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

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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。

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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.

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来源期刊

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

审稿时长

111 days