Spatial Transcriptomics at Subspot Resolution with BayesSpace

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2021 Jun 4

PMID 34083791

Citations 286

Authors

Edward Zhao

Matthew R Stone

Xing Ren

Jamie Guenthoer

Kimberly S Smythe

Thomas Pulliam

Stephen R Williams

Cedric R Uytingco

Sarah E B Taylor

Paul Nghiem

Jason H Bielas

Raphael Gottardo

Affiliations

Soon will be listed here.

Abstract

Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Here, we introduce BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. We benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, we show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. Our results illustrate BayesSpace's utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.

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