Cell2Text: Multimodal LLM for Generating Single-Cell Descriptions from RNA-Seq Data
This addresses the need for richer, contextual explanations in biology by providing interpretable outputs for characterizing unseen cells, representing a novel integration of methods rather than a foundational breakthrough.
The paper tackled the problem of single-cell RNA sequencing models being limited to discrete labels by introducing Cell2Text, a multimodal generative framework that translates scRNA-seq profiles into structured natural language descriptions, resulting in improved classification accuracy, ontological consistency, and semantic fidelity in text generation.
Single-cell RNA sequencing has transformed biology by enabling the measurement of gene expression at cellular resolution, providing information for cell types, states, and disease contexts. Recently, single-cell foundation models have emerged as powerful tools for learning transferable representations directly from expression profiles, improving performance on classification and clustering tasks. However, these models are limited to discrete prediction heads, which collapse cellular complexity into predefined labels that fail to capture the richer, contextual explanations biologists need. We introduce Cell2Text, a multimodal generative framework that translates scRNA-seq profiles into structured natural language descriptions. By integrating gene-level embeddings from single-cell foundation models with pretrained large language models, Cell2Text generates coherent summaries that capture cellular identity, tissue origin, disease associations, and pathway activity, generalizing to unseen cells. Empirically, Cell2Text outperforms baselines on classification accuracy, demonstrates strong ontological consistency using PageRank-based similarity metrics, and achieves high semantic fidelity in text generation. These results demonstrate that coupling expression data with natural language offers both stronger predictive performance and inherently interpretable outputs, pointing to a scalable path for label-efficient characterization of unseen cells.