Macrophage-Classifying AI Could Track Disease and Therapy Response

A new machine learning technique has been developed to accurately classify the state of macrophages—opening the door for interventions that modify their actions as pro- or anti-inflammatory agents in immune response.

The work, undertaken by a team at Trinity College Dublin, classifies macrophages based on their metabolic response under certain conditions and has a suite of implications. The method could be of use to drug designers looking to create therapies targeting diseases and auto-immune conditions such as diabetes, cancer and rheumatoid arthritis, all of which are impacted by cellular metabolism and macrophage function. It could be used as a diagnosis tool or to highlight the role of a particular cell type in a disease environment. 

“Currently, there are no other methods that employ artificial intelligence-based, machine learning approaches to macrophage classification,” says Michael Monaghan, Associate Professor in Biomedical Engineering at Trinity, who led the study.  “A number of different techniques are currently used to classify macrophages, but all of these have significant drawbacks. Our method uses a 2-photon fluorescence lifetime imaging microscope (2P-FLIM), which is unique to Trinity and to Ireland. 2P-FLIM does not require sample pre-treatment, can be used to follow changes in metabolism non-invasively and in real-time—which opens the door to tracking disease progression and/or physiological response to therapies—and it also requires a lower number of cells compared with conventional techniques.”

The work brought together biomedical engineers, computer scientists and immunologists. “It is becoming increasingly clear that to solve many of society’s greatest problems, we need to take multi-disciplinary approaches to harness the expertise of people working in different fields,” says Nuno Neto, PhD Candidate in the School of Engineering at Trinity. “Trinity is rightly known as a leader in immunometabolism research, with many of our scientists focusing on how it regulates immune cell response, and how immune cell metabolism is impacted in diseases. This study benefits from that expertise, but also bridges the use of advanced computer science approaches and utilizes an advanced microscope from the Biomedical Engineering Department with a regime never reported previously. It thus serves as a prime example of inter-departmental collaboration in a multidisciplinary field.”

The findings were published recently in eLife.