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：随着大语言模型（LLMs）的迅速发展，其在科学领域的影响力日益凸显。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发展。然而，作为生命有机体基本构建单元的单细胞生物学领域仍面临诸多挑战。现有方法存在的高知识门槛与有限的可扩展性，限制了LLMs在掌握单细胞数据方面的全面应用，阻碍了直接可访问性与快速迭代。为此，我们提出ChatCell，通过利用自然语言促进单细胞分析，标志着范式转变。通过词汇适应与统一序列生成，ChatCell获得了单细胞生物学领域的深厚专业知识，并能适应多样化的分析任务。广泛实验进一步证明了ChatCell的稳健性能及其深化单细胞见解的潜力，为这一关键领域的更易访问与直观探索铺平道路。我们的项目主页位于https://zjunlp.github.io/project/ChatCell。