and 1,847, respectively), CD8+ T cells (n = 76, 272 and 913, respectively) and NK cells (n = 73, 352 and 601, respectively). P values indicate results of two-sided t-tests between the Smart-seq3 and Smart-seq3xpress. e, Differential gene expression analysis (Wilcoxon test, Padj < 0.01) between naive CD4− T cell cluster (n = 2,476) and clonal CD4− T cell cluster (n = 682). Indicated are the top five TCR genes driving the clonal CD4− T cell cluster separation. f, Dot plot showing expression of selected marker genes for MAIT, gamma-delta and clonal CD4+/CD8+ T cells in all annotated clusters, with size of the dot denoting the detection of a gene within the cells of the cluster and color scale denoting the average expression level. g, Analysis of captured transcribed genetic variation in donor-matched Smart-seq3xpress and 10x Genomics 3′ version 3.1 data. For each cell passing QC (n = 2,938 and 9,846, respectively), the number of SNPs with alternate allele coverage per cell are indicated (left) as well as the average SNP coverage normalized by the sequencing depth (right). h, Frequency of RNA-velocity-informative fully spliced reads in donor-matched Smart-seq3xpress and 10x Genomics 3′ version 3.1 data. For each cell in representative cell types—B cells (n = 404 and 642, respectively), CD4+ T cells (n = 1,320 and 1,317, respectively), CD8+ T cells (n = 417 and 1,181, respectively) and NK cells (n = 441 and 498, respectively)—we summarized the percentage of reads spanning exon–exon junctions, with nominal P values for a two-sided t-test. i, Differential splicing isoform analysis using BRIE2. Shown is a volcano plot of tested skipped-exon events color-coded by significant variation when testing for any cell type (ELBO gain >20). ELBO gain is a surrogate for the Bayes factor (that is, the likelihood ratio of two hypotheses). The x axis denotes the effect size on the distribution of PSI values in a cell type. j, Overlays of color-coded PSI values inferred for each sequenced cell (n = 26,260) in genes with significant cell type splicing variation (PTPRC, GUSBP11 and ISG20). The box plots (in d, g and h) show the median and first and third quartiles as a box, and the whiskers show the most extreme data points within 1.5 lengths of the box. HSPC, hematopoietic stem and progenitor cell.

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Next, we performed a direct comparison of Smart-seq3xpress to droplet-based 10x Genomics (10x Genomics 3′ version 3.1; Methods) on a matched hPBMC donor. Tabulating the coverage over transcribed single-nucleotide polymorphism (SNP) positions—which is, for example, important for studies of allele-specific expression and cancer—showed that more SNPs were covered in the full-length Smart-seq3xpress data (9.2 million versus 2.1 million positions, over all cells). On a per-cell level, the number of SNPs with alternate allele coverage was approximately nine-fold higher with Smart-seq3xpress and with a three-fold-higher coverage per sequenced read (Fig. 2g). Similarly, full-length coverage with Smart-seq3xpress resulted in significantly (2–5-fold) increased read support over exon–exon and exon–intron splice junctions, which form the basis for RNA velocity inference16 (Fig. 2h). To perform a direct comparison of cluster granularity, we sampled an equal number of mean sequenced reads and cells for both 10x Genomics and Smart-seq3xpress and performed independent cluster analyses (Extended Data Fig. 9). Although both methods had approximately similar granularity at this number of cells and read numbers, cell type prediction based on Azimuth was slightly more consistent with clustering in Smart-seq3xpress (adjusted Rand index (ARI) of 0.59 and 0.49, respectively).

To study alternative splicing across the hPBMC Smart-seq3xpress data, we used BRIE2 (ref. 17) to identify 968 skipped exons with significant variation in inclusion levels across cell types. (Fig. 2i). Overlaying cell-level percent spliced-in (PSI) estimates for alternative exons onto the UMAP revealed distinct splicing patterns across cell types. These included switching, graded changes, as well as heterogeneous cell types with variable inclusion levels (Fig. 2j), and, notably, they were independent of their gene-level expression levels between cell types (Extended Data Fig. 10). Thus, Smart-seq3xpress can generate unprecedented high-quality, full-transcript-coverage scRNA-seq data that enable scalable analyses of alternative splicing across complex human samples.

