Data enhancement in the age of spatial biology.

Advances in cancer research Pub Date : 2024-01-01 Epub Date: 2024-07-09 DOI:10.1016/bs.acr.2024.06.008
Linbu Liao, Patrick C N Martin, Hyobin Kim, Sanaz Panahandeh, Kyoung Jae Won
{"title":"Data enhancement in the age of spatial biology.","authors":"Linbu Liao, Patrick C N Martin, Hyobin Kim, Sanaz Panahandeh, Kyoung Jae Won","doi":"10.1016/bs.acr.2024.06.008","DOIUrl":null,"url":null,"abstract":"<p><p>Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.</p>","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"163 ","pages":"39-70"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in cancer research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/bs.acr.2024.06.008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/9 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

空间生物学时代的数据增强。
揭示细胞在其原生环境中错综复杂的相互作用是了解基本生物过程和揭示疾病机制的核心,尤其是在癌症等复杂疾病中。空间转录组学(ST)为研究组织内基因表达的空间组织提供了一个革命性的视角,使研究人员有能力研究健康和疾病中的细胞异质性和微环境。然而,目前的表观基因组学技术往往在分辨率或同时分析的基因数量方面受到限制。将 ST 数据与单细胞转录组学和详细的组织染色图像等补充来源进行整合,是克服这些局限性的强大解决方案。本综述深入探讨了推动空间转录组学与其他数据类型整合的计算方法。通过阐明关键挑战和概述当前的算法解决方案,我们旨在强调这些方法在彻底改变我们对癌症生物学的理解方面所具有的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信