The role of stem cells in customized regenerative therapy

Researchers used an intracellular toolkit to identify effective cell therapies.


Stem cells have the unique ability to differentiate into different types of cells in the body, making them a promising tool for regenerative medicine. However, not all stem cells are created equal, and it is essential to understand which types of stem cells are most effective for specific therapies.

By studying the intricate intracellular toolkit of stem cells, researchers are gaining insights into the characteristics that make specific stem cells ideal for personalized regenerative medicine.

This approach could revolutionize regenerative medicine, offering customized therapies that can address various medical conditions. A new study explores the latest research on stem cells and their role in developing personalized regenerative medicine.

Organelles are important components of cells that play crucial roles in maintaining human health and disease, including regulating growth, generating energy, and maintaining homeostasis. Researchers have found that organelle diversity exists not only between different cell types but also within individual cells.

By studying these differences, researchers can gain a better understanding of cell function and develop more effective therapeutics for various diseases. In two recent papers from the lab of Ahmet F. Coskun, researchers examined a specific type of stem cell with an intracellular toolkit to determine the most effective cells for creating cell therapies.

Coskun from Georgia Institute of Technology said, “We are studying the placement of organelles within cells, and how they communicate to help better treat disease, Our recent work proposes the use of an intracellular toolkit to map organelle bio-geography in stem cells that could lead to more precise therapies.”

Scientists created a subcellular omics toolkit to develop personalized stem cell therapies. They used a single-cell approach with rapid subcellular proteomic imaging to study mesenchymal stem cells. Using a rapid multiplexed immunofluorescence technique with targeted antibodies, they could create maps of cells‘ spatial organization and identify the best cell types for treating different diseases.

He said, “Usually, the stem cells are used to repair defective cells or treat immune diseases, but our micro-study of these specific cells showed just how different they can be from one another. This proved that the patient treatment population and customized isolation of the stem cell identities and their bioenergetic organelle function should be considered when selecting the tissue source. In other words, in treating a specific disease, it might be better to harvest the same type of cell from different locations depending on the patient’s needs.”

Scientists developed a toolkit to study the spatial organization of neighboring RNA molecules in single cells. The toolkit combines machine learning and spatial transcriptomics to analyze variations of gene proximity for the classification of cell types, which was found to be more accurate than analyzing gene expression only.

The experiment involved developing computational methods and labeling RNA molecules with fluorescents to locate them in single cells. The tools can be used to identify more cells of the same type and isolate distinct stem cell subsets with uncommon gene programs, aiding in the development of cell therapies.

Researchers have used a data-driven, single-cell approach to create a subcellular omics toolkit to aid in developing personalized stem cell therapies. By studying the spatial organization of neighboring RNA molecules in single cells, the researchers could identify more cells of the same type and isolate distinct stem cell subsets with uncommon gene programs, aiding in the development of cell therapies.

Journal References:

  1. Fang, Z., Ford, A., Hu, T., Zhang, etal. Subcellular spatially resolved gene neighborhood networks in single cells. Cell Reports Methods DOI:10.1016/j.crmeth.2023.100476
  2. Venkatesan, M., Zhang, etal. Spatial subcellular organelle networks in single cells. Scientific Reports. DOI: 10.1038/s41598-023-32474-y
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