Overcoming Analytical Challenges in Proximity Labeling Proteomics

ABSTRACT

Proximity labeling (PL) proteomics has emerged as a powerful tool to capture both stable and transient protein interactions and subcellular networks. Despite the wide biological applications, PL still faces technical challenges in robustness, reproducibility, specificity, and sensitivity. Here, we discuss major analytical challenges in PL proteomics and highlight how the field is advancing to address these challenges by refining study design, tackling interferences, overcoming variation, developing novel tools, and establishing more robust platforms. We also provide our perspectives on best practices and the need for more robust, scalable, and quantitative PL technologies.

1. Introduction

Proximity labeling (PL) has emerged as a powerful tool to elucidate protein environments and interactions in their native cellular contexts. The major advantage of PL compared to traditional immunoprecipitation is the ability to capture transient and weak protein interactions with high spatial specificity. PL enzyme is genetically fused to a “bait” protein of interest and catalyzes covalent labeling of vicinity “prey” proteins within 10–20 nm of radius [1]. Commonly used PL enzymes include biotin ligases (e.g., BioID and TurboID) and peroxidases (e.g., HRP and APEX) [2, 3, 4, 5]. Biotin ligase‐based PL enzymes work under mild conditions and require no exogenous cofactors, allowing broad applications in living cells and whole organisms [6]. However, their slower labeling kinetics may limit the capture of rapidly changing interactions. In contrast, peroxidase‐based enzymes provide better temporal resolution with rapid labeling (~1 min) but require peroxide treatment that may cause oxidative stress or toxicity for the applications in vivo. Other recently developed PL methods, such as photocatalytic PL [7, 8, 9] and split‐enzyme PL [4], offer improved spatiotemporal precision, enabling precise control over the labeling process.

PL has been coupled with mass spectrometry (MS)‐based proteomics to provide comprehensive identification and quantification of proteins labeled in the PL reaction [2]. PL‐proteomics has transformed our understanding of protein networks and subcellular microenvironment with various biological applications [1, 10, 11]. However, technical challenges still exist, such as interferences from nonspecific labeling and contaminations, experimental variation, and quantitative accuracy. Addressing these issues remains essential for improving the robustness, data quality, and for unlocking the full potential of PL in elucidating complex biological systems (Figure 1).

FIGURE 1.

Overview of analytical challenges in proximity labeling proteomics. Abbreviations: B: biotin; iTRAQ: isobaric tags for relative and absolute quantitation; LFQ: label‐free quantification; SA: streptavidin; SILAC: stable isotope labeling by amino acids in cell culture; TMT: tandem mass tag.

2. Study Design: The Foundation for Success

A meticulous study design is key to produce reliable data and meaningful biological insights in PL experiments. Several critical factors include cell line establishment, the selection and validation of the expression of bait‐PL construct, the use of effective controls, and the selection of enrichment strategies. PL probe requires the genetic fusion of a PL enzyme to the bait protein which can be achieved by establishing a stable cell line or transient transfection. Therefore, users need to balance specific experimental needs for consistent long‐term study (stable cell line) versus the flexibility for rapid assessment (transient transfection) [12]. The expression level of the bait protein‐PL construct also influences the sensitivity and selectivity of resulted interactors. Endogenously knocking in the PL enzyme to the bait protein preserves the physiological relevance with great labeling specificity but compromises sensitivity and requires larger amount of starting material compared to the overexpression probe [13, 14]. Different PL enzymes catalyze reactions that label different protein residues. Biotin ligase‐based PL reaction labels the primary amine group (lysine residues), whereas peroxidase‐catalyzed reaction labels the electron‐rich structure (tyrosine residues) [14, 15]. Most proteins are rich in lysine and tyrosine and therefore should not be significantly influenced. However, proteins that do not have surface‐exposed lysine or tyrosine residues may not be labeled during PL reaction despite their proximity to the bait protein.

