Drop-Seq

Drop-Seq

Method Category: Transcriptome > RNA Low-Level Detection

Description: Drop-Seq analyzes mRNA transcripts from droplets of individual cells in a highly parallel fashion. This single-cell sequencing method uses a microfluidic device to compartmentalize droplets containing a single cell, lysis buffer, and a microbead covered with barcoded primers. Each primer contains: 1) a 30 bp oligo(dT) sequence to bind mRNAs; 2) an 8 bp molecular index to identify each mRNA strand uniquely; 3) a 12 bp barcode unique to each cell and 4) a universal sequence identical across all beads. Following compartmentalization, cells in the droplets are lysed and the released mRNA hybridizes to the oligo(dT) tract of the primer beads. Next, all droplets are pooled and broken to release the beads within. After the beads are isolated, they are reverse-transcribed with template switching. This generates the first cDNA strand with a PCR primer sequence in place of the universal sequence. cDNAs are PCR-amplified, and sequencing adapters are added using the Nextera XT Library Preparation Kit. The barcoded mRNA samples are ready for sequencing.

Similar methods: CEL-Seq, Quartz-Seq, MARS-Seq, CytoSeq, inDrop, Hi-SCL.

Pros:

• Analyze sequences of single-cells in a highly parallel manner
• Unique molecular and cell barcodes enables cell and gene specific identification of mRNA strands
• Reverse transcription with template-switching PCR produce high-yield reads from single cells
• Low cost - $0.07 per cell ($653 per 10,000 cells) and fast library prep (10,000 cells per day)

Cons:

• Requires custom microfluidics device to perform droplet separation
• Low gene-per-cell sensitivity compared to other scRNA-Seq methods¹
• Limited to mRNA transcripts

• Drop-Seq: Macosko E. Z., Basu A., Satija R., Nemesh J., Shekhar K., et al. (2015) Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161: 1202-1214
• 1. Ziegenhain C., Parekh S., Vieth B., Smets M., Leonhardt H., et al. (2016) Comparative analysis of single-cell RNA-sequencing methods. bioRxiv

Related Content:

1. Library Preparation
2. Gene Expression Transcriptome Analysis

Related Publications:

1. Bowen J. R., Ferris M. T. and Suthar M. S. Systems biology: A tool for charting the antiviral landscape. Virus Res. 2016;
2. Chaitankar V., Karakulah G., Ratnapriya R., Giuste F. O., Brooks M. J. and Swaroop A. Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research. Prog Retin Eye Res. 2016;
3. Zhao Q.-Y., Gratten J., Restuadi R. and Li X. Mapping and differential expression analysis from short-read RNA-Seq data in model organisms. Quantitative Biology. 2016;4:22-35
4. Conesa A., Madrigal P., Tarazona S., Gomez-Cabrero D., Cervera A., et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13
5. Friedensohn S., Khan T. A. and Reddy S. T. Advanced Methodologies in High-Throughput Sequencing of Immune Repertoires. Trends in Biotechnology. 2017;35:203-214
6. 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
7. Grun D. and van Oudenaarden A. Design and Analysis of Single-Cell Sequencing Experiments. Cell. 2015;163:799-810
8. Saadatpour A., Lai S., Guo G. and Yuan G. C. Single-Cell Analysis in Cancer Genomics. Trends Genet. 2015;31:576-586
9. Alizadeh A. A., Aranda V., Bardelli A., et al. Toward understanding and exploiting tumor heterogeneity. Nat Med. 2015;21:846-853
10. Ziegenhain C., Parekh S., Vieth B., et al. Comparative analysis of single-cell RNA-sequencing methods. bioRxiv. 2016;