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Single-Cell Combinatorial Indexed Hi-C (sci-Hi-C) Service

Scale your 3D genome research from dozens to thousands of cells. Our Single-Cell Combinatorial Indexed Hi-C (sci-Hi-C) Service leverages a split-pool barcoding strategy to profile chromatin architecture in massive numbers of single cells simultaneously. Construct high-resolution 3D genome atlases and resolve cellular heterogeneity without the need for physical single-cell isolation (RUO).

  • High Throughput: Profile 1,000s of cells per library.
  • Combinatorial Indexing: "Split-and-pool" workflow eliminates expensive single-cell sorting.
  • Heterogeneity Analysis: Cluster cells based on 3D conformation (TADs/Compartments).
Profile Your Cell Atlas

sci-Hi-C data visualization showing cell clustering and aggregate contact maps

Overview: Scaling Up 3D Genomics with Combinatorial Indexing

Constructing a comprehensive "Cell Atlas" of the 3D genome requires analyzing thousands of single cells to capture rare cell types and transitional states. Traditional single-cell Hi-C methods, which rely on physically isolating individual cells into separate wells, are labor-intensive, expensive, and difficult to scale beyond a few hundred cells.

Our Single-Cell Combinatorial Indexed Hi-C (sci-Hi-C) Service breaks this throughput barrier. By utilizing a "Split-and-Pool" combinatorial barcoding strategy, we can profile the chromatin conformation of thousands of single cells in a single library construction run—without the need for complex single-cell isolation equipment.

This approach generates sparse but highly informative interaction maps for individual cells, which can be clustered to identify distinct cell types based purely on their 3D genomic architecture. It is the ideal solution for dissecting tissue heterogeneity, mapping developmental trajectories, and identifying tumor subclones at scale.

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

Key Benefits

  • Massive Scalability: Generate libraries for 1,000 - 5,000+ cells in a single experiment.
  • Cost-Effective: Eliminates the need for single-cell sorting hardware (FACS) or microfluidics for capture.
  • Deep Insight: Aggregate single-cell data to form high-resolution "Pseudo-bulk" maps for rare subpopulations.
  • Robust Clustering: Separate cells by cell cycle stage or subtype using chromatin contacts alone.

Applications: Uncovering Heterogeneity

sci-Hi-C shifts the focus from "What is the average structure?" to "How many structural states exist?" by treating chromatin topology as a definable phenotype.

Cell Type Classification by 3D Structure

Just as single-cell RNA-seq clusters cells by gene expression, sci-Hi-C clusters cells by chromatin conformation (A/B compartments and TAD insulation). This allows researchers to distinguish cell types in complex tissues (e.g., brain, tumor microenvironment) that may look similar transcriptionally but possess distinct regulatory architectures poised for different functions.

Developmental Trajectory Analysis

During differentiation, chromatin rewiring often precedes transcriptional changes. sci-Hi-C allows for the mapping of "Pseudo-time" of 3D genome reorganization during embryogenesis or lineage specification. By capturing transient structural states, such as the switching of compartments or the establishment of insulating loops, sci-Hi-C can define cell fate decisions before markers are expressed.

Tumor Heterogeneity & Subclone Evolution

Tumors are mosaics of genetically and epigenetically distinct subclones. sci-Hi-C is particularly powerful here because Hi-C data inherently contains information about Copy Number Variations (CNVs) and translocations. We can identify tumor subclones based on these distinct structural variant patterns inferred directly from the single-cell contact maps, linking genomic instability to 3D topology.

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

The choice of single-cell method depends on your balance between Throughput (number of cells) and Resolution (reads per cell).

Feature sci-Hi-C (Combinatorial) Dip-C (High-Res) Standard scHi-C (Plate-based)
Throughput Ultra-High (1,000s to 10,000s of cells) Medium (10s to 100s of cells) Low (96-well limited)
Resolution per Cell Sparse (Sufficient for clustering & aggregation) Ultra-High (Haplotype-resolved 3D structures) Medium
Methodology Combinatorial Indexing (Split-Pool) Transposase + Amplification Physical Sorting/Isolation
Cost per Cell Lowest High High
Best Application Cell Atlasing & Heterogeneity 3D Modeling of Individual Cells Validation of specific cells

Our Workflow: The Split-and-Pool Strategy

Our sci-Hi-C protocol leverages a dual-indexing strategy to achieve exponential throughput. This clever design allows for unique tagging of thousands of cells using only standard PCR plates.

