As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-form dialogue can significantly advance fields like chemistry and biology. However, the field of single-cell biology, which forms the foundational building blocks of living organisms, still faces several challenges. High knowledge barriers and limited scalability in current methods restrict the full exploitation of LLMs in mastering single-cell data, impeding direct accessibility and rapid iteration. To this end, we introduce ChatCell, which signifies a paradigm shift by facilitating single-cell analysis with natural language. Leveraging vocabulary adaptation and unified sequence generation, ChatCell has acquired profound expertise in single-cell biology and the capability to accommodate a diverse range of analysis tasks. Extensive experiments further demonstrate ChatCell's robust performance and potential to deepen single-cell insights, paving the way for more accessible and intuitive exploration in this pivotal field. Our project homepage is available at https://zjunlp.github.io/project/ChatCell.

翻译：随着大型语言模型（LLMs）的快速发展，其在科学领域的影响力日益显著。LLMs在任务泛化与自由对话方面的新兴能力，能够显著推动化学与生物学等领域的进步。然而，作为生命体基本构成单元的单细胞生物学领域仍面临诸多挑战。现有方法中存在的知识门槛高、可扩展性有限等问题，限制了LLMs在单细胞数据掌握方面的充分应用，阻碍了直接可访问性与快速迭代的实现。为此，我们提出ChatCell——通过自然语言促进单细胞分析的范式变革。通过词汇适应与统一序列生成技术，ChatCell不仅掌握了单细胞生物学的深厚专业知识，还能适配多样化的分析任务需求。大量实验进一步证明了ChatCell在深化单细胞洞察方面的稳健性能与潜力，为该关键领域的可及性与直观化探索开辟了新路径。项目主页请访问：https://zjunlp.github.io/project/ChatCell。