Access through your institution

通过您的机构访问

Buy or subscribe

购买或订阅

One of my first experiences of analysing biomedical data, following my training in applied statistics, was to perform differential gene expression analysis of microarray data. Nowadays, microarray technologies have been largely superseded by three major biotechnological advances: RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq) and spatial ‘omics’.

在接受应用统计学培训后，我分析生物医学数据的第一次经验之一是对微阵列数据进行差异基因表达分析。如今，微阵列技术已在很大程度上被三大生物技术进步所取代：RNA测序（RNA-seq），单细胞RNA测序（scRNA-seq）和空间“组学”。

The 2020s has witnessed a massive rise in the take-up of spatial omics technologies, particularly at subcellular resolution, but what biotechnological and methodological advances were necessary for this spatial omics revolution to occur?One key enabling approach — combinatorial labelling of probes to capture the presence of RNAs in situ in single cells — was described by Levsky et al.

20世纪20年代，空间组学技术的使用率大幅上升，特别是在亚细胞分辨率方面，但要实现这场空间组学革命，需要哪些生物技术和方法学进步？Levsky等人描述了一种关键的使能方法-探针的组合标记以在单细胞中原位捕获RNA的存在。

in 2002. In this study, the authors used their new approach of ‘single-cell gene expression profiling’ to assess the transcription of 10–11 genes in approximately 2,500 single-cell nuclei from cell lines in situ. The use of combinations of fluorescent colours for labelling each gene was key to overcoming the limit of 3–4 optical colour channels in standard fluorescence in situ hybridization.

2002年。。使用荧光颜色组合标记每个基因是克服标准荧光原位杂交中3-4个光学颜色通道限制的关键。

By representing each gene as a barcoded combination of four fluorescent colours positive for at least two colours, the authors were able to assign individual ‘pseudo-colours’ to each gene being profiled..

通过将每个基因表示为至少两种颜色呈阳性的四种荧光颜色的条形码组合，作者能够为每个被分析的基因分配单独的“伪颜色”。。

This is a preview of subscription content, access via your institution

这是订阅内容的预览，可通过您的机构访问

Access options

访问选项

Access through your institution

通过您的机构访问

Access through your institution

通过您的机构访问

Change institution

变革机构

Buy or subscribe

购买或订阅

Access Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription24,99 € / 30 dayscancel any timeLearn moreSubscription info for Chinese customersWe have a dedicated website for our Chinese customers. Please go to naturechina.com to subscribe to this journal.Go to naturechina.comBuy this articlePurchase on SpringerLinkInstant access to full article PDFBuy nowPrices may be subject to local taxes which are calculated during checkout.

Access Nature和54篇其他Nature Portfolio journalsGet Nature+，我们最有价值的在线订阅24,99欧元/30天，随时为中国客户获取更多订阅信息我们为中国客户提供了一个专门的网站。请访问naturechina.com订阅本期刊。访问naturechina.comBuy本文在Springerlink上购买即时访问完整文章PDFBuy now价格可能需要缴纳结帐时计算的地方税。

Additional access options:

其他访问选项：

Log in

登录

Learn about institutional subscriptions

了解机构订阅

Read our FAQs

阅读我们的常见问题

Contact customer support

联系客户支持

ReferencesOriginal articleLevsky, J. M. et al. Single-cell gene expression profiling. Science 297, 836–840 (2002)Article

参考原始文章Levsky，J.M。等人。单细胞基因表达谱。科学297836-840（2002）文章

PubMed

PubMed

Google Scholar

谷歌学者

Download referencesAuthor informationAuthors and AffiliationsSchool of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, AustraliaShila GhazanfarSydney Precision Data Science Centre, University of Sydney, Sydney, New South Wales, AustraliaShila GhazanfarCharles Perkins Centre, The University of Sydney, Sydney, New South Wales, AustraliaShila GhazanfarAuthorsShila GhazanfarView author publicationsYou can also search for this author in.

下载参考文献作者信息作者和所属机构悉尼大学数学与统计学院，新南威尔士州悉尼，澳大利亚拉希拉·加赞法尔悉尼精密数据科学中心，悉尼大学，新南威尔士州悉尼，澳大利亚拉希拉·加赞法尔·查尔斯·珀金斯中心，悉尼大学，新南威尔士州悉尼，澳大利亚拉希拉·加赞法尔作者Shila GhazanfarView作者出版物您也可以在中搜索此作者。

PubMed Google ScholarCorresponding authorCorrespondence to

PubMed谷歌学者通讯社

Shila Ghazanfar.Ethics declarations

希拉·加赞法尔。道德宣言

Competing interests

相互竞争的利益

The author declares no competing interests.

作者声明没有利益冲突。

Rights and permissionsReprints and permissionsAbout this articleCite this articleGhazanfar, S. Single-cell expression profiling has its roots in in situ techniques.

权利和许可打印和许可本文引用本文Ghazanfar，S。单细胞表达谱分析有其原位技术的根源。

Nat Rev Genet (2024). https://doi.org/10.1038/s41576-024-00787-xDownload citationPublished: 09 October 2024DOI: https://doi.org/10.1038/s41576-024-00787-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Nat Rev Genet（2024年）。https://doi.org/10.1038/s41576-024-00787-xDownload引文发布日期：2024年10月9日OI：https://doi.org/10.1038/s41576-024-00787-xShare本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供