Stem cell biology has enabled researchers to produce heart muscle cells, or cardiomyocytes, which could be used for screening drugs or in cell-based therapies for heart disease.
However, those stem-cell-generated heart muscle cells are immature—meaning they do not behave like adult human cardiomyocytes. That’s a big limit on their usefulness.
Unlike mature cardiomyocytes, the immature heart muscle cells have poorly organized sarcomeres—the internal structures that enable cardiomyocytes to produce the contracting force that allows the heart to pump blood.
“We want to produce mature cardiomyocytes with internal structures that are nicely organized and aligned, which allows them to work in a coordinated way with their neighboring cells to generate functional muscle behavior,” says Wendy Crone, a professor of engineering physics at the University of Wisconsin-Madison.
Now, a team of UW-Madison researchers led by Crone has created a powerful tool to help assess what experimental factors achieve this goal. The researchers developed an algorithm for image analysis that quantifies the organization and alignment of sarcomere structures within single cardiomyocytes, as well as a population of cells, with considerable accuracy. The team described its advance in a paper published May 19, 2020, in the Journal of Applied Physics.
Crone says this new technique, called the scanning gradient Fourier transform (SGFT) method, gives researchers a valuable tool for evaluating cells and determining the most effective interventions for coaxing immature cardiomyocytes to become mature cells with highly organized internal structures.
The UW-Madison researchers’ method has a key advantage over other image analysis techniques: It is adept at analyzing cells that have some level of organization to their internal structures but fall short of being extremely well-organized.
“We started this project because we needed a way to quantify the organization of cells in these intermediate states,” Crone says. “It’s particularly important for our research because we want to be able to look at cultured cells as they mature—where they start out being very disorganized and then end up developing a much more organized state over time.”
Crone’s research focuses on culturing cardiomyocytes and other cell types derived from stem cells, so writing code for image analysis was a unique undertaking for her research group. She credits former graduate student Max Salick (MSEM ’10, PhDMSE ’14), the first author on the paper, for developing a deep understanding of image analysis and writing the primary code for the algorithm.
The usefulness of the new algorithm also extends beyond cardiomyocytes. Researchers can use it with a variety of imaging techniques and different cell types to effectively assess organization in general, Crone says.
For example, she already has collaborated with colleague Randolph Ashton, an associate professor of biomedical engineering at UW-Madison, to demonstrate that the algorithm can quantify the organization of neural rosettes, which are early stage neurons derived from stem cells.
In the Journal of Applied Physics paper, the researchers also reported using their method to analyze a breast cancer tissue sample. They showed their technique was able to clearly detect the organization of the collagen fibers within the tissue. “That’s important because if the collagen in breast tissue is very well organized, it’s a strong indicator of the malignancy of the tumor,” Crone says. “So organization can be good or bad depending on the situation, and our method allows us to quantify it in a variety of different scenarios.”
Crone is the Karen Thompson Medhi Professor of Engineering Physics. This research was supported in part by grants from the Heart, Blood and Lung Institute and the National Institute of Neurological Disorders and Stroke of the National Institutes of Health, and the UW-Madison Graduate School.
Author: Adam Malecek