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. 2024 Apr 12;72(16):8879–8889. doi: 10.1021/acs.jafc.3c09188

Immunoassays and Mass Spectrometry for Determination of Protein Concentrations in Genetically Modified Crops

Yanfei Wang †,*, Mark Bednarcik ‡, Christopher Ament §, Matthew L Cheever ∥, Simone Cummings ‡, Tao Geng †, Dulan B Gunasekara ∥, Norma Houston ⊥, Kristen Kouba ⊥, Zi Liu †, Jeffrey Shippar §
PMCID: PMC11046482  PMID: 38607999

Abstract

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Quantifying protein levels in genetically modified (GM) crops is crucial in every phase of development, deregulation, and seed production. Immunoassays, particularly enzyme-linked immunosorbent assay, have been the primary protein quantitation techniques for decades within the industry due to their efficiency, adaptability, and credibility. Newer immunoassay technologies like Meso Scale Discovery and Luminex offer enhanced sensitivity and multiplexing capabilities. While mass spectrometry (MS) has been widely used for small molecules and protein detection in the pharmaceutical and agricultural industries (e.g., biomarkers, endogenous allergens), its use in quantifying protein levels in GM crops has been limited. However, as trait portfolios for GM crop have expanded, MS has been increasingly adopted due to its comparable sensitivity, increased specificity, and multiplexing capabilities. This review contrasts the benefits and limitations of immunoassays and MS technologies for protein measurement in GM crops, considering factors such as cost, convenience, and specific analytical needs. Ultimately, both techniques are suitable for assessing protein concentrations in GM crops, with MS offering complementary capabilities to immunoassays. This comparison aims to provide insights into selecting between these techniques based on the user’s end point needs.

Keywords: quantitation, quantification, protein concentration, GM crops, immunoassay, mass spectrometry, LC−MS/MS

Introduction

The global genetically modified (GM) crops market is expected to grow from US$19 billion in 2018 to reach US$26 billion by 2024 (Genetically Modified (GM) Food Market—Forecasts from 2019 to 2024 (researchandmarkets.com)), reflecting the demand for technologies to better manage pressures from weeds, plant pests, and environmental challenges. Most GM crop products are designed to express novel proteins that contribute to the desired trait technologies. Developing methods to detect and quantify these proteins in GM crops is essential to support GM crop product research, development, seed production, monitoring, and international trade. From a regulatory perspective, precise and accurate protein quantitation tools are crucial to ensure the safety of GM crops, whether to demonstrate the presence or absence of a transgenic trait or to quantify protein levels for regulatory compliance and safety evaluation.1 This becomes more challenging when dealing with the expression of multiple trait proteins in a crop product1,2 or when the proteins are difficult to isolate due to low expression levels3 or other factors.4 As a result, alternative protein analytical techniques may be required for some complex scenarios.

Immunoassays, which rely on specific interactions between antibodies and their target proteins, have historically been powerful techniques for protein detection and quantitation in GM crops.5−11 Immunoassays can provide a relatively high throughput method with readily available reagents and instrumentation, resulting in widespread adoption across various industries.12,13 Newer immunoassay technologies support the simultaneous analysis of multiple proteins, often termed multiplexing, and the potential for greater sensitivity.14 However, the development of immunoassay methods can be time-consuming, due to the need for production of antibodies, and standard proteins.15,16 Additionally, when the analyte proteins are expressed at very low levels or are membrane proteins, the challenge of developing suitable immunoassay methods is increased.17,18 Multiplexing individual immunoassays for GM products with multiple trait proteins can lead to difficulties when there is homology between proteins or when the assay conditions are not compatible. For these reasons, the availability of alternative analytical techniques for quantifying GM proteins can be important.

In recent years, the use of liquid chromatography (LC) coupled to mass spectrometry (MS) has become an alternative approach for protein quantitation in a wide range of sectors, including biotechnology, environmental monitoring, food processing, pharmaceuticals, agrochemicals, and cosmetics.19,20 The pairing of LC to tandem MS (LC–MS/MS) combines the physical separation capabilities of LC with the specific mass identification capabilities of MS. The LC–MS/MS technology also supports multiplexed and high through put protein analysis.

In this article, we review and compare the advantages and disadvantages of immunoassay and LC–MS/MS techniques for protein quantitation in the agriculture biotech industry and discuss their suitability for detecting and quantifying GM proteins.

Description of Techniques

Immunoassays

Immunoassays have been used for decades by the agricultural biotech industry to detect and quantify proteins in GM crops.21,22 This widespread adoption of immunoassay methods is attributed to their inherent specificity, high sensitivity, and relatively high level of throughput. In addition, they are amenable to automation, which further enables throughput and minimizes human errors in sample handling, storage, and testing. Lastly, immunoassays can be readily transferred across global markets due to the low cost of instrumentation and simple analytical procedures.

Immunoassays are critical tools for protein analysis due to their ability to quantify biological molecules through specific antibody interactions. Analysis with immunoassays involves measuring the activity from certain signaling molecules, which provide colorimetric, radioactive, fluorescent, or electrochemiluminescent signaling.23−25 A calibration curve or standard curve representing the output signal as a function of analyte concentration is generated by using known concentrations of the target protein, and a curve fit is used to interpolate protein concentrations within the samples.

Immunoassays can be developed to be very specific and sensitive by selecting antibodies that exhibit high affinity for the analyte protein and low binding or cross-reactivity with other molecules in the sample matrix. Sample dilution and the use of blocking reagents may help overcome some nonspecific background signal, however, it may challenging to avoid cross-reactivity for related endogenous proteins or homologous proteins that are expressed together in a GM crop,1 such as different members of the Cry protein family.26 It may also be difficult to create antibodies that recognize post-translational modifications on the analyte protein.

Another challenge related to antibody selection is the need for a constant supply of antibodies and other method critical reagent(s) throughout the lifecycle of the method. Preparing and maintaining a large batch of critical reagents is one way to overcome this challenge but may be costly. Changes in any of the critical reagents may trigger the need for revalidation of the entire method. When necessary, bridging an old lot of critical reagents to a newer lot of reagents should be performed.

Enzyme-Linked Immunosorbent Assay (ELISA)

ELISA is widely used to quantify newly expressed proteins in GM crops. ELISA requires one (direct) or two (indirect or sandwich) antibodies that bind specifically to the protein analyte. In the most common form of a sandwich ELISA (Figure 1), one antibody (capture antibody) is immobilized on solid surface and binds the analyte present in the sample, after which a second antibody (detection antibody) is added to form a complex with any captured protein analyte. The detection antibody is either conjugated directly to a signaling enzyme or recognized by an antispecies secondary antibody conjugated to the enzyme. The inclusion of a standard curve on the ELISA plate, with known amounts of analyte protein, turns the ELISA method into a quantitative assay. On the basis of a pair of antibodies that bind different epitopes of the protein, ELISA methods can be highly specific. Commercial ELISA kits can demonstrate sensitivity as low as 0.1 to 1 ng/mL (or ppb) of the target protein in a plant tissue extract and a quantitative range spanning two to 3 orders of magnitude.27 ELISA requires a purified and characterized protein standard that is either isolated directly from a trait-specific crop or expressed and purified using a heterologous system (e.g., microbial expression or chemical synthesis).

