Proximity Labeling Proteomics Service

Q: Should I choose BioID, TurboID, miniTurbo, or APEX/APEX2?

The choice depends on bait, cell model, labeling window, background tolerance, and biological question. TurboID and miniTurbo are often used for efficient biotin-ligase labeling, BioID may fit longer labeling windows, and APEX/APEX2 may fit fast peroxidase-based labeling designs.

Q: What sample information should I prepare before starting?

Prepare the bait protein, enzyme system, fusion orientation, cell model, treatment conditions, control groups, replicate plan, and any existing labeling or enrichment information.

Creative Proteomics provides Proximity Labeling Proteomics Service to help researchers identify proteins located near a bait protein, organelle, membrane region, or signaling complex in living cells.

We combine proximity-dependent labeling, streptavidin enrichment, LC-MS/MS, and bioinformatics analysis to support protein-neighborhood mapping, transient-interaction discovery, and drug-perturbed interactome studies.

With this service, we help you move from a biological question to an interpretable proximity proteomics dataset. Instead of relying only on stable protein complexes that survive extraction, proximity labeling marks nearby proteins before cell lysis. This makes it especially useful for weak, transient, spatially restricted, or condition-dependent associations.

Key service strengths

Map weak, transient, or spatially restricted protein neighborhoods
Support BioID, TurboID, miniTurbo, APEX, and APEX2 strategies
Compare untreated, treated, mutant, or stimulated conditions
Combine enrichment proteomics with LC-MS/MS data analysis
Deliver candidate lists, QC summaries, and interpretable result tables

Discuss Your Proximity Proteomics Project

Proximity labeling proteomics workflow showing bait fusion, biotin labeling, enrichment, LC-MS/MS, and bioinformatics outputs.

Protein Neighborhoods Capabilities Labeling Strategy Workflow Sample Deliverables Demo Applications Comparison Case Study FAQ References Disclaimer

Map Protein Neighborhoods That Conventional Pull-Downs May Miss

Proximity labeling proteomics identifies proteins located near a bait protein or defined cellular region. A labeling enzyme is fused to a bait protein or targeted to a compartment. After activation, nearby proteins are biotinylated, enriched with streptavidin-based capture, digested into peptides, and identified by LC-MS/MS.

This approach is useful when conventional Co-IP-MS or pull-down MS does not fully answer the question. Many signaling interactions are weak, short-lived, membrane-associated, or dependent on cellular context. These interactions can be lost during cell lysis or affinity purification.

Proximity labeling does not automatically prove direct physical binding. It identifies proteins that are close enough to be labeled under the chosen experimental conditions. For that reason, we help clients design controls, compare conditions, and prioritize candidates for follow-up validation.

What Proximity Labeling Proteomics Detects

This service can help identify:

Proteins near a bait protein
Proteins enriched around an organelle or membrane region
Proteins that become more or less proximal after compound treatment
Signaling-complex-associated proteins
Candidate interactors that may be missed by harsh purification conditions

When Proximity Is More Informative Than Direct Pull-Down

Proximity labeling may be a strong fit when your project involves:

Weak or transient protein interactions
Compartment-specific protein environments
Membrane proteins or receptors
Drug-induced changes in protein neighborhoods
Large signaling assemblies that are hard to preserve during extraction

What the Data Can and Cannot Prove

The data can support candidate discovery, protein neighborhood mapping, and condition-dependent enrichment analysis. It should be interpreted as proximity-based evidence, not as standalone proof of direct binding. For top candidates, follow-up validation may include Co-IP, targeted MS, imaging, mutagenesis, binding assays, or orthogonal chemical proteomics.

Our Proximity Labeling Proteomics Capabilities

We provide a project-focused workflow that connects experimental design, sample preparation, enrichment proteomics, LC-MS/MS, and data interpretation. Our role is not only to run mass spectrometry, but also to help you design a proximity labeling experiment that produces useful, interpretable data.

