A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following

2025-01-14

AI Summary

To address the complexity, high technical barriers, and poor cross-task generalization in scRNA-seq analysis workflows, this paper introduces InstructCell—the first multimodal AI assistant tailored for single-cell analysis. Methodologically, it proposes (1) a novel “cell language” multimodal architecture that jointly encodes gene expression matrices, cell metadata, and natural language; (2) a comprehensive scRNA-seq instruction-tuning dataset spanning multiple tissues and species; and (3) cross-modal alignment with biological knowledge injection to enable end-to-end semantic understanding of diverse tasks—including cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction—from natural language instructions. Experimental results demonstrate that InstructCell significantly outperforms existing foundation models across multiple benchmarks, supports flexible experimental configuration, substantially lowers the barrier to entry for scRNA-seq analysis, and accelerates the generation of biologically meaningful insights.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing (scRNA-seq) data serves as the"language of cellular biology", capturing intricate gene expression patterns at the single-cell level. However, interacting with this"language"through conventional tools is often inefficient and unintuitive, posing challenges for researchers. To address these limitations, we present InstructCell, a multi-modal AI copilot that leverages natural language as a medium for more direct and flexible single-cell analysis. We construct a comprehensive multi-modal instruction dataset that pairs text-based instructions with scRNA-seq profiles from diverse tissues and species. Building on this, we develop a multi-modal cell language architecture capable of simultaneously interpreting and processing both modalities. InstructCell empowers researchers to accomplish critical tasks-such as cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction-using straightforward natural language commands. Extensive evaluations demonstrate that InstructCell consistently meets or exceeds the performance of existing single-cell foundation models, while adapting to diverse experimental conditions. More importantly, InstructCell provides an accessible and intuitive tool for exploring complex single-cell data, lowering technical barriers and enabling deeper biological insights.

Problem

• Single-cell RNA sequencing
• Data analysis
• Gene expression

Innovation

• InstructCell
• Multimodal AI Assistant
• Natural Language Processing in Single-Cell Analysis