The study of gene expression is fundamental to our understanding of how cells function. We propose to develop a highly sensitive and precise method to measure global gene expression in single cells. Our novel method is simple, yet is capable of accurately counting individual transcripts across all genes expressed within a single cell. The measurements obtained are absolute numbers of gene transcripts, and represents a significant improvement over modern gene expression techniques which typically only provide relative measurements. Our method is based on the concept of """"""""stochastic labeling"""""""" that we recently published where we validated the novel approach by randomly labeling every single copy of fragments of genomic DNA in a sample with a set of molecular barcodes. Once labeled, the original DNA fragments were amplified with PCR and detected by massively parallel sequencing. Counting the number of different barcodes reveals the number of original copies of that DNA fragment, easily distinguishing the plurality of copies of a fragment of identical DNA sequence from additional clones of itself created by PCR replication. We successfully transform the challenging task of counting identical copies of single DNA molecules into the simple process of identifying the number of different barcodes present on identical sequences. Our current objective for phase I is to develop the technique into an application for single cell gene expression analysis where quantitative measurements are especially difficult due to the small amount of starting material present. Another significant challenge in single cell analysis is that the high degree of DNA amplification required creates biases in the gene abundance representation. We circumvent the effects of amplitude distortions by barcoding molecules before any amplification steps, and counting barcodes instead of sequence reads to determine the number of original molecules present. For phase II, we will develop a commercial assay kit containing reagents and protocols to carry out the technique. Our company is comprised of an exceptionally strong team of successful innovators and scientists/engineers with significant achievements in both research and product commercialization settings. Members include an established investigator in the area of single cell/single molecule research, and a key inventor and developer of the most widely used gene expression platform over the past decade. Additionally, we have established collaborations with scientists at the Stanford University Genome Technology Center, giving us access to instruments and expertise available at this world class research facility.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1-IMST-K (14))
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Schloss, Jeffery
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Cellular Research, Inc.
Palo Alto
United States
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Fu, Glenn K; Wilhelmy, Julie; Stern, David et al. (2014) Digital encoding of cellular mRNAs enabling precise and absolute gene expression measurement by single-molecule counting. Anal Chem 86:2867-70
Fu, Glenn K; Xu, Weihong; Wilhelmy, Julie et al. (2014) Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations. Proc Natl Acad Sci U S A 111:1891-6