Organelle-Resolved Proximity Maps
Define neighborhood partners in mitochondria, ER, Golgi, endosomes, or nuclear lamina to clarify compartment-specific biology.
Proximity-dependent Biotin Identification (BioID) Service
BioID proximity labeling captures protein neighbors in live cells, revealing weak, transient, and compartment-specific associations that extraction-based pull-downs miss.
What this service solves.
- Preserves in-cell context; reduces losses from lysis.
- Works for membrane and chromatin targets that are hard to solubilize.
- Enables condition/variant comparisons to expose network rewiring.
- Delivers ranked, QC-backed candidates for follow-up validation.
BioID Background: How Proximity Labeling Maps Interaction Neighborhoods
Proximity‑dependent Biotin Identification (BioID) uses a promiscuous biotin ligase fused to your protein of interest to label nearby partners in living cells. Labeled proteins are enriched and identified by LC–MS/MS, capturing transient, weak, or spatially restricted interactions that traditional pull-downs often miss.
By tagging neighbors before lysis, BioID preserves in situ context and expands detection to membrane-bound, chromatin-associated, or dynamic complexes. It complements AP-MS and crosslinking methods by offering a broader view of interaction landscapes.
What Problems Our BioID Proximity Labeling Service Solves
- Missed transient interactions. Proximity labeling preserves short-lived contacts lost during lysis.
- Weak or indirect associations. Captures neighbors that co-IP often under-enriches.
- Membrane and chromatin challenges. Maps interaction neighborhoods in hard-to-solubilize compartments.
- Context loss in extraction workflows. Labels in living cells, keeping native spatial context.
- High background from sticky proteins. Control-driven filtering reduces non-specific enrichments.
- Unclear bait localization effects. Tag orientation and targeting signals clarify compartment-specific partners.
- Condition or variant comparisons. Designed contrasts reveal rewired networks under perturbations.
- Prioritization for validation. Ranked candidates streamline follow-up assays such as AP-MS or microscopy.
Advantages of Our BioID-MS Service
Live-Cell Labeling for Native Context
Labeling occurs in intact cells, preserving interactions in their physiological environment and minimizing artifacts from extraction.
High Sensitivity for Weak or Transient Interactors
Captures low-affinity or short-lived neighbors that are often missed by co-IP or AP-MS approaches.
Replicate-Aware Quantification — CV Typically ≤20%
Consistent enrichment across replicates supports confident comparison between conditions, variants, or treatments.
Stringent Background Control — ≤1% Protein-Level FDR
Decoy modeling and proper control samples allow robust filtering of non-specific binders, improving data quality.
Broad Compartment Access with Targeted Localization
Supports mapping in difficult compartments like the ER, nuclear lamina, or chromatin via signal-directed bait constructs.
Flexible Bait Design Support
Guidance on tag orientation, linker length, and expression strategy minimizes mislocalization and maximizes proximity coverage.
Technical Services
Service Scope
How to Choose
Workflow
Platform
Input Requirements
Deliverables
Use Cases
FAQ
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BioID Proximity Labeling: What We Offer at Creative Proteomics
Membrane & Receptor Microdomain Profiling
Characterize receptors, adaptors, and scaffolds in native bilayers to support signaling and trafficking studies.
Chromatin-Associated Neighborhood Discovery
Map proximate regulators and remodelers around DNA-bound factors to inform transcriptional mechanisms.
Condition/Variant Contrast Mapping
Compare proximity landscapes across treatments, time points, or protein variants to reveal network rewiring.
Mechanism-of-Action Triangulation
Combine proximity evidence with pathway context to generate testable hypotheses for target function or compound effects.
Mislocalization Assessment & Rescue Guidance
Evaluate tag orientation and targeting signals via neighborhood signatures to mitigate off-target localization artifacts.
Candidate Prioritization for Orthogonal Validation
Provide shortlists aligned to pathways or complexes to streamline follow-up by AP-MS, imaging, or genetic perturbation.
Secretory & Vesicular Context Linking
Connect intracellular neighborhoods with secreted or EV-associated partners to complete signaling or trafficking stories.