Bait-Centered Proximal Interactome Mapping

For bait-centered studies, we help map proteins enriched around your protein of interest. This can be useful for receptor neighborhoods, signaling proteins, transcriptional regulators, organelle-localized proteins, and proteins with poorly defined interaction partners.

We can support projects using client-provided constructs, cell models, enriched materials, or project designs that require feasibility discussion before sample submission.

Drug-Treated vs Control Differential Proximity Analysis

For mechanism-focused projects, proximity labeling can compare protein neighborhoods between untreated and treated conditions. This is useful when a compound may shift protein localization, disrupt a signaling complex, induce a new interaction environment, or alter pathway organization.

Typical comparisons include compound-treated vs untreated cells, mutant vs wild-type systems, stimulated vs baseline conditions, and knockdown or perturbation models vs controls.

Organelle, Membrane, and Signaling-Complex Neighborhoods

We can support compartment-focused proximity proteomics when the biological question depends on where proteins are located, not only whether they can bind in solution. Examples include mitochondria, ER, nucleus, plasma membrane, receptor microdomains, and pathway-specific signaling assemblies.

LC-MS/MS-Based Enrichment Proteomics and Data Reporting

After enrichment, proteins are processed for LC-MS/MS analysis. We provide protein identification and quantification tables, enrichment-based candidate ranking, QC summaries, and analysis-ready result files.

Project Feasibility Review

Before the experiment begins, we review key project details: bait protein or target compartment, labeling system preference, cell model or sample type, treatment conditions, control design, replicate plan, and expected downstream interpretation.

This step helps reduce avoidable background, misleading enrichment, and underpowered comparisons.

Choose the Right Labeling Strategy: BioID, TurboID, miniTurbo, APEX/APEX2

Different proximity labeling systems are not interchangeable. The best choice depends on the research question, cell model, labeling window, background tolerance, and the type of biological event you want to capture.

Biotin Ligase-Based Labeling: BioID, TurboID, miniTurbo

BioID, TurboID, and miniTurbo are biotin ligase-based systems. They label nearby proteins through proximity-dependent biotinylation. TurboID and miniTurbo were developed to improve labeling efficiency compared with earlier BioID systems, and TurboID has become widely used for living-cell proximity proteomics.

These approaches are often useful when the project needs broad protein-neighborhood capture around a bait, organelle, or defined cellular region.

Peroxidase-Based Labeling: APEX and APEX2

APEX and APEX2 use peroxidase chemistry to label nearby proteins. These methods can support fast labeling windows and compartment-focused mapping, but they require careful control of labeling conditions and cell compatibility.

Recent literature comparing APEX2 and TurboID shows that both can enrich compartment-specific proteomes, but they may produce different protein profiles. This means enzyme choice can shape the biological picture, so method selection should be part of the project design rather than an afterthought.

Selection Rules by Research Question

Research Goal	More Suitable Strategy	Why It May Fit
Broad bait-centered protein neighborhood mapping	TurboID / miniTurbo	Efficient labeling and strong fit for many cell-based studies
Longer labeling window with lower reactivity needs	BioID	Useful when slower labeling is acceptable
Fast compartment or microdomain labeling	APEX / APEX2	Useful when short labeling windows are important
Drug-treated vs control comparison	TurboID, miniTurbo, or APEX2	Choice depends on treatment time, biology, and cell tolerance
Membrane or receptor neighborhood mapping	TurboID / APEX2	Selection depends on localization and labeling chemistry
Discovery-stage candidate generation	Multi-method feasibility review	Best method depends on bait, model, and control design

Experimental Workflow with QC Checkpoints

Our workflow covers both the technical process and the service process, from project intake through final data delivery.

Project Design and Control Planning

We start by reviewing the bait protein, target compartment, cell model, perturbation design, and expected biological question. We also discuss controls before samples enter the LC-MS/MS workflow.

QC focus: Does the control design allow real enrichment to be separated from background labeling?

