小柯机器人

哈佛大学和麻省理工学院Fei Chen和Evan Z. Macosko研究组合作的最新研究，介绍了可在近细胞分辨率水平完成高灵敏空间转录组学的测序工具Slide-seqV2。这一研究成果于2020年12月7日发表在《自然-生物技术》上。

之前，该研究组开发了Slide-seq技术，该技术可实现全转录组范围内RNA的检测，其空间分辨率为10μm。

在本研究中，研究人员研发了Slide-seqV2，它结合和改进了文库生成、磁珠合成和阵列索引方面的功能，使RNA捕获效率可达到单细胞RNA-seq的50％（比Slide-seq提高了约10倍），接近于液滴单细胞RNA-seq技术的检测效率。

首先，研究人员利用Slide-seqV2检测和识别了小鼠海马神经元中树状定位的mRNA。其次，研究人员将Slide-seqV2数据的空间信息与单细胞轨迹分析工具整合在一起，以表征小鼠新皮层的时空发育，从而可以鉴定出因Slide-seq采样不足而遗漏的潜在遗传程序。近细胞水平分辨率和高转录本检测效率的结合使Slide-seqV2在许多实验中都大有可为。

研究人员介绍，测量组织中的分子位置对于了解组织形成和功能至关重要。

附：英文原文

Title: Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

Author: Robert R. Stickels, Evan Murray, Pawan Kumar, Jilong Li, Jamie L. Marshall, Daniela J. Di Bella, Paola Arlotta, Evan Z. Macosko, Fei Chen

Issue&Volume: 2020-12-07

Abstract: Measurement of the location of molecules in tissues is essential for understanding tissue formation and function. Previously, we developed Slide-seq, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10μm. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq), approaching the detection efficiency of droplet-based single-cell RNA-seq techniques. First, we leverage the detection efficiency of Slide-seqV2 to identify dendritically localized mRNAs in neurons of the mouse hippocampus. Second, we integrate the spatial information of Slide-seqV2 data with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex, identifying underlying genetic programs that were poorly sampled with Slide-seq. The combination of near-cellular resolution and high transcript detection efficiency makes Slide-seqV2 useful across many experimental contexts.

DOI: 10.1038/s41587-020-0739-1

Source: https://www.nature.com/articles/s41587-020-0739-1

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
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