Figure 1.

Figure 1

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Overview of the sandwich ELISA immunoassay technique. (a) The wells of an ELISA plate are coated with analyte-specific capture antibodies. (b) When analyte is present, interactions with the capture antibody and a separate analyte-specific detection antibody allow formation of a “sandwich” complex, which provides for analyte detection and quantitation. The signaling enzyme (e.g., Horseradish Peroxidase) can be directly conjugated to the detection antibody or to a secondary antibody with specificity for the detection antibody. (c) In the ELISA workflow, (1) samples containing a complex mixture of molecules (e.g., plant tissue extracts) are added to the ELISA plate wells; (2) analyte molecules are specifically bound by the capture antibody and the wells are washed to remove nonspecific molecules; (3) a solution of detection antibody is added to the wells and the antibody binds specifically to the captured analyte; and (4) the enzyme substrate is added to the wells and allowed to develop a visible colored signal. (d) Known amounts of an analyte standard are added to wells on the plate to generate a standard curve. The intensity of the signal in the sample wells is directly proportional to those produced by the standard curve allowing for quantitation of the analyte.

Luminex

Luminex is another newer type of immunoassay technology and these assays have been developed for the clinical diagnostics, pharmaceutical, and life science research markets.28−32 The use of Luminex in the agricultural biotechnology field is increasing.33 Currently, most commercially available Luminex assays for protein analysis are based on the use of a Luminex 100/200 instrument with xMAP technology (Luminex Corp., Austin, TX) (Figure 2). Luminex xMAP is a multiplexing capable technology that combines microfluidics, optics, and digital processing with antibody-linked magnetic microbeads. The microscopic size and low density of the beads used in Luminex assays allow reactions to exhibit virtually solution-phase kinetics, while the incorporated magnetic properties further simplify analysis. The separation of beads containing different antibodies that specifically bind different analyte proteins can be conducted based on color coding, bead size, or magnetic force. Fluorescence is a commonly used detection system for Luminex assays since it typically detects samples with a high signal-to-noise ratio. A highly multiplexed Luminex immunoassay can potentially measure hundreds of analytes simultaneously.

Figure 2.

Figure 2

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Overview of the Luminex xMAP immunoassay detection scheme. (a) The presence of the analyte protein targeted by the specific capture and detection antibodies allows the formation of a bead-phycoerythrin complex. (b) Individual bead-phycoerythrin complexes are separated in a flow cell and detected by fluorescence excitation. (c) The bead fluorescence is excited to allow indexing of the specific bead color address being detected. Bead addresses are correlated with different analyte-specific antibodies, allowing identification of the analyte in each complex. Excitation of the phycoerythrin fluorescence level from each complex provides the output signal that allows quantitation.

As with other immunoassays, antibody specificity is a critical parameter during method development and validation in the multiplexed Luminex environment. Typically, assays are developed and validated for accuracy either individually for single trait proteins or together for stacked traits. Protein buffer compatibility and the dilution factor required for each protein also need to be considered during method development. Luminex also requires extra effort for the proper storage and handling of the microbeads. Freezing will damage the antibody-conjugated beads, which may incur some practical inconvenience for transferring them between locations.

On top of the advantages of multiplexing capability, Luminex assays can have dynamic ranges of up to 5 orders of magnitude. Reading time for a Luminex plate is relatively long (e.g., 15–20 min for the FlexMAP 3D system) compared to the under 1 min read time for an ELISA plate. However, while the results from an ELISA plate must be captured within approximately 30 min, the Luminex fluorescence is quite stable and can be measured after a few days storage at 4 °C.34

Meso Scale Discovery

Meso Scale Diagnostics (MSD, LLC., Rockville, MD) is another immunoassay technology that incorporates an electrochemiluminescent SULFO-TAG detection technology and micro plates with carbon electrodes integrated into each well (Figure 3).

Figure 3.

Figure 3

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Overview of the MSD immunoassay detection scheme. (a) Each well of an MSD plate can have multiple electrode positions that can each be set up for detection of a specific analyte contained in the sample extract. (b) Analyte specific capture antibodies are coated onto the respective electrode positions within the wells. (c) Following incubation steps first with samples and second with detection antibodies labeled with a SULFO-TAG label, bound analyte levels are detected by adding an electrochemiluminescent substrate solution and applying an electrical charge across the well electrodes. (d) The amount of light emitted by each electrode position corresponds to the amount of bound analyte and SULFO-TAG, and the relative light levels for the electrodes are quantified using an MSD instrument.

The wells of MSD plates are divided into several well-defined spots that can be coated with different protein-specific capture antibodies and the plates are available with up to 10 spots per well. Similar to ELISA, the samples are incubated in the wells with the capture antibodies and then the wells are washed to remove unbound molecules. A mixture of detection antibodies that have been conjugated to SULFO-TAG labels are then added to each well for binding to the protein analytes captured on each spot. Following electrical stimulation of the electrodes in each well by the MSD instrument, the light emitted by the SULFO-TAG labels is measured for each spot. The intensity of the light emitted at each spot is proportional to the amount of protein captured and the amount of protein in the samples is interpolated from a standard curve produced with known amounts of proteins. Like the single plex ELISA, it is important to use highly specific antibodies for MSD assays to minimize cross-reactivity.

Few publications address the use of MSD multiplex technology for protein expression in the agriculture biotechnology industry.35 Nevertheless, there has been widespread use of this technology in the pharmaceutical industry and in clinical research.36−40 MSD technology is very sensitive, with ultralow pico-gram level detection limits and a wide dynamic range of five or more orders of magnitude.14 This is particularly useful when analyzing samples with multiple traits that express trait proteins at widely varying levels. Despite the wide dynamic range, dilution factors still need to be optimized to minimize matrix effects. If considerations for multiple analyte proteins cannot be balanced in one multiplexed assay, then it may become necessary to split off some analytes into a separate assay.

Immunoassay Summary

Overall, immunoassays are powerful bioanalytical methods for quantifying traits and endogenous proteins in GM crops. However, the relatively costly and time-consuming method development process for immunoassays, the potential cross-reactivity with other proteins and multiplex assay reagents,40 and the lower tolerance for the harsh extraction buffers required for some analyte proteins may limit their applicability for quantifying some proteins in agricultural biotechnology (Table 1). Therefore, MS technology is being implemented more often as an alternative approach for the quantification of proteins of GM crops.