Bait-Enzyme Fusion or Model Review

If the project uses a bait fusion, we review the fusion orientation, tag position, expression strategy, and localization evidence. If the project uses a client-prepared system, we review the available documentation before enrichment and MS analysis.

QC focus: Does the bait-enzyme fusion localize where the biology suggests it should?

Proximity Labeling in the Cell Model

Cells are exposed to the appropriate labeling conditions for the selected enzyme system. Nearby proteins become biotin-labeled in the cellular environment. Labeling conditions should match the biological question and avoid unnecessary stress to the model.

QC focus: Is labeling detectable and consistent across samples?

Cell Collection, Lysis, and Streptavidin Enrichment

After labeling, cells are collected and lysed. Biotinylated proteins are captured through streptavidin-based enrichment. Wash conditions are selected to reduce nonspecific background while preserving the enriched labeled-protein pool.

QC focus: Is enrichment strong enough for LC-MS/MS, and are background signals controlled?

Protein Digestion and LC-MS/MS Acquisition

Enriched proteins are prepared for mass spectrometry analysis. Peptides are analyzed by LC-MS/MS to identify and quantify proteins across experimental groups.

QC focus: Are peptide recovery, instrument performance, and sample-level signal quality acceptable?

Data Filtering, Ranking, and Interpretation

We process the proteomics data to generate protein identification tables, quantification results, enrichment patterns, and candidate ranking. Depending on the project design, we can compare conditions and perform pathway or network-level analysis.

QC focus: Are candidate proteins supported by enrichment, controls, reproducibility, and biological context?

Vertical workflow diagram for proximity labeling proteomics with QC checkpoints from project design to data interpretation.

Common control types include:

Empty-vector or enzyme-only controls

Localization-matched controls

Untreated or vehicle controls

Mutant or inactive bait controls

Biological replicate groups

Sample Requirements and Project Intake Checklist

Proximity labeling projects are often cell-model dependent, so exact input should be confirmed before submission. The table below provides practical starting points based on Creative Proteomics’ general proteomics and sample-handling guidance.

Sample / Material Type	Recommended Amount or Input	Container / Format	Storage and Shipping	Key QC or Intake Notes
Suspension or adherent cultured cells for standard quantitative proteomics	5 × 10⁶ cells for label-free analysis; 1 × 10⁷ cells for DIA-style workflows	Frozen cell pellet in labeled centrifuge tubes	Store at -80°C and ship on dry ice	Keep cell numbers consistent across groups
Trace cell proteomics sample	200–5,000 cells for trace DIA workflows	Low-bind tube if applicable	Store frozen and ship on dry ice	Feasibility review required before submission
Animal soft tissue	100 mg for label-free; 200 mg for DIA-style workflows	Cryovial or centrifuge tube	Flash-freeze and ship on dry ice	Remove non-target tissue and record tissue source
Animal hard tissue	200 mg for label-free; 300–500 mg for DIA-style workflows	Cryovial or centrifuge tube	Flash-freeze and ship on dry ice	Discuss homogenization needs before submission
Plasma / serum / CSF without high-abundance protein depletion	20 μL	Labeled low-bind tube	Freeze and ship on dry ice	Avoid hemolysis and repeated freeze-thaw cycles
Plasma / serum / CSF with high-abundance protein depletion	50–100 μL for label-free; 100 μL for DIA-style workflows	Labeled low-bind tube	Freeze and ship on dry ice	EDTA plasma may be preferred for depletion workflows
Pure protein or enriched protein material	150 μg for label-free; 300 μg for DIA-style workflows	Tube with buffer details	Keep frozen unless otherwise discussed	Provide buffer composition and preparation history
Cell culture supernatant	10 mL for label-free; 20 mL for DIA-style workflows	Screw-cap tube	Freeze and ship on dry ice	Serum-containing medium may complicate interpretation
FFPE material	10 slices for label-free; 15–20 slices for DIA-style workflows	Labeled sample tube	Ship under agreed conditions	Each slice: about 10 μm thickness and 1.5 × 2 cm area

For proximity labeling studies, please also prepare:

Bait protein name and sequence
Enzyme system used or preferred
Fusion orientation and tag information
Cell line or model description
Treatment conditions and controls
Replicate design
Labeling and enrichment history if already performed
Any known stress, drug treatment, infection model, or special sample handling

Samples should be collected consistently across groups, labeled clearly, frozen promptly when required, and protected from repeated freeze-thaw cycles.

