Drop-Seq

Analysis of mRNA Transcripts from Individual Cells in Droplets

Drop-Seq analyzes mRNA transcripts from droplets of individual cells in a highly parallel fashion (Macosko et al., 2015). 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:One) 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

Advantages:

  • Analyzes sequences of single cells in a highly parallel manner
  • Unique molecular and cell barcodes enable cell- and gene-specific identification of mRNA strands
  • RT with template-switching PCR produces high-yield reads from single cells
  • Low cost: $0.07 per cell ($653 per 10,000 cells) and fast library preparation (10,000 cells per day)

Disadvantages:

  • Requires custom microfluidics device to perform droplet separation
  • Low gene-per-cell sensitivity compared to other scRNA-Seq methods (Ziegenhain et al., 2016)</li<
  • Limited to mRNA transcripts

Reagents:
Illumina Library prep and Array Kit Selector

Reviews:
Bowen J. R., Ferris M. T. and Suthar M. S. Systems biology: A tool for charting the antiviral landscape. Virus Res. 2016;
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;
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
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
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
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
Grun D. and van Oudenaarden A. Design and Analysis of Single-Cell Sequencing Experiments. Cell. 2015;163:799-810
Saadatpour A., Lai S., Guo G. and Yuan G. C. Single-Cell Analysis in Cancer Genomics. Trends Genet. 2015;31:576-586
Alizadeh A. A., Aranda V., Bardelli A., et al. Toward understanding and exploiting tumor heterogeneity. Nat Med. 2015;21:846-853

References:
Ziegenhain C., Parekh S., Vieth B., et al. Comparative analysis of single-cell RNA-sequencing methods. bioRxiv. 2016;