Label-Free Quantitative Proteomics in Protein-Protein Interactions (PPIs) Studies

What Is Label-Free Quantitative Proteomics?

Label-free quantitative proteomics refers to a suite of methodologies. These methodologies enable relative or absolute measurement of protein abundance without isotopic or chemical tags. Instead, label-free quantitative proteomics relies on direct measurement of mass spectrometric signals. The signals derive from peptide precursor ion intensities or spectral counts. Label-free quantitative proteomics offers flexibility for diverse sample types. It also reduces cost and sample preparation complexity. This approach has become central for large-scale proteome profiling. It is especially relevant when isotopic labelling is not feasible. Thus, it provides a robust alternative to label-based methods. It is well suited for studies of protein-protein interactions (PPIs).

Label-free quantitative proteomics workflows generally include:

• Protein Extraction: Obtain proteins from cells, tissues, or biofluids.
• Proteolytic Digestion: Convert proteins into peptides via enzymes such as trypsin.
• Chromatographic Separation and Mass Analysis: Use high-resolution liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS).
• Data Processing: Extract peptide intensities or spectral counts.
• Statistical Analysis: Determine differential protein abundance across samples.

Why Quantifying PPIs Matters

Protein-protein interactions underpin most biological processes. They orchestrate signal transduction, metabolic flux, and structural assembly. Quantification of PPIs yields insights into dynamic interaction networks. Quantitative PPI data reveal how interaction strengths change under different conditions. This knowledge is critical in:

• Disease Pathogenesis: Understanding dysregulated interactomes in cancer, neurodegeneration, and infection.
• Drug Discovery: Identifying target engagement, off-target effects, and efficacy biomarkers.
• Systems Biology: Constructing quantitative models of cellular networks and pathways.

Moreover, quantitative measures discriminate between constitutive and regulated interactions. They help map transient complexes and allow comparison of interactomes across physiological states. In drug development, quantitation identifies ligand-induced changes in protein binding. In clinical proteomics, quantitation of interaction dynamics can reveal early disease biomarkers. Thus, label-free quantitative-based PPI studies have far-reaching implications for biology and medicine.

Comparison with Label-Based Quantitative Proteomics Techniques

Label-based methods include stable isotope labeling by amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ), and tandem mass tags (TMT). These approaches introduce isotopic or isobaric labels into peptides. They allow multiplexing of multiple samples in a single run. However, they also have limitations.

Label-based methods provide accurate relative quantitation. They minimize run-to-run variation by analyzing mixed labeled samples in a single injection. SILAC offers high precision in cell culture studies. iTRAQ and TMT enable multiplexing of up to eight or ten samples. However, these methods incur high reagent costs and require complex workflows.

In contrast, label-free quantitative proteomics offers broad applicability and reduced cost. It allows analysis of any sample, including tissues and body fluids. No metabolic or chemical labeling is needed. However, label-free quantitative proteomics requires rigorous data processing. Accurate retention time alignment and signal normalization are essential. Label-free quantitative proteomics also demands high-quality LC-MS instrumentation. When these prerequisites are met, accuracy can rival label-based approaches.

Figure 1. Generalized comparison of different quantitative proteomic strategies.

Principles of Label-Free Quantitative Proteomics

Spectral Counting

Spectral counting counts the number of tandem mass spectra matched to peptides of a protein. The underlying assumption is that larger or more abundant proteins yield more spectra. Spectral counting methods include:

• Protein Abundance Index (PAI): Ratio of observed peptides to theoretically observable peptides.
• Exponentially Modified Protein Abundance Index (emPAI): An adapted PAI that correlates more closely with absolute protein abundance.
• Normalized Spectral Abundance Factor (NSAF): Adjusts spectral counts by protein length to reduce size bias.

Although simple and easy to implement, spectral counting has limited dynamic range and is less sensitive to small fold changes.

Peptide Intensity-Based Quantification

Peptide intensity methods measure the area under the extracted ion chromatogram (XIC). Each peptide's precursor ion signal is integrated over retention time. Key steps include:

• Peptide Feature Detection: Identify chromatographic peaks corresponding to peptide precursors.
• Retention Time Alignment: Align retention times across multiple runs to match features.
• Area Integration: Integrate signal intensities across aligned peaks to obtain area under the curve (AUC).
• Protein Summation: Sum or average peptide intensities to derive protein-level abundance.

Intensity-based methods offer high sensitivity and dynamic range. They allow detection of subtle abundance changes in low-abundance proteins. However, they require robust software for feature extraction and alignment.

