发表机构
Yale University; University of Pennsylvania; University of Michigan, Ann Arbor（耶鲁大学; 宾夕法尼亚大学; 密歇根大学安娜堡分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出泛癌空间转录组学基础模型GITIII-scale，通过建模细胞间相互作用及配体-受体通路学习肿瘤微环境表征，在未见过的癌症类型中更准确恢复生态位状态变化，可识别潜在药物靶点。
AI 中文摘要
在肿瘤微环境中，细胞状态受其生态位内邻近细胞的细胞间相互作用（CCIs）影响。识别与致病过程相关的失调CCIs可为药物发现确定靶点。基于成像的空间转录组学和单细胞RNA测序分别提供了研究CCIs所需的单细胞空间信息和全转录组测量，但两种模态均无法同时提供这两类信息。现有的空间转录组学基础模型也无法有效从具有全转录组覆盖的空间分辨单细胞数据中学习，无法明确推断驱动细胞状态-生态位关联的CCI机制，或无法具备足够的可解释性以支持直接的生物学解读。在此，我们提出GITIII-scale，这是一种用于肿瘤微环境（TME）表征学习的分层可解释泛癌空间转录组学基础模型，用于研究细胞状态-生态位关联及其潜在的配体-受体（LR）信号通路。GITIII-scale使用Transformer对定义空间距离的细胞对之间的相互作用进行建模，采用无前馈网络的可解释单层图Transformer分解受体细胞中每个基因受每个邻近发送细胞的影响方式，并使用图Transformer生成细胞邻域嵌入。在我们组装的标本匹配scRNA-seq和基于成像的空间转录组学数据集的泛癌数据库上训练后，GITIII-scale生成的TME嵌入在训练期间未见过的癌症类型中，比现有空间转录组学基础模型更准确地恢复了生态位相关的状态变化。对一个未见过的乳腺癌数据集的案例研究进一步通过识别与内皮过度生长和肿瘤发生相关的潜在可药物靶向LR通路，证明了该模型的可解释性。
英文摘要
In the tumor microenvironment, a cell's state is influenced by cell-cell interactions (CCIs) with neighboring cells in its niches. Identifying dysregulated CCIs that are associated with pathogenic processes pinpoints targets for drug discovery. Imaging-based spatial transcriptomics and single-cell RNA sequencing provide, respectively, single-cell spatial information and transcriptome-wide measurements needed to study CCIs, but neither modality provides both. Existing spatial transcriptomics foundation models also cannot effectively learn from spatially resolved single-cell data with full-transcriptome coverage, explicitly infer the CCI mechanisms driving cell state-niche associations, or be interpretable enough to support direct biological interpretations. Here, we present GITIII-scale, a hierarchical, interpretable pan-cancer spatial transcriptomics foundation model for TME representation learning that investigates cell state-niche associations and their underlying ligand-receptor (LR) signaling pathways. GITIII-scale uses transformers to model interactions between pairs of cells at defined spatial distances, an interpretable single-layer graph transformer without a feed-forward network to decompose how each gene in a receiver cell is influenced by each neighboring sender cell, and a graph transformer to generate cellular-neighborhood embeddings. Trained on our assembled pan-cancer database of specimen-matched scRNA-seq and imaging-based spatial transcriptomics datasets, GITIII-scale generated TME embeddings that recovered niche-associated state changes more accurately than existing spatial transcriptomics foundation models in cancer types unseen during training. A case study of an unseen breast cancer dataset further demonstrated the model's interpretability by identifying potentially drug-targetable LR pathways associated with endothelial overgrowth and tumorigenesis.
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