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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Construction of a public CHO cell line transcript database using versatile bioinformatics analysis pipelines.

Oliver Rupp¹, Jennifer Becker², Karina Brinkrolf³

¹Center for Biotechnology, Bielefeld University, Bielefeld, Germany ; Cell Culture Technology, Bielefeld University, Bielefeld, Germany ; Bioinformatics and Systems Biology, Justus-Liebig-University, Giessen, Germany.

|January 16, 2014

Summary

Researchers developed new methods to assemble Chinese hamster ovary (CHO) cell transcripts, significantly expanding the available data for this key mammalian expression system used in biopharmaceutical production.

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A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

Published on: September 22, 2011

Area of Science:

Biotechnology
Genomics
Molecular Biology

Background:

Chinese hamster ovary (CHO) cells are the primary mammalian expression system for therapeutic protein production.
Limited public data on CHO cell transcriptomes hinders biotechnological process optimization.
Existing annotation systems (GenDBE, SAMS) are foundational but require expanded transcript data.

Purpose of the Study:

To develop a high-quality CHO cell transcript set from extensive sequencing data.
To establish robust pipelines for assembling and improving CHO cell transcripts.
To create a publicly accessible platform for CHO cell genome/transcriptome analysis.

Main Methods:

Constructed cDNA libraries from diverse CHO cell lines and culture conditions.
Employed Roche/454 and Illumina sequencing technologies.
Utilized de novo assembly (Trinity, Oases, CAP3) and reference-based assembly (TopHat/Cufflinks, GMAP, cuffmerge) pipelines.

Main Results:

Assembled 28,874 transcripts from 16,492 gene loci using a combined approach.
Identified a total of 65,561 transcripts for CHO cell lines.
Clustered transcripts into 17,598 distinct gene clusters based on sequence identity.

Conclusions:

The developed strategy significantly enhances the available CHO cell transcript data.
This expanded dataset is crucial for advancing research and optimizing biopharmaceutical production using CHO cells.
The established pipelines and data contribute to a more comprehensive CHO cell analysis platform.