小柯机器人

美国麻省理工学院Aviv Regev、Tommaso Biancalani等研究人员合作利用Tangram对空间分辨率的单细胞转录组进行深度学习和比对。相关论文于2021年10月28日在线发表在《自然—方法学》杂志上。

研究人员表示，绘制器官的生物图谱需要研究者在空间上解析整个单细胞转录组，并将这种细胞特征与解剖学尺度联系起来。单细胞和单核RNA-seq（sc/snRNA-seq）可以全面地描述细胞，但会失去空间信息。空间转录组学可以进行空间测量，但分辨率较低，灵敏度有限。靶向原位技术解决了这两个问题，但在基因通量方面是有限的。

为了克服这些限制，研究人员提出了Tangram，一种将sc/snRNA-seq数据与从同一区域收集的各种形式的空间数据相匹配的方法，包括MERFISH、STARmap、smFISH、Spatial Transcriptomics（Visium）和组织学图像。Tangram可以映射任何类型的sc/snRNA-seq数据，包括多模态数据，如来自SHARE-seq的数据，研究人员用它来揭示了染色质可及性的空间模式。研究人员在健康的小鼠脑组织上展示了Tangram，以单细胞分辨率重建了视觉和躯体运动区的全基因组解剖学综合空间图谱。

附：英文原文

Title: Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Author: Biancalani, Tommaso, Scalia, Gabriele, Buffoni, Lorenzo, Avasthi, Raghav, Lu, Ziqing, Sanger, Aman, Tokcan, Neriman, Vanderburg, Charles R., Segerstolpe, sa, Zhang, Meng, Avraham-Davidi, Inbal, Vickovic, Sanja, Nitzan, Mor, Ma, Sai, Subramanian, Ayshwarya, Lipinski, Michal, Buenrostro, Jason, Brown, Nik Bear, Fanelli, Duccio, Zhuang, Xiaowei, Macosko, Evan Z., Regev, Aviv

Issue&Volume: 2021-10-28

Abstract: Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

DOI: 10.1038/s41592-021-01264-7

Source: https://www.nature.com/articles/s41592-021-01264-7

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
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