奥地利科学院Christoph Bock小组的一项最新研究发现了多模态学习允许对单细胞数据进行基于聊天的探索。2025年11月11日出版的《自然—生物技术》杂志发表了这项成果。

研究人员提出了CellWhisperer，一个人工智能（AI）模型和软件工具，用于基于聊天的基因表达询问。研究组建立了转录组及其文本注释的多模态嵌入，在100万个具有人工智能策划描述的RNA测序图谱上进行主题对比学习。这种嵌入通知了一个大型语言模型，该模型可以在自然语言聊天中回答他们提供的关于细胞和基因的问题。

课题组研究人员对CellWhisperer在细胞类型和其他生物学注释的零预测方面的表现进行了基准测试，并在人类胚胎发育的荟萃分析中展示了其生物学发现的主题。该课题组人员将CellWhisperer聊天框与CELLxGENE浏览器集成在一起，允许主题通过组合图形和聊天界面交互式地探索基因表达。总之，CellWhisperer利用大型社区规模的数据存储库连接转录组和文本，从而使单细胞rna测序数据与自然语言聊天的交互式探索成为可能。

据介绍，单细胞测序以前所未有的规模和细节表征生物样品，但数据解释仍然具有挑战性。

附：英文原文

Title: Multimodal learning enables chat-based exploration of single-cell data

Author: Schaefer, Moritz, Peneder, Peter, Malzl, Daniel, Lombardo, Salvo Danilo, Peycheva, Mihaela, Burton, Jake, Hakobyan, Anna, Sharma, Varun, Krausgruber, Thomas, Sin, Celine, Menche, Jrg, Tomazou, Eleni M., Bock, Christoph

Issue&Volume: 2025-11-11

Abstract: Single-cell sequencing characterizes biological samples at unprecedented scale and detail, but data interpretation remains challenging. Here, we present CellWhisperer, an artificial intelligence (AI) model and software tool for chat-based interrogation of gene expression. We establish a multimodal embedding of transcriptomes and their textual annotations, using contrastive learning on 1 million RNA sequencing profiles with AI-curated descriptions. This embedding informs a large language model that answers user-provided questions about cells and genes in natural-language chats. We benchmark CellWhisperer’s performance for zero-shot prediction of cell types and other biological annotations and demonstrate its use for biological discovery in a meta-analysis of human embryonic development. We integrate a CellWhisperer chat box with the CELLxGENE browser, allowing users to interactively explore gene expression through a combined graphical and chat interface. In summary, CellWhisperer leverages large community-scale data repositories to connect transcriptomes and text, thereby enabling interactive exploration of single-cell RNA-sequencing data with natural-language chats.

DOI: 10.1038/s41587-025-02857-9

Source: https://www.nature.com/articles/s41587-025-02857-9