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Single-cell Hi-C sequencing and analysis service with standardized QC, protocol selection, and auditable deliverables (.hic/.cool). RUO only.
Single-cell Hi-C measures chromatin contacts at single-cell resolution, enabling you to see how 3D genome architecture varies across cell states, perturbations, or mixed populations. This service is designed for research use only (RUO) and delivers auditable outputs suitable for downstream exploration, figure generation, and handoff to internal bioinformatics teams.
Single-cell Hi-C sequencing and analysis service generates per-cell chromatin contact maps to study 3D genome organization and cellular heterogeneity. We provide protocol selection, library construction, sequencing, and standardized bioinformatics outputs with QC summaries and interpretable matrices. Research use only (RUO), not for diagnostic or clinical decision-making.
At a high level, single-cell Hi-C converts proximity-ligated DNA fragments into paired-end reads that can be mapped back to the genome to reconstruct contact pairs. Compared with bulk Hi-C, single-cell data are inherently sparse per cell, so the workflow must emphasize per-cell QC and clear reporting on data usability and limitations.
Core modules included:
Provide sample type, organism/build, and desired deliverables to receive a scope-aligned quote.
Use-case fit matters more than a generic "sequencing service" label. Single-cell Hi-C is typically selected when you need cell-to-cell variation in 3D genome structure, or when bulk averages would mask subpopulations.
Single-cell Hi-C can help characterize heterogeneous systems (e.g., mixed differentiation states, perturbed vs unperturbed cells, rare subpopulations). Because each cell contains fewer contacts than bulk datasets, study designs often rely on population-level summaries across many cells plus per-cell QC distributions to separate biological variation from technical sparsity.
Single-cell contact data can support hypothesis generation about regulatory interactions (e.g., candidate enhancer–promoter proximity patterns) and can be used alongside orthogonal measurements (expression, chromatin accessibility) in RUO research workflows. Outputs can be prepared for integration with downstream variant-to-gene (V2G) prioritization frameworks, where appropriate.
3D genome organization can vary with cell cycle and state transitions. Single-cell analyses should explicitly consider cell-cycle confounding, batch effects, and sparsity. We report QC summaries and provide recommended interpretation cautions in the final report.
Share your biological question and sample constraints to confirm whether single-cell Hi-C (or an alternative) is the most efficient approach.
Selecting the right protocol is a primary driver of data usability and budget control. We guide selection using sample attributes (cells vs nuclei, fixation constraints, throughput needs) and the type of downstream interpretation you prioritize.
Best when: you need per-cell contact maps with clear cell identity tracking and manageable batch structure.
Key considerations: per-cell sparsity is expected; study designs often focus on robust QC reporting and aggregated summaries across many cells.
Best when: you need higher-throughput single-cell contact profiling via combinatorial indexing strategies.
Key considerations: indexing design and collision/assignment controls matter; reporting should include per-cell QC distributions and clear cell filtering logic.
If your samples are more compatible with nuclei (e.g., difficult-to-dissociate tissues, preserved material), nuclei-based workflows can be more feasible. (If snHi-C is selected, the quote and deliverables remain aligned to single-cell contact-map outputs and per-cell QC reporting.)
If your primary goal is targeted loci (rather than genome-wide discovery), a capture strategy may be more efficient and interpretable.
Protocol selection consult: Send sample type, preservation method, and goals (heterogeneity vs targeted loci vs multi-omics) to receive a protocol recommendation and a scoped quote.
The wet-lab workflow is managed with QC checkpoints to ensure issues are detected early and outputs remain auditable.
Typical steps include crosslinking, restriction/fragmentation strategy (protocol-dependent), proximity ligation, library construction, and sequencing-ready QC. Each stage includes checks appropriate to the protocol (e.g., library size distribution, contamination signals, and consistency across batches).
QC focuses on:
We provide QC flagging in the report (e.g., low usable contacts, abnormal cis/trans patterns, excessive duplicates, or outlier libraries). Flagging does not imply "failure," but guides interpretation and next-step decisions.
Request QC spec sheet: Ask for a QC checklist aligned to your protocol choice and deliverables.
This service includes standard processing and QC reporting to produce auditable contact-pair files and matrices suitable for downstream exploration.
Standard processing typically covers:
Because you selected standard bioinformatics depth, we focus on deliverables and QC reporting rather than committing to advanced downstream inference. The report can still include interpretation notes about:
If requested as an add-on (scope-defined), higher-level features such as compartments, TADs/domains, or loops can be explored—often more robustly at aggregated levels than per-cell—along with guidance on validation and interpretation boundaries.
We can package outputs to support V2G hypothesis workflows (RUO), emphasizing that 3D contact evidence is supporting context and should be interpreted alongside orthogonal evidence.
| Artifact | Format | Notes (verbatim terms) |
|---|---|---|
| Raw sequencing reads | FASTQ | Raw sequencing reads (FASTQ) |
| Alignments | BAM | Alignments (BAM) where applicable/required for handoff |
| Filtered contacts | pairs format | Filtered contact pairs (pairs format) and summary tables |
| Interaction matrices | .hic / .cool/.mcool | Interaction matrices in .hic and/or .cool/.mcool (as requested) |
To support evaluation-to-decision purchasing, projects can be scoped in phases without committing to unnecessary scale upfront.
Starting is simplest when the request includes sample realities and deliverable expectations.
For an accurate quote and scope:
CTA — Deliverables-first quote: Tell us your required output formats and downstream tools; we will scope deliverables and provide a quote aligned to those needs.
We recommend confirming feasibility early, especially for complex tissues, low-input samples, or preserved material. We will provide a feasibility assessment based on sample description and requested deliverables.
Typical inputs include cell suspensions or isolated nuclei. Feasibility depends on factors such as sample integrity, fixation constraints, and compatibility with the intended protocol.
To reduce rework risk, provide:
For higher-risk samples (e.g., limited material, difficult tissues), a staged approach can be used where feasibility signals and QC outcomes are reviewed before scaling.
| Item | What to provide | Why it matters | Common risk flags (RUO) |
|---|---|---|---|
| Sample format | Cells or nuclei; description of preparation | Determines protocol feasibility | Debris-heavy prep; inconsistent isolation |
| Sample quality notes | Viability/integrity observations; storage/preservation method | Impacts library complexity and mapping | Over-fixation; degraded input |
| Organism & genome | Species and preferred reference build | Required for mapping and reporting | Uncertain build; mixed genomes |
| Experimental groups | Conditions, controls, batch structure | Enables interpretable comparisons | Confounded batches; missing controls |
| Study goal | Heterogeneity, regulatory hypothesis, V2G support | Drives analysis/reporting choices | Goal mismatch with protocol choice |
| Output preference | .hic vs .cool/.mcool; handoff needs | Ensures compatible deliverables | Downstream tool mismatch |
Deliverables are designed to be auditable and compatible with common downstream tools. Final deliverables depend on the selected protocol and the agreed scope.
Standard deliverables (RUO):
A consolidated QC report typically includes:
Figure-ready content can include:
CTA — Deliverables-first quote: Tell us your required output formats and downstream tools; we will scope deliverables and provide a quote aligned to those needs.
References
Compliance & scope statement (RUO)
This Single-cell Hi-C sequencing and analysis service is provided for research use only (RUO). It is not intended for diagnostic use, patient stratification, or clinical decision-making. Interpretation guidance and outputs are provided for scientific research workflows and should be validated with appropriate orthogonal methods.
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