Table 1. Key Advantages and Limitations for Immunoassays (ELISA, MSD, Luminex, etc.).

advantages readily transferrable between laboratories
amenable to automation and high throughput
high sensitivity
   
limitations potential for cross-reactivity or nonspecific detection
antibody development and maintenance required
maintenance of critical reagents needed
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Mass Spectrometry

MS has been widely used in qualitative and quantitative protein analysis. MS enables the direct identification of molecules based on their mass-to-charge ratios with tandem MS (MS/MS), allowing exploration of unique fragmentation patterns, serving as a fingerprint for each molecule. MS quantitation of proteins can be categorized into nontargeted or targeted strategies.

Targeted bottom-up quantitation is the most commonly used method for protein MS quantitation and has been widely employed for the assessment of specific protein concentrations in a variety of industries.41−43 Targeted quantification strategies allow total protein to be isolated from a sample and a subset of specific proteins to be selectively compared or quantified.44−49 This approach offers high specificity due to its ability to selectively quantify compounds within complex mixtures. The quantitation typically uses selected-reaction monitoring/multiple-reaction monitoring (SRM/MRM) or parallel-reaction monitoring (PRM) with high resolution MS improvements.

The use of liquid chromatography in LC–MS/MS allows for sample molecule separation and potential automation, making it advantageous for handling multiple proteins in a high throughput manner. However, challenges include the need for sophisticated and expensive instruments, skilled analysts, and knowledge that is not easily transferred. Identifying a surrogate peptide that can specifically represent the protein of interest, has acceptable properties for MS analysis, and has no interference from the sample matrix can sometimes be difficult. Sample preparation could be limited to volatile buffers because the analyte must be in the gaseous phase and effectively ionized prior to analysis by MS. Therefore, all method parameters must be examined in detail during method development and validated extensively, similar to immunoassays. Some key advantages and limitations for LC–MS/MS are listed in Table 2.

Table 2. Key Advantages and Limitations of LC–MS/MS Protein Quantitation.

advantages wide dynamic range of 4 or 5 orders of magnitude
no need for protein standard or antibody
multiplexing
amenable to automation, medium to high throughput
high sensitivity, high specificity
   
limitations need for highly skilled personnel
limited to volatile buffer systems
difficulty in identifying surrogate peptides
efficient ionization of targets required
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Plant Tissue Protein Quantitation Workflow

For bottom-up targeted protein quantitation, proteins undergo enzymatic digestion to produce a mixture of peptides. Predicting the resultant peptides from a given enzymatic digestion can be achieved in silico, and one or several of these peptides can serve as surrogate(s) for the intact target protein. Identifying appropriate surrogate peptides that specifically represent the protein of interest without interference from the matrix can be challenging. Synthetic peptides or pure protein standards can be used to generate a standard curve to quantitate the protein(s) of interest in samples.

A typical targeted bottom-up proteomics LC–MS/MS flow diagram for plant tissue protein quantitation is shown in Figure 4. Proteins are extracted using a specialized protocol involving cell lysis, fractionation, and removal of high-abundance proteins. The use of compatible buffers is essential, as incompatible buffers can complicate the process. After extraction, proteins undergo enzymatic digestion to create peptide fragments, followed by removal of salts and detergents.

Figure 4.

Figure 4

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Protein quantitation workflow by LC–MS/MS. Proteins are prepared and extracted from plant tissue samples. After extraction, protein samples undergo enzymatic digestion to generate known or predicted peptide fragments. Heavy isotope labeled peptides are spiked in as internal standard. The peptide mixtures are separated by liquid chromatography and mass spectrometry. The ions are fragmented and detected. The data is plotted and used for quantifying samples.

Labeled Peptides

An important component for accurate quantitative MS procedures is an appropriate label or internal standard (IS). Heavy isotope labeled peptides are spiked into the digested peptides to normalize the subsequent LC–MS/MS ionization at any step in the process. Ideally, a high purity version of the analyte of interest with 13C, 15N, or 2H heavy isotope labels replacing native atoms can be used. Since the compound of interest and internal standard will have different masses. The IS can be used to correct for differential ionization at the MS source to normalize the signal observed and account for any losses, depending on where it was added in the workflow. Synthetic peptides or heavy isotope-labeled peptides have become easier and faster to source from multiple vendor’s relatively low cost. These heavy-isotope labeled peptides allow analysis of very complex mixtures of digested proteins.47

Multiple-Reaction Monitoring (MRM)

MRM, also known as SRM, is an important LC–MS/MS technique for the quantitation of target proteins in complex samples. MRMs use the signal of selected MS/MS fragment ions for quantitation and are typically performed on triple quadrupole (QQQ) or quadrupole-ion trap (Q-Trap) type mass spectrometers. Both tandem instruments use the first mass analyzer (Q1) to selectively transmit ions of a particular mass-to-charge ratio (m/z) (Figure 5); these target ions are called precursor ions. The precursor ions are then subjected to collision-induced dissociation (CID) in a collision cell to generate product ions, which give excellent specificity for a targeted peptide. The targeted product ions are detected selectively either by the third quadrupole in or by the ion trap.

Figure 5.

Figure 5

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Protein quantitation by MRM/SRM and PRM. MRM/SRM monitors each precursor ion transition at a time, while PRM analyzes all fragment ions derived from a precursor ion.

The MRM technique offers sensitive and repeatable measurements since the second mass analyzer targets selected product ions; only the specified transitions are recorded, and other product ions are not detected. Selective ion transmission affords MRM analysis with low limits of quantitation (LOQ), ranging in the nanomole to attomole levels of analyte.50 and a wide dynamic range, which can cover a 4- to 5-orders of magnitude. Also, it provides high specificity to unique peptides and can theoretically distinguish proteins that differ in sequence by a single nonisobaric amino acid between homologous proteins.

Parallel-Reaction Monitoring (PRM)

PRM, with the evolution of HRMS technology, has become an alternative platform for MS quantitative analysis.51−53 Unlike MRM, all fragment ions are measured after fragmentation of a selected precursor in PRM, as diagrammed in Figure 5. PRM is typically performed on Orbitrap or Time-of-Flight (ToF) analyzers. Both full scan and product ion scan can be used for quantitative analysis. In full scan mode, all ions present in the sample of interest can be simultaneously achieved with the quantitative data. This qualitative information can be reanalyzed or reextracted at a later stage. This full scan mode reduces MS quantitation method development time due to its comprehensive data collection and could enhance sensitivity when fragmentation efficiency is poor. Quantitative processing of PRM data is performed based on narrow mass extraction windows rather than on nominal mass product ion chromatograms, as for MRM. Parameters also need to be examined in detail and kept consistent for data processing and reporting results. PRM and MRM exhibit have comparable linearity, dynamic range, precision, and repeatability for protein quantitation.54

Literature Highlights: Plant Protein Quantification

Well-developed and validated methods using either immunoassay or MS techniques should be considered acceptable for protein quantitation. There are, however, some advantages and limitations with both techniques that may guide selection of one technique or the other (Table 3). A literature search was performed to highlight these techniques in relevance to plant protein quantification. Two case studies from the literature search that are suitable for plant protein quantitation by either technique are presented here.

