Single-cell Hi-C sequencing and analysis service (RUO)
Single-cell Hi-C sequencing and analysis service with standardized QC, protocol selection, and auditable deliverables (.For a focused follow-up, consider sn-Hi-C nuclei interaction profiling when profiling chromatin contacts from isolated nuclei. hic/.cool). RUO only.
- Protocol selection and feasibility review based on sample type and scientific question
- Single-cell Hi-C (or alternative) library construction with stepwise QC checkpoints
- Standard bioinformatics processing and QC reporting (see "Bioinformatics workflow")
Service definition & what's included
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.
What single-cell Hi-C measures (contact map per cell)
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.
Included modules: wet lab, sequencing, bioinformatics, QC, reporting
Core modules included:
- Protocol selection and feasibility review based on sample type and scientific question
- Single-cell Hi-C (or alternative) library construction with stepwise QC checkpoints
- Sequencing-ready library QC and data generation
- Standard bioinformatics processing and QC reporting (see "Bioinformatics workflow")
- Deliverables package: raw data + processed contact pairs + matrices + QC report
Provide sample type, organism/build, and desired deliverables to receive a scope-aligned quote.
When single-cell Hi-C is the right tool
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.
Cell-to-cell heterogeneity / rare states
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.
Regulatory architecture & enhancer–promoter hypotheses (RUO)
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.
Cell cycle / differentiation trajectories (with cautions)
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.
Protocol selection: single-cell Hi-C vs sci-Hi-C vs snHi-C (and when to add Capture/Methyl)
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.
Conventional single-cell Hi-C (one cell per library/partition)
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.
sci-Hi-C (high-throughput combinatorial indexing)
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.
snHi-C for nuclei/rare cell types
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.)
Add-ons: Capture Hi-C (targeted), Methyl-HiC (multi-omics) — decision rules
If your primary goal is targeted loci (rather than genome-wide discovery), a capture strategy may be more efficient and interpretable.
- Capture Hi-C is considered when you have defined regions of interest and want higher effective coverage there.
- Methyl-HiC (multi-omics) can be considered where methylation-informed stratification is essential.
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. For a focused follow-up, consider T2C targeted chromatin capture when focused contact mapping around loci of interest.
Wet-lab workflow & QC checkpoints (standardized, auditable)
The wet-lab workflow is managed with QC checkpoints to ensure issues are detected early and outputs remain auditable.
Key steps: crosslink → digestion/ligation → library prep → sequencing-ready QC
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).
Library QC: complexity signals, contamination checks, batch controls
QC focuses on:
- Library integrity and size distribution
- Evidence of usable proximity-ligation signal (protocol-dependent)
- Contamination awareness (sample mix-ups, unexpected species signals)
- Batch consistency checks and documentation
Acceptance/flagging logic (what triggers re-evaluation)
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.
Bioinformatics workflow: from reads to per-cell contact maps and interpretation
This service includes standard processing and QC reporting to produce auditable contact-pair files and matrices suitable for downstream exploration.
Processing: mapping → valid pairs → per-cell QC → matrix building
Standard processing typically covers:
- Read mapping and alignment QC
- Contact pair extraction (pairs format) and filtering to define valid pairs
- Summary QC reporting including metrics such as cis/trans ratio, duplicate patterns, and contact distance behavior (e.g., contact probability P(s))
- Matrix generation into common formats (e.g., .hic and/or .cool/.mcool) suitable for visualization and downstream analysis
Single-cell analyses: clustering/embedding, batch/cell-cycle considerations
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:
- Expected sparsity and how it affects per-cell interpretability
- Batch effects and cell-cycle confounding (what to watch for)
- Recommended next steps if you plan clustering/embedding internally
Feature calling (optional): compartments/domains/loops + cross-validation guidance
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.
V2G support (RUO): linking hypotheses + handoff-ready outputs
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.
Standard output artifacts (compact view)
| 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) |
Phased packages for budget control (Starter → Standard → Expansion)
To support evaluation-to-decision purchasing, projects can be scoped in phases without committing to unnecessary scale upfront.