Step 1: Nuclei Isolation & Fixation
Tissue or cell lines are dissociated, and nuclei are isolated. The chromatin is cross-linked in situ to preserve 3D contacts, digested with a restriction enzyme (e.g., DpnII), and ends are filled with biotinylated nucleotides, preparing them for ligation.

Step 2: Round 1 Barcoding (In Situ Ligation)
The nuclei are split across a 96-well plate. A unique bridge adaptor containing the first barcode is ligated to the DNA ends within the nuclei. Crucially, the nuclei remain intact, trapping the ligated DNA inside. All nuclei are then pooled back together. This ensures every nucleus carries one of 96 distinct Round 1 tags, but they are mixed.

Step 3: Round 2 Barcoding (PCR)
The pooled nuclei are split again into a second plate (or sorted by FACS) for lysis. During PCR amplification, primers containing the second barcode are introduced. Each sequence read now contains a combination of Barcode 1 + Barcode 2. With a 96x96 combinatorial design, we can uniquely distinguish up to 9,216 distinct wells (96 × 96), allowing for massive multiplexing.

Step 4: Sequencing & Analysis
Libraries are sequenced (PE150). Data is demultiplexed, and cells are filtered to remove doublets (barcode collisions). We perform dimensionality reduction (e.g., UMAP) to visualize cell clusters and generate "Pseudo-bulk" Hi-C maps for each cluster.

Workflow of Single-Cell Combinatorial Indexed Hi-C (sci-Hi-C) using split-pool barcoding

Sample Requirements

Because sci-Hi-C relies on nuclear integrity for combinatorial indexing, sample quality is paramount. Nuclei must remain intact through the first ligation step.

Sample Type Minimum Input Preferred Input Key Notes
Cell Lines 5 × 10^5 cells 1 × 10^6 cells High viability (>90%) required. Fixation protocols available upon request.
Tissue 50 mg 100 mg Must be dissociable to single nuclei. Flash frozen tissue is preferred over FFPE.
Frozen Samples Supported Flash Frozen Nuclei integrity is critical; avoid slow freezing without cryoprotectant.

Demo Results: From Contacts to Clusters

Figure 1: Resolving Heterogeneity

sci-Hi-C allows for the visualization of 3D genomic diversity.

  • Left Panel (UMAP Clustering): sci-Hi-C data is projected into low-dimensional space (UMAP). Cells cluster not by gene expression, but by their insulation scores and compartment status. Distinct clusters (e.g., Cell Type A vs. Cell Type B) emerge clearly.
  • Right Panel (Aggregate Heatmaps): While individual single-cell maps are sparse (containing only thousands of contacts), aggregating reads from all cells in "Cluster A" yields a high-resolution "Pseudo-bulk" map. This reveals sharp TAD boundaries and loops specific to that cell type, which would be blurred in a standard bulk Hi-C experiment containing a mixture of cells.

sci-Hi-C data visualization showing cell clustering and aggregate contact mapsFigure 1: Resolving Heterogeneity

Case Study: Massively Multiplex Single-Cell Hi-C

This foundational study demonstrated the power of sci-Hi-C to resolve cellular identity and cell-cycle dynamics.

The Challenge

Standard single-cell Hi-C was limited by low throughput, making it difficult to study heterogeneous populations or dynamic processes like the cell cycle without sequencing thousands of wells individually.

The Solution

The research team developed sci-Hi-C (single-cell combinatorial indexed Hi-C). They applied the split-pool method to a mixture of human (HeLa) and mouse (HAP1) cells to test the method's ability to resolve species and cell types.

The Results

The method demonstrated a collision rate of less than 5%, effectively separating human and mouse nuclei based on sequence alignment. By analyzing the contact decay profiles (contact probability vs. genomic distance), the researchers could order the HeLa cells along a "trajectory" that perfectly matched the cell cycle phases (G1, S, G2/M). The data revealed the progressive weakening of A/B compartments as cells entered Mitosis, a feature invisible to bulk Hi-C.

sci-Hi-C data separating HeLa and HAP1 cells based on chromatin conformation

The Conclusion

sci-Hi-C successfully profiled thousands of cells in a single workflow, providing a scalable framework for constructing 3D genome atlases.

Source: Ramani, V., et al. "Massively multiplex single-cell Hi-C." Nature Methods (2017).

FAQ: Resolution & Doublets

References

  1. Ramani, V., et al. Massively multiplex single-cell Hi-C. Nature Methods. 2017;14:263–266.
  2. Collombet, S., et al. Parental-to-embryonic switch of chromosome organization in early embryogenesis. Nature. 2020;581:321–326.
  3. Nagano, T., et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502:59–64.
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