Looking inside stem cells helps develop personalized regenerative medicine


In two papers, researchers used an intracellular toolkit to test specific types of stem cells to determine which cells were most likely to produce effective cell therapies.

Nicholas Chan, Georgia Tech PhD Candidate

Nicholas Chan, Georgia Tech PhD Candidate

Organelles, fragments of RNA and proteins within cells, play important roles in human health and disease, including maintaining homeostasis, regulating growth and aging, and generating energy. Intracellular organelle diversity exists not only between cell types but also within individual cells. Studying these differences will help researchers better understand cell function, leading to improved therapies to treat various diseases.

In two papers from the lab of Georgia Tech and Ahmet F. Koskun, an early-career Bernie Marcus professor in the Coulter School of Biomedical Engineering at Emory University, researchers used an intracellular toolkit to target specific types of cells. Explore stem cells and which ones are most likely to produce effective cell therapies.

“We study the arrangement of organelles and how they communicate to improve the treatment of disease,” Koskun said. “Our recent work suggests the use of an intracellular toolkit to map the biogeography of organelles within stem cells that may lead to more precise therapeutics.”

Creating an intracellular omics toolkit

First study — published in scientific reports, be Nature portfolio journal They focused on mesenchymal stem cells (MSCs), which have historically offered promising therapeutics for repairing defective cells and modulating immune responses in patients. In a series of experiments, researchers were able to create a data-driven, single-cell approach that enables personalized stem cell therapy through rapid intracellular proteomic imaging.

The researchers then implemented a rapid multiplexed immunofluorescence technique using antibodies designed to target specific organelles. Fluorescent antibodies were used to track wavelengths and signals, and images of different cells were compiled and mapped. These maps will allow researchers to see the spatial organization of organelle contacts and geographic extent within similar cells to determine which cell types are best suited for different diseases. I was.

“Usually stem cells are used to repair defective cells or treat immune disorders, but our micro-studies on these specific cells show how different they are from each other. ‘, said Mr. Koskun. “This demonstrated that the therapeutic population of patients and the customized isolation of stem cell identity and its bioenergetic organelle function should be considered when choosing a tissue source. It may be better to harvest the same type of cells from different locations, depending on the patient’s needs, when treating these diseases.”

RNA-RNA proximity is important

The next study, published this week, found that cell report methodThe researchers took this toolkit a step further by studying the spatial organization of multiple adjacent RNA molecules within a single cell that is important for cellular function. Researchers have evolved the tool by combining machine learning and spatial transcriptomics. They found that analyzing changes in gene proximity to classify cell types was more accurate than analyzing gene expression alone.

“Physical interactions between molecules create life, so the physical location and proximity of these molecules play an important role,” Koskun said. “To probe this, we created an intracellular toolkit of intracellular gene neighborhood networks in different geographic parts of each cell.”

The experiment consisted of two parts: development of computational methods and experimentation on the bench. The researchers examined publicly available datasets and algorithms that group RNA molecules based on their physical location. This ‘nearest neighbor’ algorithm helped determine the gene groupings. Next, on the lab bench, the researchers labeled the RNA molecules with a fluorescent substance, allowing them to easily locate her RNA molecules within a single cell. Since then, many features have emerged from the distribution of RNA molecules, such as how genes are located at similar locations in cells.

Cell therapy requires many cells with very similar phenotypes, but if unknown cell subtypes are present in the therapeutic cells, researchers may wonder how these cells will behave after being injected into the patient. Unpredictable behavior. Using these tools, we can identify more cells of the same type and isolate different stem cell subsets with unusual genetic programs.

“We are expanding our toolkit for molecular spatial organization, the ‘Swiss Army knife’ of intracellular spatial omics,” said Coskun. “The goal is to measure, quantify and model multiple independent but interrelated molecular events within each cell with multiple functions. Our goal is to define modular gene neighborhood networks and cellular functions that can achieve diverse cellular decisions.”

This research was funded by Regenerative Engineering and Medicine at the Georgia Institute of Technology and the NSF Center for Cell Manufacturing Technology and Engineering Research (CMaT).

Citations: Venkatesan, M., Zhang, N., Marteau, B., Yajima, Y., Ortiz De Zarate Garcia, N., Fang, Z., Hu, T., Cai, S., Ford, A. Olszewski , H., Borst, A. & Coskun, AF Spatial organelle networks in single cells. Scientific Reports 13, 5374 (2023). doi.org/10.1038/s41598-023-32474-y

Citations: Fang, Z., Ford, A., Hu, T., Zhang, N., Mantalaris, A., Coskun, AF Intracellular spatially resolved gene neighborhood networks in single cells. cell report method. May 12, 2023. doi.org/10.1016/j.crmeth.2023.100476



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