An inside look at stem cells helps create personalized regenerative medicine

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In two papers from the lab of Ahmet F. Coskun, the Bernie Marcus Early Career Professor in the Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University, researchers examined a specific type of stem cell with a set of of intracellular tools to determine which cells are most likely to create effective cell therapies.

“We are studying the location of organelles within cells and how they communicate to help better treat disease,” Coskun said. “Our recent work proposes the use of an intracellular toolkit to map the biogeography of organelles in stem cells that could lead to more precise therapies.”

Create the Subcellular Omics Toolkit

The first study, published in scientific reports, to Nature portfolio diary — looked at mesenchymal stem cells (MSCs) that have historically offered promising treatments for repairing faulty cells or modulating the immune response in patients. In a series of experiments, the researchers were able to create a data-driven single-cell approach through rapid subcellular proteomic imaging that enabled personalized stem cell therapies.

The researchers then implemented a rapid multiplexed immunofluorescence technique using antibodies designed to target specific organelles. Using fluorescent antibodies, they tracked wavelengths and signals to compile images of many different cells, creating maps. These maps then allowed the researchers to see the spatial organization of organelle contacts and the geographic distribution in similar cells to determine which cell types would best treat various diseases.

“Typically, stem cells are used to repair faulty cells or treat immune diseases, but our microstudy of these specific cells showed just how different they can be from one another,” Coskun said. “This demonstrated that the treatment population of patients and custom isolation of stem cell identities and their bioenergetic organelle function should be taken into account when selecting the tissue source. In other words, when treating a specific disease, it could be It’s better to collect the same type of cell from different locations depending on the needs of the patient.”

ARN-ARN proximity affairs

In the forthcoming study published this week in Cell reporting methods, the researchers took the toolkit a step further and studied the spatial organization of multiple neighboring RNA molecules in individual cells, which are important for cell function. The researchers developed the tool by combining machine learning and spatial transcriptomics. They found that analyzing gene proximity variations to classify cell types was more accurate than analyzing gene expression alone.

“Physical interactions between molecules create life; therefore, the physical locations and proximity of these molecules play an important role,” Coskun said. “We created an intracellular toolkit of subcellular gene neighbor networks in the different geographic parts of each cell to look at this more closely.”

The experiment consisted of two parts: the development of computational methods and experiments on the laboratory bench. The researchers examined published data sets and an algorithm for grouping RNA molecules based on their physical location. This “nearest neighbor” algorithm helped determine gene clusters. At the bench, the researchers then tagged the RNA molecules with fluorescents to easily locate them in individual cells. They then discovered many features of the distribution of RNA molecules, such as the probability that the genes are in similar subcellular locations.

Cell therapy requires many cells with very similar phenotypes, and if there are unknown cell subtypes in the therapeutic cells, researchers cannot predict the behavior of these cells once injected into patients. With these tools, more cells of the same type can be identified and distinct subsets of stem cells with unusual genetic programs can be isolated.

“We are expanding the toolkit for the subcellular spatial organization of molecules, a ‘Swiss army knife’ for the field of subcellular spatial omics, if you will,” Coskun said. “The goal is to measure, quantify, and model multiple independent but also interrelated molecular events in each cell with multiple functionalities. The ultimate goal is to define the function of a cell that can achieve high-energy, diverse-decision, Lego-like modular gene neighborhood networks.” .

This research is funded by Regenerative Medicine and Engineering at Georgia Tech, as well as NSF’s Center for Engineering Research for Cell Manufacturing Technologies (CMaT).