Roche Invests in Stratos Genomics
News Jun 27, 2014
Roche has announced that it has made a strategic investment in Stratos Genomics and entered into a research collaboration to support further development of their unique chemistry applied to single molecule sequencing of DNA fragments using protein nanopores.
During this collaboration, a focused research team at Roche will work with Stratos scientists to support the development of efficient, low-cost sample preparation methods for DNA Xpandomers™ and also improve sequencing performance, by leveraging Roche’s expertise in protein design, polymerase mutagenesis, modified nucleotide chemistries and rare reagent manufacturing.
Stratos Genomics has pioneered a “Sequencing By Expansion™” (SBX™) method, which is a single molecule detection process that converts DNA into a larger surrogate molecule, called an Xpandomer™. These Xpandomer™ molecules, 10-100 times longer than the original DNA, pass through a nanopore which has a detector to read out the signal.
A polymerase can be used to synthesize the Xpandomers™ from a DNA template by incorporating customized expandable nucleotides and increasing the surrogate molecule through a rapid chemical reaction. SBX’s signal to noise advantage has the potential to enable accurate, high throughput sequencing on reduced cost nanopore systems.
“The recent advancements of the SBX method for detection of single molecule reads using protein nanopore detectors further demonstrates the possibility of using single molecule platforms for whole-genome sequencing,” said Dan Zabrowski, Head of the Roche Sequencing Unit. “The goal of our research collaboration with Stratos Genomics and the recent acquisition of Genia Technologies is to capitalize on the promise of nanopore sequencing and put Roche on a path to introduce a potentially disruptive technology to the sequencing market.”
“The investment by Roche will support our research team in advancing our chemistry for nanopore sequencing. We look forward to collaborating with Roche and bringing our technology to the sequencing market,” said Allan Stephan, CEO and Chairman of the Board at Stratos Genomics.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.