领域：学术论文 · 自然科学

Quantitative Transcriptional Analysis of Single-Cell RNA-Sequencing Methods

SUMMARY
Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the per- formance 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.

INTRODUCTION
Genome-wide quantification of mRNA transcripts is highly informative for characterizing cellular states and molecular circuitries (ENCODE Project Consortium, 2012). Ideally, such data are collected with high spatial resolution, and single-cell RNA sequencing (scRNA-seq) now allows for transcriptome-wide analyses of individual cells, revealing exciting biological and med- ical insights (Kolodziejczyk et al., 2015a; Wagner et al., 2016). scRNA-seq requires the isolation and lysis of single cells, the conversion of their RNA into cDNA, and the amplification of cDNA to generate high-throughput sequencing libraries. As the
amount of starting material is so small, this process results in substantial technical variation (Kolodziejczyk et al., 2015a; Wagner et al., 2016).
One type of technical variable is the sensitivity of a scRNA- seq method (i.e., the probability to capture and convert a particular mRNA transcript present in a single cell into a cDNA molecule present in the library). Another variable of interest is the accuracy (i.e., how well the read quantification corresponds to the actual concentration of mRNAs), and a third type is the precision with which this amplification occurs (i.e., the technical variation of the quantification). The combination of sensitivity, precision, and number of cells analyzed determines the power to detect relative differences in expression levels. Finally, the monetary cost to reach a desired level of power is of high practical relevance. To make a well- informed choice among available scRNA-seq methods, it is important to quantify these parameters comparably. Some strengths and weaknesses of different methods are already known. For example, it has previously been shown that scRNA-seq conducted in the small volumes available in the automated microfluidic platform from Fluidigm (C1 platform) outperforms CEL-seq2, Smart-seq, or other commercially available kits in microliter volumes (Hashimshony et al., 2016; Wu et al., 2014). Furthermore, the Smart-seq protocol has been optimized for sensitivity, more even full-length coverage, accuracy, and cost (Picelli et al., 2013), and this improved Smart-seq2 protocol (Picelli et al., 2014b) has also become widely used (Gokce et al., 2016; Reinius et al., 2016; Tirosh et al., 2016).

Other protocols have sacrificed full-length coverage in order to sequence part of the primer used for cDNA generation. This enables early barcoding of libraries (i.e., the incorporation of cell-specific barcodes), allowing for multiplexing the cDNA amplification and thereby increasing the throughput of scRNA- seq library generation by one to three orders of magnitude (Hashimshony et al., 2012; Jaitin et al., 2014; Klein et al., 2015; Macosko et al., 2015; Soumillon et al., 2014). Additionally, this approach allows the incorporation of unique molecular identifiers (UMIs), random nucleotide sequences that tag individual mRNA molecules and, hence, allow for the distinction between original molecules and amplification duplicates that derive from the cDNA or library amplification (Kivioja et al., 2011). Utilization of UMI information improves quantification of mRNA molecules (Grun et al., 2014; Islam et al., 2014), and it has been implemented in several scRNA-seq protocols, such as STRT (Islam et al., 2014), CEL-seq (Gru€n et al., 2014; Hashimshony et al., 2016), CEL-seq2 (Hashimshony et al., 2016), Drop-seq (Ma- cosko et al., 2015), inDrop (Klein et al., 2015), MARS-seq (Jaitin et al., 2014), and SCRB-seq (Soumillon et al., 2014).
However, a thorough and systematic comparison of relevant parameters across scRNA-seq methods is still lacking. To address this issue, we generated 583 scRNA-seq libraries from mouse embryonic stem cells (mESCs), using six different methods in two replicates, and we compared their sensitivity, accuracy, precision, power, and efficiency (Figure 1).

