Stem cells are like the emergency tool kit of the human body, possessing the unique ability to transform into various specialized cells, from immune cells to brain cells. They can divide and regenerate indefinitely to repair and replenish our system on command. The ability to culture stem cells in the lab and grow them into any cell type needed is the Holy Grail of medicine. This capability could enable clinicians to create an endless supply of new cells for repairing damaged tissues and organs. However, achieving this requires a comprehensive understanding of how stem cells replicate and transition into different cell types.

New research from USC’s Alfred E. Mann Department of Biomedical Engineering brings us closer to unraveling the mysteries of these essential cells. Associate Professor Keyue Shen and his team have harnessed machine learning to develop a non-invasive system that offers unprecedented insight into how stem cells proliferate and regenerate into specialized cells. Their work, published in Science Advances, represents a significant breakthrough.

Shen explains that stem cell behavior is still quite mysterious, and the process of understanding how they divide and change has traditionally been invasive, requiring stem cells to be extracted and destroyed in the laboratory. This new study examines hematopoietic stem cells, which reside in our bone marrow and give rise to all blood cells, such as red blood cells and immune cells. For the stem cells to expand their population, they must divide symmetrically. To renew themselves while creating a new, different cell type (such as a red or white blood cell), they must divide asymmetrically.

“In the case of bone marrow transplants, we want the stem cells to divide symmetrically to give us as many stem cells as possible so that we can use them on different patients. But right now, the blood stem cells cannot really be expanded outside a body in the clinic,” Shen said. “If we can achieve that—to make a huge stock of hematopoietic stem cells for bone marrow transplantation—it will solve a significant problem for many patients.”

Shen’s team focused on the stem cell’s metabolic behavior—how it breaks down glucose into energy—using real-time imaging technology known as fluorescence lifetime imaging microscopy. Stem cells produce their own fluorescent material—autofluorescence—allowing the imaging to track their metabolism. This metabolism is strongly connected to how the cells function and transition.

“For example, NADH is one of these molecules that’s autofluorescent, and when they bond to a metabolic enzyme, they show different optical fluorescent properties that we can measure. So in this way, we can measure them non-invasively without killing the cells,” Shen explained. Using a mouse model, Shen and his team extracted fluorescent features from stem cell images, developing a library of 205 metabolic optical biomarker features from each stem cell, 56 of which were associated with the differentiation of hematopoietic stem cells.

The machine learning approach allowed the team to create a clustering map of stem cells versus non-stem cells and track their behavior and differentiation over time. This approach assigned a score to determine whether a daughter cell is likely a stem cell or not, and whether the stem cells were dividing asymmetrically or symmetrically.

“It’s very exciting because we are not killing the cells. We are merely just taking images of the cell and then extracting those features. That can give us so much information about them,” Shen said.

The team’s real-time approach to understanding the metabolic state of stem cells will provide foundational knowledge that could aid drug discovery and cutting-edge stem cell treatments, as well as regenerative medicine treatments where human cells, tissues, and organs can be grown and replaced. “There are other applications nowadays, such as cell therapy. People have been trying to make, for example, T cells, macrophages, and other types of cells that have their own specific utility in different kinds of disease contexts,” Shen noted. “For stem cell researchers, this is an exciting technology because it allows them to look at a stem cell state in real-time and then track each cell over time, which is not currently possible.”

By Impact Lab