🤖 AI Summary
Spatial transcriptomics (ST) faces challenges in achieving high-throughput, subcellular-scale profiling of thousands of genes while preserving single-cell-resolution spatial context. This work proposes a cross-modal translation approach based on adversarial fine-tuning that leverages unpaired single-cell RNA sequencing (scRNA-seq) and ST data. By fine-tuning a pre-trained single-cell foundation model, the method maps scRNA-seq profiles to spatial coordinates without requiring paired multi-omic samples. It represents the first application of adversarial fine-tuning to single-cell foundation models, thereby overcoming the longstanding dependency on matched datasets for multi-omic integration. The approach significantly outperforms existing methods in reconstructing native cellular neighborhood structures, offering a powerful framework for spatially informed single-cell analysis.
📝 Abstract
Spatial transcriptomics (ST) is a powerful tool for exploring biological properties dependent on structure, proximity, and interaction in tissue. The methods underpinning ST are developing rapidly but are limited in their ability to profile many thousands of genes at a subcellular scale. Although dissociated from tissue, it is known that the whole-transcriptome readouts of cells in single-cell RNA sequencing (scRNA-seq) retain information about their former in situ neighbourhoods, motivating computational methods to recover it. While paired ST and scRNA-seq datasets are scarce, each modality in its own right is abundantly available. We therefore propose to perform cross-modal translation between unpaired ST and scRNA-seq data. In this work we show that a single-cell foundation model can perform this translation via adversarial fine-tuning. We demonstrate that our method performs favourably against methods built for multi-omics translation.