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Dip-C Service: Haplotype-Resolved Single-Cell 3D Genomes

Go beyond population averages and visualize the true 3D structure of the diploid genome. Our Dip-C Service (Diploid Chromatin Conformation Capture) delivers ultra-high-resolution, haplotype-resolved single-cell interaction maps. By distinguishing between maternal and paternal alleles, Dip-C enables the computational reconstruction of 3D genome models for individual cells.

  • Haplotype-Resolved: Separate maternal and paternal chromatin interactions.
  • 3D Modeling: Generate physical 3D structures (PDB format) for single cells.
  • High Coverage: Detect >500k unique contacts per cell for robust modeling.
Model Your Single Cells

3D reconstructed genome structure of a single diploid cell using Dip-C

Overview: Reconstructing the True Diploid Architecture

Standard Hi-C methods face a fundamental limitation: they average the signals from millions of cells, and often merge the maternal and paternal genomes into a single "haploid" map. This obscures the reality that in diploid organisms, the two alleles of a gene can fold into vastly different 3D structures—a phenomenon critical for processes like X-chromosome inactivation, genomic imprinting, and allele-specific gene expression.

Our Dip-C Service (Diploid Chromatin Conformation Capture) offers the highest resolution available for single-cell 3D genomics. Unlike methods optimized for high throughput (like sci-Hi-C), Dip-C is optimized for depth and structural fidelity. We treat each cell not just as a data point, but as a unique physical entity to be modeled.

By utilizing a highly sensitive transposase-based protocol (METAT-Tn5), we generate ultra-high-coverage contact maps (>500,000 unique contacts per cell). Combined with Whole Genome Sequencing (WGS) data, this allows us to "phase" the interaction reads, separating them into maternal and paternal datasets based on Single Nucleotide Polymorphisms (SNPs). The result is a mathematically reconstructed 3D physical model of the single cell's genome, revealing the distinct shape of every chromosome copy.

(Note: This service is for Research Use Only. It is not intended for use in diagnostic procedures or clinical decision-making.)

Key Features

  • Phased Interactions: Distinct maps for maternal vs. paternal alleles.
  • Physical Reconstruction: Output includes PDB files for 3D visualization.
  • Ultra-High Density: Sufficient contacts to resolve loops at the single-cell level.
  • Allele Specificity: Study X-inactivation and imprinting directly.

Applications: Seeing the Difference Between Alleles

Dip-C acts as a "computational microscope," allowing you to visualize genome folding anomalies that are invisible in population-averaged data.

3D Structure Modeling (The "Ball of Wool")

Dip-C data is dense enough to solve the "3D puzzle" of the genome. We generate PDB (Protein Data Bank) files for individual cells, visualizing chromatin as 3D polymer chains. This allows you to calculate biophysical parameters like gyration radius and radial positioning, visualizing how chromosomes intertwine in the nuclear space. You can literally rotate the genome of a single neuron or cancer cell on your screen.

Imprinting & X-Inactivation

In female mammals, one X chromosome is compacted (inactive) while the other is open (active). Dip-C can spatially resolve the "Barr body" structure of the inactive X and contrast it with the active X within the same cell. It is also ideal for studying imprinted loci (e.g., H19/Igf2, Snrpn) where topology differs strictly by parental origin, revealing how structure enforces monoallelic expression.

Haplotype-Specific Structural Variants (SVs)

Cancer genomes often harbor complex SVs that affect only one allele. Dip-C can detect "haplotype-specific" chromatin loops formed by translocations or inversions. This distinguishes whether an enhancer hijack event is occurring on the mutated allele or the wild-type allele, providing mechanistic insight into oncogene activation that standard short-read sequencing cannot resolve.

Comparison: Dip-C vs. sci-Hi-C vs. Standard scHi-C

The choice of single-cell method depends on whether you need a "Census" (many cells, low res) or a "Blueprint" (few cells, high res).

Feature Dip-C (Modeling) sci-Hi-C (Atlas) Standard scHi-C
Primary Goal 3D Structure Modeling Cell Typing / Clustering Basic Interactions
Resolution per Cell Ultra-High (>500k contacts) Sparse (10k-50k) Medium
Haplotype Phasing Yes (Maternal/Paternal) Difficult (Sparse data) No
Throughput Low (10s-100s cells) High (1000s cells) Low/Medium
Data Output 3D Models (PDB) + Maps Clusters + Aggregate Maps Contact Maps

Our Workflow: High-Coverage Tagmentation & Phasing

The Dip-C workflow requires precision at the single-cell level to ensure sufficient coverage for phasing. We utilize a transposase-based approach to maximize efficiency.

