How many cells are required for a sci-Hi-C run?

sci-Hi-C is designed for scale. While the protocol can be adjusted, we typically recommend a pool of at least 10,000–50,000 nuclei to ensure efficient combinatorial indexing across the resulting cell population.

Can Dip-C be used on frozen tissue?

Yes, though fresh tissue is preferred for optimal chromatin integrity. Dip-C can be performed on nuclei isolated from frozen samples, provided they have been stored correctly at -80°C to preserve the high interaction density required for modeling.

What is the difference between single-cell Hi-C and single-nucleus Hi-C?

The difference primarily lies in the starting material. Single-cell Hi-C often begins with whole, intact cells (ideal for FACS), whereas single-nucleus Hi-C (sn-Hi-C) begins with isolated nuclei. sn-Hi-C is significantly more compatible with frozen or difficult-to-dissociate tissues.

Do you provide bioinformatics for clustering single-cell Hi-C data?

Yes. We offer comprehensive bioinformatics packages that include QC for single cells, dimensionality reduction, cell clustering based on 3D structure, and the generation of aggregated heatmaps for defined clusters.

Is haplotype phasing required for all single-cell methods?

Not necessarily. While haplotype phasing (distinguishing maternal vs. paternal alleles) is a major strength of Dip-C, you can still perform cell clustering and domain analysis with methods like sci-Hi-C without full phasing.

Single-Cell 3D Genomics Services: Resolving Cellular Heterogeneity

Uncover the true structural heterogeneity of complex tissues with our Single-Cell 3D Genomics Services. Moving beyond population averages, we offer a complete suite of technologies—from high-throughput sci-Hi-C for cell atlasing to haplotype-resolved Dip-C for 3D structural modeling. Whether you are mapping developmental trajectories or dissecting tumor subclones, access the resolution you need to define the genome's shape in every single cell. RUO.

High Throughput: Profile thousands of cells simultaneously with sci-Hi-C.
3D Modeling: Reconstruct diploid genome structures with Dip-C.
Tissue Compatible: Analyze frozen samples and complex tissues with sn-Hi-C.
Multi-Omics: Options for co-assaying chromatin structure and methylation.

Overview: From Average Profiles to Individual Cell Topology

Standard bulk Hi-C provides a useful "population average" of genome organization, but it effectively masks the high degree of structural variability between individual cells. In complex tissues, tumors, or developing embryos, this cell-to-cell heterogeneity is critical for understanding gene regulation and cell state transitions.

Our Single-Cell 3D Genomics Services break the averaging barrier. By capturing chromatin conformation in individual cells, we enable researchers to resolve the unique 3D topology of specific cell types within a mixed population. From building high-throughput cell atlases to reconstructing high-resolution 3D models of diploid genomes, our suite of single-cell technologies provides the ultimate view of structural diversity.

By leveraging advanced combinatorial indexing and diploid-aware algorithms, we provide insights into how individual genomes fold, interact, and vary across biological states. This is essential for precision oncology, developmental trajectory mapping, and fundamental epigenetic research.

Comparison of bulk Hi-C average vs single-cell heterogeneity showing distinct interaction matrices.

Our Single-Cell 3D Portfolio

We provide a specialized range of single-cell technologies tailored to your specific requirements for throughput, resolution, and sample type.

Method Selection Guide: Throughput vs. Resolution

Selecting the right single-cell method depends on whether your project prioritizes the number of cells analyzed or the depth of information per cell.

Feature	sci-Hi-C	Dip-C	sn-Hi-C / scHi-C
Cell Throughput	High (1,000s per run)	Low (10s to 100s)	Medium
Contacts per Cell	Low (Sparse)	High (Dense)	Moderate
Primary Goal	Cell Clustering & Atlasing	3D Structural Modeling	Cell State Characterization
Phasing (Diploid)	Limited	Excellent	Variable
Sample Complexity	Ideal for large populations	Ideal for deep locus analysis	Ideal for complex tissues

Chart comparing throughput and resolution of single-cell Hi-C methods.

Key Applications of Single-Cell 3D Genomics

Oncology: Dissecting chromatin folding heterogeneity within tumor subclones and mapping rare structural variations. Single-cell structure reveals sub-clonal chromosomal rearrangements that are averaged out in bulk samples.
Neuroscience: Mapping cell-type-specific 3D architecture in the brain from single nuclei (sn-Hi-C). Resolve how different neuron subtypes utilize distinct 3D landscapes to regulate complex gene programs.
Developmental Biology: Tracing the trajectory of chromatin reorganization during differentiation or embryogenesis. Characterize "structural snapshots" of cells as they progress through developmental lineages.
Allele-Specific Regulation: Analyzing imprinting and haplotype-resolved interactions at single-cell resolution. Use diploid-aware data to see how paternal and maternal alleles occupy different volumes and interact with different regulators.
Complex Tissue Mapping: Analyzing frozen biopsies and clinical specimens to create high-resolution 3D cell atlases without losing spatial or cellular context.

Single-Cell 3D Genomics Services: Resolving Cellular Heterogeneity

Overview: From Average Profiles to Individual Cell Topology

Our Single-Cell 3D Portfolio

sci-Hi-C (Single-Cell Combinatorial Indexing)

Dip-C (Diploid Chromatin Capture)

sn-Hi-C (Single-Nucleus Hi-C)

Single-Cell Hi-C

Method Selection Guide: Throughput vs. Resolution

Key Applications of Single-Cell 3D Genomics

Frequently Asked Questions