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

被引：379

作者：

Biancalani, Tommaso ^{[1
,8
]}

Scalia, Gabriele ^{[1
,9
]}

Buffoni, Lorenzo ^{[2
]}

Avasthi, Raghav ^{[1
,3
]}

Lu, Ziqing ^{[1
,3
]}

Sanger, Aman ^{[1
]}

Tokcan, Neriman ^{[1
]}

Vanderburg, Charles R. ^{[1
]}

Segerstolpe, Asa ^{[1
]}

Zhang, Meng ^{[4
,11
]}

Avraham-Davidi, Inbal ^{[1
]}

Vickovic, Sanja ^{[1
]}

Nitzan, Mor ^{[1
,5
,10
]}

Ma, Sai ^{[1
,6
,7
]}

Subramanian, Ayshwarya ^{[1
]}

Lipinski, Michal ^{[1
,7
]}

Buenrostro, Jason ^{[1
,7
]}

Brown, Nik Bear ^{[3
]}

Fanelli, Duccio ^{[2
]}

Zhuang, Xiaowei ^{[4
,11
]}

Macosko, Evan Z. ^{[1
]}

Regev, Aviv ^{[1
,6
,8
,11
]}

机构：

[1] Broad Inst MIT & Harvard, Cambridge, MA 02142 USA

[2] Univ Florence, Dept Phys & Astrophys, Florence, Italy

[3] Northeastern Univ, Boston, MA 02115 USA

[4] Harvard Univ, Dept Phys, Dept Chem & Chem Biol, Cambridge, MA 02138 USA

[5] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA

[6] MIT, Dept Biol, Cambridge, MA 02139 USA

[7] Harvard Univ, Dept Stem Cell & Regenerat Biol, Cambridge, MA 02138 USA

[8] Genentech Inc, San Francisco, CA 94080 USA

[9] Roche, Monza, Italy

[10] Hebrew Univ Jerusalem, Fac Med, Sch Comp Sci & Engn, Racah Inst Phys, Jerusalem, Israel

[11] Howard Hughes Med Inst, Chevy Chase, MD 20815 USA

来源：

NATURE METHODS | 2021年 / 18卷 / 11期

关键词：

COMMON COORDINATE FRAMEWORK; GENOME-WIDE EXPRESSION; GENE-EXPRESSION; RNA; TISSUE; SEQ; CHROMATIN;

D O I：

10.1038/s41592-021-01264-7

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Tangram is a versatile tool for aligning single-cell and single-nucleus RNA-seq data to spatially resolved transcriptomics data using deep learning. 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.

引用

页码：1352 / +

页数：25

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