LATTICE：用于多模态空间组学整合的图自监督学习

中文摘要 AI 辅助

空间分辨组学研究越来越多地结合转录组学和表观基因组学分析，但下游分析仍常使用单模态管道。我们提出了LATTICE（用于跨模态嵌入的组织水平和转录组信息的潜在对齐），这是一个基于图的自监督框架，可从协调的多模态特征中学习点级表示。LATTICE为每个Visium点整合五个对齐的模态块：Visium RNA、scMultiome RNA、scMultiome ATAC、空间ATAC和空间CUT&Tag。这些模态在统一的晶格表示中捕获空间转录组测量、单细胞推断的调控活性以及原位染色质和组蛋白状态。LATTICE构建空间邻域图，并使用掩码重建、跨模态对齐和空间平滑目标训练TransformerConv编码器。在一个来自匿名临床合作者的包含54912个总斑点的11样本黑色素瘤队列上，LATTICE展示了稳定的优化行为、跨分析种子的可重复嵌入以及所有样本的完全多模态整合。单独将scMultiome RNA添加到Visium RNA中显著提高了与Space Ranger聚类的一致性。其他模态进一步改善了空间连续性和多模态效用得分，尽管有时会降低与RNA衍生参考标签的一致性。这些结果将LATTICE定位为多模态空间组学整合的实用且基于经验的框架，同时也强调了更强监督和更广泛外部基准测试的必要性。

英文摘要

Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines. We present LATTICE (Latent Alignment of Tissue-level and Transcriptomic Information for Cross-modal Embedding), a graph-based self-supervised framework that learns spot-level representations from harmonized multimodal features. LATTICE integrates five aligned modality blocks per Visium spot: Visium RNA, scMultiome RNA, scMultiome ATAC, spatial ATAC, and spatial CUT\&Tag. These modalities capture spatial transcriptomic measurements, single-cell inferred regulatory activity, and in situ chromatin and histone states within a unified lattice representation. LATTICE constructs a spatial neighborhood graph and trains a TransformerConv encoder using masked reconstruction, cross-modal alignment, and spatial smoothness objectives. On a private 11-sample melanoma cohort from an anonymized clinical collaborator comprising 54{,}912 total spots, LATTICE demonstrated stable optimization behavior, reproducible embeddings across analysis seeds, and complete multimodal integration across all samples. Adding scMultiome RNA to Visium RNA alone substantially improved concordance with Space Ranger clusters across 11 runs (adjusted Rand index [ARI] +0.157, normalized mutual information [NMI] +0.143, and spatial contiguity +0.174). Additional modalities further improved spatial contiguity and multimodal utility score (MUS), although they sometimes reduced agreement with RNA-derived reference labels, likely because the learned embeddings captured chromatin and regulatory structure beyond transcriptomic similarity alone. These results position LATTICE as a practical and empirically grounded framework for multimodal spatial omics integration, while also highlighting the need for stronger supervision and broader external benchmarking.

发表机构

• University of Houston（休斯顿大学）
• MD Anderson Cancer Center（MD安德森癌症中心）

机构由 AI 辅助整理，请以论文原文为准。