New Method for Direct Identification of Antigens

The immune system is a vital part of our defenses against pathogens, but it can also attack host tissues, resulting in autoimmune disease. The antigens that induce destructive immune reactions can now be identified directly - without any prior knowledge of their possible structure.

Molecules that activate immune responses, generically termed antigens, are recognized by circulating immune cells. In the case of autoimmune reactions, such responses may lead to the destruction of body tissues. A new method that can identify the antigens that initiate such reactions may help to prevent misdirected attacks in the future. Using genetic engineering techniques, researchers at Ludwig-Maximilians-Universität (LMU) and the Max Planck Institute for Neurobiology have generated cells that emit green fluorescent light when stimulated by the binding of a cognate antigen.

The immunological needle in a haystack
The new method is based on the isolation of T cells present in samples of affected tissues obtained from patients with autoimmune diseases. The research team, led by Dr. Klaus Dornmair (Institute for Clinical Neuroimmunology at LMU and the Department of Neuroimmunology at the MPI for Neurobiology), first recovered the genetic blueprints for the specific antigen-binding T-cell receptors (TCRs) produced by these cells, and transferred them into a cultured cell line that grows well in the laboratory.

This line also contains a version of the gene for the Green Fluorescent Protein (GFP) that is specifically expressed if a TCR is activated. Finally, the cells are incubated with a collection of some 100 million peptides - short amino acid sequences like those normally recognized by TCRs. If even a single peptide represented in the library is recognized by a specific TCR, the corresponding cell synthesizes GFP and can be detected by its green fluorescence, allowing the bound antigen to be identified. The method thus provides a relatively simple way of identifying single autoimmune antigens from huge numbers of possible suspects.

An initial test carried out using cells specific for a known influenza antigen confirmed the efficacy of the method. The researchers were able unequivocally to select out and identify the correct antigen from all the other peptides used in the test. The technique is so rapid and so sensitive that several million antigens can be analyzed in a matter of hours. This opens up a wide range of possible applications – ranging from the analysis of the reactive antigens responsible for autoimmune diseases like multiple sclerosis or psoriasis to the identification of new tumor or viral antigens. Indeed, its practical potential is so significant that the method is the subject of a patent application.

Publication: "Unbiased identification of target antigens of CD8+ T cells with combinatorial libraries coding for short peptides", K. Siewert, J. Malotka, N. Kawakami, H. Wekerle, R. Hohlfeld & K. Dornmair, Nature Medicine Advanced Online publication, 8.4.2012 doi:10.1038/nm.2720

Most Popular Now

Do Fitness Apps do More Harm than Good?

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may...

AI Tool Beats Humans at Detecting Parasi…

Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose...

Making Cancer Vaccines More Personal

In a new study, University of Arizona researchers created a model for cutaneous squamous cell carcinoma, a type of skin cancer, and identified two mutated tumor proteins, or neoantigens, that...

AI, Health, and Health Care Today and To…

Artificial intelligence (AI) carries promise and uncertainty for clinicians, patients, and health systems. This JAMA Summit Report presents expert perspectives on the opportunities, risks, and challenges of AI in health...

AI can Better Predict Future Risk for He…

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack. The study, conducted by an international...

A New AI Model Improves the Prediction o…

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the...

AI System Finds Crucial Clues for Diagno…

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly...

New AI Tool Makes Medical Imaging Proces…

When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is...