MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation

Modeling gene regulation in single cells requires jointly representing DNA sequence, chromatin accessibility, and gene expression across regulatory scales. Sequence-based models capture DNA-level regulatory grammar but typically lack cell-level resolution, whereas single-cell omics models capture cell-state semantics but treat genes and peaks as fixed vocabulary tokens, and thus lack a unified sequence-based view of regulation. Here, we present MUSS, a sequence-based multi-scale model for single-cell gene regulation that preserves both DNA-level and cell-level resolution. MUSS derives peak- and gene-level representations from DNA using a frozen pre-trained sequence encoder, integrates them with a multi-scale encoder combining peak-peak self-attention and distance-biased peak-gene interaction, and trains on paired scATAC-seq and scRNA-seq to align sequence-derived representations with cell-specific regulatory states. Across gene expression prediction, enhancer-gene linking, TF-target gene recovery, and zero-shot cell clustering, MUSS outperforms both sequence-based and single-cell omics baselines, and further generalizes to genes and peaks unseen during training. Together, these results demonstrate that MUSS learns biologically meaningful and broadly generalizable regulatory representations for decoding cell-type-specific regulatory programs from DNA sequence.