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AGBT19 Poster - Providing large scale single-cell RNA-seq in the Genomics Platform at the Broad Institute

Corey Nolet, Cole Walsh, Brian Granger, Tim Desmet, Tera Bowers, Niall Lennon

Broad Institute of MIT & Harvard

 

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique to study gene expression, cellular heterogeneity, and delineation of cell states. Interest in single-cell research continues to grow and gain momentum, which can be seen in projects such as the Human Cell Atlas.

 

Single-cell sample preparation has been around for almost a decade, however it has not been until recently that single-cell RNA-seq has reached the tipping point into truly high scale. Due to the availability of more methods of sample preparation, along with lowered sequencing costs, the demand is increasing for reproducible and high quality methods. In response, Broad Genomics has expanded our portfolio to include single-cell services that utilize automated workflows and integrated sample tracking to support 10X Genomics Chromium Single-Cell 3’single-cell, and a modified SMART-Seq2 mRNA library construction, sequencing, and analysis at scale. Throughout the development of SMART-Seq2 we had to deal with major design challenges, such as managing the minimal input concentrations, high viscosity master mixes, and low volumes at full scale automation. We accomplished this through using a variety of in-house created automation liquid classes, labware, and protocol designs.


Data delivery and analysis from single-cell sequencing is made available through our cloud based platform, Firecloud. Each cell receives a best practices QC and analysis workflow, mirroring the methods being made publically available via the Human Cell Atlas consortium, consisting of alignment with HISAT2, sequencing quality assessment with Picard tools, and determination of expression with RSEM. This data is then aggregated and run through a second workflow to visualize the results at the plate level allowing for troubleshooting of lab processes and identification of systematic biases.

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