Novel Imaging Technique Predicts Cardiomyocyte Differentiation Efficiency

In a new study—published today in Nature Communications—researchers from the Morgridge Institute for Research developed a non-invasive imaging technique that can predict the efficiency of cardiomyocyte differentiation as a method of quality control.

“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,” says Tongcheng Qian, lead author. The researchers measured the autofluorescence of NAD(P)H and FAD at various time points throughout the differentiation process. Since this method uses the innate autofluorescence of the cells, it is non-invasive and can be performed in real-time without damaging the cells.

While there have been many studies that look at metabolic changes during stem cell differentiation, the researchers say the predictive modeling of their research is important. “For applications or biomanufacturing, we can then intervene at early time points to change media conditions or change confluency to improve the outcome,” says lead researcher Melissa Skala. The team saw metabolic changes as early as day 1, with low versus high differentiation efficiency conditions. 

Qian collected the imaging data from tens of thousands of cells, and then passed on the data to co-first author Tiffany Heaster.  “It’s a simple logistic regression model,” says Skala. “But Tiffany built it in a rigorous way with separate testing and validation; all the checks you need to do robust science,” Qian adds that in the future, he’d like to incorporate more factors into the model to have a really precise prediction.