STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

Shijia Zhu (), Naoto Kubota, Shidan Wang, Tao Wang, Guanghua Xiao and Yujin Hoshida ()
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Shijia Zhu: University of Minnesota
Naoto Kubota: University of Texas Southwestern Medical Center
Shidan Wang: University of Texas Southwestern Medical Center
Tao Wang: University of Texas Southwestern Medical Center
Guanghua Xiao: University of Texas Southwestern Medical Center
Yujin Hoshida: University of Texas Southwestern Medical Center

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.

Date: 2024
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DOI: 10.1038/s41467-024-51728-5

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