Drop-Seq is based on a strategy to rapidly analyze thousands of individual cells using microfluidics by encapsulating them in droplets for parallel analysis. Drop-seq enables highly parallel analysis of individual cells by RNA-seq. These nanoliter aqueous chambers have been used for many applications in microfluidic devices: nanofabrication, emulsions and foams, drug delivery, but also as tiny reaction chambers for PCR and reverse transcription.


Cells are the basic unit of biological structure and function, and their types and states vary greatly especially for complex tissues like the nervous system (brain cells). Studying the characterization of single-cell identity and function will accelerate biological discoveries that could potentially be used in cancer and virtually any subject that may have diversity in cell populations. Therefore, fast, scalable methods are needed to characterize complex tissues with many cell types and states. Drop-Seq uses droplets to divide cells into nanoliter-sized reaction chambers for the analysis of their mRNA transcripts, while using a molecular barcoding strategy to record the cell of origin of the transcripts. Using this technique, a scientist can simultaneously make 10,000 single-cell libraries per day while performing inexpensive and simple experiments. The method will therefore allow the creation of molecular maps of gene expression for known cell classes and new candidate cell subtypes.

Drop-Seq can use two types of beads: "simple" microparticles and hydrogel microparticles. Drop-seq utilizes microfluidics to its advantage: high throughput sequencing in a short time, as well as minimized consumption of expensive samples. Drop-seq falls into the following steps: (1) Prepare single-cell suspension from tissue; (2) Prepare barcoded primers (either on the surface, interior of microparticles); (3) Use a microfluidic device to individually co-encapsulate each cell with visibly barcoded microparticles in droplets; (4) Once isolated in droplets, cells are lysed to release their mRNAs, which are then hybridized with primers; (5) Break the droplets and generate STAMPs (single-cell transcriptomes attached to microparticles); (6) Amplify STAMP; (7) Sequencing and analysis: STAMP barcodes were used to infer the original cells of each transcript.


Specific Identification of mRNA High-throughput at Low Cost Single-Cell Insights Multiple Applications
Unique molecular and cell barcodes enables cell and gene specific identification of mRNA strands. High throughput sequencing in a short time; Minimized consumption of expensive samples. Analyze sequences of single-cells in a highly parallel manner. Understanding complex tissues, tumor heterogeneity and clonal evolution.

Project Workflow

Sample Preparation

1. Sample Preparation

Isolation of viable, single cells from a given sample

Library Preparation

2. Library Preparation

Preparation and purification of the cDNA library and analysis on the BioAnalyzer.


3. Sequencing

Illumina MiSeq; Illumina NextSeq

Data Analysis

4. Data Analysis

Visualize and preprocess results, and perform custom bioinformatics analysis.


Bioinformatics Analysis Pipeline

In-depth data analysis:

  • Estimation of sequencing depth and coverage
  • Differentially expressed gene analysis
  • SNP/InDel/CNV/SV calling
  • GO and KEGG enrichment analysis
  • Gene interaction network
  • Alternative pre-mRNA splicing
  • Detection of RNA editing, novel transcripts, and fusion genes

Sample Requirements

RNA amount: Total RNA ≥ 5 ug (without degradation or DNA contamination); RNA purity: OD260/280 = 1.8~2.2; OD260/230 ≥ 1.5; RNA quality: 28S:18S ≥ 1.5,RIN ≥ 7

Please make sure that the RNA is not significantly degraded.

Sample storage: RNA can be dissolved in ethanol or RNA-free ultra-pure water and stored at -80°C. RNA should avoid repeated freezing and thawing.

Shipping Method: When shipping RNA samples, the RNA sample is stored in a 1.5 mL Eppendorf tube, sealed with sealing film. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.

Deliverable: FastQ, BAM, coverage summary, QC report, custom bioinformatics analysis.


  1. Fan J, Slowikowski K, Zhang F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Experimental & Molecular Medicine. 2020 Sep;52(9).
  2. Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nature Reviews Genetics. 2015 Mar;16(3).
* For Research Use Only. Not for use in diagnostic procedures.

Research Areas
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