The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 30 sources its claims cite is checked for support yet.
Comparative Analysis of Single-Cell RNA Sequencing Methods
Open accessPublished version of a preprint
Abstract
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.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That amplification noise can be a major factor is seen by the strong increase of extra Poisson variability when ignoring UMIs and considering read counts only (Figure 5B, left; Figure S7A).”
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In the paper
“A 50% detection probability was reached at 7, 11, 14, 16, 17, and 28 ERCC molecules for Smart-seq2, Smart-seq/C1, CEL-seq2/C1, SCRB-seq, Drop-seq, and MARS-seq, respectively (Figure S4C).”
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In the paper
“Hence, among the UMI methods, CEL-seq2/C1 and SCRB-seq libraries had the highest complexity of mRNA molecules”
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In the paper
“As expected, the 3 0 counting methods showed a strong bias of reads mapped to the 3 0 end (Figure S3D).”
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In the paper
“cell (Figure 3C). Smart-seq2 detectedthe highestnumberofgenes per cell with a median of 9,138.”
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In the paper
“While Smartseq2 was the most sensitive method in both cases”
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In the paper
“This was possible for 96.5% (MARS-seq) to 98.9% (Smart-seq2) of all the 13,361 genes.”
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In the paper
“Overall, between 48% (Smart-seq2) and 30% (Smart-seq/C1) of all reads were exonic and, thus, were used to quantify gene expression levels.”
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In the paper
“In particular, the deep sequencing of Smart-seq2 libraries showed clearly that the number of detected genes did not change when increasing the sequencing depth from one million to five million reads per cell (Figure S2B).”
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In the paper
“As expected, accuracy was worse for narrower and especially for lower concentration ranges (Figure S5C).”
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In the paper
“Taking the number of detected genes per cell as a measure of sensitivity, we found that Drop-seq and MARS-seq had the lowest sensitivity, with a median of 4,811 and 4,763 genes detected per cell, respectively”
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In the paper
“A general difference in expression values between 3 0 counting and full-length methods also was reflected in their strong separation by the first principal component, explaining 37% of the total variance”
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In the paper
“while CEL-seq2/C1, SCRB-seq, and Smartseq/C1 detected a median of 7,536, 7,906, and 7,572 genes per”
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In the paper
“While the majority of genes ( 13,000) were detected by all methods, 400 genes were specific to each of the 3 0 counting methods, and 1,000 genes were specific to each of the two full-length methods (Figure S3A).”
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In the paper
“Drop-seq performed better and SCRB-seq and MARS-seq performed worse when using ERCCs.”
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In the paper
“To compare the total number of genes detected across many cells, we pooled the sequence data of 65 cells per method, and we detected 19,000 genes for CEL-Seq2/C1, 17,000 for MARS-seq, 18,000 for Drop-seq and SCRB-Seq, 20,000 for Smart-seq/C1, and 21,000 for Smart-seq2 (Figure 3D).”
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In the paper
“As expected from the number of detected genes per cell (Figure 3C), MARS-seq had the highest median dropout probability (74%) and Smart-seq2 had the lowest (26%) (Figure 5A).”
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In the paper
“When we stratified our analysis (considering UMIs) across five bins of expression levels, the ranking of methods was recapitulated and showed that the lowest expression bin strongly limited the TPR in all methods (Figure S9B).”
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In the paper
“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.”
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In the paper
“While Smart-seq2 and CEL-seq2/C1 needed just 1.3-fold more cells at 0.25 million reads than at one million reads to reach 80% power, SCRB-seq and Drop-seq required 2.6-fold morecells (Table 1).”
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In the paper
“In summary, we find that Drop-seq, SCRB-seq, and MARS-seq are the most cost-effective methods, closely followed by Smart-seq2, if using an in-house-produced transposase.”
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In the paper
“In summary, SCRB-seq is the most powerful method at one million reads and half a million reads, but CELseq2/C1 is the most powerful method at a sequencing depth of 250,000 reads.”
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In the paper
“Calculating minimal costs this way, Drop-seq (690$) is the most cost-effective method when sequencing 254 cells at a depth of 250,000 reads, and SCRB-seq (810$), MARS-seq (820$), and Smart-seq2 (1,090$) are slightly more expensive at the same performance (Table 1).”
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- The authors state this as an assumption: Sequencing costs are the same for all methods.
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In the paper
“The p value of that difference was lowest for SCRB-seq, the most powerful method, and highest for Smartseq/C1, the least powerful method (Figure S10D).”
