Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.
Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.
复制标题
用七巧板对空间分辨的单细胞转录本进行深度学习和比对。
DOI：
10.1038/s41592-021-01264-7
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发表时间：
2021-11
期刊：
影响因子：
48
通讯作者：
Regev A
中科院分区：
文献类型：
--
作者：
Biancalani T;Scalia G;Buffoni L;Avasthi R;Lu Z;Sanger A;Tokcan N;Vanderburg CR;Segerstolpe Å;Zhang M;Avraham-Davidi I;Vickovic S;Nitzan M;Ma S;Subramanian A;Lipinski M;Buenrostro J;Brown NB;Fanelli D;Zhuang X;Macosko EZ;Regev A
绘制器官的生物图谱需要我们在空间上解析整个单细胞转录组，并将这些细胞特征与解剖尺度相关联。单细胞和单核RNA-seq（sc/snRNA-seq）可以全面地分析细胞，但丢失了空间信息。空间转录组学允许空间测量，但分辨率较低且灵敏度有限。靶向原位技术解决了这两个问题，但基因通量有限。为了克服这些限制，我们提出了Tangram，这是一种将sc/snRNA-seq数据与从同一区域收集的各种形式的空间数据进行比对的方法，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，包括多模式数据，例如来自SHARE-seq的数据，我们用来揭示染色质可及性的空间模式。我们证明七巧板对健康小鼠脑组织，通过重建全基因组的解剖学整合的空间地图在单细胞分辨率的视觉和somatomotor地区。Tangram是一种多功能工具，用于使用深度学习将单细胞和单核RNA-seq数据与空间分辨转录组学数据进行比对。
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. Tangram is a versatile tool for aligning single-cell and single-nucleus RNA-seq data to spatially resolved transcriptomics data using deep learning.
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影响因子：
5.9
作者：
Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
通讯作者：
Lundeberg J
影响因子：
48
作者：
Qian, Xiaoyan;Harris, Kenneth D.;Nilsson, Mats
通讯作者：
Nilsson, Mats
影响因子：
46.9
作者：
Cadwell CR;Palasantza A;Jiang X;Berens P;Deng Q;Yilmaz M;Reimer J;Shen S;Bethge M;Tolias KF;Sandberg R;Tolias AS
通讯作者：
Tolias AS
影响因子：
64.8
作者：
Nitzan, Mor;Karaiskos, Nikos;Rajewsky, Nikolaus
通讯作者：
Rajewsky, Nikolaus
单细胞染色质可及性揭示了调节变化的原理。
影响因子：
64.8
作者：
Buenrostro JD;Wu B;Litzenburger UM;Ruff D;Gonzales ML;Snyder MP;Chang HY;Greenleaf WJ
通讯作者：
Greenleaf WJ