The proper use of controls is perhaps the most crucial aspect of study design. A negative control without the PL enzyme expression can provide insights into contaminations and nonspecific bindings associated with the enrichment strategies. A spatial control expressing PL probe at a specific subcellular location can reduce possible stochastic labeling of high abundant bystanders during the PL reaction [16]. The control group is often used to compare with the experimental group to generate relative fold changes/ratios in the proteomics results. It is beneficial to tune the expression level of PL probes to be at similar levels for ratiometric comparison. If ratio is highly skewed, normalization strategies can also be used during sample preparation or data analysis. Validating the labeling reaction and subcellular location of the PL probe also involves the use of control groups in fluorescence microscopy and western blot experiments. Whenever possible, including both negative control and spatial control will improve the confidence of the results.

PL labeling involves the use of biotin or biotin derivatives as the substrate. Biotinylated proteins are then enriched at the protein or peptide level. Protein‐level enrichment purifies intact biotinylated proteins using affinity matrices such as streptavidin or neutravidin‐conjugated beads. In contrast, peptide‐level enrichment specifically captures biotinylated peptides after enzymatic digestion by using biotin antibody, neutravidin, or reversible biotin‐binding entities [17, 18, 19, 20]. The selection of protein‐level or peptide‐level enrichment strategy should align with the specific objectives of the PL study, as each approach offers distinct advantages and limitations [21]. Protein‐level enrichment identifies more proteins and unique peptides per protein for more confident protein identification and quantification compared to the peptide‐level enrichment. Protein‐level enrichment also allows flexible buffer exchange for various downstream experiments, such as MS‐based proteomics and western blotting [2, 3]. But protein‐level enrichment often copurifies nonspecific labeling or secondary protein interactors, leading to increased false positives. On the other hand, peptide‐level enrichment that captures biotinylation residue provides direct evidence that the candidate protein was once near the bait protein during its life span. Since biotinylation only occurs at surface‐exposed amino acid residues, biotinylation sites also provide valuable information for characterizing protein structure and membrane protein topology [19]. However, peptide‐level enrichment may be subject to false negatives due to limited peptide coverage of each protein [22]. A large amount of starting material is often needed for protease digestion before peptide‐level enrichment, which significantly increases experimental cost.

3. Tackling Interferences Throughout the Workflow

PL experiments are prone to various interferences that can bury true interactors and impede biological interpretations. It is essential to understand the causes of interferences and implementing optimizations to reduce interferences, such as nonspecific biotin labeling during PL reaction, nonspecific binding during enrichment, and contaminations throughout the proteomics pipeline. During the PL reaction, the diffusion of reactive biotin radicals beyond the target region will compromise the spatial and temporal resolution of the PL proteomics results. Therefore, it is critical to precisely control labeling time and promptly quench the reaction to limit nonspecific labeling. Biotin‐dependent carboxylases within the mitochondria, such as PC, PCC, ACACA, and MCC, are highly enriched in PL‐proteomics dataset [23, 24]. Together with their interactors inside mitochondria, PL‐proteomics data may therefore present a false positive GO term enrichment at the mitochondrial location. We previously found that using a cleavable biotin substrate can significantly reduce the levels of these endogenously biotinylated proteins [15]. Although difficult to remove, endogenously biotinylated proteins can be used to normalize different batches of PL labeling experiments to reduce variations in the proteomics data [14]. Bioinformatic tools such as SAINT [25] can be used to assign confidence scores to proteins based on quantitative data and the negative control to filter out interference proteins from the proteomics results.

Besides false‐positive interactors during the labeling reaction, nonspecific bindings during the enrichment step represent another layer of interferences in PL experiments. Insufficient removal of nonbiotinylated proteins during enrichment can originate from the limited specificity of affinity beads, high viscosity of cell lysate, and insufficient washing step. Several methods can be used to decrease the viscosity of cell lysate, such as removing nucleic acids from the samples by sonication or RNase treatment and diluting the cell lysate before enrichment. Stringent beads washing with high‐salt and detergent‐containing buffers could effectively disrupt nonspecific bindings to the beads. At the data analysis level, a common interference protein list can be helpful to mark and remove interference proteins, such as the CRAPome repository for affinity purification MS data [26].

