Nature communications · 2024
METI: deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Jiang Jiahui, Liu Yunhe, Qin Jiangjiang, Chen Jianfeng, Wu Jingjing, Pizzi Melissa P, Lazcano Rossana, Yamashita Kohei, Xu Zhiyuan, Pei Guangsheng, Cho Kyung Serk, Chu Yanshuo, Sinjab Ansam, Peng Fuduan, Yan Xinmiao, Han Guangchun, Wang Ruiping, Dai Enyu, Dai Yibo, Czerniak Bogdan A, Futreal Andrew, Maitra Anirban, Lazar Alexander, Kadara Humam, Jazaeri Amir A, Cheng Xiangdong, Ajani Jaffer, Gao Jianjun, Hu Jian, Wang Linghua
Аннотация
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.