Table 3. Key Points/Differences for Immunoassays and Mass Spectrometry.

information immunoassay (ELISAs, Luminex, MSD) mass spectrometry
specificity measures whole/intact protein Measured surrogate peptides must be correlated to intact protein value.
Quantitation might be affected by conformational changes. dependent upon physiochemical properties of protein/peptide
dependent upon immunogenicity
sample extraction and preparation no need for cleanup required but harsh reagent use is limited amenable to harsh reagents but need later treatment.
Internal standard is used to normalize, can be advantageous when the analyte is in low abundance.
cost considerations after kit development, inexpensive and rapid assay with, minimal equipment needed for ELISA; widely available with reasonable maintenance cost; cost added up with multiplexing instruments and standard protein, antibody production and method development relative expensive instrumentation and software at start; increased cost associated with more sophisticated instruments and maintenance. The cost could be saved due to shorter development time and easy to add new analytes.
ease of implementation and acceptance easier to implement and validate after kit development and hence easily adaptable to less skilled laboratories. high level of technical expertise needs for data interpretation and troubleshooting aberrant data; recent development of performance and interface make the technique easier to use
method development time development of a new assay is a lengthy process, months for polyclonal antibodies and years for monoclonal antibodies relative quick process with target and matrix protein sequence information available, few weeks to months
multiplexing well-developed multiplexing technique; sometimes difficult to develop capture antibody due to cross-reactivity or reduced sensitivity and specificity for certain proteins well-developed multiplexing technique; attractive of specificity/sensitivity due to surrogate peptide detection
sensitivity Good protein/antibody sensitivity ELISA kits can achieve LOQs as low as 0.1 to 1 ng/mL (or ppb) and multiplexing assays can reach similar or even lowers, down to 0.05 ng/mL. newer instruments can have enhanced sensitivity and LOQ, with current LOQs ranging from low nanomole to attomole levels peptide dependent
accuracy and precision The effect of the sample matrix is considered to impact specificity. high specificity due to the use of antibodies, provides accurate quantification of target proteins; limitations in accurately measuring multiple proteins simultaneously. also faces challenges with extraction recovery and the labeled internal standards may obviate the need for matrix assessment
precise quantitation with careful calibration and validation to ensure accuracy
need consistence of sample preparation, instrument performance, and data analysis
intractable protein challenge due to lack of protein production and antibody advantage due to the use of peptide surrogates
validation Most method validation parameters are common. Multiplexing immunoassays can be complex. technique specific parameters need to be evaluated such as antibody specificity and cross-reactivity testing for immunoassays, etc. Most method validation parameters are common. Validation can be complex compared to ELISA, technique specific parameters need to be evaluated such as carry-over, digestion time course, interference, etc.
regulatory acceptance Immunoassay analysis has been accepted by many global regulatory agencies. currently accepted by FDA and EFSA, also accepted by many global reg agencies
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Literature Case Study 1: Endogenous Allergen Quantitation by ELISA and LC–MS/MS

One aspect of the safety assessment of GM crops is to evaluate whether the transformation process impacts the levels of endogenous allergens relative to conventional non-GM varieties.55 To comply with the mandatory assessment of individual soybean allergens implemented by the European Commission,56 Geng et al. (2017)8 developed and validated ELISA methods for each of the allergen proteins Gly m 4, Gly m 5, Gly m 6, Gly m Bd 28k, and Gly m Bd 30k. Due to the wide range of expression levels (Figure 6) and allergen complexity (three or five subunits in Gly m 5 or Gly m 6, respectively), recombinant and natively purified allergens were used as standards to generate the calibration curves. Also, Geng et al. (2017)8 produced and purified mouse monoclonal antibodies, goat polyclonal antibodies, goat peptide polyclonal antibodies, and rabbit polyclonal antibodies to develop five specific and sensitive soy allergen ELISA methods.

Figure 6.

Figure 6

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Summary of value ranges of five endogenous soybean allergens measured by using ELISA or LC–MS/MS. (A) Value ranges of endogenous soybean allergens Gly m 4, Gly m Bd 28K, and Gly m Bd 30K. (B) Value ranges of endogenous soybean allergens Gly m 5 and Gly m 6.

Due to the increasing numbers of endogenous soybean allergens being requested for the GM crop safety assessment,57 multiplexing protein detection methods such as LC–MS/MS have been investigated. For example, Hill et al. (2017)58 showed that mass spectrometric analysis is ideal for endogenous protein assays due its high specificity. While there are differences in the specific methodology between the two protocols, and the growing seasons and soybean varieties are different for these two studies, endogenous allergen levels detected by Hill et al., using the described LC–MS/MS protocols are in the range of data from the ELISA methodologies8,43 published by Geng et al. This agreement between methodologies demonstrates the acceptability of both techniques for collecting quantitative endogenous allergen data to address EFSA requirements.

Literature Case Study 2: GM Trait Proteins

In Hu and Owens,45 a LC–MS/MS method was developed and validated to quantify three GM trait proteins expressed in GM maize leaves; the three proteins were a gene-shuffled glyphosate acetyltransferase variant (GAT4621), a highly resistant allele mutant of maize acetolactate synthase (zmHRA), and phosphinothricin acetyltransferase (PAT). These proteins are expressed in maize to achieve multiple herbicide resistance against a single herbicide or a combination of herbicides. Hu and Owens (2011)45 quantified the protein levels in 10 transgenic GM leaf samples and compared using both ELISA and LC–MS/MS methods. The proteins were detected in a range of roughly 8–80 ppm/dry weight for both methods. Of the three proteins, GAT4261 had the highest detected values followed by PAT and zmHRA. The detected values suggested a lower day-to-day variation with LC–MS/MS compared to ELISA. The ranges of expression levels determined using LC–MS/MS and ELISA were similar for the three proteins but not identical. This was to be expected due to the different techniques and different experimental conditions. On the basis of statistical analyses, GAT4621 and zmHRA LC–MS/MS values were within ±3 standard deviations (SD), while ELISA values were within ± SD limits. LC–MS/MS results for PAT were consistently higher in comparison with ELISA values, and most of the LC–MS/MS values (78%) were well within the ±2 SD with ELISA value. Overall, Hu and Owens45 found that all results demonstrated that the two independent analytical techniques, LC–MS/MS and ELISA, generated comparable results, indicating that both techniques were suitable for quantifying the GM trait proteins.

