Deep Learning and Alignment of Spatially Resolved Single-cell Transcriptomes with Tangram

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2021 Oct 29

PMID 34711971

Citations 247

Authors

Tommaso Biancalani

Gabriele Scalia

Lorenzo Buffoni

Raghav Avasthi

Ziqing Lu

Aman Sanger

Neriman Tokcan

Charles R Vanderburg

Asa Segerstolpe

Meng Zhang

Inbal Avraham-Davidi

Sanja Vickovic

Mor Nitzan

Sai Ma

Ayshwarya Subramanian

Michal Lipinski

Jason Buenrostro

Nik Bear Brown

Duccio Fanelli

Xiaowei Zhuang

Evan Z Macosko

Aviv Regev

Affiliations

Soon will be listed here.

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.

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