Mapping protein-DNA interactions across biological scales: principles, scalability and applications.

Decoding the regulatory genome: single-cell four-omics integration

In a recent study published in Nature, Chen et al. introduced CHARM (single-cell assay for Chromatin conformation, Histone modi fi cation, chromatin Accessibility, and RNA expression Multi-omics pro fi ling), a platform that simultaneously captures four regulatory modalities within the same nucleus. 1 This integrated strategy provides a comprehensive framework for dissecting how multiple layers of epigenetic regulation converge to control gene expression at single-cell resolution. Gene regulation in eukaryotic cells is governed by a complex interplay of molecular and spatial mechanisms. Chromatin accessibility determines whether regulatory elements such as promoters and enhancers are available for transcription factor binding. Histone modi fi cations de fi ne chromatin states that either promote or repress transcription. In parallel, the three-dimensional organization of the genome establishes spatial proximity between distal regulatory elements and their target genes. Although each of these regulatory layers has been extensively studied, understanding how they operate together within the same cell has remained a major challenge. 2 Previous technologies have provided valuable insights into individual modalities. ATAC-seq pro fi les chromatin accessibility, CUT&Tag captures histone modi fi cations, and Hi-C reveals three-dimensional genome architecture. Recent single-cell platforms such as ChAIR and scHiCAR jointly pro fi le chromatin accessibility, RNA, and 3D contacts, but their 3D contact capture is anchored at accessible chromatin or candidate cis-regulatory elements, introducing structural bias into chromatin architecture reconstruction. 3,4 Moreover, regulatory modalities not captured by these platforms, such as histone modi fi cations, require separate pro fi ling and computational integration for broader cross-modality analysis. CHARM is distinguished from previous platforms by adding histone modi fi cation as a fourth same-cell modality and by using restriction-enzyme-based Hi-C

High-Efficiency Genomic Mapping of Chromatin-Associated Targets with CUT&RUN.

The precise regulation of chromatin composition is critical to gene expression and cellular identity, and thus a key component in development and disease. Robust assays to study chromatin features, including histone post-translational modifications (PTMs) and chromatin-associated proteins (e.g., transcription factors or PTM readers), are crucial for understanding their function and identifying novel therapeutic strategies. To this end, Cleavage Under Targets and Release Using Nuclease (CUT&RUN) has emerged as a powerful tool for high-resolution epigenomic profiling. The approach has been successfully applied to numerous cell and tissue types, providing insights into target genomic distribution with unprecedented sensitivity and throughput. Here, we provide a detailed CUT&RUN protocol from sample collection through data analysis, including best practices and defined controls to ensure specific, efficient, and robust target profiling.

FENGC: Targeted, Multiplexed, and Integrated Single-Molecule Profiling of Chromatin Accessibility and DNA Methylation.

The human RNA-DNA interactome is cell type-specific and dynamic

Experimental and Computational Approaches to Identify RNA–Protein Interactions

RNA-binding proteins (RBPs) are essential regulators of RNA metabolism and gene expression, influencing processes such as splicing, stability, localization, and translation. Despite their critical roles in health and disease, including cancer, identifying RNA–protein interactions remains challenging due to technical limitations and biases of existing methods. Here we review and compare experimental techniques—including in vitro affinity purification, in vivo crosslinking, and proximity labeling—and computational prediction tools for RBP identification. We assess their strengths, limitations, and applicability across biological contexts, emphasizing the benefits of integrating experimental and computational strategies. Our analysis provides practical guidelines for selecting appropriate methodologies tailored to different cell types and research goals. These insights aim to facilitate more accurate mapping of RNA–protein interactomes, thereby advancing understanding of RBP functions and supporting the development of novel therapeutic interventions targeting RNA–protein complexes.

Complex assembly and activity states as multifaceted protein attributes explaining phenotypic variability.

The state of a cell depends not only on protein abundance, but also on the biochemical and cellular activities of proteins, which are largely invisible to abundance profiling alone. Here, we introduce a multi-omics framework that infers context-specific protein activities from transcriptomic, phosphoproteomic, and protein correlation-based protein-protein interaction data, integrating modality-specific algorithms via network diffusion. Applying it to a panel of phenotypically diverse HeLa cell lines, whose genetic drift provides a natural perturbation system, we make three findings. First, physical separation of monomeric and assembled protein fractions by protein correlation profiling provides direct evidence that complex assembly buffers variation in gene copy number and transcription, a mechanism previously only inferred from bulk measurements. Second, using Let7 perturbation data, CRISPR gene dependency scores, and subcellular localization, we orthogonally validate that inferred protein activities capture functional regulation linked to cellular phenotypes inaccessible from abundance data alone. Third, differential analysis of context-specific activity profiles identifies molecular mechanisms underlying phenotypic divergence, including a WIPF1/WIPF2--Arp2/3 axis governing invadopodium formation and infection susceptibility, and an immunoproteasome switch linked to immune adaptation.