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Organelles — bits and pieces of RNA and protein within a cell — play important roles in human health and disease, such as maintaining homeostasis, regulating growth and aging, and generating energy. Organic diversity in cells is not only found between cell types but also between individual cells. Studying these differences helps researchers better understand cell function, which can lead to better therapies for treating various diseases.
In two papers from Ahmed F. Lab. Coskun, Bernie Marcus Early Career Professor in the Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, researchers examined a specific type of stem cell using an intracellular toolkit to identify the cells most likely to create effective cell therapies.
“We’re studying the placement of organelles inside cells and how they communicate to help better treat disease,” Coskun said. “Our recent work suggests using an intracellular toolkit to map the biogeography of organelles in stem cells that could lead to more precise therapies.”
Create the Omics Subcellular Toolkit
The first study – published in Scientific reports a nature Portfolio Journal – She looked at mesenchymal stem cells (MSCs), which have historically offered promising therapies for repairing defective 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 applied a rapid multiplex immunofluorescence technique where they used antibodies designed to target specific organelles. By fluorescing the antibodies, they tracked the wavelengths and signals to compile images of many different cells, creating maps. These maps then enabled the researchers to see the spatial organization of organelle connections and geographic spread in similar cells to better identify which cell types would treat different diseases.
“Stem cells are usually used to repair defective cells or treat immune diseases, but our careful study of these specific cells showed just how different they are from each other,” Coskun said. “This demonstrated that the treatment group of patients and the specific isolation of stem cell identities and the function of their vital organelles must be considered when selecting a tissue source. In other words, in the treatment of a particular disease, it may be preferable to harvest the same type of cells from different sites depending on the needs of the patient.”
RNA-RNA Proximity Matters
In the following study, published this week in Methods for cell reportsTaking the toolkit a step further, the researchers investigated the spatial organization of multiple neighboring RNA molecules in single cells, which are important for cellular function. The researchers developed the tool by combining machine learning with spatial transcriptomics. They found that analyzing differences in genetic proximity 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 important roles,” Coskun said. “We created an intracellular toolkit of subcellular gene networks in the different geographic compartments of each cell to take a closer look at this.”
The experiment consisted of two parts: development of computational methods and benchtop experiments. The researchers examined published data sets and an algorithm to group RNA molecules based on their physical location. The Nearest Neighbor algorithm helped identify gene clusters. On the bench, the researchers then labeled the RNA molecules with fluorescent materials to easily locate them in single cells. They then explored several features from the distribution of the RNA molecules, such as how genes are likely to be 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 they are injected into patients. With these tools, more cells of the same type can be identified, and distinct subsets of stem cells with non-common gene programs can be isolated.
“We’re expanding the toolkit of subcellular spatial organization of molecules — which is the ‘Swiss army knife’ 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 functions. The ultimate purpose is to identify which cell function can achieve high power, Lego-like modular gene networks and diverse cellular decisions.”
This research is funded by Regenerative Engineering and Medicine at Georgia Tech, as well as the NSF Engineering Research Center for Cell Manufacturing Technologies (CMaT).
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