Single-Cell RNA-Seq

Single-cell RNA sequencing (scRNA-seq) is a next-generation sequencing (NGS)-based method to amplify and sequence the whole transcriptome of a single cell. It is becoming a powerful tool and has been applied to research related to stem cell differentiation, embryogenesis, whole tissue analysis, and even tumors.


Single-cell RNA-seq is a method that enables simple and comprehensive access to the transcriptome of thousands of single cells. The principle is to separate multicellular organisms into single cells, and then the RNA of single cells is efficiently amplified using mature whole genome amplification (WGA) kits before being subjected to high-throughput sequencing. Single-cell RNA-seq allows a comparison of the transcriptome of individual cells and reveals the similarities and differences of the transcriptome in cell populations. scRNA-seq involves single-cell isolation, cell lysis, reverse transcription of RNA to cDNA, optional polyA RNA selection, followed by DNA sequencing. Unlike the standard bulk RNA sequencing, scRNA-seq provides unparalleled details of different cells in a sample, allowing researchers to move from average data for large numbers of tissues to individualized cellular expression data.

From basic research to clinical applications, there are many applications for scRNA-seq. It provides transcriptome information about single cells, enabling researchers to analyze the unique gene expression patterns of each cell, understand cellular heterogeneity, and explore how different cells promote disease progression and clinical response. Currently, this technique has been changing our understanding of basic biological processes, such as development, immunity or cell-related pathology.


Any Species Single-Cell Insights High Resolution Multiple Applications
This method can be applied to any species, from microorganisms to humans. Quantitative analysis down to single-cell levels for input samples. Discovery of more cellular differences based on high resolution analysis. 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

Total RNA or poly-A RNA; Smart-seq2


3. Sequencing

Illumina HiSeq; PE50/75/100/150; >10G clean data

Data Analysis

4. Data Analysis

Provide customized bioinformatics analyses and services for users.

Bioinformatics Analysis Pipeline

Bioinformatics Analysis Pipeline

Bioinformatics analysis:

  • Raw data QC and clean-up
  • 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

Deliverable: FastQ, raw data, coverage summary, QC report, experiment results, custom bioinformatics analysis.


  1. Picelli S. Single-cell RNA-sequencing: The future of genome biology is now. RNA Biol, 2017,5, 14(5): 637-650.
  2. Haque A, Engel J, Teichmann S A, et al. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med, 2017, 18, 9(1): 75.
  3. Hedlund E, Deng Q. Single-cell RNA sequencing: Technical advancements and biological applications. Mol Aspects Med, 2018, 4, 59: 36-46.
  4. Angela R W, Norma F N, Tomer K, et al. Quantitative assessment of single-cell RNA-sequencing methods. Nature Methods, 2014, 1, 11(1): 41–46.
* For Research Use Only. Not for use in diagnostic procedures.


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