Scientists have created a special set of tools that can be used inside cells to study how different parts of the cell communicate with each other and how they vary in stem cells. These tools include a way to quickly take pictures of the proteins inside the cell, a technique called immunofluorescence, and a method called spatial transcriptomics.
By using this toolkit, researchers can classify and identify different types of stem cells more accurately. This could help in developing better personalized treatments by isolating specific groups of stem cells.
Internal cell structure
Inside a cell, there are tiny structures called organelles. They are made up of RNA and proteins and have important roles in maintaining the body’s balance, controlling growth and aging, and producing energy.
Not only do different types of cells have different organelles, but even individual cells can have variations. By studying these differences, scientists can gain a better understanding of how cells work and develop improved therapies for various diseases.
In the work led by Ahmet F. Coskun, a professor in the Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, two research papers focused on a particular type of stem cell. They used the intracellular toolkit to figure out which cells are most likely to be effective for cell therapies.
Subcellular omics toolkit
Scientists have made significant progress in a field called “subcellular omics” which involves studying the tiny components within cells. One recent study, published in Scientific Reports, a prestigious journal in the Nature portfolio, focused on a type of cell called mesenchymal stem cells (MSCs).
These cells have shown great promise in treating damaged cells and regulating the immune system in patients. The researchers conducted a series of experiments and successfully developed a new method to analyze individual cells based on their proteins, which could lead to personalized stem cell therapies.
To achieve this breakthrough, the scientists used a technique called “Rapid Subcellular Proteomic Imaging.” They employed specially designed antibodies that can bind to organelles. By using fluorescent antibodies, they were able to track the wavelengths and signals emitted by these antibodies, creating detailed images of various cells.
These images formed maps that showed how organelles were distributed within cells and how they interacted with each other. By studying these maps, the researchers gained valuable insights into which types of cells could be most effective in treating different diseases, based on their spatial organization and similarities.
Coskun said, “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.”
He further said, “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.”
The physical interactions and close proximity of molecules are vital for the creation and sustenance of life. The experiment involved two main components: computational methods and laboratory experiments.
First, the researchers examined existing datasets and devised an algorithm that groups RNA molecules based on their physical locations. This algorithm, known as the “nearest neighbor” approach, aided in determining gene groupings.
In the laboratory, the scientists tagged RNA molecules with fluorescent markers to facilitate their identification within individual cells. This enabled them to uncover various characteristics related to the distribution of RNA molecules, such as the tendency for genes to occupy similar subcellular regions.
Findings of the study and implications for cell therapy
The findings of this study have important implications for cell therapy. In order to achieve successful cell therapy, it is crucial to have a large number of cells with highly similar characteristics. However, if there are unknown subtypes of cells mixed in with therapeutic cells, it becomes challenging to predict how these cells will behave once they are injected into patients.
By utilizing these tools, researchers can identify more cells of the same type and isolate distinct subsets of stem cells with unique gene programs.
Coskun emphasizes the significance of expanding the toolkit for understanding the spatial organization of molecules within cells. It can be likened to a versatile “Swiss Army Knife” for researchers in the field of subcellular spatial omics.
The ultimate goal is to measure, quantify, and model multiple molecular events that occur independently yet interactively within each cell, leading to diverse cellular decisions.
By achieving this, researchers aim to define a cell’s function, which involves intricate gene neighborhood networks, much like building blocks in a Lego set, and the ability to attain high energy levels.