Large-scale efforts to enumerate cell types and states across human tissues and in model organisms are dominated by scRNA-seq methods that count RNAs by sequencing their ends. With the development of Smart-seq3xpress, we demonstrate a scalable solution for full-transcript-coverage scRNA-seq. Not only has Smart-seq3xpress overcome the main limitations of plate-based assays in terms of resources and material needed per cell, the data obtained also challenge the existing notions of the cell numbers required for efficient and finely resolved clustering of cells. Therefore, high-sensitivity scRNA-seq with isoform-specific and allele-specific resolution can, for the first time, be performed with Smart-seq3xpress at a scale suitable for large-scale cell atlas building.

Methods

Cell sources, culturing and sorting

HEK293FT cells (Invitrogen) were grown in DMEM medium (4.5 g L−1 of glucose and 6 mM L-glutamine, Gibco), supplemented with 10% FBS (Sigma-Aldrich), 0.1 mM MEM non-essential amino acids (Gibco), 1 mM sodium pyruvate (Gibco) and 100 µg ml−1 of pencillin–streptomycin (Gibco) at 37 °C. For sort of single cells, cells were harvested by incubation with TrypLE Express (Gibco). K562 (ATCC) cells were grown in RPMI medium, supplemented 10% FBS (Sigma-Aldrich) and 1% pencillin–streptomycin (Gibco). Frozen aliquots of 10 million hPBMCs from healthy individuals were purchased from Lonza, requiring healthy donors only. Written informed consent was obtained at sampling point from all donors by Lonza, and our analyses of hPBMCs were approved by the Regional Ethical Review Board in Stockholm, Sweden (2020-05070). hPBMCs were gently thawed and stained with PE mouse anti-human CCR7 (2-L1-A,1:100), PE-Cy7 mouse anti-human CD4 (SK3,1:250), FITC mouse anti-human CD45RA (HI100, 1:100), PerCP-Cy5.5/BB700 mouse anti-human CD8 (RPA-T8, 1:250) and PE-Cy5 mouse anti-human CD45RO (UCHL1, 1:250) (BD Biosciences) before sorting. For all cell types, dead cells were gated out after staining with propidium iodide (Thermo Fisher Scientific). Live, single cells were sorted into 384-well plates containing lysis buffer using a BD FACSMelody (BD FACSChorus version 1.3 software) equipped with a 100-µm nozzle and plate cooling with index sorting on (BD Biosciences). After sorting, each plate was immediately spun down and stored at −80 °C.

Smart-seq3 library preparation

Full-volume Smart-seq3 library preparations were performed as previously described2. PCR was carried out using 20 cycles of amplification.

Low-volume Smart-seq3 and Smart-seq3xpress library preparation

For experiments including overlays, including Vapor-Lock (Qiagen), silicon oils (Sigma-Aldrich) and tri-/tetradecane (Sigma-Aldrich), 3 µl of designated overlay was added to each well and stored at room temperature until use. Lysis buffer of various volumes (0.1–3 µL) was dispensed using either Formulatrix Mantis or Dispendix I.Dot One liquid dispenser to each well, all containing 0.1% Triton X-100, 5% PEG8000 adjusted to RT volume, 0.5 µM oligo(dT) adjusted to RT volume, 0.5 mM dNTPs each adjusted to RT volume and 0.5 U RNase Inhibitor (Takara, 40 U µl−1). After dispensing, lysis plates were briefly centrifuged down to ensure that lysis is properly collected and stored under the overlay. Stored plates of sorted cells were denatured at 72 °C for 10 minutes, followed by the addition of indicated volumes of RT mix; common for all is that the reagent concentrations are stable: 25 mM Tris-HCl ~pH 8.3 (Sigma-Aldrich), 30 mM NaCl (Ambion), 0.5 mM GTP (Thermo Fisher Scientific), 2.5 mM MgCl2 (Ambion), 8 mM DTT (Thermo Fisher Scientific), 0.25 U µl−1 of RNase Inhibitor (Takara), 2 µM TSO (5′-Biotin-AGAGACAGATTGCGCAATGNNNNNNNNrGrGrG-3′; IDT) and 2 U µl−1 of Maxima H Minus reverse transcriptase (Thermo Fisher Scientific). After RT mix dispensing, the plate was spun down to ensure merge of RT and lysis reactions. RT was performed at 42 °C for 90 minutes, followed by ten cycles of 50 °C for 2 minutes and 42 °C for 2 minutes. Indicated volumes of PCR master mix were dispensed, all containing constant reaction concentrations of 1× KAPA HiFi PCR buffer (Roche), 0.3 mM dNTPs each (Roche), 0.5 mM MgCl2 (Ambion), 0.5 µM Smart-seq3 forward primer (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGATTGCGCAATG-3′; IDT), 0.1 µM Smart-seq3 reverse primer (5′-ACGAGCATCAGCAGCATACGA-3′; IDT) and 0.02 U µl−1 of KAPA HiFi DNA polymerase (Roche). After dispensing, the plate was quickly spun down before being incubated in PCR as follows: 3 minutes at 98 °C for initial denaturation, 10–24 cycles of 20 seconds at 98 °C, 30 seconds at 65 °C and 2–6 min at 72 °C. Final elongation was performed for 5 minutes at 72 °C. For conditions after cDNA pre-amplification clean-up: 100 nl of water, ExoSAP-IT express (Thermo Fisher Scientific) or 0.5 U ExoI (NEB) + 0.05 FastAP (Thermo Fisher Scientific) was dispensed per well and incubated at 37 °C for 15 minutes, followed by inactivation at 85 °C for 5 minutes.

