Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Michael Y. Fatemi, Yunrui Lu, Zarif L. Azher, Zarif L. Azher, Cyril Sharma, Eric Feng, Alos B. Diallo, Alos B. Diallo, Alos B. Diallo, Gokul Srinivasan, Grace M. Rosner, Grace M. Rosner, Kelli B. Pointer, Kelli B. Pointer, Brock C. Christensen, Lucas A. Salas, Gregory J. Tsongalis, Scott M. Palisoul, Laurent Perreard, Fred W. Kolling IV, Louis J. Vaickus, Joshua J. Levy, Joshua J. Levy, Joshua J. Levy, Joshua J. Levy, Joshua J. Levy

2025

Abstract

Spatial transcriptomics (ST) enables studying spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. Recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. Elucidating such information from histology alone presents a significant challenge, but if solved can enable spatial molecular analysis at cellular resolution even where ST data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in colorectal cancer whole slide images. A cell-graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA, facilitating the analysis of cellular groupings and gene relationships. We demonstrate that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within it. Our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional computer vision methods which did not incorporate single cell expression during the model training. This innovative approach augments the resolution of spatial molecular assays utilizing histology as sole input through co-mapping of histological and transcriptomic datasets at the single-cell level.

Paper Citation

in Harvard Style

Fatemi M., Lu Y., Azher Z., Sharma C., Feng E., Diallo A., Srinivasan G., Rosner G., Pointer K., Christensen B., Salas L., Tsongalis G., Palisoul S., Perreard L., Kolling IV F., Vaickus L. and Levy J. (2025). Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-731-3, SciTePress, pages 444-458. DOI: 10.5220/0013157300003911

in Bibtex Style

@conference{bioinformatics25,
author={Michael Fatemi and Yunrui Lu and Zarif Azher and Cyril Sharma and Eric Feng and Alos Diallo and Gokul Srinivasan and Grace Rosner and Kelli Pointer and Brock Christensen and Lucas Salas and Gregory Tsongalis and Scott Palisoul and Laurent Perreard and Fred Kolling IV and Louis Vaickus and Joshua Levy},
title={Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={444-458},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013157300003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity
SN - 978-989-758-731-3
AU - Fatemi M.
AU - Lu Y.
AU - Azher Z.
AU - Sharma C.
AU - Feng E.
AU - Diallo A.
AU - Srinivasan G.
AU - Rosner G.
AU - Pointer K.
AU - Christensen B.
AU - Salas L.
AU - Tsongalis G.
AU - Palisoul S.
AU - Perreard L.
AU - Kolling IV F.
AU - Vaickus L.
AU - Levy J.
PY - 2025
SP - 444
EP - 458
DO - 10.5220/0013157300003911
PB - SciTePress