Dendritic cells (DCs) are a heterogenous population of professional antigen presenting cells that orchestrate the immune system by initiating and shaping adaptive immune responses. Immunophenotypic and transcriptomic profiling are defining DC subsets with increasingly high resolution, moving beyond the classical plasmacytoid and conventional DC paradigm to identify novel, functionally distinct subpopulations.

   Regulatory elements are non-coding DNA-segments that include enhancers, promoters, silencers, and insulators, and determine the subset-specific expression of genes in different cell types or developmental stages. The regulatory elements involved in the identity of DC subsets are still poorly characterized. Yet, their identification would help to provide a unified and standardized description of DC heterogeneity that is still elusive.

   Multiomic approaches that provide simultaneous profiling of chromatin accessibility and gene expression at single-cell resolution allow to directly link regulatory element activity to gene expression in heterogeneous cell populations, unlocking deeper insights into gene regulation and cell fate.

   Here, we enriched DCs from peripheral blood of healthy donors, followed by single-nucleus multiomic profiling, combining ATACseq and RNAseq, to dissect DC heterogeneity and identifying key transcription factors and cis-regulatory elements driving DC subset identity. This approach yielded an effective enrichment of DCs, enabling the identification of major DC subsets, including conventional DC1s (~1000 cells), conventional DC2s (~9,000 cells) and plasmacytoid DCs (~9,000 cells). These subsets were further subdivided in sub-clusters, each with peculiar transcriptomic signatures. By integrating data from chromatin accessibility and transcriptomic profile, we delineated key transcription factors, regulatory elements, metabolic traits and biological functions of each DC subset, and obtained subcluster-specific gene scores and modules. Their application to independent datasets of healthy individuals and patients affected by different pathological conditions will improve cell-state annotation and cross-disease comparison.

   Altogether, this study provides a high-resolution multiomic resource of DC regulatory programs to map DC heterogeneity in human health and disease.