For Smartseq3xpress with SeqAmp (Takara), lysis and RT was carried out with 0.125 µM or 0.5 µM oligodT30VN and 0.75 µM or 2 µM TSO unless otherwise indicated as described above. Original Smartseq3 TSO (5′-Biotin-AGAGACAGATTGCGCAATGNNNNNNNNrGrGrG-3′; IDT). Improved TSO (5′-Biotin-AGAGACAGATTGCGCAATGNNNNNNNNWWrGrGrG-3′; IDT). PCR mastermix was dispensed at 0.6 µl per cell containing 1× SeqAmp PCR buffer, 0.025 U µl−1 of SeqAmp polymerase and 0.5 µM/1 µM Smartseq3 forward and reverse primer. After dispensing PCR mastermix, the plate was quickly spun down before being incubated as follows: 1 minute at 95 °C for initial denaturation, 6–18 cycles of 10 seconds at 98 °C, 30 seconds at 65 °C and 2–6 minutes at 68 °C. Final elongation was performed for 10 minutes at 72 °C. For Smartseq3xpress with NEBNext Ultra II Q5 Master Mix (NEB), PCR mastermix consisted of 1× NEBNext Ultra II Q5 Master Mix and 0.5 µM/1 µM Smartseq3 forward and reverse primer and PCR was performed at 30 seconds at 98 °C for initial denaturation, 12 cycles of 10 seconds at 98 °C, 30 seconds at 65 °C and 6 minutes at 72 °C. Final elongation was performed for 5 minutes at 72 °C. For Smartseq3xpress with NEBNext Q5 Hot Start HiFi PCR Master Mix (NEB), PCR mastermix consisted of 1× NEBNext Q5 Hot Start HiFi PCR Master Mix and 0.5 µM/1 µM Smartseq3 forward and reverse primer and PCR was performed for 30 seconds at 98 °C for initial denaturation, 12 cycles of 10 seconds at 98 °C, 30 seconds at 65 °C and 1 minute at 65 °C. Final elongation was performed for 5 minutes at 65 °C. For Smartseq3xpress with Platinum SuperFi II DNA polymerase (Invitrogen), PCR mastermix consisted of 1× SuperFi II Master Mix, 0.2 µM dNTPs and 0.5 µM/1 µM Smartseq3 forward and reverse primer and PCR was performed for 30 seconds at 98 °C for initial denaturation, 12 cycles of 10 seconds at 98 °C, 30 seconds at 60 °C and 6 minutes at 72 °C. Final elongation was performed for 5 minutes at 72 °C. For Smartseq3xpress with Platinum II Taq Hot Start DNA polymerase (Invitrogen), PCR mastermix consisted of 1× Platinum II Taq Master Mix, 0.2 µM dNTPs and 0.5 µM/1 µM Smartseq3 forward and reverse primer and PCR was performed for 2 minutes at 94 °C for initial denaturation, 12 cycles of 15 seconds at 94 °C, 30 seconds at 60 °C and 6 minutes at 68 °C. Final elongation was performed for 5 minutes at 68 °C.

A full and comprehensive protocol of Smart-seq3xpress has been deposited on protocols.io18.

