ISMB 2016 - Industry Posters

Authors

Paul Ryvkin, 10x Genomics, Pleasanton, California, United States
Grace X.Y. Zheng, 10x Genomics, Pleasanton, California, United States
Tarjei Mikkelsen, 10x Genomics, Pleasanton, California, United States
Jessica Terry, 10x Genomics, Pleasanton, California, United States
Phil Belgrader, 10x Genomics, Pleasanton, California, United States
Ryan Wilson, 10x Genomics, Pleasanton, California, United States
Jeff Mellen, 10x Genomics, Pleasanton, California, United States
Tobias Wheeler, 10x Genomics, Pleasanton, California, United States
Zachary Bent, 10x Genomics, Pleasanton, California, United States
Geoff McDermott, 10x Genomics, Pleasanton, California, United States
Solongo Ziraldo, 10x Genomics, Pleasanton, California, United States
Luz Montesclaros, 10x Genomics, Pleasanton, California, United States
Joe Shuga, 10x Genomics, Pleasanton, California, United States
Stefanie Nishimura, 10x Genomics, Pleasanton, California, United States
Michael Schnall-Levin, 10x Genomics, Pleasanton, California, United States
Ben Hindson, 10x Genomics, Pleasanton, California, United States

Presentation Overview

Characterizing the transcriptome of numerous individual cells is fundamental to understanding complex biological systems such as the immune system, the nervous system and heterogenous tumors. However, existing single cell RNA-sequencing methods often require custom equipment or laborious experimental protocols that limit throughput. 10x Genomics has developed a fully integrated droplet-based system within a bench top instrument that enables 3’ mRNA counting from up to forty-eight thousand single cells per run. Here, we demonstrate the application of the system to profile heterogeneous samples, including tens of thousands of peripheral blood mononuclear cells from human donors.

The core of the system is a picoliter-sized droplet that encapsulates a single cell and a single gel bead containing barcoded oligonucleotides. The system captures 50% of loaded cells with a low doublet rate of <1%, making it suitable for profiling of rare and precious cell populations. Barcoded reverse transcription takes place inside each droplet and sequencing libraries are finished in a single bulk reaction. RNA capture efficiency is comparable to leading academic droplet systems, detecting thousands of genes per cell.

The 10x Chromium Single Cell system is an end-to-end solution, from reagent kits to software, that empowers users to analyze, visualize, and interact with gene expression data from tens of thousands of single cells. Our analysis pipeline, Cell Ranger, receives BCL data from an Illumina sequencer and produces per-cell gene expression matrices, as well as an automated cell clustering analysis. Additionally, it includes an R package to enable downstream analysis. We believe that our system will enable widespread adoption of high throughput single cell mRNA analysis.

Authors

David M. Tanenbaum, MITRE, United States
Eldred A. Ribeiro, MITRE, United States
Wenling E. Chang, MITRE, United States
Peter Gutgarts, MITRE, United States
Ari Abrams-Kudan, MITRE, United States
William Kim, MITRE, United States
Lisa Tutterow, MITRE, United States
Erin Williams, MITRE, United States

The National Institutes of Health (NIH) invested more than $30 billion in biomedical research during fiscal year 2015, for which Digital Objects such as data, metadata, software, or workflows are among the highest value creations. Effective reuse of Digital Objects permits the greatest scientific and societal return on the investments made by NIH and other funders of biomedical research. The traditional funding paradigm for Digital Objects relies on locally provisioned information technology resources, but use of high-volume data generation and analytic technologies have strained this model. Access to scalable storage and compute to support research is essential for the success of NIH-funded research programs in the face of ever bigger and richer data and informatics.

NIH is proposing instantiation of a cloud-based electronic environment (Commons) where researchers can store, share, and make computations on digital data using sharable software, workflows, metadata, and other Digital Objects. The Commons should support the needs of the biomedical research community at reduced cost by leveraging advances in cloud computing and storage. The Commons will be supported, in part, by NIH-provided resources called Commons Credits.

MITRE is conducting a three-year pilot for NIH to evaluate the use of the Commons Credits Model for obtaining cloud services to perform biomedical computational research. Interested research investigators are encouraged to apply during open Credits Request Cycles, starting in fall of 2016. This work is sponsored by the Office of the NIH Associate Director for Data Science (ADDS), and is part of the Big Data To Knowledge (BD2K) Initiative.