DiffGSP: reversing mRNA diffusion to unlock high-fidelity spatial transcriptomics
- 1Shandong University
Abstract
Spatial transcriptomics enables gene expression profiling within intact tissues while preserving spatial context, providing unprecedented insights into cellular organization and function. However, mRNA diffusion during tissue processing can cause transcripts originating from adjacent cells to be captured at a given spot. This spatial misalignment challenges a fundamental premise of spatial transcriptomics that measured gene expression faithfully corresponds to its spatial origin, thereby compromising spatial fidelity and potentially biasing biological interpretation. Here, we present DiffGSP, a physics-informed framework that integrates Fick's law with graph signal processing to explicitly model diffusion-induced distortions and recover the underlying spatial gene expression landscape. Comprehensive benchmarking across diverse datasets and evaluation metrics demonstrates the consistent ability of DiffGSP to restore spatial gene expression patterns. By computationally reversing diffusion-induced distortions, DiffGSP enables the discovery of fine anatomical structures in the mouse brain, spatially organized gene modules in the kidney, intratumoral heterogeneity in colorectal cancer, and tertiary lymphoid structures in lung adenocarcinoma. Built on a physically interpretable framework and broadly applicable to sequencing-based spatial transcriptomics technologies, DiffGSP improves the fidelity of spatial gene expression reconstruction and enables more reliable biological interpretation.
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BibTeX
@misc{liu2026diffgsp,
title = {{DiffGSP: reversing mRNA diffusion to unlock high-fidelity spatial transcriptomics}},
author = {J. Liu and S. Sun and Y. Xu and S. Jiang and S. Cao and G. Li and X. Zhao and B. Liu},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.10.750095},
url = {https://www.biorxiv.org/content/10.64898/2026.09.10.750095v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - DiffGSP: reversing mRNA diffusion to unlock high-fidelity spatial transcriptomics
AU - J. Liu
AU - S. Sun
AU - Y. Xu
AU - S. Jiang
AU - S. Cao
AU - G. Li
AU - X. Zhao
AU - B. Liu
PY - 2026
DA - 2026/09/10
PB - bioRxiv
DO - 10.64898/2026.09.10.750095
UR - https://www.biorxiv.org/content/10.64898/2026.09.10.750095v1
AB - Spatial transcriptomics enables gene expression profiling within intact tissues while preserving spatial context, providing unprecedented insights into cellular organization and function. However, mRNA diffusion during tissue processing can cause transcripts originating from adjacent cells to be captured at a given spot. This spatial misalignment challenges a fundamental premise of spatial transcriptomics that measured gene expression faithfully corresponds to its spatial origin, thereby compromising spatial fidelity and potentially biasing biological interpretation. Here, we present DiffGSP, a physics-informed framework that integrates Fick's law with graph signal processing to explicitly model diffusion-induced distortions and recover the underlying spatial gene expression landscape. Comprehensive benchmarking across diverse datasets and evaluation metrics demonstrates the consistent ability of DiffGSP to restore spatial gene expression patterns. By computationally reversing diffusion-induced distortions, DiffGSP enables the discovery of fine anatomical structures in the mouse brain, spatially organized gene modules in the kidney, intratumoral heterogeneity in colorectal cancer, and tertiary lymphoid structures in lung adenocarcinoma. Built on a physically interpretable framework and broadly applicable to sequencing-based spatial transcriptomics technologies, DiffGSP improves the fidelity of spatial gene expression reconstruction and enables more reliable biological interpretation.
ER -