Beckman Coulter Life Sciences, New England Biolabs Partner
News Oct 22, 2014
Beckman Coulter Life Sciences, through a partnership with New England Biolabs®, Inc., offers automated methods to improve processes and throughput in next generation sequencing (NGS) sample preparation. Under the agreement, Beckman Coulter will use its extensive experience in automated NGS sample prep to develop, distribute and support automation for NEB’s NEBNext® sample preparation reagent kits. NEB will provide technical expertise on the reagents, chemistry and protocols.
Fiona Stewart, Product Marketing Manager for Next Generation Sequencing at NEB said: “We are delighted to have formed this productive partnership with Beckman Coulter Life Sciences. With fast, streamlined workflows requiring fewer components and fewer steps, the NEBNext kits are ideally suited for automation. In combination with Beckman’s trusted automation solutions, NEBNext reagents enable robust performance, even with low input amounts and challenging samples.”
Optimized methods for the NEBNext kits are built on Beckman Coulter’s proven Biomek liquid handling platforms, and each solution includes a unique group of Biomek methods to address a specific NEBNext kit protocol. To improve overall workflows, methods are also included that automate Beckman Coulter’s AMPure XP kit for DNA purification, the SPRIselect kit for high throughput DNA size selection, and the qPCR setup and normalization processes.
“Beckman Coulter’s focus on providing our NGS customers a top-notch portfolio of automated sample prep solutions makes a partnership with NEB, a world leading provider of quality reagent systems for molecular biology research, a natural fit,” said Alisa Jackson, Senior Marketing Manager, Automated Genomics Solutions at Beckman. “Our joint collaborations with scientific researchers have already led to the development of high-quality methods demonstrated to improve efficiency, throughput and results for some of the most challenging sample inputs.”
The first collection of automated NEBNext methods were developed on the NGS configurations of the Biomek 4000 and the Biomek FXP Dual Arm Multi 96 and Span 8 platforms in collaboration with scientists from several institutions, including the European Molecular Biology Laboratory (EMBL), and the Genomics and Molecular Biology Shared Resource (GMBSR) at the Geisel School of Medicine at Dartmouth and the Norris Cotton Cancer Center. The methods create up to 96 sequence-ready libraries that generate quality results on Illumina® and Ion Torrent™ sequencing platform.
“We currently provide NGS services for over 400 scientists within EMBL and its European network. They expect fast delivery and high-quality results. Protocols must be robust to address small input amounts or difficult sample types,” said Dr. Jürgen Zimmermann, Senior Engineer Automation at GeneCore, EMBL based in Heidelberg, Germany. “We rely on automated solutions developed in collaboration with leading companies like Beckman and NEB to produce high-quality DNA and RNA libraries for our customers.”
“The fully automated methods we’ve co-developed will result in a substantial increase in our productivity with consistent quality,” said Dr. Vladimir Benes, Head of GeneCore. “As well, our small team gains time to focus on difficult samples and establish new methods.”
“By automating the NEBNext Library Preparation method on the Biomek 4000, we were able to generate more reproducible libraries compared to those prepared manually, our labour costs were reduced and we were able to offer a faster turnaround time to customers performing NGS projects through our core facility” said Joanna Hamilton, PhD, Co-Director of the GMBSR, Geisel School of Medicine at Dartmouth and Dartmouth-Hitchcock Norris Cotton Cancer Center.
Methods available today are NEBNext Ultra™ Directional RNA, NEBNext Ultra DNA (including for ChIP-Seq) for Illumina NGS and NEBNext Fast DNA Fragmentation & Library Prep for Ion Torrent. Other methods, including for NEBNext ribosomal RNA depletion and the NEBNext Small RNA reagent kits, are expected to follow later in the year.
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.