After pre-amplification workflow

For regular Smart-seq3, pre-amplified cDNA libraries were purified with homemade 22% PEG beads at a ratio of 1:0.6. Library sizes were observed using Agilent Bioanalyzer High Sensitivity Chip, followed by concentration quantification using QuantiFlour dsDNA assay (Promega). cDNA was subsequently diluted to 100 pg µl−1 unless otherwise specified.

For low volume, pre-amplified cDNA libraries were diluted by the addition of 9 µl of water to each well, if not indicated otherwise, followed by a quick centrifugation. Library sizes were checked on an Agilent Bioanalyzer, using the high-sensitivity DNA chip; meanwhile, concentrations were quantified using QuantiFlour dsDNA assay (Promega). cDNA was normalized to 100 pg µl−1 if nothing else was specified.

For Smart-seq3xpress, pre-amplified cDNA libraries were diluted with the addition of 9 µl of water unless stated otherwise, before transferring 1 µl of diluted cDNA from each well into tagmentation.

Sequence library preparation for Smart-seq3xpress

Tagmentation was performed in 2 µl consisting of 1 µl of either diluted or normalized pre-amplified cDNA and 1 µl of 1× tagmentation buffer (10 mM Tris pH 7.5, 5 mM MgCl2, 5% DMF), 0.025–0.5 µl of ATM (Illumina XT DNA sample preparation kit) or 0.0002–0.01 µl of tagmentation DNA enzyme 1 (TDE1;Illumina DNA sample preparation kit)). In the event of in-house Tn5, 1× tagmentation buffer used consisted of 10 mM TAPS-NaOH pH 8.4, 5 mM MgCl2 and 8% PEG8000 and indicated amounts of 0.0005–0.01 µM in-house Tn5 enzyme. Samples were incubated at 55 °C for 10 minutes, followed by the addition of 0.5 µl of 0.2% SDS to each well. After addition of 1.5 µl/3.5 µl of custom Nextera index primers (0.5 μM) carrying 10-bp dual indexes, library amplification was started by the addition of 2/4 µl of PCR mix (1× Phusion Buffer (Thermo Fisher Scientific), 0.01 U µl−1 of Phusion DNA polymerase (Thermo Fisher Scientific), 0.2 mM dNTP each) and incubated for 3 minutes at 72 °C; 30 seconds at 95 °C; 12–14 cycles of (10 seconds at 95 °C; 30 seconds at 55 °C; 30–60 seconds at 72 °C); and 5 minutes at 72 °C in a thermal cycler. Samples were pooled by spinning out each plate gently in a 300-ml robotic reservoir (Nalgene) fitted with a custom 3D-printed scaffold by pulsing to ~200g. The pooled library was purified with homemade 22% PEG beads at a ratio of 1:0.7.

10x Genomics library preparation

After thawing the PBMC sample, we stained dead cells with propidium iodide (Thermo Fisher Scientific) and sorted 200,000 live cells into a 5-ml tube. After centrifugation, the cell suspension was resuspended and concentration of cells was determined using a Countess automated cell counter (Thermo Fisher Scientific). We loaded ~13,000 cells for a target cell recovery of ~8,000 cells and prepared libraries according to the 10x Genomics version 3.1 user guide. For both pre-amplification and post-fragmentation PCR, we applied 12 cycles of PCR.

Sequencing

Smartseq3 and Smartseq3xpress libraries were sequenced on a Illumina NextSeq 500 (Illumina NextSeq Control Software 2.2.0) or MGI DNBSEQ G400RS platform (version 1.1.0.108 software). For NextSeq runs with Smart-seq3, denatured libraries were loaded on HighOutput version 2.5 cartridges at 2.1–2.3 pM. For G400RS runs, libraries were created using phosphorylated index primers or subjected to five cycles of adapter conversion PCR using the MGIEasy Universal Library Conversion Kit (MGI) and subsequently circularized from 1 pmol of dsDNA according to the manufacturer’s protocol. Next, 60 fmol of circular ssDNA library pools were used for DNA nanoball (DNB) making using a custom rolling-circle amplification primer (5′-TCGCCGTATCATTCAAGCAGAAGACG-3′). DNBs were loaded on FCL flow cells (MGI) and sequenced using SE100, PE100 or PE150 cartridges using custom sequencing primers (Read 1: 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3′; MDA: 5′-CGTATGCCGTCTTCTGCTTGAATGATACGGCGAC-3′, Read 2: 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3′; i7 index: 5′-CCGTATCATTCAAGCAGAAGACGGCATACGAGAT-3′; i5 index: 5′-CTGTCTCTTATACACATCTGACGCTGCCGACGA-3′). 10x Genomics version 3.1 libraries were sequenced on a NextSeq 500 according to manufacturer specifications (1.8 pM loading concentration; HighOutput version 2.5 150-cycle kits, 28–8–92 cycles for read1, index1 and read2).

