A Faster, Less Expensive Way To Analyze Gene Activity
News Mar 03, 2015
A team of Yale researchers has developed a simple method that could significantly reduce the time and cost of probing gene expression on a large scale. The findings were published March 2 in the journal Nature Methods.
The team, led by Dr. Abhijit Patel, assistant professor of therapeutic radiology at the Yale School of Medicine, created a tool that takes advantage of new high-throughput DNA sequencing technologies to make it easier to simultaneously measure gene activity in large numbers of cells or tissues. While DNA is considered the blueprint of life, knowledge of which genes are activated or de-activated under different conditions is fundamental to our understanding of biology and disease.
Gene expression profiling is commonly used in clinical tests. For example, in patients with breast cancer, gene expression is often measured within tumor specimens to predict the likelihood of recurrence and to determine whether chemotherapy would be beneficial. With additional validation, Patel said, this high-throughput approach could be used to measure gene expression from many patients’ tumors simultaneously. Ultimately, the method could reduce the cost of such tests, making them more broadly accessible.
“To make meaningful conclusions about complex gene expression patterns, it is usually necessary to perform statistical analysis on large numbers of clinical or experimental samples. We believe that this new technology will facilitate such work,” said Patel, senior author on the paper. “We are excited because this method makes large-scale RNA profiling studies more practical and accessible to most researchers and clinical labs.”
Research was supported by the Yale Cancer Center, The Honorable Tina Brozman Foundation, a Rudolph Anderson Fellowship, a Leslie Warner Fellowship, and Clinical and Translational Science Award grants UL1 TR000142 and KL2 TR000140 from the National Center for Advancing Translational Sciences, a component of the US National Institutes of Health.
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.