Discussion

In addition to the soybean and maize discussed in the case studies, immunoassay and LC–MS/MS techniques are suitable for quantitating proteins in various crops and agricultural matrices. The availability of protein and genetic sequence information enables the rapid development of multiple approaches to protein quantitation. Both techniques offer comparable sensitivity, providing multiplexed quantitative analytical data. When selecting a method for protein quantitation, user requirements and other criteria and various circumstances, such as breeding stacks, intractable proteins, convenience, cost, time, and personnel, play a significant role in selecting the analytical technique (immunoassay versus MS).

Specificity

One of the major differences between immunoassay and LC–MS/MS technologies is the manner in which the analyte protein of interest is targeted and quantified. Immunoassay methods target specific epitopes on the protein(s) of interest and detect these indirectly though specific antibody interactions which make the immunoassay specific. However, protein immunogenicity is a critical consideration for the assay and conformational changes may impact immunoreactivity and the quantitative results for some proteins. On the other hand, LC–MS/MS quantitation assays target direct detection of surrogate peptides for the full-length analyte protein. In this approach, peptides are quantified by monitoring several MRM/PRM transitions for each peptide (that span the intact protein) and typically one is selected for final quantification while another could be used for quality control. The surrogate peptide results are then correlated back to the amount of the full-length protein present in the sample. Correlating the peptide results to full-length protein results requires detailed method development to ensure proteins are extracted and digested to their representative fragments in a consistent manner, due to the large number of steps and modifications involved. Selecting unmodified unique peptides for proteins with multiple modifications can be challenging. LC–MS/MS methods quantify target proteins based on their primary sequence, allowing quantification regardless of conformation changes and provides high specificity to unique peptides and can theoretically distinguish proteins that differ in sequence by a single nonisobaric amino acid between homologous proteins.

Sample Extraction and Preparation

Immunoassays typically do not require a cleanup step for sample extracts, but the use of harsh reagent is limited due to potential disruption of antigen–antibody interaction. In a typical study, Geng et al. (2017)8 applied ELISA analysis after extraction with a simple buffer of phosphate-buffered saline with 0.05% (v/v) Tween-20, pH 7.4 (PBST) for Soybean allergens Gly m 4, Gly m 5, and Gly m 6. In contrast, MS is amenable to harsh extraction buffers and solvents but requires additional steps. Hill et al. (2017)58 demonstrated the use of buffer containing 5 M urea, 2 M thiourea, 50 mM Tris-HCl, and 65 mM dithiothreitol to extract proteins. Diluted extracts were denatured and reduced, followed by overnight incubation with enzyme. Typically, harsh extraction buffers are exchanged or diluted to buffers suitable for MS. Any losses throughout MS sample preparation can be assessed and normalized using IS, making the technique suitable for a wider range of proteins. The flexibility in sample handling with MS methods can be advantageous, especially when the analyte is in low abundance.

Cost Considerations

The relatively low cost of equipment and software used for immunoassay render it an attractive technology. The basic equipment for an immunoassay is the microplate reader, which is widely available and can cost less than ten thousand US dollars. Some laboratories may choose to purchase additional more expensive equipment such as plate washer or liquid handlers. The equipment and software that support multiplexing capabilities, such as Luminex or MSD, would also require increased capital expenditure. Although an increased cost is associated with these options, instrument and software costs could still be low. However, in addition to equipment costs, immunoassays require initial development and ongoing production of antigens, antibodies, and standard proteins, which increase the overall cost for assay development and cost per test. The quantitative analysis of proteins via LC–MS/MS normally involves the use of QQQ, Q-Trap, or HRMS instrumentation, coupled with LC systems. The overall cost typically starts in the hundreds of thousands of US dollars for the most basic equipment and quickly rises for more sensitive or sophisticated models. The software typically required for LC–MS/MS quantification can be both expensive and challenging to use. Due to these instrument and software needs alone, the up-front and operational costs for LC–MS/MS quantitation are typically much higher than most other analytical techniques. The added equipment maintenance, calibration, and automation for high throughput could easily lead to costs over a million US dollars. However, the technique normally needs shorter development time without antibody or even standard protein, it is also relatively easy to add new analyte proteins to a multiplexed method, which helps balance some of the increased cost of MS analysis.

Ease of Implementation and Acceptance

Immunoassay remains the technology of choice by laboratories in many geographic locations due to historical use and familiarity. ELISA methods can be streamlined and commercialized, making them readily adaptable to less experienced laboratories. In addition, there are well-established software packages available for data analysis. A tremendous amount of published data generated from immunoassay technologies exists from a broad variety of applications in laboratories across the globe, supporting broad acceptance of immunoassay methods. The complexity of the LC–MS/MS assay procedures, as well as the interpretation and troubleshooting of acquired data, demands well-trained and experienced staff to develop and validate these methods. In recent years, the performance and the software for the LC–MS/MS instruments has improved dramatically, making MS more approachable and user-friendly.

Method Development Time

When selecting one technology over the other, method development time is often an important decision factor. The development of a new immunoassay is a lengthy process. requiring purified protein, antibody development and testing for the protein(s) of interest. The design of custom antibodies also varies with the platform choice, involving immunogen design and preparation, antibody development, purification, and modification. This process normally takes two or more months for polyclonal antibodies and even longer for monoclonal antibodies, which might end up requiring years. In contrast, LC–MS/MS method development primarily uses the genetic and protein sequence information, as well as physiochemical properties of the target protein/peptide to develop the assay. This allows comparatively rapid development of an LC–MS/MS method. The target and matrix protein sequence information are evaluated to determine surrogate peptides unique to the protein of interest. LC–MS/MS methods can thus be developed and validated in weeks using preliminary protein information, making this an attractive choice when timelines are critical. If using HRMS bottom-up approaches and a variety of labels, then rich information on the observed proteome can be obtained in days.

Multiplexing

With the expansion of technological development in GM crops, more stacked event products are emerging, making multiplexing a critical consideration factor. Selectively detecting and quantitating several proteins in a single run is an extremely attractive feature for both immunoassay and LC–MS/MS based technologies.2,59,60 Although assays for single traits in stacked events have already been developed, these assays do not directly translate to a multiplex assay. In some cases, immune-based techniques may not be the method of choice, especially when simultaneous monitoring of similar proteins is of importance. Immunoassays require full antibody generation for each protein involved and may be impacted by difficulty of developing capture antibodies due to cross-reactivity or reduced sensitivity and specificity for certain proteins when multiplexed together. Closely related proteins make the lack of selectivity in the assay extremely challenging due to increased matrix effects. The difference between proteins can be minimal or buried within regions of the protein structure that are not readily available to the antibodies. Given these circumstances, LC–MS/MS technology, with its multiplexing and proteolytic capabilities, may be more attractive. LC–MS/MS assays can quantify a larger number of proteins within the same assay, so long as targets share extraction and digestion conditions. The technique can differentiate and quantitate closely related proteins, such as proteins that exist in different isoforms or newly expressed proteins and their closely related endogenous counterparts.

