MINT infers the latent single-cell spatial transcriptome from paired histology
- 1Guangzhou National Laboratory
Abstract
Sequencing-based spatial transcriptomics captures transcriptome-wide molecular information, yet the corresponding cell-resolved tissue map remains incomplete. Histology records dense cellular morphology, tissue architecture and local context. Here, we present MINT (Morphological Integrative Network for Transcriptomics), which combines this latent cellular organisation with sample-specific spatial measurements to infer tissue-wide, cell-resolved transcriptomes without an external single-cell reference. MINT reconstructs expression from sparse Slide-tags profiles across unmatched adjacent sections; its shared nucleus-centred representation also resolves pooled Visium measurements into cell-level profiles. MINT improved Slide-tags alignment and expression recovery under controlled nuclear loss and cross-section perturbation. It also outperformed reference-free Visium disaggregation methods against Xenium ground truth and was validated with Visium HD sequencing. In human lung adenocarcinoma, MINT reconstructed the tumour immune microenvironment cell by cell and resolved tertiary lymphoid structures and their maturity. These results establish paired histology as a quantitative, sample-intrinsic complement to molecularly rich but cellularly incomplete spatial-transcriptomic assays.
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BibTeX
@misc{chen2026mint,
title = {{MINT infers the latent single-cell spatial transcriptome from paired histology}},
author = {L. Chen and Z. Zhang and B. Wang and W. Ren and H. Peng and S. Lu and M. Xu and Y. Zhou and X. Luo and T. Ye and Q. Zhu and F. Yan and L. Tian},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.16.751717},
url = {https://www.biorxiv.org/content/10.64898/2026.09.16.751717v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - MINT infers the latent single-cell spatial transcriptome from paired histology
AU - L. Chen
AU - Z. Zhang
AU - B. Wang
AU - W. Ren
AU - H. Peng
AU - S. Lu
AU - M. Xu
AU - Y. Zhou
AU - X. Luo
AU - T. Ye
AU - Q. Zhu
AU - F. Yan
AU - L. Tian
PY - 2026
DA - 2026/09/16
PB - bioRxiv
DO - 10.64898/2026.09.16.751717
UR - https://www.biorxiv.org/content/10.64898/2026.09.16.751717v1
AB - Sequencing-based spatial transcriptomics captures transcriptome-wide molecular information, yet the corresponding cell-resolved tissue map remains incomplete. Histology records dense cellular morphology, tissue architecture and local context. Here, we present MINT (Morphological Integrative Network for Transcriptomics), which combines this latent cellular organisation with sample-specific spatial measurements to infer tissue-wide, cell-resolved transcriptomes without an external single-cell reference. MINT reconstructs expression from sparse Slide-tags profiles across unmatched adjacent sections; its shared nucleus-centred representation also resolves pooled Visium measurements into cell-level profiles. MINT improved Slide-tags alignment and expression recovery under controlled nuclear loss and cross-section perturbation. It also outperformed reference-free Visium disaggregation methods against Xenium ground truth and was validated with Visium HD sequencing. In human lung adenocarcinoma, MINT reconstructed the tumour immune microenvironment cell by cell and resolved tertiary lymphoid structures and their maturity. These results establish paired histology as a quantitative, sample-intrinsic complement to molecularly rich but cellularly incomplete spatial-transcriptomic assays.
ER -