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

TLDR

Charting an organ’s biological atlas requires spatially resolving the entire single‑cell transcriptome and linking cellular features to anatomy, yet sc/snRNA‑seq provides comprehensive profiles but loses spatial context, while spatial transcriptomics offers location data at lower resolution and limited sensitivity, and targeted in situ methods improve resolution but are constrained by 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 uses deep learning to map any sc/snRNA‑seq dataset—including multimodal SHARE‑seq data—to spatial modalities, revealing spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, reconstructing a genome‑wide anatomically integrated spatial map at single‑cell resolution of the visual and somatomotor areas, and show it is a versatile tool for aligning single‑cell and single‑nucleus RNA‑seq data to spatially resolved transcriptomics data.

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