Starter: feasibility + baseline QC + core deliverables
- Confirm protocol feasibility for your sample type
- Generate core deliverables and QC report for review
- Establish acceptance/flagging logic for scaling
Standard: deeper analysis aligned to hypothesis
- Expand to the agreed sample set and comparisons
- Provide standardized deliverables plus analysis add-ons as scoped (RUO)
Expansion: scale cells/samples, add multi-omics/targeted modules
- Scale throughput or broaden conditions
- Add targeted (Capture Hi-C) or multi-omics (Methyl-HiC) modules if justified by study goals
How to start (method fit + project intake + compliance)
Starting is simplest when the request includes sample realities and deliverable expectations.
Minimum info to quote: sample type, organism/build, research question, desired outputs
For an accurate quote and scope:
- Sample format (cells/nuclei), preservation method, and risk notes
- Organism and preferred genome build
- Research question (heterogeneity vs targeted loci vs multi-omics)
- Desired outputs (.hic vs .cool/.mcool, report depth, handoff needs)
RUO / privacy / data handling expectations (high level)
- RUO only: not for diagnostic use or clinical decision-making
- Data handling: de-identified project identifiers, controlled access, and documented deliverables manifest (details aligned to your institutional requirements)
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.
Sample requirements & feasibility (reduce failure risk)
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.
Sample types: cells vs nuclei; fresh/frozen considerations
Typical inputs include cell suspensions or isolated nuclei. Feasibility depends on factors such as sample integrity, fixation constraints, and compatibility with the intended protocol.
Key metadata needed: organism/build, cell type, treatments, replicates
To reduce rework risk, provide:
- Organism and reference genome build (if known)
- Cell type/tissue context and any enrichment steps
- Treatment/perturbation conditions and grouping logic
- Experimental design goals (comparisons, clusters, targeted hypotheses)
Pilot-first strategy for risky inputs (no timelines stated)
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.
Sample requirements table (standard)
| 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 you receive (files, formats, and figure-ready outputs)
Deliverables are designed to be auditable and compatible with common downstream tools. Final deliverables depend on the selected protocol and the agreed scope.
Primary data: FASTQ/BAM/pairs + matrices (.hic/.cool/.mcool)
Standard deliverables (RUO):
- Raw sequencing reads (FASTQ)
- Alignments (BAM) where applicable/required for handoff
- Filtered contact pairs (pairs format) and summary tables
- Interaction matrices in .hic and/or .cool/.mcool (as requested)
QC report: per-sample + per-cell summary tables and flags
A consolidated QC report typically includes:
- Per-sample library QC summaries
- Per-cell QC distributions (to show sparsity and outliers)
- Key interpretability notes and recommended caution flags
- Documentation of processing steps and file manifest
Summary figures: contact maps + key QC plots + optional biology figures
Figure-ready content can include:
- Representative contact map snapshots
- QC plots (distance decay behavior, per-cell contact distributions)
- Optional scoped figures aligned to your question (RUO; no clinical interpretation)
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.
Glossary
- Valid pairs: filtered read pairs interpreted as true chromatin contacts after processing.
- cis/trans ratio: proportion of intra-chromosomal vs inter-chromosomal contacts; a useful QC context signal.
- Contact probability P(s): contact frequency as a function of genomic distance; reflects expected decay behavior.
- Sparsity: limited number of contacts per cell; common in single-cell contact mapping.
- Compartments (A/B): broad chromatin segregation patterns often observed at aggregated levels.
- TADs/domains: domain-like structures; detection can depend on coverage and aggregation strategy.
- Loops: focal contact enrichments; often require stronger coverage and careful validation.
- .hic / .cool/.mcool: common matrix formats used for visualization and analysis in different tool ecosystems.
FAQ
References
- Nagano T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature (2013).
- Nagano T. et al. Single-cell Hi-C for genome-wide detection of chromatin interactions in single cells. Nature Protocols (2015).
- Ramani V. et al. Massively multiplex single-cell Hi-C. Nature Methods (2017).
- Flyamer I.M. et al. Single-nucleus Hi-C reveals chromatin reorganization during early development. Nature (2017).
- Durand N.C. et al. Juicer pipeline (Durand et al.).
- Servant N. et al. HiC-Pro pipeline (Servant et al.).
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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