Experimental Workflow for Label-Free Quantitative PPIs Studies

Sample Preparation Strategies for PPIs Enrichment

The initial step in any PPIs study is the enrichment of protein complexes. Common enrichment strategies include:

• Affinity Purification (AP): Uses antibodies or tagged bait proteins to capture interactors.
• Pull-Down Assays: Employ recombinant bait proteins immobilized on beads (e.g., NHS-activated sepharose).
• Co-Immunoprecipitation (Co-IP): Uses antibodies to capture endogenous or transfected bait proteins and their partners.

Each strategy has specific considerations:

• Choice of Epitope Tags: GST, FLAG, HA, Strep, or GFP tags can facilitate purification.
• Crosslinking: Formaldehyde or DSS crosslinkers can stabilize weak or transient interactions.
• Stringency Conditions: Salt concentration, detergent type, and wash volume influence specificity.
• After enrichment, protein complexes are eluted under mild conditions to preserve interactions. The eluted proteins are then reduced, alkylated, and digested with proteases (e.g., trypsin, Lys-C). The resulting peptide mixture is desalted and concentrated.

Quality Control and Reproducibility in Label-free Quantitative Proteomics

Ensuring data quality is vital for reliable PPI quantitation. Key quality control steps include:

• Internal Standards: Spiked-in synthetic peptides or proteins (e.g., iRT peptides) monitor instrument performance.
• Retention Time Calibration: Use retention time markers to align chromatograms.
• Mass Accuracy Checks: Calibrate mass spectrometer.
• Replicate Injections: Analyze technical replicates (n≥2) to assess LC-MS reproducibility.
• Blank Runs: Perform blank injections between samples to detect carryover.
• QC Metrics: Monitor total ion current (TIC), peptide identification rate, and peak width distribution.

Reproducibility across biological replicates is critical for statistical confidence. Based on QC metrics, any outlier runs should be excluded. Consistent sample handling and precise timing of all steps improve reproducibility.

Data Processing and Normalization in Label-Free Quantification

Preprocessing

Preprocessing begins with converting raw MS files into searchable formats (e.g., mzML). Software platforms such as MaxQuant, Proteome Discoverer, or OpenMS perform:

• Peak Detection: Identify m/z, retention time, and intensity.
• Peptide-Spectrum Matching (PSM): Use search engines (e.g., Mascot, Sequest, Andromeda) to match MS/MS spectra against a protein database.
• False Discovery Rate (FDR) Control: Employ target-decoy strategy to maintain FDR≤1% at peptide and protein levels.
• Retention Time Alignment: Align peptide features across runs using algorithms.

Intensity Normalization

Normalization corrects for technical variability. Common methods include:

• Total Ion Current (TIC) Normalization: Divides each peptide intensity by the sum of intensities across the run. This corrects for sample loading differences.
• LOESS (Locally Estimated Scatterplot Smoothing): A non-parametric regression to correct systematic biases between runs. LOESS adjusts intensity values to a reference run.
• Variance Stabilization Normalization (VSN): Transforms intensity data to stabilize variance across dynamic range. VSN reduces heteroscedasticity, improving statistical power.

Statistical Models for Differential Interaction Detection

After normalization, statistical models identify significantly enriched interactions. Models include:

• t-Test and ANOVA: Compare mean intensities or spectral counts between groups. Requires equal variances and normally distributed residuals.
• Linear Models for Microarray Data (limma): Empirical Bayes moderation improves variance estimates. Suitable for small sample sizes.
• Significance Analysis of INTeractome (SAINT): The Bayesian model was designed explicitly for AP-MS data. Evaluate the probability of true interactions versus background.
• Generalized Linear Models (GLMs): Handle spectral count data using Poisson or negative binomial distributions.

Bioinformatics Tools for Label-Free PPIs Quantification

Several bioinformatics platforms support label-free quantitative-based PPI analysis. Key tools include:

• MaxQuant: Comprehensive software for label-free quantitative intensity-based quantitation. Features MaxLFQ algorithm for accurate label-free quantification. Integrates Andromeda search engine and retention time alignment modules.
• Perseus: Companion tool for MaxQuant. Provides data filtering, normalization, statistical testing, and enrichment analysis.
• Progenesis QI (Nonlinear Dynamics): Commercial solution for peptide feature alignment, normalization, and quantification. Employs proprietary algorithms for retention time correction.
• OpenMS: Open-source C++ library with Python bindings. Offers workflow modules for label-free quantitative, including feature detection (FeatureFinder), alignment (MapAligner), and quantitation (ProteomicsLFQ).
• MSstats (R/Bioconductor): Statistical package for label-free quantitative data. Implements linear mixed-effects models for differential expression analysis. Supports both intensity and spectral count data.
• SAINT (Significance Analysis of INTeractome): Applies Bayesian statistical framework to AP-MS spectral count data. Distinguishes true interactors from nonspecific binders.
• CompPASS (Comparative Proteomics Analysis Software Suite): Scoring method for high-throughput PPI datasets. Ranks prey proteins based on frequency and abundance across multiple pulldowns.
• Cytoscape: Visualization platform for network data. Integrates PPI quantitation results to generate interaction maps. Includes plugins for cluster detection and functional enrichment (e.g., MCODE, ClueGO).