Bioinformatics Analysis and Data Deliverables

A proximity labeling experiment should not end with a long protein list. Our goal is to help you understand which candidates are enriched, which proteins are condition-dependent, and which pathways or networks may explain the biology.

Minimum Deliverables	Optional Add-On Analyses	Candidate Prioritization Factors
Raw LC-MS/MS data files Protein identification table Protein quantification table Enriched candidate proximal protein list Background-filtered ranking table Sample-level QC summary Enrichment QC summary Basic annotation table Volcano plot or heatmap-ready data Methods and parameter summary	Differential proximity enrichment analysis GO enrichment analysis KEGG or Reactome pathway enrichment Protein interaction network visualization Bait-centered candidate prioritization Drug-treated vs control comparison Subcellular marker enrichment review Figure-ready visualization package	Enrichment over control Reproducibility across replicates Condition-dependent changes Known localization or pathway relevance Background or contaminant risk Biological fit with the bait, compartment, or perturbation

This helps separate high-priority biological candidates from abundant background proteins or nonspecific enrichment.

Demo Results: What Your Proximity Proteomics Data May Look Like

The examples below describe typical result types. They are not claims about a specific project outcome.

Demo proximity labeling proteomics results showing candidate ranking.

Demo 1: Ranked Candidate Proximal Proteins

A ranked candidate table or bar chart can show which proteins are most enriched near the bait or compartment after background filtering.

Protein ID
Gene name
Enrichment score or fold-change
Detection frequency
Control comparison
Annotation notes

Demo proximity labeling proteomics results showing differential enrichment volcano plot.

Demo 2: Differential Proximity Enrichment

For treatment-control or mutant-wild-type designs, a volcano plot or heatmap can show proteins that change proximity under different conditions.

Condition-specific enrichment
Up-enriched and down-enriched candidates
Replicate-level consistency
Statistical comparison where design allows

Demo proximity labeling proteomics results showing protein interaction network and pathway interpretation.

Demo 3: Network and Pathway Interpretation

A network view can place the bait protein at the center and group enriched candidates by pathway, complex, function, or cellular compartment.

Bait-centered protein network
GO / pathway enrichment
Candidate functional clusters
Follow-up validation priorities

Applications in Drug Discovery and Mechanism Research

Proximity labeling proteomics is useful when the biological question depends on location, timing, and cellular context.

Target Deconvolution and Candidate Target Prioritization

When a compound changes the protein neighborhood around a bait, receptor, organelle, or signaling complex, proximity proteomics can help generate candidate proteins for follow-up validation. This can support target deconvolution and mechanism exploration when combined with orthogonal MS approaches such as photoaffinity labeling MS, Activity-based protein profiling, or Competitive ABPP.

Compound-Induced Network Rewiring

Some compounds do not simply increase or decrease protein abundance. They may alter localization, complex assembly, or pathway organization. Proximity labeling can compare protein neighborhoods before and after compound exposure to reveal condition-dependent network changes.

Signaling Complex and Pathway Regulation

For signaling studies, proximity labeling can map proteins near pathway members under baseline and stimulated conditions. This is helpful when signaling assemblies are dynamic and may not survive conventional purification.

Organelle and Membrane Protein Neighborhood Mapping

For mitochondria, ER, nuclear regions, plasma membrane, and receptor neighborhoods, proximity labeling can help identify local protein environments that are difficult to capture by whole-cell proteomics alone.

