Chemical Tomography of Cancer Organoids and Cyto-Proteo-Genomic Development Stages Through Chemical Communication Signals

IF 26.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY Advanced Materials Pub Date : 2025-02-11 DOI:10.1002/adma.202413017
Arnab Maity, Vivian Darsa Maidantchik, Keren Weidenfeld, Sarit Larisch, Dalit Barkan, Hossam Haick
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Abstract

Organoids mimic human organ function, offering insights into development and disease. However, non-destructive, real-time monitoring is lacking, as traditional methods are often costly, destructive, and low-throughput. In this article, a non-destructive chemical tomographic strategy is presented for decoding cyto-proteo-genomics of organoid using volatile signaling molecules, hereby, Volatile Organic Compounds (VOCs), to indicate metabolic activity and development of organoids. Combining a hierarchical design of graphene-based sensor arrays with AI-driven analysis, this method maps VOC spatiotemporal distribution and generate detailed digital profiles of organoid morphology and proteo-genomic features. Lens- and label-free, it avoids phototoxicity, distortion, and environmental disruption. Results from testing organoids with the reported chemical tomography approach demonstrate effective differentiation between cyto-proteo-genomic profiles of normal and diseased states, particularly during dynamic transitions such as epithelial-mesenchymal transition (EMT). Additionally, the reported approach identifies key VOC-related biochemical pathways, metabolic markers, and pathways associated with cancerous transformations such as aromatic acid degradation and lipid metabolism. This real-time, non-destructive approach captures subtle genetic and structural variations with high sensitivity and specificity, providing a robust platform for multi-omics integration and advancing cancer biomarker discovery.

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肿瘤类器官和细胞-蛋白质-基因组发育阶段的化学断层扫描通过化学通信信号
类器官模仿人体器官的功能,为研究发育和疾病提供了洞见。然而,缺乏非破坏性的实时监测,因为传统方法通常成本高,破坏性大,产量低。本文提出了一种非破坏性化学层析策略,利用挥发性信号分子(即挥发性有机化合物(VOCs))解码类器官的细胞-蛋白质-基因组学,以指示类器官的代谢活性和发育。将基于石墨烯的传感器阵列分层设计与人工智能驱动分析相结合,该方法绘制了VOC的时空分布,并生成了类器官形态和蛋白质基因组特征的详细数字剖面。无镜片和标签,避免光毒性,扭曲和环境破坏。用化学断层扫描方法测试类器官的结果表明,在正常和患病状态下,细胞-蛋白质-基因组谱之间存在有效的分化,特别是在上皮-间充质转化(EMT)等动态转变过程中。此外,报告的方法确定了与VOC相关的关键生化途径、代谢标志物以及与癌变相关的途径,如芳香酸降解和脂质代谢。这种实时、非破坏性的方法以高灵敏度和特异性捕获微妙的遗传和结构变异,为多组学整合和推进癌症生物标志物的发现提供了一个强大的平台。
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来源期刊
Advanced Materials
Advanced Materials 工程技术-材料科学:综合
CiteScore
43.00
自引率
4.10%
发文量
2182
审稿时长
2 months
期刊介绍: Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.
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