Case Studies in Label-Free PPI Quantitation

Mapping BCL-2 Interactions in Apoptotic Regulation

Case: Quantitative interactome of a membrane Bcl-2 network identifies a hierarchy of complexes for apoptosis regulation

Experimental Design:

• Objective: To map the membrane-associated BCL-2 protein interaction network involved in apoptosis regulation.
• Approach: Fluorescence cross-correlation spectroscopy (FCCS) combined with label-free mass spectrometry to quantify the relative abundance and interaction strength of BCL-2 family proteins in membrane environments.
• Samples: Recombinant BCL-2 family proteins reconstituted in artificial membranes.
• Analysis: Comparative spectral counting and intensity-based label-free quantification across apoptotic conditions.

Key Findings:

• Identified a hierarchical interaction map within the BCL-2 protein family.
• Membrane integration alters the interaction dynamics and promotes specific complex formations.
• Demonstrated that interaction strength correlates with pro-apoptotic or anti-apoptotic activity, offering insights into therapeutic targeting.

Figure 2. Analysis of interactions between cBid, Bax, and Bcl-xL in solution.

Quantitative Analysis of Signalosome Dynamics in Immune Activation

Case: Quantitative proteomics analysis of signalosome dynamics in primary T cells identifies the surface receptor CD6 as a Lat adaptor-independent TCR signaling hub

Experimental Design:

• Objective: To dissect the molecular organization of the T cell receptor (TCR) signalosome using a label-free proteomics approach.
• Approach: Affinity purification of signaling complexes followed by high-resolution LC-MS/MS and label-free quantification.
• Samples: Primary murine T cells under TCR activation vs. resting conditions.
• Analysis: Spectral counting and NSAF normalization for abundance comparisons of signaling components.

Key Findings:

• Identified CD6 as a novel, Lat-independent TCR signaling hub.
• Discovered dynamic reorganization of adaptor proteins and kinases during immune activation.
• Provided evidence for distinct signaling modules within the broader signalosome network.

Figure 3. Protein-protein interactions in the Zap70-Lat-SLP-76 interaction network.

Label-Free Quantitative-Based Dissection of Host-Virus Interactomes in SARS-CoV-2 Research

Case: Interactomes of SARS‐CoV‐2 and human coronaviruses reveal host factors potentially affecting pathogenesis

Experimental Design:

• Objective: Using label-free quantitative proteomics to profile host proteins interacting with SARS-CoV-2 proteins systematically.
• Approach: Affinity-tagged viral proteins expressed in human cells, followed by pull-down assays and label-free quantitative using high-resolution LC-MS/MS.
• Samples: Human HEK293 cells expressing individual SARS-CoV-2 proteins.
• Analysis: Intensity-based label-free quantitative with MaxQuant; data normalized using label-free quantitative intensity and statistical modeling.

Key Findings:

• Identified novel host interactors of SARS-CoV-2 proteins, including translation components, RNA processing, and vesicular transport.
• Highlighted differential interactomes between SARS-CoV-2 and other coronaviruses, providing clues to pathogenicity.
• Revealed several druggable host targets potentially exploitable for antiviral therapy.

Figure 4. Building a host-virus protein-protein interaction network.

References

• Paul F E, Hosp F, Selbach M. Analyzing protein–protein interactions by quantitative mass spectrometry. Methods, 2011, 54(4): 387-395. DOI: 10.1016/j.ymeth.2011.03.001
• Calvete J J, et al. Quantification of snake venom proteomes by mass spectrometry‐considerations and perspectives. Mass Spectrometry Reviews, 2024, 43(5): 977-997. DOI: 10.1002/mas.21850
• Bleicken S, et al. Quantitative interactome of a membrane Bcl-2 network identifies a hierarchy of complexes for apoptosis regulation. Nature communications, 2017, 8(1): 73. DOI: 10.1038/s41467-017-00086-6
• Roncagalli, R., et al. Quantitative proteomics analysis of signalosome dynamics in primary T cells identifies the surface receptor CD6 as a Lat adaptor-independent TCR signaling hub. Nature immunology, 15, 384–392 (2014). DOI: 10.1038/ni.2843
• Chen Z, et al. Interactomes of SARS-CoV-2 and human coronaviruses reveal host factors potentially affecting pathogenesis. The EMBO journal, 2021, 40(17): e107776. DOI: 10.15252/embj.2021107776