Maverix Wins $150,000 STTR Award
News Feb 12, 2015
Maverix Biomics, Inc. has announced the receipt of a National Institutes of Health $150,000 Small Business Technology Transfer (STTR) Phase I Award, funded by the National Institute on Drug Abuse, to improve the detection of modifications in RNA transcripts.
Maverix is collaborating with the University of Rochester to develop a high-throughput, robust analytic kit that will enable researchers to detect multiple types of RNA modifications in human cells to accelerate the study of neurological disease.
RNA modification is a key type of cellular processing event that plays an important but incompletely understood role in the development of the nervous system and is known to be involved in a number of neurological diseases.
In contrast with well-studied DNA methylation modification patterns, which are central in the field of epigenetics (e.g., 5-methyl cytosine found in “CpG islands”), there are a very large number of distinct RNA modifications which existing DNA methylation mapping methods do not detect.
Currently, researchers must rely on low-throughput methods such as mass spectrometry or specialized biochemical methods to observe these small but numerous molecular decorations. Furthermore, custom computational analytics are needed to quantitatively determine RNA modifications with high throughput sequencing-based methods, making the barrier to rapid RNA modification detection prohibitively high for typical researchers.
The STTR Phase I Award will help fund development of a wet-lab/dry-lab kit in collaboration with Eric Phizicky at the University of Rochester, which should significantly advance the knowledge of RNA modifications and its many effects on gene regulation and human health.
The final product will include all required molecular biology reagents, a set of simple and robust experimental manipulations for facile analysis of modifications, and provide cloud-based analysis software through the Maverix Analytic Platform, which requires no bioinformatics skill or computing hardware.
“In order to push forward a broad-based understanding of the regulatory role(s) of RNA modifications, we believe it is essential to provide simplified, standardized methods, which include both innovative molecular detection chemistries, as well as straight-forward data analytics so any RNA sample can be scrutinized for new biological discoveries,” Said Todd Lowe, Chief Scientist for Maverix Biomics. “High throughput sequencing has become commonplace thanks to high-quality sample preparation kits, and we believe we can advance the state of the art in RNA modification research similarly with this project."
“We are excited to be working together with the people at Maverix on developing this new technology - since the project will effectively combine our biochemical expertise with the computational expertise at Maverix,” said Phizicky, a renowned biochemical expert in the RNA modification research field.
“By engaging in scientific research and development through grants and collaborations, Maverix is enabling researchers to better understand the complex RNA modifications that may have a critical impact on biological or disease function investigations,” said Dave Mandelkern, Maverix president and co-founder.
The STTR program, coordinated by the US Small Business Administration, is a highly competitive program that reserves a percentage of federal R&D funding for awards to small businesses and Unites States nonprofit research institutions. The program’s mission is to support scientific “excellence and technological innovation through the investment of Federal research funds in critical American priorities to build a strong national economy.
The unique feature of the STTR program is the requirement for the small business to formally collaborate with a research institution in Phase I and Phase II. STTR's most important role is to bridge the gap between performance of basic science and commercialization of resulting innovations.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.