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捕捉药物研发中的三维信息:用于探究复杂三维细胞模型的空间分辨工具。

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models.

作者信息

Simão Daniel, Gomes Catarina M, Alves Paula M, Brito Catarina

机构信息

iBET, Instituto de Biologia Experimental e Biológica, Oeiras, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal.

iBET, Instituto de Biologia Experimental e Biológica, Oeiras, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal; The Discoveries Center for Regenerative and Precision Medicine, Lisbon Campus, Oeiras, Portugal.

出版信息

Biotechnol Adv. 2022 Mar-Apr;55:107883. doi: 10.1016/j.biotechadv.2021.107883. Epub 2021 Dec 4.

Abstract

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

摘要

先进的三维(3D)细胞模型已被证明能够描绘多种组织的结构和微环境特征。通过提供具有更高生理和病理生理相关性的数据,3D细胞模型有助于更好地理解人类发育、病理发生和进展机制,以及用于基于3D细胞的药物发现检测。尽管如此,对这些类组织结构的表征和研究给传统分析方法带来了重大挑战,推动了空间分辨技术的发展。在此,我们综述了适用于研究多细胞3D模型且能够保留生物空间信息的最新进展和前沿技术。我们重点关注成像技术和组学工具,即转录组学、蛋白质组学和代谢组学。讨论了这些新方法的优缺点,以及整合来自不同工具数据的机会。

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