Scientists at the Francis Crick Institute and Imperial College London have developed a new way to study proteins released by cells, which may spawn new tools to track diseases, including cancer.
Biomarkers are invaluable tools that enable physicians to study biology and disease, for example, diagnose disease from blood or tissue samples, predict whether a treatment is effective for an individual, or observe how much a drug reaches diseased cells.
However, finding these biomarkers is challenging. To help diagnose disease, scientists need to identify proteins that are unique to diseased cells or cancer cells but not released by healthy cells.
In their study, published recently in Nature Communications, the team developed a new method that identifies proteins released by specific types of cells, even if these cells live in a complex environment with many other types of cells.
"When you have a sample that contains a different cell line, it is difficult to identify proteins from a specific cell line. Of course, in the laboratory, we can use only one type of cell for experiments, but these conditions do not reflect what happens in vivo, and complex interactions between cells may affect their behavior and thus the proteins they release," explained Ben Schumann, lead author and group leader at Crick and Imperial College.
The core of the new method is to add chemical tags to sugar molecules added to cells. Although all cells absorb sugar, the researchers genetically modified the cell type they wanted to study, and only this type of cell added sugar to the protein. When cells make these proteins, they still carry chemical tags, which means researchers can identify them.
The method uses bioorthogonal or "click" chemistry, which was awarded this year's Nobel Prize in Chemistry. One of the winners of the award, Caberlin Beltoz from Stanford University, was a co-author of the study. The chemical tag was selected so that it could "click" with another molecule to help researchers separate the desired protein or add fluorescent tags to them.
The researchers showed that their method, called bioorthogonal cell line-specific glycoprotein marker (BOCTAG), was effective in cell culture in a variety of cell lines and in mice, and the researchers successfully labeled proteins from specific cancer cells.
"In this study, we looked at proteins produced by cancer cells, but our method can also be used in other fields, including the study of immunology or infectious diseases. It can also be used to better understand disease biology, including how tumor cells change in response to complex interactions in vivo," said Anna Theos, lead author and postdoctoral training researcher at Crick University.
"The next step for our team will be to continue to develop this approach and learn more about how cells produce different proteins according to the environment," the researcher added.
Collected by Profacgen that has developed a series of protein labeling techniques and reagents to meet your research requirements. The most popular labels our customers choose include fluorescent probes (Phycoerythrin, allophycocyanin, fluorescein, tetramethylrhodamine, fluor dyes), enzyme conjugates (horseradish peroxidase and alkaline phosphatase) and biotin.
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Profacgen, a biotechnology company that offers bioinformatics platform to help academic and industrial researchers to explore, understand and analyze complex biological data in various biotechnological programs and projects, recently updated its in silico protein-protein interactions prediction service to meet diversified needs of its customers.
As one of the most common molecules in cells, proteins are of great significance for regulating various metabolic pathways and numerous biological processes in cells. In general, proteins do not function individually, but through interactions with each other, known as protein-protein interactions (PPIs), to accomplish their corresponding tasks. In addition, the study of protein interactions can provide new perspectives for medical diagnosis and treatment, and promote the design of new drugs and the development of biomedicine.
At present, the methods for predicting protein interactions mainly include biological methods and computational methods. In the traditional biological field, the collection of interaction data can be completed by methods such as yeast two-hybrid, protein chip, and synthetic lethal analysis. However, these methods are time-consuming and labor-intensive, resulting in insufficient prediction efficiency, and this proportion of false negatives and false positives is often observed in the prediction results. Therefore, with the rapid development of computer technology, computational methods, which were originally used as auxiliary means, have now become the mainstream methods for predicting protein interactions.
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