METI: deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics
Jiahui Jiang, Yunhe Liu, Jiang‐Jiang Qin, Jianfeng Chen, Jingjing Wu, Melissa Pool Pizzi, Rossana Lazcano, Kohei Yamashita, Zhiyuan Xu, Guangsheng Pei, Kyung Serk Cho, Yanshuo Chu, Ansam Sinjab, Fuduan Peng, Xinmiao Yan, Guangchun Han, Ruiping Wang, Enyu Dai, Yibo Dai, Bogdan Czerniak, P. Andrew Futreal, Anirban Maitra, Alexander J. Lazar, Humam Kadara, Amir A. Jazaeri, Xiangdong Cheng, Jaffer A. Ajani, Jianjun Gao, Jian Hu, Linghua Wang
Abstract
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. Integrating tissue histology with spatial transcriptomics (ST) can significantly enhance the analysis of tumor heterogeneity and the tumor microenvironment (TME). Here, the authors present METI, a computational framework to analyze cancer cells and the complex TME by integrating ST with histology imaging.