Sensitivity

With the progress of technical advancements and improved instruments, most proteins can be detected with sufficient sensitivity. For immunoassays, protein/antibody sensitivity is typically very high,61 ELISA kits can demonstrate limits of quantitation (LOQ) as low as 0.1 to 1 ng/mL (or ppb),27 which is conserved excellent for many applications. Multiplexing techniques, offer comparable or even lower LOQ levels, and can reach approximately 0.05 ng/mL depending on the specific analyte and assay conditions.14,61 For example, in the study by Yeaman et al. (2016), a multiplexed set of sandwich assays for quantifying multiple proteins in a multitrait GM cotton product was developed and validated using the Luminex system, and the results were compared with the ELISA technique.62 In the study, they found that the LOQs of ELISA are in the 0.3–1.6 ng/mL range, while Luminex assays ranged from 0.1 to 1.7 ng/mL. Protein expression levels determined with this multiplexed Luminex assay were comparable to those determined by ELISA. Similarly, Bastarache et al. (2014) investigated the performance of a set of multiplexed MSD assays and compared the results to those from validated individual ELISA methods. The precision and accuracy of the MSD and ELISA methods were comparable, and protein measurements determined with both technologies were well correlated, with LOQs reported to be slightly more sensitive.63 In the past, LC–MS/MS faced challenges in detection and quantitative sensitivity, but recent technical advancements have significantly enhanced instrument detection sensitivity. Currently, LC–MS/MS techniques can achieve low limits of quantitation, typically ranging from the low nanomole to attomole levels of analyte,50 which are comparable to those of immunoassay techniques. This improvement in sensitivity has made LC–MS/MS a powerful tool for protein quantification in complex biological matrices.

Accuracy and Precision

When comparing the quantification of proteins, both immunoassay and MS offer similar ranges of accuracy and precision. According to the survey of Settlage et al.,64 In ELISA and LC–MS/MS assays, the recovery accuracy ideally is at 70–120%, but currently accepted at 60–130%. The ELISA assay accuracy is impacted by sample matrix issues that reduce specificity. LC–MS/MS also faces challenges with extraction recovery, while the labeled internal standards may obviate the need for matrix assessment. The ELISA assay precision is ideally within 20% of the individual values but currently accepts 25%. Mass spec assay precision has a similar accept rate. While immunoassays provide accurate quantification, especially due to their high specificity, they may have limitations in measuring multiple proteins simultaneously. Conversely, mass spectrometry offers multiplexing capabilities and precise quantitation, but requires careful calibration and validation for accuracy. Factors such as sample preparation, instrument performance, and data analysis are critical in achieving precise protein quantification using mass spectrometry.

Intractable Protein

When dealing with challenging proteins, both technologies need to be carefully evaluated to determine the most suitable approach. For some challenging proteins, such as intractable proteins, purification, or enrichment can be difficult due to their low expression or poor solubility. In some cases, an immunoassays cleanup step is even incorporated into an LC-MS/MS assay to overcome technical challenges. When it is difficult to produce protein standards and furthermore to generate antibodies, immunoassay can be a challenging for this situation and MS is likely the best choice for quantifying the target protein, especially when it is in a complex matrix, such as membrane bound proteins. Peptide standards unique to the protein(s) of interest can easily be synthesized and quantitated when peptides are soluble and stable, while interfering proteins and matrix components can be removed or digested prior to separation by liquid chromatography.

Validation

Analytical methods must be validated to demonstrate method performance and ensure quality. Guidelines on analytical method validation, including both immunoassays and MS assays, have been published in various sources for different applications.65−69 Most method validation parameters, such as accuracy, precision, specificity, limits of quantitation, linearity, quantitative range, extraction efficiency, and stability, are common for both techniques. However, there are technique specific parameters as well, such as carry-over and digestion time courses in MS methods, and antibody specificity and cross-reactivity testing for immunoassays. MS method validation can be complex and time-consuming compared to ELISA, and other multiplexing immunoassays could also be complex due to additional technique specific parameters, method complexity and serial analysis.

Regulatory Acceptance

Regulatory authorities do not typically dictate the technique of choice for regulatory data collection. Although LC–MS/MS methods have gained increased recognition over the past decade, immunoassays are still preferred techniques of choice in many geographic regions due to its easy implementation, lower requirements for analyst training, relatively low cost, and ease of transfer. Protein expression data packages containing quantitative immunoassay analysis have been accepted by many global regulatory agencies, including United States Department of Agriculture (USDA), Food and Drug Association (FDA), Environmental Protection Agency (EPA), and European Food Safety Authority (EFSA).70 MS-based protein quantification has been used in the pharmaceutical industry and accepted by the FDA. The FDA guidance for Bioanalytical Method Validation Guidance for Industry71 which is based on the guidance document drafted by the International Council for Harmonization (ICH),65 also listed the suggestion for the usage of LC–MS/MS methods for quantitative analysis. In the guidance on allergenicity assessment of GM plants, EFSA clearly stated that “either enzyme-linked immunosorbent assay (ELISA) or mass spectrometry (MS) approaches are appropriate methods for the quantification of endogenous allergens, both allowing the specific detection and quantification of single known allergens.72

With the fast-growing global population and the increasing concerns about food security, modern agriculture has embraced biotechnology as a promising solution. Looking ahead to the future application perspectives emphasize the critical need for well-developed and validated quantitative technologies to support the development of genetically modified crops, ensuring high specificity, sensitivity, multiplexing, and accurate quantitation. The application perspectives underscore that both immunoassays and MS-based methods are crucial for all stages of trait development, including early development and regulatory studies of trait protein quantitation in support of GM product registration. As we move forward, the application of these techniques will be pivotal in addressing scientific questions and specific requirements related to the protein being quantified. The future application will carefully require consideration of factors such as targeted measurement, sample preparation, cost, personnel expertise, method development time, multiplexing, and throughput when choosing between immunoassay or LC–MS/MS for protein quantitation. Ultimately, the future application of these techniques will be driven by the specific circumstances and needs of end users, ushering in an era of advanced quantitative technologies that will play a crucial role in modern agriculture, addressing food security concerns for the expanding global population.

Acknowledgments

The authors thank the Analytical Excellence through Industry Collaboration (AEIC) for their support in preparing and publishing this review paper. The authors thank Shveta Bagga, Luis Burzio, Durba Ghoshal, Rod Herman, Donna Houchins, Penny Hunst, Pamela Jensen, Shawn Motyka, Luke Muschinske, Hector Serrano, John Vicini, Scott Young, and John Zheng for their comments that greatly improved the manuscript.

