10 X Genomics Single-cell Transcriptome Sequencing

Next-generation Sequencing

Service Overview

Single-cell transcriptome sequencing is a novel sequencing strategy that reveals gene expression at single-cell level. Single-cell transcriptome sequencing enables distinguish of different cell populations from tissue samples and studies on single-cell behaviour at molecular level. 10X Genomics enables single cell capturing and individual library construction with help of microfluidics, oil droplets of nanoliters and up to millions of different barcodes. Transcriptome information of 500 to 10,000 single cells can be isolated within one run. Accompany with high-throughput sequencing, transcriptome study can be extended to single cell level, which makes it a perfect technique to reveal single cell phenotyping and differential expression analysis between cell populations. It has been widely applied in various biomedical arena including diseases, immunology, cancer research ,etc.

10 X GENOMICS Workflow

Gel Bead-in-Emulsions

Single-cell sequencing platform consist of single cell separation, microfluidics-based amplification and data analysis, which overcomes the difficulties: 1) single cell separation and 2) Low quantity RNA reverse transcription.

Results Demo

Cell differentiation tracking

Marker gene identification

Cell type identification

tSNE clustering on cells


1How to identify the cell type of a cluster?
1. Based on marker gene: For cell types that have been extensively studied, marker genes that could represent cell type features can be found in literatures. For example, T cells are marked as CD3+/CD4+ and B cells are marked as CD19+/CD20+.

1) Loupe Cell Browser or similar software is able to define cell type of a cluster based on corresponding expression of marker genes.

2) Cell-Ranger and monocle2 or similar single-cell analyzing software are able to identify differentially expressed genes between clusters. Cell type can be identified by matching marker genes with DEG list.

2. Based on functional pathways of genes: For cell types without typical marker genes or clusters without known marker genes, DEGs between clusters can be identified and annotated with KEGG and GO database. Function of the clusters can be predicted.
2What are the differences between the software mentioned in report: Cell-Ranger, Monocle2 or other analyzing tools?
Each software is relatively independent. However, some analysis are overlapped between each other, e.g. cell clustering, differential genes identification, functional analysis, etc. Cell-Range is the official analyzing software provided by 10X. Its output is included in conclusive report (html ver. ). These analysis is less flexible for customized analysis. monocl2 is one of the most commonly used software to analyze single-cell data, which is developed by Cole Trapnell from Washington University (Developer of TopHat and Cufflinks). It covers most of single-cell analysis and highly flexible-for parameter setting ,etc. SC3 software is also available for customized analysis of specific research goal.