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In the paper
“Overall, the decrease in power was moderate (Figure 6C; Table 1) and followed the drop in sensitivity at different sequencing depths (Figure 3B).”
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In the paper
“CEL-seq2/C1, Drop-seq, MARS-seq, and Smart-seq2 performed slightly worse, reaching 80% power with 86, 99, 110, and 95 cells per group, respectively, while Smart-seq/C1 needed 150 cells to reach 80% power (Figure 6A).”
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In the paper
“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 MARSseq detect nearly 50% fewer genes per cell (Figures 3B and 3C).”
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In the paper
“TPRs differed considerably among methods and SCRB-seq performed best, reaching a median TPR of 80% with 64 cells.”
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In the paper
“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.”
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In the paper
“We used mESCs cultured under two inhibitor/leukemia inhibitory factor (2i/LIF) conditions to obtain a relatively homogeneous cell population (Gr € un et al., 2014; Kolodziejczyk et al., 2015b), so that biological variation was similar among experiments and, hence, we mainly compared technical variation.”
What the sources say
- Not checked yetcites Gr € un et al., 2014
- Not checked yetcites Kolodziejczyk et al., 2015b
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In the paper
“This extra Poisson variability includes biological variation (assumed to be the same across methods in our data) and technical variation, and the latter includes noise introduced by amplification (Brennecke et al., 2013; Gr € un et al., 2014; Stegle et al., 2015).”
What the sources say
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In the paper
“Indeed, a mixture model including a dropout probability and a negative binomial distribution, modeling the overdispersion in the count data, have been shown to represent scRNA-seq data better than the negative binomial alone (Finak et al., 2015; Kharchenko et al., 2014).”
What the sources say
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In the paper
“Hence, it seems that full-length methods turn a slightly higher fraction of transcripts into sequenceable molecules than 3 0 counting methods and are more sensitive in this respect.”
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In the paper
“Among the 3 0 counting methods, CEL-seq2/C1 and SCRB-seq detect about as many genes per cell as Smart-seq/C1, whereas Drop-seq and MARS-seq detect considerably fewer genes.”
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In the paper
“This suggests that they are unlikely to remain method specific at higher expression levels and that their impact on conclusions drawn from scRNAseq data is rather limited (Lun et al., 2016).”
What the sources say
- Not checked yetcites Lun et al., 2016 source
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In the paper
“Furthermore, the Smart-seq protocol has been optimized for sensitivity, more even full-length coverage, accuracy, and cost (Picelli et al., 2013)”
What the sources say
- Not checked yetcites Picelli et al., 2013 source
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In the paper
“In summary, we find that Smart-seq2 is the most sensitive method, as it detects the highest number of genes per cell and the most genes in total across cells and has the most even coverage across transcripts.”
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In the paper
“Utilization of UMI information improves quantification of mRNA molecules (Gr € un et al., 2014; Islam et al., 2014)”
What the sources say
- Not checked yetcites Gr € un et al., 2014
- Not checked yetcites Islam et al., 2014 source
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In the paper
“Smart-seq/C1 is slightly less sensitive per cell and detects almost the same number of genes across cells with slightly less even coverage.”
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In the paper
“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 scRNAseq library generation by one to three orders of magnitude (Hashimshony et al., 2012; Jaiti”
What the sources say
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None of the sources cited for this claim have been checked yet.
In the paper
“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).”
What the sources say
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None of the sources cited for this claim have been checked yet.
In the paper
“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).”
What the sources say
- Not checked yetcites Hashimshony et al., 2016 source
- Not checked yetcites Wu et al., 2014
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In the paper
“To this end, we generated data for all six compared methods from the same cells, cultured under the same condition in the same laboratory.”
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In the paper
“Hence, our power simulation framework considers the full range of dropout rates and is not biased against more sensitive methods.”
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In the paper
“successful establishment of the method in our lab was confirmed by a species-mixing experiment (Figure S1A).”
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In the paper
“These estimates were very similar among our technical replicates, and, hence, our method comparison is valid with respect to batch variations (Figures S10B-S10D).”
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In the paper
“J1 embryonic stem cells were obtained from E. Li and T. Chen and mycoplasma free determined by a PCR-based test.”
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In the paper
“Furthermore, the presence of batch effects shows that experiments need to be designed in a way that does not confound batches with biological factors (Hicks et al., 2015).”
