Single Cell Hi-C: Revealing 3D Chromatin in Individual Cells

Understanding how DNA is organized within the nucleus is essential for decoding gene regulation and cellular function. Traditional methods provide an averaged view across many cells, missing critical details about chromatin architecture variations between individual cells.

Single-cell Hi-C overcomes this limitation by capturing three-dimensional chromatin interactions at the level of individual cells. This technique has revealed significant variability in genome organization, offering new insights into gene expression, development, and disease.

Key Principles In Single Cell Hi-C

Single-cell Hi-C captures chromatin interactions at the resolution of individual cells, allowing researchers to study genome organization heterogeneity. Unlike bulk Hi-C, which averages contact frequencies across a population, this approach preserves cell-to-cell variability, revealing unique topological features that may be masked in ensemble data. By isolating chromatin conformation in single nuclei, researchers can examine how structural differences influence gene regulation, cellular identity, and development.

A defining aspect of single-cell Hi-C is its reliance on proximity ligation to detect spatially adjacent DNA segments. This process generates contact maps representing the three-dimensional folding of chromatin within a single nucleus. Due to the limited DNA available per cell, the technique results in sparse data, capturing only a fraction of possible interactions. Computational methods such as imputation and probabilistic modeling infer missing interactions, improving resolution and interpretability.

Single-cell Hi-C also uncovers rare or transient chromatin conformations that may be undetectable in bulk experiments. Studies have shown that certain topologically associating domains (TADs) reorganize dynamically across individual cells, challenging the notion of a static genome architecture. This variability plays a role in processes such as differentiation, where chromatin remodeling establishes lineage-specific regulatory landscapes. By capturing these changes, single-cell Hi-C provides a more nuanced understanding of chromatin structure’s role in cellular function and fate.

Differences From Population-Based Approaches

Single-cell Hi-C differs from population-based Hi-C by capturing chromatin interactions at single-cell resolution instead of averaging contact frequencies across a bulk population. Traditional Hi-C aggregates signals from thousands to millions of cells, generating a consensus map that obscures variability between individual cells. In contrast, single-cell Hi-C preserves this heterogeneity, enabling researchers to study chromatin structure variations within tissues and developmental contexts.

Bulk Hi-C effectively delineates large-scale chromatin domains such as TADs and compartments but cannot determine whether these features are universally conserved across all cells. Single-cell Hi-C has revealed that while some genome folding patterns are maintained across populations, others are highly dynamic and context-dependent. For example, TAD boundaries can shift between individual cells, indicating a more adaptable genome organization than previously thought. This level of detail is particularly valuable in studying differentiation, where chromatin undergoes remodeling to establish cell-type-specific regulatory landscapes.

Another key difference is the detection of rare or transient chromatin interactions. Population-based Hi-C averages out low-frequency contacts, making it difficult to identify interactions that occur in only a subset of cells. Single-cell Hi-C preserves these uncommon interactions, revealing regulatory elements such as long-range enhancer-promoter contacts that influence gene expression in specific cell states. This finer resolution enhances our understanding of how chromatin folding orchestrates gene regulation and how disruptions may contribute to disease.

DNA Preparation And 3D Chromatin Mapping

Single-cell Hi-C involves several key steps to capture chromatin interactions while preserving DNA’s spatial organization within individual nuclei. These include fixation and fragmentation of chromatin, proximity ligation, sequencing, and quality control to ensure reliable data. Each stage is optimized to maximize meaningful interaction recovery while addressing the inherent sparsity of single-cell data.

Fixation And Chromatin Fragmentation

The process begins with chemically fixing cells to preserve their native chromatin conformation. Formaldehyde crosslinks proteins and DNA, stabilizing chromatin interactions at the moment of fixation. After fixation, restriction enzymes such as HindIII or MboI fragment the chromatin by cutting DNA at specific recognition sites. The choice of enzyme affects resolution, as more frequent cut sites yield higher-resolution data. Efficient fragmentation is crucial—insufficient digestion introduces bias, while excessive fragmentation can disrupt meaningful contacts.

Ligation And Labeling

Following fragmentation, proximity ligation links spatially adjacent DNA fragments using DNA ligase. In single-cell Hi-C, limited DNA per cell necessitates careful optimization to maximize ligation efficiency. Unique molecular identifiers (UMIs) or barcodes may be incorporated to track individual DNA molecules and reduce amplification biases. The ligation process generates chimeric DNA fragments containing chromatin interaction information, which are then purified and prepared for sequencing.

Sequencing And Data Output

Once ligated, DNA fragments undergo library preparation and high-throughput sequencing, typically using platforms such as Illumina’s NovaSeq or HiSeq. Sequencing generates millions of paired-end reads, each representing a chromatin contact within a single cell. Unlike bulk Hi-C, which relies on deep sequencing for high-resolution maps, single-cell Hi-C employs computational methods to infer missing interactions. The raw sequencing reads are aligned to a reference genome, and interaction matrices are constructed to visualize chromatin folding patterns. Advanced bioinformatics approaches, including machine learning-based imputation, enhance resolution and interpretability.

Quality Checks And Validation

Ensuring data quality is critical, as single-cell Hi-C is inherently sparse and prone to technical noise. Key quality control metrics include the number of unique contacts detected per cell and the ratio of intra-chromosomal to inter-chromosomal interactions. An unusually high number of inter-chromosomal contacts may indicate experimental artifacts. Computational filtering removes spurious interactions and corrects biases introduced during library preparation. Validation methods include comparing single-cell Hi-C data with bulk Hi-C datasets or orthogonal techniques like fluorescence in situ hybridization (FISH) to confirm chromatin interactions.

Capturing Cell-Specific Chromosomal Interactions

Single-cell Hi-C has revealed previously hidden layers of genome organization. While population-based approaches provide an averaged view of chromatin conformation, they fail to capture variability between individual cells. Chromosomal interactions fluctuate dynamically depending on cell state, lineage commitment, and external stimuli. These variations influence gene expression by modulating enhancer-promoter contacts, insulating regulatory elements, or altering long-range chromatin loops that control transcription.

One striking finding is the presence of rare or transient chromatin interactions masked in bulk data. Some promoter-enhancer loops appear only in a subset of cells, suggesting that gene regulation occurs probabilistically rather than through a uniform process. This observation aligns with single-cell RNA sequencing data, which shows transcriptional variability even among genetically identical cells. Capturing these stochastic interactions provides a deeper understanding of chromatin architecture’s role in cellular heterogeneity, particularly in tissues with diverse functional subpopulations.

Observed Variations Across Diverse Cell Types

Examining chromatin interactions at the single-cell level has revealed striking differences in genome organization across cell types. While population-based Hi-C captures the most prevalent chromatin contacts within a tissue, it overlooks three-dimensional genome folding diversity among individual cells. Even within seemingly homogeneous populations, chromatin architecture can vary significantly, reflecting differences in gene expression, developmental stage, or environmental influences.

Pluripotent stem cells exhibit a more flexible, less compartmentalized chromatin structure than differentiated cells. Stem cells maintain a more disordered genome organization, allowing for rapid gene expression changes during lineage commitment. In contrast, terminally differentiated cells display more stable chromatin interactions, reinforcing established transcriptional programs. This shift from a permissive to a more restrictive chromatin state is essential for maintaining cellular identity and function.

Comparisons between healthy and diseased cells have shown that chromatin folding disruptions contribute to pathological states. In cancer cells, alterations in TADs can lead to aberrant gene activation, driving tumor progression. Understanding these variations provides valuable insights into how genome organization influences cellular behavior and disease mechanisms.