SCXM: Single Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models

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 multiplex 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 methods to recover such information. While paired ST and scRNA-seq datasets are scarce, each modality in its own right is abundantly available. We therefore pro-pose 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 both multi-omic translation and spatial context inference.