OCT 17, 2013 7:00 AM PDT

Advancing Translational Cancer Research through Automated RNASeq Sample Preparation from Archival FFPE Tissue Specimens

Speaker

Abstract

RNA-Seq allows the simultaneous observation of gene expression levels, mutations in the coding sequences, splice variants and gene fusions, which are especially important in cancer studies. Archived FFPE collections, which are stored for decades, must be unlocked to discover clinically relevant biomarkers. With the advent of new molecular techniques, specifically targeting FFPE and advances in automation, it is foreseeable that faster, more cost-efficient and reliable tools will facilitate the discovery of clinically relevant biomarkers. This study describes automated RNA isolation from archival FFPE tissue specimens using Agencourt FormaPure Kit and RNA library construction using the Beckman Coulter Biomek FXP. The library preparation method is based on the ScriptSeq RNA sample preparation protocol (Complete Gold low input). The ScriptSeq method is a streamlined, time-saving method that proved amenable to automation with limited optimization required. This automated method is suitable for up to ninety-six library constructions in parallel. As part of this automated method development we assessed; the overall RNA quality of the FFPE sample cohort, the total RNA input amount, SPRI bead-based clean-up strategies and optimization of key preparation steps. Libraries prepped on the Biomek automated platform provide end-users with a more reliable way to generate libraries from precious clinical samples in a faster turnaround time.


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OCT 17, 2013 7:00 AM PDT

Advancing Translational Cancer Research through Automated RNASeq Sample Preparation from Archival FFPE Tissue Specimens


Specialty

Antibodies

Virology

Immunology

Dna Sequencing

Personalized Medicine

Clinical Diagnostics

Immunity

Cancer Diagnostics

Immunotherapy

Bioinformatics

Flow Cytometry

Immuno-Oncology

Gene Expression

Dna

Big Data

Geography

Europe100%

Registration Source

Website Visitors100%

Job Title

Student100%

Organization

Academic Institution100%


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