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.
Overview
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.
Features
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
1. Sample Preparation
Isolation of viable, single cells from a given sample
2. Library Preparation
Total RNA or poly-A RNA; Smart-seq2
3. Sequencing
Illumina HiSeq; PE50/75/100/150; >10G clean data
4. Data Analysis
Provide customized bioinformatics analyses and services for users.
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
Live cells: 1-1000 intact cells from all vertebrate species. 96-well plates of live cells in suspension are recommended.
Picelli S. Single-cell RNA-sequencing: The future of genome biology is now. RNA Biol, 2017,5, 14(5): 637-650.
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.
Hedlund E, Deng Q. Single-cell RNA sequencing: Technical advancements and biological applications. Mol Aspects Med, 2018, 4, 59: 36-46.
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.