DISCUSSION
Here we have provided an in-depth comparison of six prominent scRNA-seq protocols. To this end, we generated data for all six compared methods from the same cells, cultured under the same condition in the same laboratory. While there would be many more datasets and methods for a comparison of the sensitivity and accuracy of the ERCCs (Svensson et al., 2016), our approach provides a more controlled and comprehensive comparison across thousands of endogenous genes. This is important, as can be seen by the different sensitivity estimates that we obtained for Drop-seq, MARS-seq, and SCRB-seq using the ERCCs. In our comparison, we clearly find that Smart-seq2 is the most sensitive method, closely followed by SCRB-seq, Smart-seq/C1, and CEL-seq2/C1, while Drop-seq and MARS- seq detect nearly 50% fewer genes per cell (Figures 3B and 3C). In addition, Smart-seq2 shows the most even read coverage across transcripts (Figure S3D), making it the most appropriate method for the detection of alternative splice forms and for analyses of allele-specific expression using SNPs (Deng et al., 2014; Reinius et al., 2016). Hence, Smart-seq2 is certainly the most suitable method when an annotation of single-cell transcriptomes is the focus. Furthermore, we find that Smart-seq2 is also the most accurate method (i.e., it has the highest correlation of known ERCC spike-in concentrations and read counts per million), which is probably related to its higher sensitivity. Hence, differences in expression values across transcripts within the same cell predict differences in the actual concentrations of these transcripts well. All methods do this rather well, at least for higher expression levels, and we think that the small differences among methods will rarely be a decisive factor. Importantly, the accuracy of estimating transcript concentrations across cells (relevant, e.g., for comparing the total RNA content of cells) depends on different factors and cannot be compared well among the tested methods as it would require known concentration differences of transcripts across cells. However, it is likely that methods that can use UMIs and ERCCs (CEL-seq2/ C1, MARS-seq, and SCRB-seq) would have a strong advantage in this respect.
How well relative expression levels of the same genes can be compared across cells depends on two factors. First, how often (i.e., in how many cells and from how many molecules) it is measured. Second, with how much technical variation (i.e., with how much noise, e.g., from amplification) it is measured. For the first factor (dropout probability), we find Smart-seq2 to be the best method (Figure 5A), as expected from its high gene detection sensitivity. For the second factor (extra Poisson variability), we find the four UMI methods to perform better (Figure 5B), as expected from their ability to eliminate variation introduced by amplification. To assess the combined effect of these two factors, we performed simulations for differential gene expression scenarios (Figure 6). This allowed us to translate the sensitivity and precision parameters into the practically relevant power to detect differentially expressed genes. Of note, our power estimates include the variation that is caused by the two different replicates per method that constitutes an important part of the variation. Our simulations show that, at a sequencing depth of one million reads, SCRB-seq has the highest power, probably due to a good balance of high sensitivity and low amplification noise. Furthermore, amplification noise and power strongly depend on the use of UMIs, especially for the PCR- based methods (Figures 5B and 6B; Figure S7). Notably, this is due to the large amount of amplification needed for scRNA- seq libraries, as the effect of UMIs on power for bulk RNA-seq libraries is negligible (Parekh et al., 2016).
Perhaps practically most important, our power simulations also allow us to compare the efficiency of the methods by calculating the costs to generate the data for a given level of power. Using minimal cost calculations, we find that Drop-seq is the most cost-effective method, closely followed by SCRB-seq, MARS-seq, and Smart-seq2. However, Drop-seq costs are likely to be more underestimated, due to lower flexibility in generating a specified number of libraries and the higher fraction of reads that come from bad cells. Hence, all four UMI methods are in practice probably similarly cost-effective. In contrast, for Smart-seq2 to be similarly cost-effective it is absolutely necessary to use in-house-produced transposase or to drastically reduce volumes of commercial transposase kits (Lamble et al., 2013; Mora-Castilla et al., 2016).
Given comparable efficiencies of Drop-seq, MARS-seq, SCRB-seq, and Smart-seq2, additional factors will play a role when choosing a suitable method for a particular question. Due to its low library costs, Drop-seq is probably prefer- able when analyzing large numbers of cells at low coverage (e.g., to find rare cell types). On the other hand, Drop-seq in its current setup requires a relatively large amount of cells (>6,500 for 1 min of flow). Hence, if few and/or unstable cells are isolated by FACS, the SCRB-seq, MARS-seq, or Smart- seq2 protocols are probably preferable. Additional advantages of these methods over Drop-seq include that technical variation can be estimated from ERCCs for each cell, which can be helpful to estimate biological variation (Kim et al., 2015; Vallejos et al., 2016), and that the exact same setup can be used to generate bulk RNA-seq libraries. While SCRB-seq is slightly more cost-effective than MARS-seq and has the advantage that one does not need to produce the transposase in-house, Smart-seq2 is preferable when transcriptome annotation, identification of sequence variants, or the quantification of different splice forms is of interest. Furthermore, the presence of batch effects shows that experiments need to be designed in a way that does not confound batches with bio- logical factors (Hicks et al., 2015). Practically, plate-based methods might currently accommodate complex experimental designs with various biological factors more easily than microfluidic chips.

翻译考点：
考点1: “Dropout Probability”必须译为“漏检概率”（非“缺失概率”）
考点2:“unique molecular identifiers”必须译为“独特分子标识符”首现可保留英文 UMI 并加注）
考点3: “quantification”应译为“量化”
考点4: “full-length coverage”应译为“全长覆盖度”（保持“度”的一致性）
考点5: “spike-in“必须译为”掺入对照“（“加标”可备注为行业俗称，但不作首选）
考点6:“Poisson variability”应译为“泊松变异性”
考点7:“>6,500 for 1 min of flow”必须译为“流速＞6,500/分钟”
考点8:“plate-based methods”推荐译为“基于微孔板的方法”
考点9: “multiplexing”必须译为“多重化/多路复用”（不应只保留英文）
考点10: “amplification duplicates”必须译为“扩增重复（序列副本）/重复扩增子”（非“扩增产物”）
考点11: “sequence part of the primer”推荐译为“对引物部分进行测序”（非“捕获/捕集”）
考点12: “in how many cells / from how many molecules”推荐译为“在多少细胞中/由多少分子（覆盖范围/数量维度）”（非“被测量的频率”）
考点13: “monetary cost”推荐译为“成本/费用”（非“成本控制”）