Label-free imaging, plus data science, means better quality control for biomanufacturing stem cells

By reprogramming cells from a patient to yield induced pluripotent stem cells, scientists and clinicians can wield a fertile and versatile resource for personalizing patient healthcare via regenerative medicine, cell therapy or disease modeling for drug testing.

But this reprogramming is far from a neat-and-tidy process; not all cells follow the desired pathway, and sifting through batches to pick out those precious stem cells generally requires techniques that are slow, laborious and oftentimes destructive to the cells themselves.

A team of University of Wisconsin-Madison biomedical engineers has devised an innovative method that leverages micropatterning, label-free imaging and machine learning to enable real-time, noninvasive monitoring of reprogramming. Their proof-of-concept, presented in a new paper in the journal GEN Biotechnology, offers a high-throughput option for quality control in biomanufacturing of induced pluripotent stem cells (iPSCs), which can in turn be used to develop cutting-edge personalized therapies and disease models.

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Note: See also this D2P innovator profile of Melissa Skala.