Proximity-Labeling Methods Compared: BioID, TurboID, miniTurbo, BioID2, APEX2, Split-TurboID
| Technique | BioID (BirA*) | TurboID | miniTurbo | BioID2 | APEX2 | Split-TurboID / Split-BioID |
| What it captures | Proximal neighborhood | Proximal neighborhood | Proximal neighborhood | Proximal neighborhood | Proximal proteome | Proximity contingent on complex formation |
| Labeling speed | Moderate | Fast | Fast | Moderate | Very fast (sub-minute) | Fast–moderate (upon reassembly) |
| Live-cell use | Yes | Yes | Yes | Yes | Yes (peroxide required) | Yes |
| Spatial radius | Nanoscale | Nanoscale | Nanoscale | Nanoscale | Very small, sharp | Nanoscale (conditional) |
| Best use cases | Steady-state mapping | Rapid signaling snapshots | Size-sensitive baits | Crowded organelles | Fine spatiotemporal control | Inducible/conditional assemblies |
| Key advantages | Robust enrichment; simple setup | High efficiency; short windows | Smaller tag footprint | Compact ligase | High temporal precision | Specificity tied to interaction |
| Typical caveats | Longer windows ↑ background | Needs strict control to curb nonspecific | Slightly less active vs TurboID | Activity depends on compartment | Requires careful quenching | Construct complexity; signal strength varies |
| Choose when… | Broad maps with good specificity | You need speed and can tune controls | Tag size matters and speed helps | Space around bait is limited | You need sub-minute capture | You want labeling only if partners meet |
Step-by-Step Workflow for BioID-MS Service
1
Project Intake & Goal Setting
Define the biological question, bait strategy, compartment targeting, and control plan.
2
Construct Review & Localization Check
Evaluate tag orientation, linker design, and expected subcellular residency to reduce artifacts.
3
Live-Cell Proximity Labeling
Apply controlled biotin labeling aligned to your model and perturbation design.
4
Affinity Capture with Stringent Washes
Enrich labeled proteins using streptavidin matrices and high-stringency wash regimes.
5
Proteomic Identification by LC–MS/MS
Acquire high-resolution spectra and perform database searching with strict error control.
6
Quantification, Normalization, and Background Modeling
Combine replicates, normalize signals, and estimate non-specific binders using controls.
7
Candidate Ranking and Biological Context
Prioritize neighbors with pathway, complex, and compartment annotations for interpretation.
8
Reporting & Data Handover
Deliver clean tables, QC summaries, and concise guidance for follow-up validation.
BioID Instrumentation & Proximity-Labeling Platform Capabilities
LC–MS/MS Platforms
- Orbitrap Exploris / Q Exactive HF-X (HRAM)
- timsTOF Pro / TOF-class instruments for speed and depth
NanoLC Systems & Columns
- Ultralow-dead-volume nanoLC with trap-and-elute
- Typical analytical column: 75 µm × 25 cm, C18, 1.6–2.0 µm
- Stable nano-ESI emitters (1–2 kV) for consistent spray
Acquisition Parameters (typical ranges)
- Full-scan resolution: 60k–120k (m/z 200)
- MS/MS isolation window: 0.7–1.6 m/z; dynamic exclusion enabled
- Mass range: ~350–1,800 m/z; mass accuracy ≤5 ppm (internal calibration)
- Data-dependent top-N or PASEF (for timsTOF) with optimized duty cycle
Orbitrap Exploris 480
Q Exactive HF-X
BioID Sample Types and Input Guidelines
| Item | What to submit | Key notes |
| Sample types | Adherent cells, suspension cells, primary cells; optional organoids or nuclei preps | Expression of bait–ligase fusion required; provide localization intent |
| Sample amount | Cell pellets: ~5–20 million cells per condition Lysates: ~1–2 mg total protein |
Lower inputs may be feasible with optimized capture; discuss during scoping |
| Cell material | Snap-frozen pellets or clarified lysates | Avoid thaw cycles; keep identifiers consistent across conditions/replicates |
| Construct info | Construct map, amino acid sequence, tag orientation | Note targeting signals (e.g., NLS, ER, mito) and any linkers |
| Controls | Negative control sample | Non-tagged or inactive-tag control for background modeling |
| Labeling details | Biotin addition status, duration, concentration | Brief washout helps reduce free-biotin carryover before harvest |
| Lysis compatibility | Buffer recipe used | Compatible with streptavidin capture; list detergents/salts used |
| Additives | Inhibitors and special reagents | Include protease/phosphatase inhibitors; avoid biotin analogs that hinder capture |
| Prohibited/avoid | Problematic chemicals | High levels of PEG/polymers, heparin, or MS-unfriendly surfactants |
| Shipping | Packaging and temperature | Cold packs or dry ice; label tubes with project ID, condition, replicate |
Deliverables: What You Get from Our BioID-MS Service
- Ranked proximity list — gene IDs, enrichment scores, concise annotations.