Primary data processing

zUMIs13 version 2.8.2 or newer was used to process raw FASTQ files. Reads were filtered for low-quality barcodes and UMIs (4 bases < phred 20, 3 bases < phred 20, respectively) and UMI-containing reads parsed by detection of the pattern (ATTGCGCAATG) while allowing up to two mismatches. Reads were mapped to the human genome (hg38) using STAR version 2.7.3, and error-corrected UMI counts were calculated from Ensembl gene annotations (GRCh38.95). zUMIs was also used to downsample cells to equal raw sequencing depth to facilitate method benchmarking.

Analysis of Smartseq3xpress hPBMC data

Cells were filtered for low-quality libraries, requiring (1) more than 50% of read pairs mapped to exons+introns, (2) more than 20,000 read pairs sequenced, (3) more than 500 genes (exon+intron quantification) detected per cell and (4) less than 15% of read pairs mapped to mitochondrial genes. Furthermore, a gene was required to be expressed in at least ten cells. Analysis was done using Seurat v4.0.1 (ref. 14). Data were normalized (‘LogNormalize’), scaled to 10,000 and total number of counts, and mitochondrial fraction was regressed out. Using the Seurat integration function, the donor effect from the seven different donors in the dataset was removed. The top 10,000 variable genes were considered and 35 principal components for shared nearest neighbor (SNN) neighborhood construction and UMAP dimensionality reduction. Cell clusters were produced using Louvain algorithm at a resolution of 0.8. Cell types were identified by using the R package Presto (Wilcoxon & AUC, version 1.0.0). For the Azimuth predictions, a QC-filtered count matrix was uploaded to the Azimuth web-based application and processed according to the Azimuth app. For the direct donor comparison with 10x Genomics version 3.1 data, read counts from only donor 7 were downsampled to similar median sequencing depth as the comparable 10x dataset and quality filtered as follows: at least 10,000 read pairs, more than 50% of read pairs mapped to exons+introns and less than 15% read pairs mapped to mitochondrial genes. A gene was required to be expressed in at least ten cells. Data were subset to 3,000 cells for analysis in Seurat. Data were LogNormalized, scaled to 10,000 and mitochondrial genes regressed out. Default Seurat settings were used for neighborhood construction and dimensionality reduction. Cell clusters were assigned using the Louvain algorithm at a resolution of 0.8. Cell type identification was performed as above.

Analysis of 10x Genomics version 3.1 donor 7 hPBMC data

Raw sequencing data in FASTQ format were processed using zUMIs version 2.9.3 with automatic barcode detection based on the 10x Genomics version 3.1 allow-list. After completion, we exported full count tables including empty droplets and assigned ambient RNA droplets and real cells using the CellBender (version 0.2.0) remove-background function19. To filter for doublets, the CellBender output.h5 file was used with Solo20 (version 0.6). Additional doublets were discarded by manually inspecting the distribution of total UMI counts per droplet and discarding those greater than 45,000. For downstream analysis in Seurat, a low-quality filter was applied based on requiring at least 10,000 read pairs and less than 10% read pairs mapped to mitochondrial genes. A gene should be expressed in at least ten cells to be included. A subset of 3,000 cells out of 6,483 passing QC was used for direct comparison to Smart-seq3xpress. Seurat was run at default settings using SCTransform, and cell clusters were assigned at resolution 0.8 using the Louvain algorithm. Cell types were identified using Presto (Wilcoxon & AUC) together with reference-based approach performed by the Azimuth app.

TCR reconstruction

TCR sequences were reconstructed using TraCeR version 0.6.0 (ref. 21) run in the teichlab/tracer Docker environment and using the --loci A B G D --species Hsap flags. Scirpy22 (version 0.8.0) was used to summarize and QC the output from TraCer.

