Integrating Spatial and Single-cell Transcriptomics Data Using Deep Generative Models with SpatialScope

Overview

Journal Nat Commun

Specialty Biology

Date 2023 Nov 29

PMID 38030617

Authors

Xiaomeng Wan

Jiashun Xiao

Sindy Sing Ting Tam

Mingxuan Cai

Ryohichi Sugimura

Yang Wang

Xiang Wan

Zhixiang Lin

Angela Ruohao Wu

Can Yang

Affiliations

Soon will be listed here.

Abstract

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.

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