STIE: Single-Cell Level Deconvolution, Convolution, and Clustering in In Situ Capturing-Based Spatial Transcriptomics

Authors

Shijia Zhu
Naoto Kubota
Shidan Wang
Tao Wang
Guanghua Xiao
Yujin Hoshida

Language

English

Publication Date

8-30-2024

Journal

Nature Communications

DOI

10.1038/s41467-024-51728-5

PMID

39214995

PMCID

PMC11364663

PubMedCentral® Posted Date

8-30-2024

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Single-Cell Analysis, Transcriptome, Algorithms, Gene Expression Profiling, Cluster Analysis, Animals, Humans, Image Processing, Computer-Assisted, Mice, Software, Computational models, Bioinformatics, RNA sequencing, Data integration

Published Open-Access

yes

Recommended Citation

Zhu, Shijia; Kubota, Naoto; Wang, Shidan; et al., "STIE: Single-Cell Level Deconvolution, Convolution, and Clustering in In Situ Capturing-Based Spatial Transcriptomics" (2024). Faculty, Staff and Student Publications. 6732.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6732