Molecular spike data processing and analysis

Molecular spike data were extracted from aligned zUMIs BAM files and analyzed using the UMIcountR package10 (https://github.com/cziegenhain/UMIcountR, version 0.1.1). After loading the data using the ‘extract_spike_dat’ function, overrepresented spikes were discarded with a read cutoff of 25 and higher. We next used molecular spike observations across all cells and conditions with at least five reads per molecule to sample 26 ground truth mean expression levels from 1 to 316 molecules per cell using the ‘subsample_recompute’ function. We then plotted the mean counting difference shaded by the standard deviation.

Identification of TSO strand invasion artifact

To identify UMI reads with the TSO mis-primed artifact, we loaded sequencing reads into R using Rsamtools (version 2.6.0). In the case of paired-end sequencing, only first-in-pair reads were selected using the appropriate SAM flags. The strand orientation of the mapped reads was also determined from SAM flags. Then, we extracted a 20-bp window of genome sequence upstream of the read start position on the positive strand (+stranded mappings) or downstream of the read start position+read length on the negative strand (−stranded mappings) using the BSgenome package (version 1.62.0, human hg38). Afterwards, we checked for presence of the UMI sequence (with or without addition of GGG overhang) in the genomic window using R’s fuzzy string matching function (allowing 0, 1 or 2 mismatches). This identification procedure of artifactual UMI reads was also implemented in Python3 to process aligned BAM files and remove all artifactual reads/read pairs (available on GitHub: https://github.com/cziegenhain/pyTSOfilter).

Isoform-based analysis

For analysis of skipped-exon (SE) isoform differences, we retrieved annotations from GenCode (Human v39) and produced the SE annotation in GFF file format using BRIEkit-event (version 0.2.2). We filtered SE events using BRIEkit-event-filter with the following criteria: (1) retain SE events on autosomes and X/Y chromosomes; (2) SE events not overlapped by any other AS-exon; (3) surrounding introns are no shorter than a fixed length (100 bp); (4) presence of specific splice sites (that is, surrounded by AG-GT); and (5) SE events have a minimum distance (10 bp) from transcription start site or transcription termination site. Next, we summarized the coverage over the filtered SE events for each cell using the brie-count command from BRIE2 (version 2.0.6) using per-cell demultiplexed, aligned and TSO-artifact-filtered (see above) BAM files as input. The resulting count files in h5ad format were used as input for the Bayesian regression-based inference of PSI values and variable splicing detection over cell types. We applied the aggregated imputation mode introduced by BRIE2 to fit the gene-wise prior distribution through aggregation of data over all cells for each gene. Default settings for Monte Carlo EM were applied. Genes were filtered by requiring at least 50 counts, ten unique counts and at least 30 cells with unique counts. The minimum required minor isoform frequency was 0.001 (default settings). For variable splicing detection, we annotated each cell with a binarized dummy factor of cell type identity (Seurat clustering; Louvain resolution 2.0), removing the most common cell type to avoid collinearity of the design matrix. We loaded the resulting h5ad file into scanpy23 (version 1.8.2) for visualization of PSI values. For selection of SE events with significant cell type difference, we selected the highest evidence lower bound (ELBO) value per SE for each of the cell type LRT indices. Gene model plots to visualize significant cell-type-variable SE events were generated using the Gviz (version 1.38.1L, https://link.springer.com/protocol/10.1007%2F978-1-4939-3578-9_16) and rtracklayer (version 1.54.0, https://academic.oup.com/bioinformatics/article/25/14/1841/225816) R packages.

SNP and junction coverage analysis

Coverage over transcribed SNPs was analyzed per cell using the cellsnp-lite24 package (version 1.0.0) over the most common human polymorphisms (1000 Genomes Project minor allele frequency >0.005, 36 million positions). We applied default settings of minimum aggregated count over cells 20 and minimum MAPQ for read filtering of 20 (essentially discarding multimapping reads due to the mapping quality encoding of the STAR aligner). Coverage on RNA velocity informative positions was tabulated from zUMIs output BAM files. Fully spliced exon–exon reads were identified by the presence of splicing in their CIGAR value and exclusive assignment to exon regions, whereas nascent (that is, unspliced or partially spliced) exon–intron spanning reads were identified by the overlap with both exonic and intronic regions of the same gene.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.