In cases of blood and lymph node cancers, specialists must analyze large amounts of complex single-cell data. Researchers at the University Medical Center Freiburg and the Weizmann Institute of Science in Israel have now developed an artificial intelligence-based model that can automate key parts of this analysis. In cases of chronic lymphocytic leukemia (CLL), the model specifically identified typical cancer cells among millions of measured cells. The results correlated very well with the manual evaluations performed by specialists. The open-source model CytoVI (short for “Variational Inference for Cytometry”) could thus reduce the workload on staff, standardize examinations, and make disease-relevant changes more easily visible. The method was published in *Nature Methods* on September 30, 2026.

“This will allow us to evaluate blood samples—which are collected in clinical settings anyway—in a much more automated manner in the future,” says Dr. Florian Ingelfinger, first author of the study and a researcher at the Department of Internal Medicine I at the University Medical Center Freiburg, as well as a junior research group leader at the German Consortium for Translational Cancer Research (DKTK), Freiburg site. “The model reduces the workload for hospitals while also helping to discover new cell populations that are of interest as diagnostic markers or potential targets for therapies.” Ingelfinger heads the Laboratory for Generative Single Cell Immunology at the University Medical Center Freiburg.

From Research to Routine Diagnostics

CytoVI analyzes single-cell data generated by flow cytometry. No images are captured in the process. Laser measurements are used to record the characteristics of individual cells, such as specific proteins, cell size, and internal structure. This creates a digital profile for each cell. CytoVI recognizes typical patterns in these profiles and can, for example, automatically distinguish leukemia cells from other blood cells.

A key strength of the model is that it mathematically accounts for technical differences between various measurements—such as those caused by different devices, measurement times, or laboratories—and helps distinguish them from biological differences. This enables a more comparable analysis.

“Above all, patients need a reliable diagnosis and clarity as quickly as possible about what disease they have. CytoVI could help us with this in the future,” says Prof. Dr. Robert Zeiser, head of the Department of Tumor Immunology and Immunoregulation at the Division of Internal Medicine I at the University Medical Center Freiburg, who contributed to the publication.

In 63 patients with various B-cell lymphomas, CytoVI automatically identified T-cell clusters associated with specific forms of the disease. These included a rare cell population that had not been described in the original manual analysis. Such cell clusters can provide clues to diagnostic biomarkers and potential therapeutic targets.

The evaluation of CLL data from routine diagnostics was particularly clinically relevant. “We were able to see that the automatic identification correlated very well with the manual evaluation,” says Ingelfinger. The reliable identification of very few cancer cells—for example, following cancer treatment—remains a challenge at present. The reliability of the analysis is reported to physicians in each case, enabling targeted reviews. “CytoVI is intended to support medical assessment, not completely replace it,” Ingelfinger adds.

In this way, the large volumes of flow cytometry data generated over many years in routine diagnostics can be more easily compared and evaluated collectively. This unlocks a treasure trove of data for research that was previously of limited use.

Researchers at the University Medical Center Freiburg now aim to apply the method to other types of blood cancer. CytoVI is freely available as open-source software at https://scvi-tools.org. Researchers can use the method to analyze their own single-cell data.

SOURCE: UKF