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

Kalin Nonchev, Glib Manaiev, Viktor Koelzer, Gunnar Ratsch

Submission Track: Short papers presenting ongoing research or work submitted to other venues (up to 5 pages, excluding references)

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 quantify genome-wide gene expression and preserve spatial 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 DeepSet neural network that leverages pretrained pathology foundation models and spatial multi-level context 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/DeepSpot2Cell

Submission Number: 4