Nova-ST:用于空间转录组学的纳米图案超密集平台。

IF 4.3 Q1 BIOCHEMICAL RESEARCH METHODS Cell Reports Methods Pub Date : 2024-08-19 Epub Date: 2024-08-06 DOI:10.1016/j.crmeth.2024.100831
Suresh Poovathingal, Kristofer Davie, Lars E Borm, Roel Vandepoel, Nicolas Poulvellarie, Annelien Verfaillie, Nikky Corthout, Stein Aerts
{"title":"Nova-ST:用于空间转录组学的纳米图案超密集平台。","authors":"Suresh Poovathingal, Kristofer Davie, Lars E Borm, Roel Vandepoel, Nicolas Poulvellarie, Annelien Verfaillie, Nikky Corthout, Stein Aerts","doi":"10.1016/j.crmeth.2024.100831","DOIUrl":null,"url":null,"abstract":"<p><p>Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.</p>","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384075/pdf/","citationCount":"0","resultStr":"{\"title\":\"Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics.\",\"authors\":\"Suresh Poovathingal, Kristofer Davie, Lars E Borm, Roel Vandepoel, Nicolas Poulvellarie, Annelien Verfaillie, Nikky Corthout, Stein Aerts\",\"doi\":\"10.1016/j.crmeth.2024.100831\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.</p>\",\"PeriodicalId\":29773,\"journal\":{\"name\":\"Cell Reports Methods\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11384075/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Reports Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.crmeth.2024.100831\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.crmeth.2024.100831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/6 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

使用条形码捕获阵列的空间转录组学工作流程通常用于解析组织中的基因表达。然而,现有技术要么受限于捕获阵列密度,要么成本过高,无法进行大规模图谱绘制。我们介绍的 Nova-ST 是一种高密度纳米图案空间转录组学技术,源自随机条形编码的 Illumina 测序流式细胞。Nova-ST 可以对大型组织切片进行定制化、低成本、灵活和高分辨率的空间剖析。对小鼠大脑切片的基准测试表明,与现有方法相比,Nova-ST 的灵敏度明显更高,而且成本更低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics.

Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cell Reports Methods
Cell Reports Methods Chemistry (General), Biochemistry, Genetics and Molecular Biology (General), Immunology and Microbiology (General)
CiteScore
3.80
自引率
0.00%
发文量
0
审稿时长
111 days
期刊最新文献
Optimized full-spectrum flow cytometry panel for deep immunophenotyping of murine lungs. A deep learning framework combining molecular image and protein structural representations identifies candidate drugs for pain. Adult zebrafish can learn Morris water maze-like tasks in a two-dimensional virtual reality system. Recovering single-cell expression profiles from spatial transcriptomics with scResolve. Mimicking and analyzing the tumor microenvironment.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1