Under the title of “High Content – Less Mess, More Mesh”, GEN (Genetic, Engeneering & Biotechnology news) has published this month an article about the advances that have made it possible to generate vaste datasets with decreasing costs and the need for comparing results and using informations collected from experiments on different cell types with different imaging systems.
“The biggest thing that needs to happen in the next few years is a more extensive interoperability of the information that is obtained from high-content screening and analysis,” insists Robert F. Murphy, Ph.D., the Ray and Stephanie Lane Professor of Computational Biology and professor of biological sciences, biomedical engineering, and machine learning at Carnegie Mellon University.
When descriptive features are used in the analysis of microscopy images, one of the challenges is to compare and integrate data across experiments, particularly when specific features captured using different experimental platforms may have different meanings for different investigators.
“One potential way to address this is to make the features interpretable,” suggests Dr. Murphy. “But that can be impossible if people use different microscopes, conditions, and objectives—and often different cells.”
Efforts to develop generative models of cellular organization and protein distribution from fluorescence microscopy images have been undertaken in Dr. Murphy’s laboratory. These efforts have led to the development of an open-source platform, the CellOrganizer project.
More details on GEN !