Biologically informed generation of protein levels with flow matching

Cellular protein levels are governed by RNA and other complex factors making them hard to predict computationally. Limited protein experimental data, particularly at single cell (sc) and spatial resolution, often necessitates the use of scRNA-seq as a proxy despite poor direct correlation. Current methods estimating single cell protein levels largely use discriminative predictors while omics data is inherently stochastic. Since proteins measured are often few and widely differ across experiments, existing methods require repeated training and tuning with limited generalizability. They fail to incorporate batch and sample effects; phenotypic priors such as cell type, disease, or condition; and mechanistic priors such as structure or function encoded in sequence. Most methods fail to incorporate spatial locations of cells in tissue slices. We address these issues with RnaProFlo, a conditional generative foundation model using rectified flow matching. It generates surface or intracellular protein levels at single cell or spatial spot resolution. Protein generation is conditioned on RNA expression, cell technical and biological priors, sequence and spatial location, if present. We develop a tokenizer that modulates expression levels based on protein language model derived sequence embeddings which inherently contain structure and functional information. It enables learning from non-overlapping protein feature sets across experiments and supports generalization to proteins unseen during training. Batch, disease, cell type and data source abstract metadata are embedded using a large language model (LLM) and globally condition the transformer ensuring dataset specific technical effects and biological conditions are incorporated. We further introduce gated graph structured token level cross attention with spatial distance bias during fine-tuning to enable generation of spatially informed proteomic data despite limited spatial training data. RnaProFlo outperforms other methods on paired single cell and spatial RNA-protein data effectively predicting held-out protein levels. Detailed ablation tests, zero-shot and fine-tuning experiments across seen and unseen proteins, tissues, biological conditions, sequencing technologies and species, collectively show RnaProFlo's superiority in estimating cellular and spatial protein from scRNA-seq or spatial transcriptomics (ST) expression and metadata.