癌症生物学中空间转录组学的当前计算方法。

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-07-25 DOI:10.1016/bs.acr.2024.06.006
Jaewoo Mo, Junseong Bae, Jahanzeb Saqib, Dohyun Hwang, Yunjung Jin, Beomsu Park, Jeongbin Park, Junil Kim
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引用次数: 0

摘要

多细胞生物体中的细胞通过与邻近细胞的相互作用,构成了一个自组织社会。癌症就源于这种自组织系统中细胞行为的失常。单个肿瘤细胞的身份或特征可以通过基因表达或转录组的标志来体现,这可以通过单细胞解离后的 RNA 测序来解决。然而,单细胞解离过程会导致失去每个肿瘤细胞在组织中的细胞地址或邻近信息,而这些信息对于了解微环境中细胞的失常行为至关重要。空间转录组学技术可以测量以组织内地址为标记的转录组。然而,要了解自组织社会中的细胞行为,我们需要应用数学或统计方法。在此,我们将综述当前癌症生物学中空间转录组学的计算方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Current computational methods for spatial transcriptomics in cancer biology.

Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.

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