How do we achieve a Google Maps of chemical activity at single cell level?
Ahmet Coskun and his collaborators plan to create a chemical atlas of all the immune cells in the human body, a 3D micromap to help clinicians navigate the complex role of the entire immune system in the presence of different diseases.
It’s the kind of massive undertaking that would result in vastly improved precision therapies for patients. And it’s the kind of journey that starts with a single cell. Coskun and team are off to a fast start with the introduction of a new integrative technique for profiling human tissue that enables researchers to capture the geography, structure, movement, and function of molecules in a 3D picture. The researchers described their new approach, the Single Cell Spatially resolved Metabolic (scSpaMet) framework, in the journal Nature Communications on Dec. 13.The study builds on a technique Coskun’s team developed and described in a 2021 article, “3D Spatially resolved Metabolomic profiling Framework,” published in Science Advances. In that work, the team introduced a technique that measures the activity of metabolites and proteins as part of a comprehensive profile of human tissue samples. “Earlier we couldn’t achieve single-cell resolution, but with this new approach, we can,” said Coskun, Bernie Marcus Early Career Professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. “With this new approach, we can get spatial details of proteins and metabolites in single cells– no one else has yet reached this level of high subcellular resolution.” He added, “We’re pioneering a new field of research with this work, single cell spatial metabolomics.”
Shambavi Ganesh, Thomas Hu, Eric Woods, Mayar Allam, Shuangyi Cai, Walter Henderson, and Ahmet F. Coskun. Spatially resolved 3D metabolomic profiling in tissues Science Advances, Vol. 7, no. 5, eabd0957 (2021)
(2) We develop Subcellular Spatial Transcriptomics Toolkits
How do we model subcellular organization of intracellular molecular networks?
The first study — published in Scientific Reports, a Nature portfolio journal — 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.
The 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.
“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,” said Coskun. “This 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’s needs.”
RNA-RNA Proximity Matters
In the next study published this week in Cell Reports Methods, 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.
“The physical interactions between molecules create life; therefore, the physical locations and proximity of these molecules play important roles,” said Coskun. “We created an intracellular toolkit of subcellular gene neighborhood networks in each cell's different geographical parts to take a closer look at this.”
The 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 “nearest neighbor” 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.
Cell 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.
“We are expanding the toolkit for the subcellular spatial organization of molecules – a ‘Swiss Army Knife’ for the subcellular spatial omics field, if you will,” said Coskun. “The 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’s function that can achieve high energy, Lego-like modular gene neighborhood networks and diverse cellular decisions.”
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., and Coskun, A. F. Spatial subcellular organelle networks in single cells. Scientific Reports 13, 5374 (2023). doi.org/10.1038/s41598-023-32474-y
Fang, Z., Ford, A., Hu, T., Zhang, N., Mantalaris, A., Coskun, A.F. Subcellular spatially resolved gene neighborhood networks in single cells. Cell Reports Methods. May 12, 2023. doi.org/10.1016/j.crmeth.2023.100476