What the sources say
- Not checked yetcites Hicks et al., 2015
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, we systematically compared six prominent scRNA-seq methods and found that Drop-seq is preferable when quantifying transcriptomes of large numbers of cells with low sequencing depth, SCRB-seq and MARS-seq is preferable when quantifying transcriptomes of fewer cells, and Smart-seq2 is preferable when annotating and/or quantifying transcriptomes of fewer cells as long one can use in-house”
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In the paper
“Cell line authentication was not recently performed.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We trimmed cDNA reads to the same length and processed raw reads in the same way as data sequenced for this study.”
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Similar sequencing qualities were confirmed by FastQC v0.10.1 (Figure S1B).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
A thorough and systematic comparison of relevant parameters across scRNA-seq methods is still lacking.
“However, a thorough and systematic comparison of relevant parameters across scRNA-seq methods is still lacking.”
“However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking.”
The authors state this as an assumption.
Differences in total variation among methods are due to differences in their technical variation.
“Hence, we can assume that differences in the total variation among methods are due to differences in their technical variation.”
The paper relies on this without support.
The amount of biological variation is the same in cells analyzed by each of the six methods.
“As we used the same cell type under the same culture conditions for all methods, the amount of biological variation should be the same in the cells analyzed by each of the six methods.”
The authors state this as an assumption.
Sequencing costs are the same for all methods.
“we assume that sequencing costs are the same for all methods.”
The authors state this as an assumption.
Detections of ERCC molecules are independent from each other and can be regarded as independent Bernoulli trials.
“Detections are independent from each other and are thus regarded as independent Bernoulli trials.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Combinatorial indexing and droplet-based methods have higher throughput and lower cost per cell, whereas plate-based methods provide more sensitive gene detection and are more customizable (Ziegenhain et al., 2017).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Ziegenhain et al., 2017
In the citing paper
“In scRNA-seq experiments, individual cells are typically not sequenced to saturation, leading to sparse count matrices, where only highly expressed genes are detected with counts greater than zero.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [36]
In the citing paper
“Specifically, we found the optimal read depths to be ~10,000 in most evaluations, which is relatively low compared to previous recommendations34–36,80,81.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [36]
In the citing paper
“Furthermore, scRNA‐seq techniques can be divided into full‐length and 3′ enrichment methods (Svensson et al, 2017; Ziegenhain et al, 2017).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Ziegenhain et al, 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Commercial Smart-seq2 total conflicts with the stated cost calculation
MajorAt one million reads, 95 cells per group means 190 libraries. At the listed $30 per library and $5 per million reads, the stated calculation gives $6,650, not the table's $10,470; the other two commercial Smart-seq2 totals are also too high under that calculation.
“| Smart-seq2 (commercial) | 0.8 | 5.2 | 95/105/128 | 30 | 10,470/11,040/13,160 |”
“c Assuming $5 per one million reads.”
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
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Cutadapt removes adapter sequences from high-throughput sequencing reads
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Mapping Cell Identity from scRNA-seq: A primer on computational methods
Traversa, D., & Chiara, M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.051 · Cited by 5
Luo, Q.-W., Shi, Y.-C., Jia, W.-Q., Zhou, X.-H., Yang, Z.-R., Wang, Y., & Guan, Q. (2025) · Immune Network · doi:10.4110/in.2025.25.e38 · Cited by 4
Metadata
- Authors
- Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Ludwig Cancer Research (BE)
- Universitat Pompeu Fabra (ES)
- Center for Integrated Protein Science Munich (DE)
- Corresponding author
- Enard, Wolfgang
- ORCID
- Venue
- Molecular Cell
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 65
- Issue
- 4
- Pages
- 631-643.e4
- ISSN
- 1097-2765 (ISSN-L)
- OpenAlex
- W2887910031
- Cited by
- 1,494
- References
- 62
- Citations by year
- 2026: 86 · 2025: 156 · 2024: 158 · 2023: 152 · 2022: 160 · 2021: 219 · 2020: 197 · 2019: 174 · 2018: 125 · 2017: 60
- FWCI
- 63.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Molecular Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, other-oa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, other-oa)
- Annals of Hematology
- Funders
- Deutsche Forschungsgemeinschaft (DFG) (SFB1243, A01/A14/A15)
- Boehringer Ingelheim Fonds
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, mouse embryonic stem cells, Drop-seq, SmartSeq2, SMART-seq, unique molecular identifiers, sequencing depth, power simulation, cost efficiency, transcriptome quantification
- MeSH terms
- Animals, Base Sequence, Cell Line, Computer Simulation, Cost-Benefit Analysis, RNA, Sequence Analysis, RNA, Models, Economic, Mice, Embryonic Stem Cells, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
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