MARS-Seq
Massively Parallel RNA Single-Cell Sequencing Framework
MARS-Seq profiles the transcriptional dynamics of single cells in an automated and massively parallel workflow with high resolution (Jaitin et al., 2014). MARS-Seq can be used with in vivo samples containing a wide variety of different cell subpopulations.
Single cells are first isolated into individual wells using FACS. Each cell is lysed, and the 3′ ends of mRNAs are annealed to unique molecular identifiers containing a T7 promoter. The mRNA is reverse-transcribed to generate the first cDNA strand and treated with exonuclease I to remove leftover RT primers. Next, the cellular lysates are pooled together and converted to double-stranded cDNA. The DNA strands are transcribed to RNA and treated with DNase to remove leftover DNA templates in the mixture. The RNA strands are fragmented and annealed to sequencing adapters, followed by RT to generate barcoded cDNA libraries that are ready for sequencing.
Similar methods: CEL-Seq, Quartz-Seq, Drop-seq, CytoSeq, inDrop
Advantages:
- High-throughput transcriptional profiling of single cells
- in vivo sampling of thousands of cells
- Three barcode levels (molecular, cellular, and plate-level tags) facilitate robust multiplexing capabilities
- Processes 100 to 1000 single cells
- Pooling all single cells into 1 flow cell reduces the cost to less than 50 cents per cell (Jaitin et al., 2015)
Disadvantages:
- 3′ bias can occur during the purification step
- Fragmentation step eliminates strand-specific information (Hrdlickova et al., 2016)
Reagents:
Illumina Library prep and Array Kit Selector
Reviews:
Hrdlickova R., Toloue M. and Tian B. RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA. 2016;
Wen L. and Tang F. Single-cell sequencing in stem cell biology. Genome Biol. 2016;17:71
Jaitin D. A., Keren-Shaul H., Elefant N. and Amit I. Each cell counts: Hematopoiesis and immunity research in the era of single cell genomics. Semin Immunol. 2015;27:67-71
References:
Paul F., Arkin Y., Giladi A., et al. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. Cell. 2015;163:1663-1677
Baruch K., Deczkowska A., Rosenzweig N., et al. PD-1 immune checkpoint blockade reduces pathology and improves memory in mouse models of Alzheimer’s disease. Nat Med. 2016;22:135-137
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History: MARS-Seq
Revision by sbrumpton on 2017-06-21 07:50:22 - Show/Hide
Massively Parallel RNA Single-Cell Sequencing Framework
MARS-Seq profiles the transcriptional dynamics of single cells in an automated and massively parallel workflow with high resolution (Jaitin et al., 2014). MARS-Seq can be used with in vivo samples containing a wide variety of different cell subpopulations.
Single cells are first isolated into individual wells using FACS. Each cell is lysed, and the 3' ends of mRNAs are annealed to unique molecular identifiers containing a T7 promoter. The mRNA is reverse-transcribed to generate the first cDNA strand and treated with exonuclease I to remove leftover RT primers. Next, the cellular lysates are pooled together and converted to double-stranded cDNA. The DNA strands are transcribed to RNA and treated with DNase to remove leftover DNA templates in the mixture. The RNA strands are fragmented and annealed to sequencing adapters, followed by RT to generate barcoded cDNA libraries that are ready for sequencing.
Similar methods: CEL-Seq, Quartz-Seq, Drop-seq, CytoSeq, inDrop
Advantages:- High-throughput transcriptional profiling of single cells
- in vivo sampling of thousands of cells
- Three barcode levels (molecular, cellular, and plate-level tags) facilitate robust multiplexing capabilities
- Processes 100 to 1000 single cells
- Pooling all single cells into 1 flow cell reduces the cost to less than 50 cents per cell (Jaitin et al., 2015)
Disadvantages:- 3' bias can occur during the purification step
- Fragmentation step eliminates strand-specific information (Hrdlickova et al., 2016)
Reagents:Illumina Library prep and Array Kit SelectorReviews:Hrdlickova R., Toloue M. and Tian B. RNA-Seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA. 2016;Wen L. and Tang F. Single-cell sequencing in stem cell biology. Genome Biol. 2016;17:71Jaitin D. A., Keren-Shaul H., Elefant N. and Amit I. Each cell counts: Hematopoiesis and immunity research in the era of single cell genomics. Semin Immunol. 2015;27:67-71References:Paul F., Arkin Y., Giladi A., et al. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. Cell. 2015;163:1663-1677Baruch K., Deczkowska A., Rosenzweig N., et al. PD-1 immune checkpoint blockade reduces pathology and improves memory in mouse models of Alzheimer's disease. Nat Med. 2016;22:135-137