Unraveling the Mysteries of Stem Cells – Technology Org

New research uses machine learning and imaging to provide unprecedented insight into stem cell behavior, which could enable life-saving treatments in the future.

Human stem cells. Image Credit: Wikimedia Commons

Stem cells are like the human body’s emergency tool kit. They have the unique ability to be transformed into other specialized cells – from immune to brain cells. They can divide and regenerate indefinitely to repair and replenish our systems on command.

The ability to culture stem cells in the laboratory and grow them into any type of cell we need is the hallmark of medicine. For example, this ability could enable physicians to create an endless supply of new cells to repair damaged tissues and organs. However, to find that Holy Grail, we need a comprehensive understanding of how stem cells replicate and transform into different types of cells.

USC’s new research Alfred E. Mann Department of Biomedical Engineering This brings us one step closer to unraveling the secrets of these essential cells. Associate Professor of Biomedical Engineering qiu shen And his team has used machine learning to develop a non-invasive system that provides an unseen insight into how stem cells proliferate and regenerate into specialized cells. The work has been published science advancement,

Shen said the behavior of stem cells is still quite mysterious, and the process of understanding how they divide and change is often invasive, requiring the stem cells to be extracted and ultimately destroyed in the laboratory. .

The new work examines hematopoietic stem cells, which live in our bone marrow and give rise to all the cells in our blood, such as red blood cells and immune cells. Shen said stem cells need to divide symmetrically to expand their population and asymmetrically to renew themselves while forming a new, different cell type (such as a red or white blood cell). need to be divided.

Single hematopoietic stem cells divide metabolically (M) symmetrically and asymmetrically, as measured by the research team’s imaging technology. Image Credit: Hoa Zhou and Qiu Shen

“In the case of bone marrow transplants, we want the stem cells to divide symmetrically so that we get as many stem cells as possible so that we can use them on different patients. But right now, blood stem cells can’t actually be expanded outside the body in the clinic,” Shen said. “If we can achieve this – to create a large reservoir of hematopoietic stem cells for bone marrow transplantation – it would solve a huge problem for a lot of patients.”

Shen’s team looked at the metabolic behavior of the stem cell – how it breaks down glucose into energy – using a real-time imaging technique known as fluorescence lifetime imaging microscopy.

Stem cells produce their own fluorescent material – known as autofluorescence – which allows imaging to track the cells’ metabolism. This metabolism is strongly linked to how cells will function and transition.

“For example, NADH is one of these molecules that is autofluorescent and when they bind to a metabolic enzyme, they also show different optical fluorescent properties that we can measure. So this way, we can measure the cells non-invasively without killing them,” Shen said.

Using a mouse model, Shen and his team took this information and extracted fluorescent features from stem cell images, developing a library of 205 metabolic optical biomarker features from each individual stem cell, 56 of which indicate the differentiation of hematopoietic stem cells. Were associated with.

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. The approach gave a score to determine whether a daughter cell was potentially a stem cell or not, or whether the stem cells were dividing asymmetrically or symmetrically.

“It’s very exciting because we’re not killing the cells. We are simply taking pictures of the cell and then extracting those features. “This can give us a lot of information about them.”

The team’s real-time approach to understanding the metabolic state of stem cells will provide further fundamental knowledge that could aid drug discovery and cutting-edge stem cell therapies, as well as regenerative medicine treatments where human cells, tissues and organs grow Can be done and took place.

“Nowadays there are other applications too, such as cell therapy. People, for example, are trying to create T cells, macrophages and other types of cells that have their own specific utility in a variety of disease contexts,” Shen said. “For the stem cell people, this is an exciting technology because we allow them to see the status of the stem cells in real time and then track each cell over time, which is not currently possible.”

Source: USC