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下一代空间转录组学:释放推动转化肿瘤学发展的力量。

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology.

作者信息

Wang Nan, Hong Weifeng, Wu Yixing, Chen Zhe-Sheng, Bai Minghua, Wang Weixin, Zhu Ji

机构信息

Cosmos Wisdom Biotech Co. Ltd Hangzhou China.

Department of Radiation Oncology Zhejiang Cancer Hospital Hangzhou China.

出版信息

MedComm (2020). 2024 Oct 6;5(10):e765. doi: 10.1002/mco2.765. eCollection 2024 Oct.

Abstract

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

摘要

空间转录组学(ST)的不断发展成为新的前沿领域,在转化肿瘤学领域带来了前所未有的影响。这引发了系统性的实验设计、分析范围和深度,同时在过去几年中,全面的生物信息学方法也在不断发展。然而,利用空间生物学的力量并简化一系列ST工具以实现指定的研究目标是至关重要的,并且需要实际经验。我们通过按时间顺序更新ST在不同主要基础上的技术范围,对其进行了系统综述,提示了该领域普遍采用的ST技术。我们还回顾了生物信息学工具的当前进展,并提出了一个流水线式工作流程以及一个可用于ST数据探索的工具箱。特别关注ST被广泛应用的肿瘤微环境,我们通过叙述多项癌症类型中基于ST的技术在机制阐明或生物标志物分析(转化肿瘤学)方面的研究及其通过ST开展研究的方式,总结了最新进展。这篇更新后的综述提供了一种指导,许多高分辨率ST工具支持的前瞻性观点可用于解开可能在未来具有临床意义的生物学问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be78/11456678/85189a74106c/MCO2-5-e765-g003.jpg

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