Massively Parallel Single Cell Transcriptomics

The over-arching goal of DOEs genomics program is to understand biological systems with such precision that we can eventually engineer biological systems to help solve the worlds energy problems. All cell populations have some measure of heterogeneity, but conventional bulk expression analysis obscures such complexity. Single cell transcriptomics has the potential to unlock the complexity inherent to cell populations. However, conventional techniques for cell isolation are very low-throughput. The goal of this DOE-SBIR grant proposal is to develop GigaGens patent-pending GigaLink microfluidic technology into a novel method for massively parallel single cell expression analysis. GigaGens technology will enable genomics researchers to characterize gene expression with more biological precision than has ever been possible. GigaGen has raised $1 million in SBIR and venture funds to commercialize a technology for analysis of mouse and human T cells. In this proposal, we outline a plan to adapt our current technology for applications of interest to DOE. The modified technology will use advanced microfluidics to isolate single cells into millions of aqueous-in-oil picoliter reactors, fuse gene products with DNA barcode tags, and then sequence barcoded gene products by next-generation sequencing. The barcodes enable trace back of each gene product to single cells. The team is led by Dr. David Johnson, an expert in single cell genomics and former ENCODE Project Director at the Stanford Human Genome Center. GigaGens team boasts several PhDs with expertise in genomics, microfluidics, colloidal chemistry, bioinformatics, and molecular biology. In Phase I we propose to validate a technology for massively parallel single cell expression analysis in bacterial cells. In our T cell analysis current system, multiple genetic loci are amplified and linked in single cell emulsions, emulsions are reversed and pooled, and then the nucleic acid complexes are sequenced en masse with next-gen platforms. In Phase I, we propose two barcoding methods to trace transcripts back to single cell sources. The Phase I effort will form a basis for Phase II studies that will scale the technology to the full transcriptome and for analysis of different cell types, such as yeast and algae. The most important application for our technology is in the development of biofuels through engineering of (i) microorganisms that convert solar energy to carbon energy; and (ii) microorganisms that convert cellulosic energy to biofuels. Our tools will enable researchers to quantify thousands of genes in thousands of single cells in parallel, which will help to monitor, diagnose, and engineer single-cell systems with more precision than has ever before been possible.