Streptavidin‐coated bead is the most commonly used method to enrich biotinylated proteins because of the strong affinity between streptavidin and biotin (Kd ~ 10⁻¹⁴). Streptavidin is a 52‐kDa protein that will be inevitably leaked and/or digested into the samples during biotinylation enrichment, on‐bead digestion, or elution steps [22, 27]. Several methods have been developed in recent years to mitigate streptavidin contamination. Beads titration assay needs to be used for each PL probe to obtain an optimal ratio between streptavidin beads and input protein ratio to avoid excessive use of streptavidin [4, 24]. Protease‐resistant beads can be used to reduce streptavidin peptides during on‐bead digestion [28]. Cleavable biotin probes were developed to allow convenient release of biotinylated proteins without the need for on‐bead digestion [15, 29]. Other type of beads such as neutravidin, avidin, and biotin antibody can be used as described in the previous section. Streptavidin, together with other commonly observed contaminant proteins from the proteomics workflow, should be marked by introducing a contaminant FASTA library and removed after data analysis [30]. Because of the use of detergent during cell lysis and beads washing, PL proteomics is also prone to detergent contamination. Detection and removal of residue detergent are critical to ensure data quality and protect LC–MS instruments. Recently developed ContamSPOT assay [31] can be used to detect and quantify trace levels of detergents from 1 μL of sample input via colorimetric and fluorometric assays on a TLC plate, which can be coupled with ethyl‐acetate liquid–liquid extraction to remove residue detergent prior to MS injection.

4. Overcoming Variation and Improving Scalability

PL proteomics is influenced by multiple sources of variations, from in situ PL labeling reaction, complex sample preparation procedures, and LC–MS analysis [32]. First, the efficiency of the labeling reaction can fluctuate. Biotin substrate may be difficult to dissolve, which influences the local biotin concentration for PL reaction. Prewarming, sonication, and predissolving in warm cell culture medium can help dissolve the reagent for homogenous labeling among cell populations. Likewise, PL labeling reaction duration should be precisely controlled and promptly quenched to reduce variations. Additionally, the lot‐to‐lot production variations of enrichment beads, as well as beads from different vendors have been found to influence binding capacity, coating density, and beads concentration [33]. This highlights the importance of rigorous quality control and vendor consistency in bead manufacturing, as well as the necessity of standardizing the test for binding capacity by dot blot or colorimetric assays [24, 27]. Stable isotope labeling technique with multiplexing capabilities can be used to reduce experimental and instrumental variations, such as TMT [34], iTRAQ [35], and SILAC labeling [36]. Trysin/Lys‐C mix could be used instead of trypsin for protein digestion to improve digestion efficiency and reproducibility [37]. Additionally, data normalization and batch corrections are important for large‐scale studies to enhance comparability across runs [38]. Emerging machine learning‐based bioinformatic tools are transforming LC–MS data normalization by enhancing peak classification and retention time alignment [39, 40]. With recent advancements in robotics for proteomics sample preparation, such as using liquid handlers [41, 42] or magnetic beads processors [43], automating PL sample preparation is an attractive route to mitigate human error, reduce handling time, ensure consistent sample handling, and increase throughput (with 96‐well plate) [21, 44, 45].

5. Conclusions

Addressing the analytical challenges in PL proteomics is essential to ensure reliable and reproducible results and unlock the full potential of PL. In this perspective, we discussed critical technical issues in PL proteomics experiments, including study design, the origins of interferences, and sources of variability, and highlighted recent strategies to overcome these challenges. Developing next‐generation PL probes will further improve its spatial and temporal resolution. To achieve these advances, it is important to foster interdisciplinary collaborations among biologists, chemists, engineers, instrumentation experts, and data scientists to transform the PL technique into a more quantitative, robust, scalable, and versatile tool. These advancements will ultimately provide deeper insights into cellular microenvironments, dynamic molecular interactions, and signaling networks of biological systems.

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.