The Landscape of Single-Cell Foundation Models: Design Principles, Applications, and Open Challenges

Recent advances in large-scale single-cell atlases and large language models (LLMs) have enabled the emergence of single-cell foundation models (scFMs)-pretrained models that aim to learn transferable representations of cellular states across tissues, conditions, and molecular modalities. These models hold promise for moving single-cell analysis beyond task-specific pipelines towards unified, generalizable and predictive frameworks, yet their biological fidelity, generalization and scaling capacity, as well as utility for perturbation modeling remain challenging. In this review, we summarize recent progress in scFMs across key design dimensions, including data representation, model architecture and learning objective, and examine how these frameworks are being applied to biological discovery. We further critically assess current challenges and opportunities from multimodal pretraining atlas, emerging pretraining paradigms, downstream applications, and real-world barriers. This review provides a structured guide for both computational scientists and experimental biologists seeking to understand, develop, and apply foundation models for single-cell research.