Quartz-Seq
Whole-Transcript Amplification for Single Cells
The Quartz-Seq method optimizes whole-transcript amplification (WTA) of single cells (Sasagawa et al., 2013). In this method, an RT primer with a T7 promoter and PCR target is first added to the extracted mRNA. RT synthesizes first-strand cDNA, after which the RT primer is digested by exonuclease I. Next, a poly(A) tail is added to the 3′ ends of first-strand cDNA, along with a poly(dT) primer containing a PCR target. After second-strand generation, a blocking primer is added to ensure PCR enrichment in sufficient quantity for sequencing. Deep sequencing allows for accurate, high-resolution representation of the whole transcriptome of a single cell.
Similar methods: CEL-Seq, Drop-seq, MARS-Seq, CytoSeq, inDrop, Hi-SCL
Advantages:
- Single-tube reaction suitable for automation
- Digestion of RT primers by exonuclease I eliminates amplification of byproducts
- Short fragments and byproducts are suppressed during enrichment
Disadvantages:
- PCR biases can underrepresent GC-rich templates
- Amplification errors caused by polymerases will be represented and sequenced incorrectly
- Targets smaller than 500 bp are preferentially amplified by polymerases during PCR
Reagents:
Illumina Library prep and Array Kit Selector
Reviews:
Zhang X., Marjani S. L., Hu Z., Weissman S. M., Pan X. and Wu S. Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects. Cancer Res. 2016;76:1305-1312
Poulin J. F., Tasic B., Hjerling-Leffler J., Trimarchi J. M. and Awatramani R. Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci. 2016;19:1131-1141
Sun H. J., Chen J., Ni B., Yang X. and Wu Y. Z. Recent advances and current issues in single-cell sequencing of tumors. Cancer Lett. 2015;365:1-10
Grun D. and van Oudenaarden A. Design and Analysis of Single-Cell Sequencing Experiments. Cell. 2015;163:799-810
Navin N. E. Cancer genomics: one cell at a time. Genome Biol. 2014;15:452
Liang J., Cai W. and Sun Z. Single-Cell Sequencing Technologies: Current and Future. J Genet Genomics. 2014;41:513-528
References:
Takeuchi M., Yamaguchi S., Sakakibara Y., et al. Gene expression profiling of granule cells and Purkinje cells in the zebrafish cerebellum. J Comp Neurol. 2016;
Archer N., Walsh M. D., Shahrezaei V. and Hebenstreit D. Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways. Cell Syst. 2016;
Scialdone A., Natarajan K. N., Saraiva L. R., et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015;
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History: Quartz-Seq
Revision by sbrumpton on 2017-06-21 07:50:22 - Show/Hide
Whole-Transcript Amplification for Single Cells
The Quartz-Seq method optimizes whole-transcript amplification (WTA) of single cells (Sasagawa et al., 2013). In this method, an RT primer with a T7 promoter and PCR target is first added to the extracted mRNA. RT synthesizes first-strand cDNA, after which the RT primer is digested by exonuclease I. Next, a poly(A) tail is added to the 3' ends of first-strand cDNA, along with a poly(dT) primer containing a PCR target. After second-strand generation, a blocking primer is added to ensure PCR enrichment in sufficient quantity for sequencing. Deep sequencing allows for accurate, high-resolution representation of the whole transcriptome of a single cell.
Similar methods: CEL-Seq, Drop-seq, MARS-Seq, CytoSeq, inDrop, Hi-SCL
Advantages:- Single-tube reaction suitable for automation
- Digestion of RT primers by exonuclease I eliminates amplification of byproducts
- Short fragments and byproducts are suppressed during enrichment
Disadvantages:- PCR biases can underrepresent GC-rich templates
- Amplification errors caused by polymerases will be represented and sequenced incorrectly
- Targets smaller than 500 bp are preferentially amplified by polymerases during PCR
Reagents:Illumina Library prep and Array Kit SelectorReviews:Zhang X., Marjani S. L., Hu Z., Weissman S. M., Pan X. and Wu S. Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects. Cancer Res. 2016;76:1305-1312Poulin J. F., Tasic B., Hjerling-Leffler J., Trimarchi J. M. and Awatramani R. Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci. 2016;19:1131-1141Sun H. J., Chen J., Ni B., Yang X. and Wu Y. Z. Recent advances and current issues in single-cell sequencing of tumors. Cancer Lett. 2015;365:1-10Grun D. and van Oudenaarden A. Design and Analysis of Single-Cell Sequencing Experiments. Cell. 2015;163:799-810Navin N. E. Cancer genomics: one cell at a time. Genome Biol. 2014;15:452Liang J., Cai W. and Sun Z. Single-Cell Sequencing Technologies: Current and Future. J Genet Genomics. 2014;41:513-528References:Takeuchi M., Yamaguchi S., Sakakibara Y., et al. Gene expression profiling of granule cells and Purkinje cells in the zebrafish cerebellum. J Comp Neurol. 2016;Archer N., Walsh M. D., Shahrezaei V. and Hebenstreit D. Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways. Cell Syst. 2016;Scialdone A., Natarajan K. N., Saraiva L. R., et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015;