CytoBERT: A Foundation Model for Cytometry Data

Researchers released CytoBERT, an open-source foundation model for single-cell cytometry data, pretrained on 15 human datasets with over 50 million cells to learn transferable inter-marker relationships. Fine-tuning for sample-level classification showed transfer learning across heterogeneous datasets is feasible, offering a scalable approach for cytometry analysis.

arXiv:2608.14414v1 Announce Type: new Abstract: Cytometry measures the complex characteristics of single cells e.g., counts and protein expression of immune cells and is widely used across immunological research and clinical settings. However, cytometry data is highly heterogeneous and unstandardized due to experimental protocols and the choice of measured features. While machine learning methods hold the potential to gain deeper insights into cell biology, these challenges make them difficult to apply and transfer across studies. Recent advances in foundation models can alleviate these issues, but corresponding approaches are still scarce in this field. To address this, we provide CytoBERT, a publicly available, open-source, open-weight foundation model for single-cell cytometry data with variable marker panels. CytoBERT is pretrained in a self-supervised manner on a large-scale cytometry corpus 15 human datasets with heterogeneous marker panels and more than 50 million cells curated through marker standardization, enabling it to learn transferable inter-marker relationships within cells. Fine-tuning CytoBERT for sample-level classification demonstrates that transfer learning across heterogeneous cytometry datasets is feasible, providing a starting point for scalable, generalizable cytometry analysis. Code is available at GitHub.