DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision

Kalin Nonchev, Glib Manaiev, Viktor Koelzer, Gunnar Ratsch

Keywords: Computational Pathology, Spatial Transcriptomics, Deconvolution, Single-cell prediction, Virtual Cells, Representation Learning, Machine Learning in Medicine

TL;DR: We developed DeepSpot2Cell, a novel DeepSet network that uses pathology foundation models and spatial multi-level tissue context to effectively predict single-cell spatial transcriptomics from histology images using spot-level supervision

Abstract:

Spot-based spatial transcriptomics (ST) technologies like 10x Visium combine RNA sequencing with spatial imaging to quantify genome-wide gene expression while retaining tissue organization. However, their coarse spot-level resolution aggregates signals from multiple cells, preventing accurate single-cell analysis and detailed cellular characterization. Here, we present DeepSpot2Cell, a novel multi-modal DeepSet neural network that fuses sub‑spot image detail and neighborhood context with pathology foundation-model features to effectively predict virtual single-cell gene expression from histopathological images using spot-level supervision. DeepSpot2Cell substantially improves gene expression correlations on a newly curated benchmark we specifically designed for single-cell ST deconvolution and prediction from H&E images. The benchmark includes 20 lung, 7 breast, and 2 pancreatic cancer samples, across which DeepSpot2Cell outperformed previous super-resolution methods, achieving respective improvements of 46%, 65%, and 38% in cell expression correlation for the top 100 genes. We hope that DeepSpot2Cell and this benchmark will stimulate further advancements in virtual single-cell ST, enabling more precise delineation of cell-type-specific expression patterns and facilitating enhanced downstream analyses.
Code availability: https://github.com/ratschlab/DeepSpot

Submission Number: 40