A pre-trained large generative model for translating single-cell transcriptomes to proteomes

Measuring protein abundance at the single-cell level can facilitate a high-resolution understanding of biological mechanisms in cellular processes and disease progression. However, current single-cell proteomic technologies face challenges such as limited coverage, constrained throughput and sensitivity, batch effects, high costs and stringent experimental operations. Inspired by the translation procedure in both natural language processing and the genetic central dogma, we propose a pre-trained, large generative model named single-cell translator (scTranslator). scTranslator can generate multi-omics data by inferring the missing single-cell proteome based on the transcriptome. Through systematic benchmarking and validation on independent datasets, we have confirmed the accuracy, stability and flexibility of scTranslator across various profiling techniques (for example, CITE-seq, spatial CITE-seq, REAP-seq, NEAT-seq), cell types (for example, monocytes, macrophages, T cells, B cells), tissues (for example, blood, lung, brain) and a wide range of disease contexts, including infectious, metabolic and oncologic conditions. Furthermore, scTranslator shows its superiority in assisting various downstream analyses and applications, including gene/protein interaction inference, perturbation prediction, cell clustering, batch correction and cell origin recognition in pan-cancer data.