Method
Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity
- † Equal contributors
1 Functional Genomics Unit, RIKEN Center for Developmental Biology, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
2 Genome Resource and Analysis Unit, RIKEN Center for Developmental Biology, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
3 Laboratory for Systems biology, RIKEN Center for Developmental Biology, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
4 Laboratory for Sensory Circuit Formation, RIKEN Center for Developmental Biology, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
5 JST, PRESTO, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
6 Laboratory for Synthetic Biology, Quantitative Biology Center, RIKEN, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
7 Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Genome Biology 2013, 14:R31 doi:10.1186/gb-2013-14-4-r31
Published: 17 April 2013Additional files
Additional file 1:
Figure S1: Schematic of the whole-transcript amplification methods based on the poly-A-tailing reaction. Figure S2: Improvement parameters of whole-transcript amplification for Quartz-Seq. Figure S3: Key steps for robust suppression of byproducts. Figure S4: Optimization of suppression PCR for Quartz-Seq. Figure S5: Optimal DNA polymerase for whole-transcript amplification. Figure S6: Quality check of the library preparation for single-cell Quartz-Seq. Figure S8: Percentage of sequence reads of the suppression PCR primer or rRNA. Figure S9: Relationship between the read number and the reproducibility. Figure S10: Optimization of cDNA length in technical development for single-cell Quartz-Seq. Figure S11: Trend of unamplified isoforms in each single-cell RNA-seq method. Figure S12: Amplified cDNA lengths resulting from single-cell RNA-seq methods. Figure S13: Success rate of whole-transcript amplification from single cells sorted by fluorescence-activated cell sorting (FACS). Figure S14: Amount of total RNA from a single cell at each cell-cycle phase. Figure S15: Principal component analysis (PCA) of single cells from different cell types at different cell-cycle phases. Figure S16: Over-representation analyses for principal component (PC) of single cells from same cell types in the same cell-cycle phase (G1). Figure S17: Scatter plots of conventional RNA-seq and Quartz-Seq using 50 ES cells in the G1 phase of the cell cycle and Quartz-Seq using 10 pg of total ES RNA. Figure S18: Effect of carried-over buffer for PCR efficiency.
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Additional file 3:
Figure S7: All scatter plots
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Additional file 4:
Table S1. All results of linear regression and correlation analyses.
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Additional file 5:
Supplementary movie 1. Principal component analysis (PCA) with single-cell Quartz-Seq data of embryonic stem (ES) and primitive endoderm (PrE) single-cell preparations.
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Additional file 6:
Supplementary movie 2. Principal component analysis (PCA) with single-cell Quartz-Seq data of embryonic stem (ES) cells in different cell-cycle phases.
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Additional file 7:
Table S2. Sequencing information.
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Additional file 8:
Table S3. Primer information.
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