Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

• Published in: Nature communications Vol. 13; no. 1; p. 2339
• Main Authors: Miller, Brendan F., Huang, Feiyang, Atta, Lyla, Sahoo, Arpan, Fan, Jean
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29-04-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 38/39; 631/114; 631/337/2019; Datasets; Freeware; Gene expression; Gene Expression Profiling; Humanities and Social Sciences; multidisciplinary; Pixels; Science; Science (multidisciplinary); Software; Source code; Transcription; Transcriptome - genetics; Transcriptomics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-30033-z

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at
https://github.com/JEFworks-Lab/STdeconvolve
.
Identifying cell-type-specific spatial patterns in ST data is critical for understanding tissue organization but current methods rely on external references. Here the authors develop a reference-free method to effectively recover cell-type transcriptional profiles and proportions.