September 9, 2025 | There is a lack of reliable predictive methods that can determine how a patient will respond to certain immunotherapies and understanding how immune cells communicate with each other may be the key to understanding the differences.
To detect and destroy infections and cancer cells, immune cells have a complex system of “talking" to each other. Technologies have been developed to decipher the interactions between these cells, but they have long processing times, are expensive, and are usually limited to predefined cell types. A team of researchers from the Berlin Institute of Health at Charité (BIH), the Max Delbrück Center, the German Cancer Research Center (DKFZ), the Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM), and Queen Mary University of London have created a new technology called Interact-omics that can “listen in” on this immune cell dialogue.
Decoding a Complex Network
There are two main ways immune cells communicate, explained Viktoria Flore, doctoral student and one of the first authors of the study. The first way is that the cells send out molecules as messengers across longer distances, and the second is using molecules on their surface to attach and communicate with another cell directly. The research team focused on the second way for Interact-omics because this form of communication is crucial to recognizing and fighting infections, as well as for the mechanism-of-action of immunotherapies.
Interact-omics uses flow cytometry, a method that measures individual cells while they pass through lasers in a narrow, single-file liquid stream, said Flore. “Cells are meant to pass through the instrument one at a time, but cells that are attached to each other can also be measured as one event. The way cells deflect the light of the laser tells us something about their size and shape, and the signal for single cells looks different than for two or more cells connected to each other.”
The approach is more cost-efficient and quicker than existing approaches, and has been developed in human samples, without relying on mouse models.
In flow cytometry, cell types can be identified by marking more than 40 different proteins on the surface of the cells with fluorescently labeled antibodies. Together with the information about size and shape, these attributes all help characterize each cell or interaction in a high dimensional space, similar to single-cell transcriptomic data analysis.
“Interacting cells show characteristics of two or more connected cells, and a combination of surface proteins that is only expected when more than one cell type is present,” Flore continued.
The team’s method proved successful, as they were able to show that multiplets detected in flow cytometry data can indeed be used to measure physical interactions between cells.
“One of the most surprising findings was related to what we usually consider ‘artefacts’ in flow cytometry,” noted Schayan Yousefian, another doctoral student and first author of the study. “Typically, doublets or multiplets are gated out and discarded during the analysis. But in our work, we discovered that these cellular interactions can carry meaningful biological information.”
And by including them and studying them, the team was able to show that these cellular interactions could be used to predict responses to immunotherapies, such as bispecific antibodies.
A Cost-Saving Method for Personalized Medicine
Interact-omics is cost-effective and could therefore be a useful addition to clinics. Physicians could be able to confirm beforehand if a patient’s immune cells are able to form the physical connection between an immune cell and cancer cell and respond appropriately.
“We envision that our framework can help do exactly that,” explained Flore. “Patient samples (e.g. blood or bone marrow) could be treated ex vivo with different therapeutic agents, followed by the quantification of drug-induced cell-cell interactions using our Interact-omics pipeline. Selected promising therapies can then be prescribed to the patient.”
Patients can also benefit from more personalized treatment decisions if Interact-omics becomes a part of routine clinical care, said Yousefian. By testing each patient’s unique immune cell network and how they respond, it will help identify which immunotherapy is most effective and has the lowest risk of side effects.
“The next step for the team is actively applying this framework across a wide range of samples, human disease models, and therapeutic settings to uncover further applications and provide novel biological or medical insights,” relayed Yousefian. The researchers are also exploring how Interact-omics can enhance personalized medicine by leveraging data to make cell immunotherapies more effective while minimizing side effects. The team is also searching for and open to collaborations within the pharmaceutical industry for new opportunities for translation and commercialization of Interact-omics.