to embryonic stem cells. Cell 161
(5):1187–1201. https://doi.org/10.1016/j.
cell.2015.04.044
24. Ziegenhain C, Vieth B, Parekh S, Reinius B,
Guillaumet-Adkins A, Smets M, Leonhardt H,
Heyn H, Hellmann I, Enard W (2017) Comparative analysis of single-cell RNA sequencing
methods. Mol Cell 65(4):631–643. e634.
https://doi.org/10.1016/j.molcel.2017.01.
023
25. Svensson V, Natarajan KN, Ly LH, Miragaia
RJ, Labalette C, Macaulay IC, Cvejic A, Teichmann SA (2017) Power analysis of single-cell
RNA-sequencing experiments. Nat Methods
14(4):381–387. https://doi.org/10.1038/
nmeth.4220
26. Rosenberg AB, Roco CM, Muscat RA,
Kuchina A, Sample P, Yao Z, Graybuck LT,
Peeler DJ, Mukherjee S, Chen W, Pun SH,
Sellers DL, Tasic B, Seelig G (2018) Singlecell profiling of the developing mouse brain
and spinal cord with split-pool barcoding. Science 360(6385):176–182. https://doi.org/
10.1126/science.aam8999
27. Kiselev VY, Kirschner K, Schaub MT,
Andrews T, Yiu A, Chandra T, Natarajan KN,
Reik W, Barahona M, Green AR, Hemberg M
(2017) SC3: consensus clustering of single-cell
RNA-seq data. Nat Methods 14(5):483–486.
https://doi.org/10.1038/nmeth.4236
28. Smith TS, Heger A, Sudbery I (2017)
UMI-tools: modelling sequencing errors in
unique molecular identifiers to improve quantification
accuracy.
Genome
Res 27
(3):491–499. https://doi.org/10.1101/gr.
209601.116
29. Parekh S, Ziegenhain C, Vieth B, Enard W,
Hellmann I (2018) zUMIs - a fast and flexible
pipeline to process RNA sequencing data with
UMIs. Gigascience 7(6). https://doi.org/10.
1093/gigascience/giy059
30. Andrews S (2010) FastQC: a quality control
tool for high throughput sequence data
31. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina
sequence
data.
Bioinformatics
30
(15):2114–2120. https://doi.org/10.1093/
bioinformatics/btu170
32. Krueger F (2012) http://www.bioinformatics.
babraham.ac.uk/projects/trim_galore
33. Martin M (2011) Cutadapt removes adapter
sequences from high-throughput sequencing
reads. EMBnet J 17(1):10–12
34. Dobin A, Davis CA, Schlesinger F, Drenkow J,
Zaleski C, Jha S, Batut P, Chaisson M, Gingeras
TR (2013) STAR: ultrafast universal RNA-seq
aligner. Bioinformatics 29(1):15–21. https://
doi.org/10.1093/bioinformatics/bts635
35. Bray NL, Pimentel H, Melsted P, Pachter L
(2016) Near-optimal probabilistic RNA-seq
quantification.
Nat
Biotechnol
34
(5):525–527. https://doi.org/10.1038/nbt.
3519
36. Andrews TS, Hemberg M (2019) M3Drop:
dropout-based feature selection for scRNASeq.
Bioinformatics 35(16):2865–2867. https://
doi.org/10.1093/bioinformatics/bty1044
37. Eng CL, Lawson M, Zhu Q, Dries R,
Koulena N, Takei Y, Yun J, Cronin C,
Karp C, Yuan GC, Cai L (2019)
Transcriptome-scale super-resolved imaging in
tissues by RNA seqFISH. Nature 568
(7751):235–239. https://doi.org/10.1038/
s41586-019-1049-y
38. Rodriques SG, Stickels RR, Goeva A, Martin
CA, Murray E, Vanderburg CR, Welch J, Chen
LM, Chen F, Macosko EZ (2019) Slide-seq: a
scalable technology for measuring genomewide expression at high spatial resolution. Science 363(6434):1463–1467. https://doi.org/
10.1126/science.aaw1219
314
Abdull J. Massri et al.
