Being able to look at how genes are expressed in each cell type within tissue may help scientists unravel how diseases, such as cancer, progress and how they evade treatment.
SDState Assistant Professor Qin Ma leads a multi-institutional team of researchers who will develop a computational model that tracks how genes are expressed in specific cell types through a four-year, $1.04 million National Institutes of Health Research Project grant. Each year four doctoral students will also work on the project, which is administered through the National Institute of General Medical Sciences.
Ma, whose expertise is in bioinformatics and computational systems biology, has a joint faculty position in mathematics and statistics and agronomy, horticulture and plant science. He is one of the researchers working with the South Dakota’s BioSystems Networks and Translational Research (BioSNTR) center.
“We have a very strong team of early-career researchers and are excited about receiving this RO1 grant,” he said. The RO1 grant allows researchers to apply for a continuation award during the final year of the project.
The human genome has 20,000 genes. Though each cell contains the same genetic code, the unique way these genes are expressed determines how the cell functions. “Most biological techniques collect 1,000 cells in a tissue and assume the way in which the genes are expressed is identical—that is not the case,” Ma said. “Each tissue contains multiple cell types and each has its own regulatory mechanism.”
In addition, gene expression can change, particularly in complicated disease tissues, such as cancer, he explained. “For instance, scientists suspect that each cancer cell has its own evolutionary path.”
The project involves three major parts. “First, we need to have a very good mathematical formulation of the problem,” Ma said. This baseline work is the responsibility of theoretical statistician Anru Zhang, an assistant professor of statistics at the University of Wisconsin-Madison.
As the bioinformatics expert, Ma will then design algorithms that scientists can use to analyze their data. “The model should be robust so it can be applied to different organisms.”
Finally, Chi Zhang, an assistant professor of medical and molecular genetics at the Indiana University School of Medicine, will apply the computational techniques to cancer tissues. Zhang is interested in using single-cell data to understand cancer progression, metastasis and the mechanisms that lead to resistance to certain therapies.
Ma anticipates the model, which uses single-cell RNA sequencing data, can also be applied to gene expression related to interactions among microbial communities and plants and among gut microbes that colonize the gastrointestinal tract of animals and humans. “I think this project can easily be integrated into a wide range of research projects,” he added.