The specific immune response is mounted because each of the cells responsible for it, the lymphocytes, carries a receptor with a unique sequence that allows it to recognise one antigen and one antigen only. When these cells recognise the antigen, they proliferate and expand, but only those that carry a receptor sequence capable of interacting with the antigen. Therefore, if it is possible to characterise and distinguish among the millions of possible receptors those that have expanded and reacted to that antigen, it could be a valuable diagnostic tool, especially in complex cases such as autoimmune diseases.

In work led by Scott Boyd, they have interrogated which sequences react in various diseases (covid19, AIDS, lupus and type 1 diabetes) and whether these were capable of diagnosing the disease. An artificial intelligence strategy based on machine learning was used to interpret the data generated by sequencing millions of cells. The diagnoses interpreted by artificial intelligence correlate perfectly with those established by clinical and laboratory methods.

Although this strategy is still a long way from the clinical setting, it is clear that it could be very useful in the future, especially when the diagnosis is ambiguous, and in any case represents an important first step in the use of artificial intelligence tools for the diagnosis of immune diseases.

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