(5):1187–1201. https://doi.org/10.1016/j.
cell.2015.04.044
24. Ziegenhain C, Vieth B, Parekh S, Reinius B,
Guillaumet-Adkins A, Smets M, Leonhardt H,
Heyn H, Hellmann I, Enard W (2017) Comparative analysis of single-cell RNA sequencing
methods. Mol Cell 65(4):631–643. e634.
https://doi.org/10.1016/j.molcel.2017.01.
023
25. Svensson V, Natarajan KN, Ly LH, Miragaia
RJ, Labalette C, Macaulay IC, Cvejic A, Teichmann SA (2017) Power analysis of single-cell
RNA-sequencing experiments. Nat Methods
14(4):381–387. https://doi.org/10.1038/
nmeth.4220
26. Rosenberg AB, Roco CM, Muscat RA,
Kuchina A, Sample P, Yao Z, Graybuck LT,
Peeler DJ, Mukherjee S, Chen W, Pun SH,
Sellers DL, Tasic B, Seelig G (2018) Singlecell profiling of the developing mouse brain
and spinal cord with split-pool barcoding. Science 360(6385):176–182. https://doi.org/
10.1126/science.aam8999
27. Kiselev VY, Kirschner K, Schaub MT,
Andrews T, Yiu A, Chandra T, Natarajan KN,
Reik W, Barahona M, Green AR, Hemberg M
(2017) SC3: consensus clustering of single-cell
RNA-seq data. Nat Methods 14(5):483–486.
https://doi.org/10.1038/nmeth.4236
28. Smith TS, Heger A, Sudbery I (2017)
UMI-tools: modelling sequencing errors in
unique molecular identifiers to improve quantification
accuracy.
Genome
Res 27
(3):491–499. https://doi.org/10.1101/gr.
209601.116
29. Parekh S, Ziegenhain C, Vieth B, Enard W,
Hellmann I (2018) zUMIs - a fast and flexible
pipeline to process RNA sequencing data with
UMIs. Gigascience 7(6). https://doi.org/10.
1093/gigascience/giy059
30. Andrews S (2010) FastQC: a quality control
tool for high throughput sequence data
31. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina
sequence
data.
Bioinformatics
30
(15):2114–2120. https://doi.org/10.1093/
bioinformatics/btu170
32. Krueger F (2012) http://www.bioinformatics.
babraham.ac.uk/projects/trim_galore
33. Martin M (2011) Cutadapt removes adapter
sequences from high-throughput sequencing
reads. EMBnet J 17(1):10–12
34. Dobin A, Davis CA, Schlesinger F, Drenkow J,
Zaleski C, Jha S, Batut P, Chaisson M, Gingeras
TR (2013) STAR: ultrafast universal RNA-seq
aligner. Bioinformatics 29(1):15–21. https://
doi.org/10.1093/bioinformatics/bts635
35. Bray NL, Pimentel H, Melsted P, Pachter L
(2016) Near-optimal probabilistic RNA-seq
quantification.
Nat
Biotechnol
34
(5):525–527. https://doi.org/10.1038/nbt.
3519
36. Andrews TS, Hemberg M (2019) M3Drop:
dropout-based feature selection for scRNASeq.
Bioinformatics 35(16):2865–2867. https://
doi.org/10.1093/bioinformatics/bty1044
37. Eng CL, Lawson M, Zhu Q, Dries R,
Koulena N, Takei Y, Yun J, Cronin C,
Karp C, Yuan GC, Cai L (2019)
Transcriptome-scale super-resolved imaging in
tissues by RNA seqFISH. Nature 568
(7751):235–239. https://doi.org/10.1038/
s41586-019-1049-y
38. Rodriques SG, Stickels RR, Goeva A, Martin
CA, Murray E, Vanderburg CR, Welch J, Chen
LM, Chen F, Macosko EZ (2019) Slide-seq: a
scalable technology for measuring genomewide expression at high spatial resolution. Science 363(6434):1463–1467. https://doi.org/
10.1126/science.aaw1219
314
Abdull J. Massri et al.