Trustworthy super-resolution reconstruction across spatial omics modalities
- 1Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, 06030, CT, United States
Abstract
Sequencing-based spatial omics platforms provide scalable and unbiased profiling of transcriptomic, epigenomic, and isoform-resolved signals, but their spatial resolution remains limited because each capture unit aggregates molecular information from multiple cells. Existing computational enhancement methods reconstruct high-resolution spatial gene expression from spot-level data by integrating histology, but most are designed primarily for spatial transcriptomics, rely predominantly on histological features, and lack appropriate validation strategies, leading to overfitting, spurious spatial patterns, and limited reliability. Here we present spEnhance, a generalizable and trustworthy computational framework for super-resolution enhancement of spot-level spatial omics data. spEnhance integrates histological features with complementary molecular information, including single-cell RNA-seq references and gene co-expression structure, to reconstruct high-resolution spatial molecular profiles. To enable reliable model selection from a single tissue section, spEnhance introduces a count-splitting strategy that generates statistically independent training and validation sets from spot-level measurements. spEnhance further quantifies prediction reliability through predictive residuals, providing an interpretable proxy for spatially resolved uncertainty. Comprehensive benchmarking across multiple tissues, platforms, and molecular modalities demonstrates that spEnhance achieves state-of-the-art accuracy, recovers fine-grained tissue structures, mitigates overfitting, and provides calibrated reliability estimates. Beyond spatial transcriptomics, spEnhance extends to isoform-level, epigenomics, proteomic and metabolomic spatial omics. Collectively, spEnhance establishes a modality-general and trustworthy framework for enhancing spatial omics data, enabling more accurate and reliable investigation of spatially resolved molecular regulation.
Related
- SpaReg: sparsity-based 3D reconstruction of tissue microenvironments at native resolution across morphological and spatial molecular modalities
- Unifying physical and molecular coordinate systems across modalities in spatial biology
- ASTRA enables zero-shot super-resolution of spatial transcriptomics across capture geometries
- BiNVaST: niche boundary-preserving and synthetic data-guided variational autoencoders for concordant inference across high-resolution spatial transcriptomic platforms
- Optimising digital volume correlation across materials: a practical framework for accuracy and spatial resolution
BibTeX
@misc{li2026trustworthy,
title = {{Trustworthy super-resolution reconstruction across spatial omics modalities}},
author = {M. Li and X. Wang and S. Xu and Y. Yin and D. Chen and X. Guo and D. Song},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.25.754471},
url = {https://www.biorxiv.org/content/10.64898/2026.09.25.754471v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Trustworthy super-resolution reconstruction across spatial omics modalities
AU - M. Li
AU - X. Wang
AU - S. Xu
AU - Y. Yin
AU - D. Chen
AU - X. Guo
AU - D. Song
PY - 2026
DA - 2026/09/25
PB - bioRxiv
DO - 10.64898/2026.09.25.754471
UR - https://www.biorxiv.org/content/10.64898/2026.09.25.754471v1
AB - Sequencing-based spatial omics platforms provide scalable and unbiased profiling of transcriptomic, epigenomic, and isoform-resolved signals, but their spatial resolution remains limited because each capture unit aggregates molecular information from multiple cells. Existing computational enhancement methods reconstruct high-resolution spatial gene expression from spot-level data by integrating histology, but most are designed primarily for spatial transcriptomics, rely predominantly on histological features, and lack appropriate validation strategies, leading to overfitting, spurious spatial patterns, and limited reliability. Here we present spEnhance, a generalizable and trustworthy computational framework for super-resolution enhancement of spot-level spatial omics data. spEnhance integrates histological features with complementary molecular information, including single-cell RNA-seq references and gene co-expression structure, to reconstruct high-resolution spatial molecular profiles. To enable reliable model selection from a single tissue section, spEnhance introduces a count-splitting strategy that generates statistically independent training and validation sets from spot-level measurements. spEnhance further quantifies prediction reliability through predictive residuals, providing an interpretable proxy for spatially resolved uncertainty. Comprehensive benchmarking across multiple tissues, platforms, and molecular modalities demonstrates that spEnhance achieves state-of-the-art accuracy, recovers fine-grained tissue structures, mitigates overfitting, and provides calibrated reliability estimates. Beyond spatial transcriptomics, spEnhance extends to isoform-level, epigenomics, proteomic and metabolomic spatial omics. Collectively, spEnhance establishes a modality-general and trustworthy framework for enhancing spatial omics data, enabling more accurate and reliable investigation of spatially resolved molecular regulation.
ER -