Reify’s video-capture-based screening technology proves itself with in vivo and in vitro phenotypic screens
The Sixth International Conference on Systems Biology at Harvard Medical School on October provided a venue for Reify’s Visible Discovery platform to show its mettle in two very different models: an in vivo analysis of cardiovascular structure and function in zebrafish and an in vitro analysis of transcription-associated gene dynamics. The posters for these two studies were presented by researchers at the Cardiovascular Research Center at Massachusetts General Hospital (MGH) and the Dana Farber Cancer Institute (DFCI).
CAMBRIDGE, Mass.—The Sixth International Conference on Systems Biology at Harvard Medical School on October provided a venue for Reify's Visible Discovery platform to show its mettle in two very different models: an in vivo analysis of cardiovascular structure and function in zebrafish and an in vitro analysis of transcription-associated gene dynamics. The posters for these two studies were presented by researchers at the Cardiovascular Research Center at Massachusetts General Hospital (MGH) and the Dana Farber Cancer Institute (DFCI).
Although the two models are very different, notes Reify Chief Technology Officer Arman Garakani, both posters focus on something that is very important to drug discovery companies' bottom lines: Higher throughput and simplification of otherwise time-consuming processes.
Even though viewing biological systems directly is one of the oldest ways to measure how such systems change when perturbed—such as by environmental factors or manipulations of genes—Garakani says some in the research community have been slow to embrace a system that uses video capture, even one that captures cell motion at high spatial and temporal resolution and applies unsupervised learning algorithms to characterize dynamic changes in morphology. He hopes the success of Visible Discovery at MGH and DFCI will help offset that.
"Our goal is to give researchers a valuable tool that will allow them to gather data that is highly complementary to what they are getting through other means, not redundant," Garakani says. "The interesting thing about it is that when you look at the two posters that showcased Visible Discovery recently, it's hard for a lot of people to believe that the exact same analysis algorithms were used for both experiments."
Drs. Jordan Shin and Calum A. MacRae of MGH produced the in vivo-related poster, "Machine Learning Approaches to High-Throughput In Vivo Analysis of Cardiovascular Structure and Function in Zebrafish," which measured dynamic cardiovascular function in living zebrafish embryos treated with drugs. Traditional quantification of cardiac function is a laborious and time-consuming process, Shin notes, but the MGH research was able to validate Reify's novel technology for providing high-throughput study of heart function in intact animals using conventional transmitted light microscopy.
For the in vitro poster, "Transcription-Associated Gene Dynamics," Drs. David Drubin and Pamela A. Silver used Visible Discovery to produce quantitative results from an assay that discovered how chromosomes move in relation to cellular nuclei, demonstrating Reify's capabilities for measuring small-scale, subcellular movement of chromosomes in a dynamic, high-content screen.
"We were able to assess the dynamics of chromosome movement in relation to the nucleus without having to go through the tedious process of telling the computer for each cell, 'Here is the chromosome, here is the nucleus, now start tracking them,'" notes Drubin. "As a result, we were able to extract data that is predictive of the output of more labor-intensive analyses. This will enable us to develop future high-throughput screens based on chromosome movement."
"There haven't really been good ways until now of automating kinetic analyses in primary biological studies," says Sean Walter, Reify's vice president of corporate development. "Analytics tend to be slow and burdensome and researchers end up tweaking images until they get just the right parameters and a signal that looks more or less OK. We feel that we have the ability to give researchers highly repeatable, highly precise measurements that get done pretty much right out of the box. The net effect is that they save reagents, they save time—and thus they save money."