Optimization of a high-throughput shotgun immunoproteomics pipeline for antigen identification

Abstract

Identification of proteins which initiate and/or perpetuate adaptive immune responses has potential to greatly impact pre-clinical and clinical work across numerous fields. To date, however, the methodologies available to identify antigens responsible for driving adaptive immune responses have been plagued by numerous issues which have drastically limited their widespread adoption. Therefore, in this study, we sought to optimize a shotgun immunoproteomics approach to alleviate these persistent issues and create a high-throughput, quantitative methodology for antigen identification. Three individual components of a previously published approach, namely the protein extraction, antigen elution, and LC-MS/MS analysis steps, were optimized in a systematic manner. These studies determined that preparation of protein extracts using a one-step tissue disruption method in immunoprecipitation (IP) buffer, eluting antigens from affinity chromatography columns with 1% trifluoroacetic acid (TFA), and TMT-labeling & multiplexing equal volumes of eluted samples for LC-MS/MS analysis, resulted in quantitative longitudinal antigen identification, with reduced variability between replicates and increased total number of antigens identified. This optimized pipeline provides a multiplexed, highly reproducible, and fully quantitative approach to antigen identification which is broadly applicable to determine the role of antigenic proteins in inciting (i.e., primary antigens) and perpetuating (i.e., secondary antigens) a wide range of diseases. SIGNIFICANCE: Using a systematic, hypothesis-driven approach, we identified potential improvements for three individual steps of a previously published approach for antigen-identification. Optimization of each step created a methodology which resolved many of the persistent issues associated with previous antigen identification approaches. The optimized high-throughput shotgun immunoproteomics approach described herein identifies more than five times as many unique antigens as the previously published method, greatly reduces protocol cost and mass spectrometry time per experiment, minimizes both inter- and intra-experimental variability, and ensures each experiment is fully quantitative. Ultimately, this optimized antigen identification approach has the potential to facilitate novel antigen identification studies, allowing evaluation of the adaptive immune response in a longitudinal manner and encourage innovations in a wide array of fields.

Keywords: Affinity chromatography; Antigen identification; Humoral immune response; Proteomics; Tandem-mass tag (TMT) labeling; Xenogeneic biomaterials.

Fig. 1.. Schematic overview of the high-throughput shotgun immunoproteomic antigen identification pipeline.

Red boxes indicate the three steps in the protocol (protein extraction, antigen elution, and LC-MS/MS analysis) which were targeted for optimization.

Fig. 2.. Optimization of the protein extraction increases total protein yield, antigen number, and diversity in BP extracts.

A.) Percent yield of protein extracted from pieces of native BP as determined via Detergent Compatible (DC) protein assay. B.) Representative Coomassie-stained gel loaded with equal volumes of total protein extracts prepared via various experimental conditions in each lane. C.) Densitometry quantification of total protein gels as measured via ImageJ analysis. D.) Venn diagram of all proteins identified via LC-MS/MS in protein extractions prepared using the 2-step extraction protocol and the optimized 1-step method. E.) Representative Western blot loaded with equal volumes of total protein extracts prepared via various experimental conditions. F.) Densitometry quantification of all antigens identified on western blots as measured via Image J analysis. * p ≤ 0.05, *** p ≤ 0.001, **** p ≤ 0.0001, ns=no statistically significant difference found

Fig. 3.. Elution of antigens from affinity chromatography columns using 1% TFA increases the quantity and diversity, while simultaneously reducing the variability, of antigens identified.

A.) Representative Coomassie-stained gel loaded with equal volumes of affinity chromatography eluate samples. B.) Densitometry quantification of total protein in affinity chromatography eluate gels as measured via ImageJ analysis. C.) Venn diagram of all identified proteins by LC-MS/MS analysis in 1% TFA and glycine eluates from day 84 affinity chromatography columns. D.) Representative Western Blot loaded with equal volumes of affinity chromatography eluate samples. E.) Densitometry quantification of antigens in western blots of affinity chromatography eluate samples as measured via Image J analysis. F.) Venn diagram of antigens identified in day 84 columns compared to day 0 by TMT-based quantitative proteomic analysis of affinity chromatography eluate samples prepared with various elution buffers. G.) Bar graph of the coefficient of variability for the total abundance of identified proteins (via LC-MS/MS analysis) across various elution buffer groups. H.) Violin plot of the coefficients of variability for all proteins identified in affinity chromatography elution samples prepared using various elution buffers. * p ≤ 0.05, ** p ≤ 0.005, **** p ≤ 0.0001, ns=no statistically significant difference found.

Fig. 4.. Multiplexing equal volumes, not equal protein concentrations, of TMT-Labeled samples results in a greater number of antigens identified via LC-MS/MS analysis.

Volcano plots of all identified antigens in LC-MS/MS analysis of TMT-labeled affinity chromatography eluate samples multiplexed by either A.) equal protein concentration, B.) equal protein concentration (corrected for dilution factor), or C.) equal volume. D.) Venn diagram of antigens identified in each of the three LC-MS/MS loading strategies (i.e., equal protein content, equal content corrected for dilution factor, and equal volume loading).

Fig. 5.. Optimized affinity chromatography antigen identification approach is capable of quantifying longitudinal response toward over 700 individual antigens in a non-adjuvanted rabbit subpannicular implantation model.

A.) Longitudinal response curves for every individual antigen in a representative rabbit implanted with native BP. B.) Venn diagram of overlap between individual rabbits in their response toward identified antigens (proteins with a Fold Change ≥2) at endpoint (Day 56) following implantation with native BP.