发表机构
The Chinese University of Hong Kong; State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology; Institute of Automation, Chinese Academy of Sciences; Beijing Academy of Artificial Intelligence; University of Chinese Academy of Sciences（香港中文大学; 脑认知与脑启发智能技术国家重点实验室; 中国科学院自动化研究所; 北京人工智能研究院; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出LangPatch框架，利用语言接口和对比学习对齐Patch-seq转录组与电生理数据，实现跨脑区、跨物种的预测迁移，为神经科学多模态基础模型提供基础。
AI 中文摘要
整合转录组和电生理学数据对于构建神经科学的多模态基础模型至关重要。Patch-seq技术能够从同一神经元获取基因表达和内在电生理特性的配对测量，为训练跨模态模型奠定了基础。在此，我们提出LangPatch，一种基于基础模型的对比学习框架，利用配对的Patch-seq数据，通过基于语言的接口，将预训练的GenePT表示与电生理表型对齐。基因描述和言语化的电生理特征由同一个冻结的文本编码器嵌入。一个上下文适配器和投影模块通过配对对比学习连接各模态。在小鼠视觉皮层、小鼠运动皮层和人类皮层队列中，LangPatch在所评估的基础模型和表示学习方法中，实现了最高的平均转录组到电生理预测相关性。它还改善了两个小鼠队列中留出数据的跨模态对齐（FOSCTTM 0.107/0.135，对比现有跨模态Patch-seq插补方法JAMIE的0.208/0.222）。它从电生理学预测转录组家族、类型、皮层层次和标记基因表达，在大多数评估指标上优于其他基线。更重要的是，该方法可跨脑区和物种迁移：在小鼠视觉皮层上训练的模型预测运动皮层电生理时，相关性保留约70%，预测人类皮层时保留47%（急性切片记录为58%）。总之，这些结果证明了神经元分子与功能表示之间的对齐，为神经科学中的多模态基础模型提供了构建模块。
英文摘要
Integrating transcriptomic and electrophysiological data is essential for building multimodal foundation models for neuroscience. Patch-seq provides paired measurements of gene expression and intrinsic electrophysiology from the same neuron, establishing a basis for training cross-modal models. Here we introduce LangPatch, a foundation-model-based contrastive learning framework that uses paired Patch-seq data to align pretrained GenePT representations with electrophysiological phenotypes through a language-based interface. Gene descriptions and verbalized electrophysiological profiles are embedded by the same frozen text encoder. A context adapter and projection modules connect the modalities through paired contrastive learning. Across mouse visual, mouse motor, and human cortical cohorts, LangPatch achieves the highest mean transcriptome-to-electrophysiology prediction correlation among the evaluated foundation-model and representation-learning methods. It also improves held-out cross-modal alignment in the two mouse cohorts (FOSCTTM 0.107/0.135 vs. 0.208/0.222 for JAMIE, an existing cross-modal Patch-seq imputation method). It predicts transcriptomic family, type, cortical layer, and marker-gene expression from electrophysiology, exceeding other baselines on most endpoints. More importantly, the method transfers across brain areas and species: a model trained on mouse visual cortex predicts electrophysiology in motor cortex with approximately 70% correlation retention and in human cortex with 47% (58% on acute-slice recordings). Together, these results demonstrate alignment between molecular and functional representations of neurons, providing a building block for multimodal foundation models in neuroscience.
CommentsCode: https://github.com/ai4biomedicine/LangPatch