Step 1: Nuclei Isolation & Transposition
Single cells or nuclei are isolated. Instead of traditional restriction digestion, Dip-C uses a transposase (Tn5) loaded with mosaic end adaptors. This "tagmentation" step is highly efficient, fragmenting and tagging chromatin in situ while preserving 3D contacts. The Tn5 insertion provides a high density of potential ligation sites, avoiding the limitations of restriction motif distribution.

Step 2: Ligation & Amplification
Proximity ligation joins the tagged ends. The library is amplified using high-fidelity PCR. Because Tn5 captures more accessible sites and digestion is not limited by restriction motifs, Dip-C yields significantly higher library complexity than restriction-based Single-Cell Hi-C, often yielding 2-5 million reads per cell.

Step 3: Deep Sequencing & SNP Phasing
Libraries are sequenced to high depth. Using a reference list of SNPs (derived from bulk WGS of the same sample), we assign each read to the Maternal or Paternal allele. Reads that cannot be phased are used for the "backbone" structure, while phased reads resolve the allele-specific details.

Step 4: 3D Modeling & Visualization
We employ computational algorithms (e.g., RMSD analysis, particle simulation) to generate a set of candidate 3D structures that best satisfy the contact constraints. The final output includes 3D coordinates for each genomic bin (20kb or 100kb resolution).

Comparison of population-averaged Hi-C versus haplotype-resolved Dip-C structures

Sample Requirements

To achieve haplotype resolution, knowing the genotype is essential. WGS data is strongly recommended for the most accurate modeling.

Sample Type Minimum Input Preferred Input Key Notes
Cell Lines 1 × 10^5 cells 5 × 10^5 cells High viability (>90%) required.
Tissue 20 mg 50 mg Must be dissociable to single cells or nuclei.
WGS Data 30X Coverage 60X Coverage Essential. We need a high-quality SNP list (VCF) to distinguish maternal vs. paternal reads. We can generate this if not provided.

Demo Results: The 3D Genome "Fingerprint"

Figure 1: Haplotype-Resolved 3D Modeling

Dip-C results are often visualized as physical structures rather than just heatmaps.

  • Left Panel (The "Ball of Wool"): A visual reconstruction of the entire nucleus of a single cell. Each chromosome is rendered as a distinct 3D polymer chain (colored differently), occupying its own "Chromosome Territory."
  • Right Panel (Split View): The power of Dip-C is revealed when we isolate specific homologues. For example, Paternal Chromosome 1 (Blue) may show a compact, folded structure, while Maternal Chromosome 1 (Red) appears more elongated. This structural heterogeneity is quantifiable via RMSD (Root Mean Square Deviation) analysis, serving as a unique "fingerprint" of that specific cell's state.

3D reconstructed genome structure of a single diploid cell using Dip-CFigure 1: Haplotype-Resolved 3D Modeling

Case Study: Determining the 3D Genome Structure (STAR Protocols)

This protocol paper validates the Dip-C methodology for generating robust single-cell 3D models.

The Context

Reconstructing the 3D genome structure of a single mammalian cell requires a method that provides both high genomic coverage and high detection efficiency of chromatin contacts. Standard methods often yield data too sparse for reliable 3D modeling, leaving the physical shape of the genome a mystery.

The Method

The study utilized Dip-C to determine the 3D genome structures of single human cells (GM12878). The protocol involved chromatin fixation, Tn5 transposase-based tagmentation for high-efficiency fragmentation, Ligation, and high-fidelity amplification to preserve molecular complexity.

The Results

The Dip-C protocol consistently yielded >500,000 unique contacts per single cell, significantly higher than restriction-enzyme based methods. By integrating phased SNP data, the researchers successfully separated interactions into allele-specific maps. The high-density data allowed for the computation of 3D structures (particle systems) that satisfied the distance constraints imposed by the Hi-C data. These models visually revealed the separate territories of maternal and paternal chromosomes.

Workflow for determining 3D genome structure of single mammalian cells using Dip-C

The Conclusion

Dip-C provides a robust, reproducible workflow for generating "Haplotype-resolved" 3D genome structures, serving as a blueprint for understanding cell-to-cell structural variability.

Source: Tan, L., et al. "Determining the 3D genome structure of a single mammalian cell with Dip-C." STAR Protocols (2021).

FAQ: SNPs & Resolution

References

  1. Tan, L., et al. Three-dimensional genome structures of single diploid human cells. Science. 2018;361(6405):924–928.
  2. Tan, L., et al. Three-dimensional genome structures of single sensory neurons in mouse visual and olfactory systems. Nature Structural & Molecular Biology. 2019;26:297–307.
  3. Tan, L., et al. Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development. Cell. 2021;184(3):741–756.
  4. Tan, L., et al. Determining the 3D genome structure of a single mammalian cell with Dip-C. STAR Protocols. 2021;2(3):100622.
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