Glossary

Abbreviations Used

GM

genetically modified

ELISA

enzyme-linked immunosorbent assay

MSD

meso scale discovery

MS

mass spectrometry

LC–MS/MS

liquid chromatography coupled to tandem mass spectrometers

IS

internal standard

Author Contributions

Y.W. and M.B. outlined the review plan. M.B., Y.W., T.G., and K.K. contributed to the introduction. M.C., T.G., K.K., and S.C. contributed to immunoassay description, M.C. made the immunoassay figures. Y.W., N.H., J.S., and D.G. contributed to mass spectrometry description, Y.W. made the mass spectrometry figures. S.C., J.S., and D.G. contributed to discussion. T.G. contributed to references. C.A., Z.L., and J.M. reviewed and edited the manuscript. Y.W. and M.B. contributed to conclusion. Y.W. and M.B. led manuscript optimization. All authors read and approved the final manuscript.

The authors declare no competing financial interest.

References

  1. Que Q.; Chilton M. D.; de Fontes C. M.; He C.; Nuccio M.; Zhu T.; Wu Y.; Chen J. S.; Shi L. Trait stacking in transgenic crops: challenges and opportunities. GM Crops 2010, 1 (4), 220–229. 10.4161/gmcr.1.4.13439. [DOI] [PubMed] [Google Scholar]
  2. Shang Y.; Zhang B.; Zhu L.; Huang K.; Xu W. A novel quantitative technique in detecting stacked genetically modified plants by fluorescent-immunohistochemistry. Journal of Food Composition and Analysis 2020, 88, 103452. 10.1016/j.jfca.2020.103452. [DOI] [Google Scholar]
  3. Bushey D. F.; Bannon G. A.; Delaney B. F.; Graser G.; Hefford M.; Jiang X.; Lee T. C.; Madduri K. M.; Pariza M.; Privalle L. S.; et al. Characteristics and safety assessment of intractable proteins in genetically modified crops. Regulatory toxicology and pharmacology: RTP 2014, 69 (2), 154–170. 10.1016/j.yrtph.2014.03.003. [DOI] [PubMed] [Google Scholar]
  4. Bushey D. F.; Bannon G. A.; Delaney B. F.; Graser G.; Hefford M.; Jiang X.; Lee T. C.; Madduri K. M.; Pariza M.; Privalle L. S.; et al. Characteristics and safety assessment of intractable proteins in genetically modified crops. Regulatory toxicology and pharmacology: RTP 2014, 69 (2), 154–170. 10.1016/j.yrtph.2014.03.003. [DOI] [PubMed] [Google Scholar]
  5. Chen S.; Yu Z.; Wang Y.; Tang J.; Zeng Y.; Liu X.; Tang D. Block-Polymer-Restricted Sub-nanometer Pt Nanoclusters Nanozyme-Enhanced Immunoassay for Monitoring of Cardiac Troponin I. Anal. Chem. 2023, 95 (38), 14494–14501. 10.1021/acs.analchem.3c03249. [DOI] [PubMed] [Google Scholar]
  6. Zeng R.; Qiu M.; Wan Q.; Huang Z.; Liu X.; Tang D.; Knopp D. Smartphone-Based Electrochemical Immunoassay for Point-of-Care Detection of SARS-CoV-2 Nucleocapsid Protein. Anal. Chem. 2022, 94 (43), 15155–15161. 10.1021/acs.analchem.2c03606. [DOI] [PubMed] [Google Scholar]
  7. Geng T.; Liu K.; Frazier R.; Shi L. F.; Bell E.; Glenn K.; Ward J. M. Development of a Sandwich ELISA for Quantification of Gly m 4, a Soybean Allergen. J. Agric. Food Chem. 2015, 63 (20), 4947–4953. 10.1021/acs.jafc.5b00792. [DOI] [PubMed] [Google Scholar]
  8. Geng T.; Stojšin D.; Liu K.; Schaalje B.; Postin C.; Ward J.; Wang Y.; Liu Z. L.; Li B.; Glenn K. Natural Variability of Allergen Levels in Conventional Soybeans: Assessing Variation across North and South America from Five Production Years. J. Agric. Food Chem. 2017, 65 (2), 463–472. 10.1021/acs.jafc.6b04542. [DOI] [PubMed] [Google Scholar]
  9. Gu X.; Lee T.; Geng T.; Liu K.; Thoma R.; Crowley K.; Edrington T.; Ward J. M.; Wang Y.; Flint-Garcia S.; Bell E.; Glenn K. C.; et al. Assessment of Natural Variability of Maize Lipid Transfer Protein Using a Validated Sandwich ELISA. J. Agric. Food Chem. 2017, 65 (8), 1740–1749. 10.1021/acs.jafc.6b03583. [DOI] [PubMed] [Google Scholar]
  10. Ma X.; Sun P.; He P. L.; Han P. F.; Wang J. J.; Qiao S. Y.; Li D. F. Development of monoclonal antibodies and a competitive ELISA detection method for glycinin, an allergen in soybean. Food Chem. 2010, 121 (2), 546–551. 10.1016/j.foodchem.2009.12.045. [DOI] [Google Scholar]
  11. You J. M.; Li D.; Qiao S. Y.; Wang Z. R.; He P. L.; Ou D. Y.; Dong B. Development of a monoclonal antibody-based competitive ELISA for detection of beta-conglycinin, an allergen from soybean. Food Chem. 2008, 106 (1), 352–360. 10.1016/j.foodchem.2007.05.040. [DOI] [Google Scholar]
  12. Yu Z.; Qiu C.; Huang L.; Gao Y.; Tang D. Microelectromechanical Microsystems-Supported Photothermal Immunoassay for Point-of-Care Testing of Aflatoxin B1 in Foodstuff. Anal. Chem. 2023, 95 (8), 4212–4219. 10.1021/acs.analchem.2c05617. [DOI] [PubMed] [Google Scholar]
  13. Yu Z.; Gong H.; Li M.; Tang D. Hollow prussian blue nanozyme-richened liposome for artificial neural network-assisted multimodal colorimetric-photothermal immunoassay on smartphone. Biosens. Bioelectron. 2022, 218, 114751 10.1016/j.bios.2022.114751. [DOI] [PubMed] [Google Scholar]
  14. Tighe P. J.; Ryder R. R.; Todd I.; Fairclough L. C. ELISA in the multiplex era: potentials and pitfalls. Proteomics. Clinical applications 2015, 9 (3–4), 406–422. 10.1002/prca.201400130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gao Y.; Li M.; Zeng Y.; Liu X.; Tang D. Tunable Competitive Absorption-Induced Signal-On Photoelectrochemical Immunoassay for Cardiac Troponin I Based on Z-Scheme Metal–Organic Framework Heterojunctions. Anal. Chem. 2022, 94 (39), 13582–13589. 10.1021/acs.analchem.2c03263. [DOI] [PubMed] [Google Scholar]
