Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

BackgroundSingle-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI.Findings zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution.Conclusions zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data.

1 Single-cell mRNA sequencing (scRNA-seq) allows to profile heterogeneous cell 2 populations, offering exciting possibilities to tackle a variety of biological and medical 3 questions. A range of methods has been recently developed, making it necessary to 4 systematically compare their sensitivity, accuracy, precision and cost-efficiency. 5Here, we have generated and analyzed scRNA-seq data from 479 mouse ES cells and 6 spike-in controls that were prepared with four different methods in two independent 7 replicates each. We compare their sensitivity by the number of detected genes and by 8 the efficiency with which they capture spiked-in mRNAs, their accuracy by correlating 9 spiked-in mRNA concentrations with estimated expression levels, their precision by 10 power simulations and variance decomposition and their efficiency by their costs to 11 reach a given amount of power. While accuracy is similar for all methods, we find that 12Smart-seq on a microfluidic platform is the most sensitive method, CEL-seq is the 13 most precise method and SCRB-seq and Drop-seq are the most efficient methods. 14 Our analysis provides a solid basis to choose among four available scRNA-seq 15methods and to benchmark future method development. 16 17 18 transcription allowing to process hundreds or thousands of scRNA-seq libraries in one 36 reaction, increasing the throughput of scRNA-seq library generation by one to three orders 37 of magnitude 9,13-15 . However, a systematic comparison of sensitivity, accuracy and precision 38 among such recently developed methods has not been performed yet. To this end, we have 39 generated and analysed 479 scRNA-seq libraries from mouse embryonic stem (mES) cells 40 cultured in two-inhibitor (2i/LIF) medium using four different methods run in two replicates 41 each (Fig. 1). 42 43 Results 44Generation and processing of 479 scRNA-seq libraries 45We have used the Smart-seq protocol on the C1 platform from Fluidigm (Smart-seq/C1) 46 that uses microfluidic chips to automatically separate up to 96 cells 7 . After lysis and the 47

Currently, quantitative RNA-seq methods are pushed to work with increasingly small starting amounts of RNA that require amplification. However, it is unclear how much noise or bias amplification introduces and how this affects precision and accuracy of RNA quantification. To assess the effects of amplification, reads that originated from the same RNA molecule (PCR-duplicates) need to be identified. Computationally, read duplicates are defined by their mapping position, which does not distinguish PCR- from natural duplicates and hence it is unclear how to treat duplicated reads. Here, we generate and analyse RNA-seq data sets prepared using three different protocols (Smart-Seq, TruSeq and UMI-seq). We find that a large fraction of computationally identified read duplicates are not PCR duplicates and can be explained by sampling and fragmentation bias. Consequently, the computational removal of duplicates does improve neither accuracy nor precision and can actually worsen the power and the False Discovery Rate (FDR) for differential gene expression. Even when duplicates are experimentally identified by unique molecular identifiers (UMIs), power and FDR are only mildly improved. However, the pooling of samples as made possible by the early barcoding of the UMI-protocol leads to an appreciable increase in the power to detect differentially expressed genes.

Single-cell RNA sequencing (scRNA-seq) has emerged as a central genome-wide method to characterize cellular identities and processes. Consequently, improving its sensitivity, flexibility, and cost-efficiency can advance many research questions. Among the flexible plate-based methods, single-cell RNA barcoding and sequencing (SCRB-seq) is highly sensitive and efficient. Here, we systematically evaluate experimental conditions of this protocol and find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to develop a scRNA-seq library protocol we call molecular crowding SCRB-seq (mcSCRB-seq), which we show to be one of the most sensitive, efficient, and flexible scRNA-seq methods to date.