Proximity Labeling vs Co-IP-MS, AP-MS, XL-MS, and Spatial Proteomics

No single method answers every protein-interaction question. The best method depends on whether you need direct binding evidence, stable complex recovery, structural distance information, spatial context, or neighborhood-level discovery.

Method	What It Measures	Best Fit	Strengths	Key Limitations	Follow-Up Value
Proximity labeling proteomics	Proteins near a bait or compartment in cells	Weak, transient, spatially restricted, or condition-dependent neighborhoods	Captures cellular context; useful for dynamic systems; supports differential analysis	Does not prove direct binding by itself; requires careful controls and tag design	Candidate discovery, pathway mapping, MoA exploration
Co-IP-MS / Pull-down MS	Proteins retained with a bait during extraction and purification	Stable protein complexes	Strong for recoverable complexes; familiar workflow	Weak or transient interactions may be lost; lysis can disrupt context	Validation of top proximity candidates
AP-MS	Affinity-purified bait-associated proteins	Stable interactome mapping	Useful for well-behaved bait systems	Less suitable for unstable or spatially restricted neighborhoods	Complements proximity-based evidence
XL-MS	Crosslinked protein contacts	Structural interaction evidence	Can provide distance-level structural insight	Lower coverage; more complex interpretation	Supports mechanistic validation
Spatial proteomics	Protein localization or compartment assignment	Broad cellular localization studies	Useful for compartment-level mapping	Not always bait-centered; may not identify bait-specific neighborhoods	Helps interpret subcellular enrichment
Proteome-wide thermal stability profiling	Protein stability shifts after treatment	Target engagement and pathway response studies	Orthogonal MS evidence for compound response	Does not directly map local neighborhoods	Supports target and MoA confidence
chemical cross-linking mass spectrometry	Crosslinked protein contacts	Structural and complex-level questions	Adds distance-informed evidence	Requires suitable crosslinking chemistry and analysis	Helps validate physical proximity

When Proximity Labeling Is the Better Fit

Choose proximity labeling when your question involves:

Local protein neighborhoods
Dynamic signaling events
Weak or transient associations
Drug-induced changes in proximity
Organelle or membrane-localized systems
Bait-centered discovery in living cells

When Orthogonal Validation Is Still Needed

Use follow-up methods when you need to confirm:

Direct binding
Structural contact sites
Functional dependency
Compound engagement
Localization changes
Candidate biological relevance

For structural or conformational follow-up, HDX-MS epitope mapping may help answer a different but related question.

Case Study: TurboID-Based Proximal Proteome Mapping in IFN Signaling

This literature case shows how TurboID-based proximity labeling can map a dynamic signaling pathway and reveal candidate regulatory proteins.

Source: Proximal protein landscapes of the type I interferon signaling cascade reveal negative regulation by PJA2

Background

Type I interferon signaling is controlled by dynamic protein assemblies. These assemblies can change after stimulation and may include weak, transient, or context-dependent associations. Conventional interaction methods may miss part of this landscape because the signaling state can be altered during extraction or purification.

Schiefer and Hale used TurboID-based proximity labeling to map the proximal proteomes of seven canonical type I interferon signaling cascade members under basal and IFN-stimulated conditions. The study focused on IFNAR1, IFNAR2, JAK1, TYK2, STAT1, STAT2, and IRF9, covering receptor-level, kinase-level, transcription-factor-level, and regulatory pathway components.

Methods

The researchers generated cell systems expressing TurboID-tagged IFN signaling proteins. Cells were either left under basal conditions or stimulated with IFN-α2. Before harvest, biotin was added so that TurboID could label nearby proteins in the cellular environment.

After labeling, biotinylated proteins were enriched and analyzed by label-free mass spectrometry. The study then compared proximal protein profiles across signaling components and stimulation states. Figure 1 presents the system-wide strategy, including TurboID tagging, biotin labeling, enrichment, label-free MS analysis, heatmap visualization, and validation of selected proteins.

