Research Subject

Single Cell Transcriptome Analysis Pipeline

Piero Carninci

RIKEN Center for Life Science Technologies
Division of Genomic Technologies
Director

〒230-0045 神奈川県横浜市鶴見区末広町1-7-22
TEL: MAIL:Carninci@riken.jp

Cells in a given population exhibit heterogeneous (mixed) gene expression patterns, suggesting that no single cells are equal. Expression profiling of individual cells is therefore essential for the understanding of gene regulation and can detect aberrant cell types in a seemingly healthy population.

We established a Single Cell Transcriptome Anaysis Pipeline, which multiple laborious steps into one simple workflow – further incorporating single cell experiments and expression data with a centralized database for easy deposition and dissemination of single cell expression data.
The pipeline relies on the microfluidics Fluidigm C1™ system to capture single cells, followed by General Electrics’ (GE) InCell6000 imaging system for quantification of cellular features (e.g. GFP expression levels). Once the cells are captured, the pipeline executes either SMARTer® RNA-seq protocol or C1-CAGE, a RNA expression profiling method developed by us.

Sequencing data analysis is key for Next Generation Sequencing (NGS) analysis in general. Therefore we developed data analysis platform especially for CAGE analysis, which consists of simple three system; data integration, sequence data visualization (called ZENBU), and sequencer data processing workflow (called MOIRAI).
For the data integration, the data need to be integrated from wide variety of different type of data, which includes cell image source code (git hub) and sequencing data.
In ZENBU, we developed a genome browser in order to visualize sequencing data. The information contains gene names, transcript models etc. coupled with expression levels in a single cell. Also it visualizes the sequence reads as the red bars.
MOIRAI has a graphical interface allowing wet-lab researchers to create, modify and run analysis workflows. Embedded within the workflows are graphical quality control indicators allowing users assess data quality and to quickly spot potential problems.

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Team Member