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：随着大语言模型的快速发展，其在科学领域的影响力日益显著。大语言模型在任务泛化与自由对话方面的新兴能力，能够显著推动化学和生物学等领域的发展。然而，作为构成生命基本单位的单细胞生物学领域仍面临诸多挑战。当前方法中存在的较高知识门槛和有限的可扩展性，限制了大语言模型在掌握单细胞数据方面的潜力，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，标志着一种范式转变。借助词汇适应与统一序列生成技术，ChatCell已具备深厚的单细胞生物学专业知识，并能够适应多样化的分析任务。大量实验进一步证明了ChatCell在深化单细胞认知方面的稳健性能与潜力，为该关键领域更易访问、更直观的探索铺平了道路。我们的项目主页访问地址为：https://zjunlp.github.io/project/ChatCell。