- Processed data bundle — peptide/protein tables, search outputs, parameter notes.
- Raw MS files — vendor-native and open formats for full reanalysis.
- QC snapshot — capture indicators, replicate agreement, error-control metrics.
- Pathway/complex mapping — GO, pathway, and compartment context.
- Review-ready figures — volcano/enrichment charts and compact network views.
- Methods brief — capture chemistry, LC–MS settings, database versions.
Proximity Enrichment Volcano Plot
Clustered Heatmap of Proximity Profiles
Proximity Network Map with Functional Context
Targeted Validation Panel (Spectrum + Capture QC)
Case Study
Case: EBV LMP1 Proximity Interactome by BioID
Research Objective: Define the in-cell interaction neighborhood of Epstein–Barr virus latent membrane protein 1 (LMP1) to clarify signaling and trafficking mechanisms.
How BioID Was Used
LMP1-BirA* fusion enabled proximity biotinylation in living cells; biotinylated neighbors were enriched by streptavidin and identified by LC–MS/MS. Replicate datasets and SAINT scoring prioritized high-confidence interactors; N- and C-terminal tagging assessed spatial effects.
Key Findings from BioID
Recovered >1,000 LMP1-associated proteins with strong enrichment for signal transduction and vesicle/protein trafficking; exosome-pathway components (e.g., CD63, syntenin-1, ALIX, TSG101, HRS, CHMPs, sorting nexins) were prominent. Targeted validations confirmed partners and linked syntenin-1/ALIX to LMP1 exosomal packaging.
Why BioID Was Essential
Live-cell labeling captured membrane/vesicular neighbors often missed by extraction-dependent methods and provided compartment context consistent with exosome biology.
Additional Techniques
Confocal localization, streptavidin-HRP capture QC, AP-MS comparison, SAINT statistics, pathway/network analysis; related work expanded the CD63–LMP1 vesicle network model.
Reference
Rider, Mark A., et al. "The interactome of EBV LMP1 evaluated by proximity-based BioID approach." Virology 516 (2018): 55-70.
Mass spectrometry analysis of affinity purified proteins.
You May Want to Know
What types of proteins or complexes are suitable for BioID?
BioID is ideal for proteins in membrane systems, chromatin environments, or dynamic assemblies where traditional pull down fails; literature shows it handles insoluble compartments and transient interactions.
How specific are the interactions detected by BioID?
The technique labels proximal—not necessarily directly binding—proteins; statistical controls and replicate aware quantification help distinguish true neighbors from background.
Do I need to design special controls for a BioID experiment?
Yes; a negative control (bait free or inactive tag) and spatial or localization controls improve filtering of non specific biotinylation, as recommended in proximity labeling studies.
Can BioID compare different conditions or disease models?
Absolutely; one strength of the service is enabling comparative proximity maps across treatments or variants, helping identify how networks rewire in different states.
Will I get full quantitative data or just lists of hits?
You get full scale quantitative proteomics outputs—including enriched candidate lists, replicate data, and annotations—designed for downstream validation and interpretation.
Is BioID better than co immunoprecipitation or cross linking MS?
BioID complements those methods: it captures context in live cells and weaker or transient neighbors; AP MS is stronger for stable complexes, XL MS provides direct contact detail.
What sample quality is required for successful BioID?
High quality bait expression, clear localization, appropriate controls, and compatible lysis conditions are essential; we provide sample preparation guidance to ensure success and high signal to noise.
How actionable are the results for follow up studies?
Very actionable—the output is ranked by enrichment with pathway and compartment annotation, making it ready for targeted validation (e.g., microscopy, AP‑MS) or follow‑up experiments.
