Researchers create non-invasive imaging technology to predict stem cell differentiation

Stem cell research exhibits significant potential for regenerative therapies and treatments to fight against cardiovascular disease, the one responsible for more than 30% of all deaths globally.

Tongcheng Qian, assistant scientist at the Morgridge Institute for Research. Image Credit: Morgridge Institute for Research.

Highly functional cardiomyocytes are the muscle cells behind the contraction of the heart. They are significant for drug screening, disease modeling, and other regenerative medicine approaches.

The process of differentiating stem cells into cardiomyocytes is labor-intensive, expensive, and greatly variable.

There’s this need for quality control in stem cell differentiation, for scale-up for industrial applications.”

Melissa Skala, Study Investigator, Morgridge Institute for Research

Investigator Skala’s lab created a non-invasive imaging technique that helps estimate the efficiency of cardiomyocyte differentiation as a means of quality control. The findings have been published in the Nature Communications journal.

If we can predict the outcome of stem cell differentiation into cardiomyocytes at a very early stage, then we can save time, money, and speed at the manufacturing stage.”

Tongcheng Qian, Study Lead Author and Assistant Scientist, Morgridge Institute for Research

Tongcheng Qian is an assistant scientist at the Skala Lab. The human pluripotent stem cells take around 14 days to differentiate into cardiomyocytes, while the cells have extreme changes in metabolic activity.

The researchers calculated the autofluorescence of NAD(P)H and FAD, the two molecules involved in cellular metabolism, at different time points all through the differentiation process. As the process employs innate autofluorescence of the cells, it is non-invasive and can be carried out in real-time without damaging the cells.

Even when many research works are analyzing the metabolic changes during stem cell differentiation, Qian and Skala assert that the predictive modeling of their research is crucial.

“For applications or biomanufacturing, we can then intervene at early time points to change media conditions or change confluency to improve the outcome,” adds Skala who is also a professor of biomedical engineering at the University of Wisconsin–Madison.

The researchers observed metabolic changes as early as the first day, with low versus high differentiation efficiency conditions.

Qian gathered imaging data from tens of thousands of cells and then conveyed the data to co-first author Tiffany Heaster, who is now at Genentech after obtaining her PhD through the Skala Lab.

“It’s a simple logistic regression model. But Tiffany built it in a rigorous way with separate testing and validation; all the checks you need to do robust science,” adds Skala.

Qian intends to add more factors into the model in the future to get a highly precise prediction.

Just to deal with the reproducibility of that data was actually very impressive. And it was completely consistent with data taken two years previously; I was like ‘science works!’ and it makes you feel a lot more confident about what you’re seeing.”

Melissa Skala, Study Investigator, Morgridge Institute for Research

The team believes that this work offers the framework for the application of this imaging technique on a much larger scale for bulk manufacturing.

“Cardiomyocytes can be differentiated in a suspension culture, too. If we do differentiation in suspension, or organoid differentiation, then it’s more like an industry manufacturing scale,” states Qian.

Qian further adds that in the future, he would like to try to see their autofluorescence technology predicting other cell lineage differentiation, such as blood cells or immune cells, which could have varied applications.

The research is an epitome of how scientific ideas originate from collaboration—Qian’s interest in stem cells along with his background in chemical engineering paired with Skala’s expertise in optical imaging.

“This project was done by a lot of collaboration. It’s a team effort,” Qian adds acknowledging Heaster who performed the predictive modeling, and the undergraduates and lab members who supported the analysis.

“It’s a combination of expertise, which is what we love about science,” remarked Skala.