发表机构
Johnson & Johnson Innovative Medicine（强生创新医药）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PaSTel是一种整合多尺度生物先验的层级多模态预训练框架，通过三层生物先验设计解决现有空间转录组学方法的局限，在多个下游任务中性能优于现有编码器。
AI 中文摘要
空间转录组学（ST）将组织形态与分子程序关联，推动了对齐组织学图像与基因表达的多模态预训练方法发展，但现有方法存在两大关键局限：具有空间信息的基因选择常被普遍存在的管家基因主导，导致表征判别性弱；独立的斑点-图像块对齐无法捕捉对组织结构至关重要的空间依赖关系。为应对这些挑战，本文提出PaSTel，一种层级多模态预训练框架，在三个层级整合生物先验：在斑点层级，采用TF-IDF重加权识别具有空间信息的基因；在功能层级，使用经整理的KEGG通路作为锚点编码全局生物语义；在区域层级，通过空间聚类聚合相邻斑点以建模中尺度组织结构。在多个下游任务中，PaSTel始终优于现有视觉及视觉-组学编码器，证明整合多尺度生物先验可为空间转录组学生成更具信息性与可迁移性的表征。
英文摘要
Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, existing approaches suffer from two key limitations: spatially informative gene selection is often dominated by ubiquitous housekeeping genes, leading to weakly discriminative representations, and independent spot-patch alignment fails to capture spatial dependencies that are critical for tissue organization. To address these challenges, we introduce PaSTel, a hierarchical multimodal pretraining framework that integrates biological priors at three levels. At the spot level, TF-IDF reweighting is used to identify spatially informative genes; at the functional level, curated KEGG pathways serve as anchors for encoding global biological semantics; and at the regional level, spatial clustering aggregates neighboring spots to model meso-scale tissue structure. Across multiple downstream tasks, PaSTel consistently outperforms existing vision and vision-omics encoders, demonstrating that incorporating multiscale biological priors yields more informative and transferable representations for spatial transcriptomics.
CommentsThis paper was accepted to the 3rd ICML 2026 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences
