Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Nat Commun. 2024 Aug 25;15(1):7312. doi: 10.1038/s41467-024-51708-9.
Recent advances in spatial transcriptomics (ST) techniques provide valuable insights into cellular interactions within the tumor microenvironment (TME). However, most analytical tools lack consideration of histological features and rely on matched single-cell RNA sequencing data, limiting their effectiveness in TME studies. To address this, we introduce the Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI), an end-to-end framework that maps cancer cells and TME components, stratifies cell types and states, and analyzes cell co-localization. By integrating spatial transcriptomics, cell morphology, and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue. We evaluate the performance of METI on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. We also conduct a quantitative comparison of METI with existing clustering and cell deconvolution tools, demonstrating METI's robust and consistent performance.
近年来，空间转录组学 (ST) 技术的进展为深入了解肿瘤微环境 (TME) 中的细胞相互作用提供了有价值的见解。然而，大多数分析工具缺乏对组织学特征的考虑，并且依赖于匹配的单细胞 RNA 测序数据，这限制了它们在 TME 研究中的有效性。为了解决这个问题，我们引入了形态增强空间转录组分析集成器 (METI)，这是一个端到端的框架，可以对癌细胞和 TME 成分进行映射，对细胞类型和状态进行分层，并分析细胞共定位。通过整合空间转录组学、细胞形态和经过精心整理的基因特征，METI 增强了我们对组织内分子景观和细胞相互作用的理解。我们在来自各种肿瘤组织（包括胃癌、肺癌和膀胱癌以及癌前组织）的 ST 数据上评估了 METI 的性能。我们还对 METI 与现有聚类和细胞去卷积工具进行了定量比较，证明了 METI 的稳健和一致性能。