Results

The study identified 103 high-confidence proteins proximal to type I interferon signaling components. The dataset included known pathway-associated proteins as well as additional candidates that were not previously established as core type I interferon signaling components.

Figure 1 shows the workflow and heatmap-style outputs for enriched proximal proteins across the selected pathway members. The authors also used immunoblot validation for selected newly identified proximal proteins, showing that the proximity proteomics workflow produced candidates suitable for deeper biological follow-up.

A key biological finding was the identification of PJA2 as a negative regulator connected to the type I interferon signaling cascade. The study further explored this candidate beyond the discovery dataset, supporting the value of proximity labeling as a starting point for functional mechanism research.

Conclusion

This case shows why proximity labeling proteomics can be valuable for signaling and mechanism research. It can map protein neighborhoods across multiple pathway members, compare stimulated and basal states, and produce candidate regulatory proteins for deeper validation.

For drug discovery and pathway-focused projects, the same logic can support compound-response studies, target deconvolution, and mechanism exploration. The most useful output is not only a protein list, but a controlled and interpretable dataset that helps researchers decide which candidates deserve follow-up experiments.

TurboID proximity labeling workflow and heatmap outputs for mapping type I interferon signaling proximal proteomes.

Figure 1 shows the system-wide TurboID proximity labeling strategy used to map type I interferon signaling proximal proteomes.

FAQ

Frequently Asked Questions

Q: What is proximity labeling proteomics?

Proximity labeling proteomics is an LC-MS/MS-based approach for identifying proteins located near a bait protein, organelle, membrane region, or signaling complex. A labeling enzyme marks nearby proteins, which are then enriched and analyzed by mass spectrometry.

Q: How is proximity labeling different from Co-IP-MS or AP-MS?

Co-IP-MS and AP-MS usually capture proteins that remain associated during extraction and purification. Proximity labeling marks proteins inside the cellular environment before lysis, making it useful for weak, transient, or spatially restricted protein neighborhoods.

Q: Does proximity labeling prove direct protein-protein interaction?

No. It shows proximity under the experimental conditions. Direct binding or functional interaction should be validated with orthogonal methods when needed.

Q: Should I choose BioID, TurboID, miniTurbo, or APEX/APEX2?

The choice depends on your bait, cell model, labeling window, background tolerance, and biological question. TurboID and miniTurbo are often used for efficient biotin-ligase labeling, BioID may fit longer labeling windows, and APEX/APEX2 may fit fast peroxidase-based labeling designs.

Q: What controls are recommended?

Common controls include enzyme-only controls, empty-vector controls, localization-matched controls, untreated controls, mutant bait controls, and biological replicates. The best control design depends on the project question.

Q: Can this service compare drug-treated and untreated samples?

Yes. Proximity labeling can be used to compare protein neighborhoods between treated and untreated conditions, provided that the experimental design includes suitable controls and replicates.

Q: What sample information should I prepare before starting?

Please prepare the bait protein, enzyme system, fusion orientation, cell model, treatment conditions, control groups, replicate plan, and any existing labeling or enrichment information.

Q: What data deliverables are included?

Deliverables may include raw LC-MS/MS data, protein identification tables, quantification tables, enriched candidate lists, QC summaries, enrichment analysis, and visualization-ready result files.

Q: Can proximity labeling support target deconvolution or mechanism studies?

Yes. It can help identify condition-dependent proximal proteins and pathway changes that support target deconvolution or mechanism exploration, especially when combined with orthogonal MS workflows such as Affinity Selection–MS or chemical proteomics.

Q: What validation should be considered after candidate proteins are identified?

Follow-up may include Co-IP, targeted MS, imaging, mutagenesis, binding assays, pathway perturbation, or structural MS depending on the candidate and project goal.

References

Disclaimer

This service is for Research Use Only and is not intended for clinical diagnosis, treatment selection, patient management, or medical decision-making.

Online Inquiry

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