Researchers unveiled DeepDETAILS, a deep-learning framework designed to recover cell-type-specific gene regulation signals from bulk sequencing data. The approach targets a key limitation of bulk assays: averaging across cell types, which can obscure regulatory programs. DeepDETAILS is positioned as an alternative or complement to single-cell methods, enabling cross-modality deconvolution of bulk samples and improving the ability to infer which regulatory processes are active in specific cell populations. The work was published in Nature Biotechnology. For translational research and biomarker efforts, the reported capability could reduce reliance on large-scale single-cell experiments while still extracting cell-resolution regulatory insight from existing cohorts. That could accelerate hypothesis generation in areas where tissue access or costs limit single-cell profiling.