In an era where the complexity of cellular function is dissected at unprecedented resolution, the ability to simultaneously capture multiple layers of gene regulation from individual cells represents a transformative leap in biological research. The newly developed method, termed scHiCAR, breaks barriers by integrating transcriptome, epigenome, and three-dimensional (3D) genome landscapes within the same single cells. This tri-modal approach offers a comprehensive glimpse into how gene expression is orchestrated, regulated, and spatially organized, particularly within intricate tissues such as the mammalian brain.
At the heart of this innovation lies the plate-based combinatorial barcoding strategy unique to scHiCAR, enabling researchers to profile mRNA, chromatin accessibility, and chromosome conformation dynamics all at once. Prior single-cell Hi-C approaches have provided valuable insights into 3D genome architecture; however, coupling these data with transcriptomic and epigenomic states within the same cell has remained elusive. scHiCAR addresses this challenge by efficiently capturing long-range cis-interactions—physical DNA contacts within chromosomes—anchored at candidate cis-regulatory elements (cCREs), pivotal genomic regions that modulate gene activity.
Application of scHiCAR to an unparalleled dataset encompassing 1.62 million mouse brain cells has demonstrated the method’s scalability and resolution. With the aid of a sophisticated deep-learning loop detection algorithm tailored for scHiCAR’s architecture, the research delineates cell-type-specific gene expression patterns, open chromatin regions indicative of active regulation, and enhancer-promoter interactions at an unprecedented 5-kilobase resolution. This high-precision mapping across 22 distinct brain cell types reveals a diverse and nuanced regulatory landscape, which contributes greatly to understanding brain function at the cellular level.
Crucially, scHiCAR’s prowess extends beyond neural tissues. Its robust performance was validated in the highly challenging environment of skeletal muscle, further proving its versatility. By enabling simultaneous measurements of transcriptional output, chromatin accessibility, and 3D genome conformation, scHiCAR provides a dynamic view of gene regulation during muscle stem cell regeneration, a process critical for tissue repair and maintenance. This multi-dimensional insight connects spatial chromatin configuration to functional gene expression changes during regeneration, a feat hitherto unattainable with existing tools.
The technical ingenuity behind scHiCAR lies in the combination of its plate-based approach and combinatorial barcoding system, allowing high-throughput and cost-effective processing of vast cellular populations. Unlike previous methods constrained by either low throughput or limited assay modalities, scHiCAR harnesses the synergy of multi-omic data from the same cell, linking the epigenetic landscape intimately with transcriptional activity and 3D genomic topology. This integration provides a comprehensive picture of gene regulation that reflects the true complexity of biological systems.
Engineered to prioritize candidate CREs, scHiCAR’s enrichment for distal cis-interactions greatly enhances the detection of functionally relevant chromatin contacts involved in gene regulation. Many regulatory DNA elements exert their influence through long-range looping interactions that control gene expression, and scHiCAR’s capacity to capture these interactions at previously unattainable resolution offers unprecedented opportunities to decode the regulatory grammar embedded within the genome.
The importance of understanding 3D genome architecture in the context of transcriptional regulation has gained momentum over the last decade. Chromosome conformation capture methods such as Hi-C illuminated the spatial folding patterns of genomes, while assays of chromatin accessibility and RNA sequencing provided orthogonal views into regulatory potential and gene activity. However, integrating these dimensions had been limited to bulk or paired single-cell assays that sampled separate cells for transcriptomics and chromatin states. scHiCAR’s unified single-cell tri-modal design overcomes this fragmentation, enabling direct correlations within each individual cell.
Data generated by scHiCAR offer novel insights into how gene regulation is modulated across diverse cell types in complex tissues. In the brain, variations in regulatory interaction landscapes contribute to cellular identity, function, and plasticity. By resolving enhancer–promoter pairs at 5 kb resolution, scHiCAR reveals fine-scale regulatory networks that tune gene expression, advancing our understanding of neurobiology and disease mechanisms where misregulation of 3D genome structure is implicated.
Furthermore, the integrative analytical pipeline developed alongside scHiCAR, incorporating deep-learning algorithms trained on massive datasets, ensures robust identification of chromatin loops despite inherent single-cell data sparsity. This computational advancement complements the experimental innovation, pushing single-cell multi-omics into a new frontier where interpretability meets scalability.
One of the compelling applications of scHiCAR lies in dynamic biological contexts such as development, differentiation, and regeneration. Monitoring how gene-regulatory interactions evolve in muscle stem cells during regeneration provides a paradigm for studying temporal changes in 3D genome architecture linked to functional outcomes. This unprecedented capability opens doors for future research into tissue repair, stem cell biology, and regenerative medicine.
In addition to its biological significance, scHiCAR addresses practical challenges faced by researchers studying complex tissues. By offering a cost-efficient and scalable method, it lowers barriers to entry for large-scale single-cell studies in labs worldwide. As single-cell multi-omic technologies proliferate, methods like scHiCAR are poised to become foundational tools in both basic research and translational settings.
As we move forward, the confluence of biological discovery enabled by scHiCAR and related technologies will likely illuminate new dimensions of gene regulation. Understanding the interplay among transcriptomic, epigenomic, and 3D genomic states at single-cell resolution may fundamentally reshape therapeutic strategies for conditions rooted in gene regulatory dysfunction, including cancer, neurodegeneration, and developmental disorders.
The scHiCAR method exemplifies a watershed moment in genomic technologies, underscoring the power of integrated multi-omic approaches to unravel the complexity inherent in cellular systems. By simultaneously capturing the transcriptome, accessible chromatin, and chromosome conformation, it transcends the limitations of single-modality assays, providing a holistic view of gene regulation within the spatial context of the nucleus.
This advance brings us closer to constructing comprehensive cellular atlases that integrate structural genome organization with functional readouts, an essential step towards truly understanding biological systems at their intrinsic resolution. The capacity to analyze millions of cells concurrently enhances statistical power and opens avenues for novel discoveries in heterogeneous tissues with intricate cellular compositions.
In summary, scHiCAR ushers in a new era for single-cell genomics by facilitating detailed multi-dimensional characterization of gene regulatory landscapes from the same individual cells. Its innovative integration of transcriptomic, epigenomic, and 3D genome data represents a formidable leap in dissecting the regulatory code governing cell identity and function across complex biological tissues, with far-reaching implications for both research and medicine.
Subject of Research: Single-cell multi-omic profiling integrating transcriptome, epigenome, and 3D genome architecture.
Article Title: Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR.
Article References:
Wei, X., Xu, Y., Yang, D. et al. Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03013-7
