{"667734":{"#nid":"667734","#data":{"type":"news","title":"A Look Inside Stem Cells Helps Create Personalized Regenerative Medicine ","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EOrganelles \u2013 the bits and pieces of RNA and protein within a cell \u2013 play important roles in human health and disease, such as maintaining homeostasis, regulating growth and aging, and generating energy. Organelle diversity in cells not only exists between cell types but also individual cells. Studying these differences helps researchers better understand cell function, leading to improved therapeutics to treat various diseases. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn two papers out of the lab of \u003Ca href=\u0022https:\/\/clab.bme.gatech.edu\/\u0022\u003EAhmet F. Coskun\u003C\/a\u003E, a Bernie Marcus Early Career professor in the \u003Ca href=\u0022https:\/\/bme.gatech.edu\/bme\/\u0022\u003ECoulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University\u003C\/a\u003E, researchers examined a specific type of stem cell with an intracellular toolkit to determine which cells are most likely to create effective cell therapies. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cWe are studying the placement of organelles within cells and how they communicate to help better treat disease,\u201d said Coskun. \u201cOur recent work proposes the use of an intracellular toolkit to map organelle bio-geography in stem cells that could lead to more precise therapies.\u201d \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003ECreating the Subcellular Omics Toolkit\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EThe \u003Ca href=\u0022https:\/\/www.nature.com\/articles\/s41598-023-32474-y\u0022\u003Efirst study\u003C\/a\u003E \u2014 published in \u003Cem\u003EScientific Reports, \u003C\/em\u003Ea \u003Cem\u003ENature\u003C\/em\u003E\u003Cspan\u003E\u003Cspan\u003E portfolio journal\u003C\/span\u003E\u003C\/span\u003E\u003Cem\u003E \u2014\u003C\/em\u003E looked at mesenchymal stem cells (MSCs) that have historically offered promising treatments 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 therapeutics.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EThe researchers then implemented a rapid multiplexed immunofluorescence technique in which they used antibodies designed to target specific organelles. By fluorescing antibodies, they tracked wavelengths and signals to compile images of many different cells, creating maps. These maps then enabled researchers to see the spatial organization of organelle contacts and geographical spread in similar cells to determine which cell types would best treat various diseases. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cUsually, 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,\u201d said Coskun. \u201cThis proved that patient treatment population and customized isolation of the stem cells 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\u2019s needs.\u201d \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003ERNA-RNA Proximity Matters \u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn the next study published this week in \u003Cem\u003ECell Reports Methods\u003C\/em\u003E, the researchers took the toolkit a step further, studying the spatial organization of multiple neighboring RNA molecules in single cells, which are important to cellular function. The researchers evolved the tool by combining machine learning and spatial transcriptomics. They found that analyzing the variations of gene proximity for classification of cell types was more accurate that analyzing gene expression only.\u0026nbsp; \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cThe physical interactions between molecules create life; therefore, the physical locations and proximity of these molecules play important roles,\u201d said Coskun. \u201cWe created an intracellular toolkit of subcellular gene neighborhood networks in each cell\u0027s different geographical parts to take a closer look at this.\u201d \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EThe experiment consisted of two parts: the development of computational methods and experiments at the lab bench. The researchers examined published datasets and an algorithm to group RNA molecules based on their physical location. This \u201cnearest neighbor\u201d algorithm helped determine gene groupings. On the bench, researchers then labeled RNA molecules with fluorescents to easily locate them in single cells. They then uncovered many features from the distribution of RNA molecules, such as how genes are likely to be in similar subcellular locations. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003ECell therapy requires many cells with highly similar phenotypes, and if there are subtypes of unknown cells in 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 stem cell subsets with uncommon gene programs can be isolated. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cWe are expanding the toolkit for the subcellular spatial organization of molecules \u2013 a \u2018Swiss Army Knife\u2019 for the subcellular spatial omics field, if you will,\u201d said Coskun. \u201cThe goal is to measure, quantify, and model multiple independent but also interrelated molecular events in each cell with multiple functionalities. The end purpose is to define a cell\u2019s function that can achieve high energy, Lego-like modular gene neighborhood networks and diverse cellular decisions.\u201d \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EThis research is funded by \u003Ca href=\u0022https:\/\/www.regenerativeengineeringandmedicine.com\/\u0022\u003ERegenerative Engineering and Medicine at Georgia Tech\u003C\/a\u003E, as well as the \u003Ca href=\u0022https:\/\/cellmanufacturingusa.org\/\u0022\u003ENSF Engineering Research Center for Cell Manufacturing Technologies (CMaT).\u003C\/a\u003E \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003ECITATION: 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., and Coskun, A. F. \u0026nbsp;Spatial subcellular organelle networks in single cells.\u0026nbsp;Scientific Reports\u0026nbsp;13, 5374 (2023). \u003Ca href=\u0022\/\/\/C:\/Users\/acoskun7\/AppData\/Local\/Microsoft\/Windows\/INetCache\/Content.Outlook\/QNI1A4U7\/doi.org\/10.1038\/s41598-023-32474-y\u0022\u003Edoi.org\/10.1038\/s41598-023-32474-y\u003C\/a\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003ECITATION: Fang, Z., Ford, A., Hu, T., Zhang, N., Mantalaris, A., Coskun, A.F. \u0026nbsp;Subcellular spatially resolved gene neighborhood networks in single cells. \u003Cem\u003ECell Reports Methods\u003C\/em\u003E. May 12, 2023. doi.org\/10.1016\/j.crmeth.2023.100476\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn two papers out of the lab of \u003Ca href=\u0022https:\/\/clab.bme.gatech.edu\/\u0022\u003EAhmet F. Coskun\u003C\/a\u003E, a Bernie Marcus Early Career professor in the \u003Ca href=\u0022https:\/\/bme.gatech.edu\/bme\/\u0022\u003ECoulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University\u003C\/a\u003E, researchers examined a specific type of stem cell with an intracellular toolkit to determine which cells are most likely to create effective cell therapies. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"In two papers, researchers examined a specific type of stem cell with an intracellular toolkit to determine which cells are most likely to create effective cell therapies. 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