  16. Huang L.; Zeng R.; Xu J.; Tang D. Point-of-Care Immunoassay Based on a Multipixel Dual-Channel Pressure Sensor Array with Visual Sensing Capability of Full-Color Switching and Reliable Electrical Signals. Anal. Chem. 2022, 94 (38), 13278–13286. 10.1021/acs.analchem.2c03393. [DOI] [PubMed] [Google Scholar]
  17. Lin Y.; Zhou Q.; Tang D.; Niessner R.; Knopp D. Signal-On Photoelectrochemical Immunoassay for Aflatoxin B1 Based on Enzymatic Product-Etching MnO2 Nanosheets for Dissociation of Carbon Dots. Anal. Chem. 2017, 89 (10), 5637–5645. 10.1021/acs.analchem.7b00942. [DOI] [PubMed] [Google Scholar]
  18. Luo Z.; Zhang L.; Zeng R.; Su L.; Tang D. Near-Infrared Light-Excited Core–Core–Shell UCNP@Au@CdS Upconversion Nanospheres for Ultrasensitive Photoelectrochemical Enzyme Immunoassay. Anal. Chem. 2018, 90 (15), 9568–9575. 10.1021/acs.analchem.8b02421. [DOI] [PubMed] [Google Scholar]
  19. Chaimbault P.The Modern Art of Identification of Natural Substances in Whole Plants. In Recent Advances in Redox Active Plant and Microbial Products: From Basic Chemistry to Widespread Applications in Medicine and Agriculture; Jacob C., Kirsch G., Slusarenko A., Winyard P. G., Burkholz T., Eds.; Springer: Netherlands, 2014; pp 31–94. [Google Scholar]
  20. Dass . Hyphenated Separation Techniques. In Fundamentals of Contemporary Mass Spectrometry; 2007; pp 151–194. [Google Scholar]
  21. Grothaus G. D.; Bandla M.; Currier T.; Giroux R.; Jenkins G. R.; Lipp M.; Shan G.; Stave J. W.; Pantella V. Immunoassay as an analytical tool in agricultural biotechnology. J. Aoac Int. 2006, 89 (4), 913–928. 10.1093/jaoac/89.4.913. [DOI] [PubMed] [Google Scholar]
  22. Karsonova A.; Riabova K.; Villazala-Merino S.; Campana R.; Niederberger V.; Eckl-Dorna J.; Fröschl R.; Perkmann T.; Zhernov Y. V.; Elisyutina O. G.; et al. Highly sensitive ELISA-based assay for quantification of allergen-specific IgE antibody levels. Allergy 2020, 75 (10), 2668–2670. 10.1111/all.14325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ren R.; Cai G.; Yu Z.; Zeng Y.; Tang D. Metal-Polydopamine Framework: An Innovative Signal-Generation Tag for Colorimetric Immunoassay. Anal. Chem. 2018, 90 (18), 11099–11105. 10.1021/acs.analchem.8b03538. [DOI] [PubMed] [Google Scholar]
  24. Yu Z.; Tang Y.; Cai G.; Ren R.; Tang D. Paper Electrode-Based Flexible Pressure Sensor for Point-of-Care Immunoassay with Digital Multimeter. Anal. Chem. 2019, 91 (2), 1222–1226. 10.1021/acs.analchem.8b04635. [DOI] [PubMed] [Google Scholar]
  25. Huang L.; Chen J.; Yu Z.; Tang D. Self-Powered Temperature Sensor with Seebeck Effect Transduction for Photothermal–Thermoelectric Coupled Immunoassay. Anal. Chem. 2020, 92 (3), 2809–2814. 10.1021/acs.analchem.9b05218. [DOI] [PubMed] [Google Scholar]
  26. Albright V. C. 3rd; Hellmich R. L.; Coats J. R. A Review of Cry Protein Detection with Enzyme-Linked Immunosorbent Assays. J. Agric. Food Chem. 2016, 64 (11), 2175–2189. 10.1021/acs.jafc.5b03766. [DOI] [PubMed] [Google Scholar]
  27. Alarcon C. M.; Shan G.; Layton D. T.; Bell T. A.; Whipkey S.; Shillito R. D. Application of DNA- and Protein-Based Detection Methods in Agricultural Biotechnology. J. Agric. Food Chem. 2019, 67 (4), 1019–1028. 10.1021/acs.jafc.8b05157. [DOI] [PubMed] [Google Scholar]
  28. Picascia A.; Infante T.; Napoli C. Luminex and antibody detection in kidney transplantation. Clin Exp Nephrol 2012, 16 (3), 373–381. 10.1007/s10157-012-0635-1. [DOI] [PubMed] [Google Scholar]
  29. Purohit S.; Sharma A.; She J. X. Luminex and other multiplex high throughput technologies for the identification of, and host response to, environmental triggers of type 1 diabetes. Biomed Res. Int. 2015, 2015, 326918 10.1155/2015/326918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lynch H. E.; Sanchez A. M.; D’Souza M. P.; Rountree W.; Denny T. N.; Kalos M.; Sempowski G. D. Development and implementation of a proficiency testing program for Luminex bead-based cytokine assays. J. Immunol Methods 2014, 409, 62–71. 10.1016/j.jim.2014.04.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Roberts C.; Green T.; Hess E.; Matys K.; Brown M. J.; Haupt R. M.; Luxembourg A.; Vuocolo S.; Saah A.; Antonello J. Development of a human papillomavirus competitive luminex immunoassay for 9 HPV types. Hum. Vaccines Immunother. 2014, 10 (8), 2168–2174. 10.4161/hv.29205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Adamcova M.; Šimko F. Multiplex biomarker approach to cardiovascular diseases. Acta Pharmacol Sin 2018, 39 (7), 1068–1072. 10.1038/aps.2018.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Fraiture M. A.; Herman P.; Taverniers I.; De Loose M.; Deforce D.; Roosens N. H. Current and new approaches in GMO detection: challenges and solutions. Biomed Res. Int. 2015, 2015, 392872 10.1155/2015/392872. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Song E. Y.; VanDunk C.; Kuddo T.; Nelson P. G. Measurement of Vasoactive Intestinal Peptide using a Competitive Fluorescent Microsphere Immunoassay or ELISA in human blood samples. J. Immunol Methods 2005, 300 (1), 63–73. 10.1016/j.jim.2005.02.009. [DOI] [PubMed] [Google Scholar]
  35. Vicini J.; Etherton T.; Kris-Etherton P.; Ballam J.; Denham S.; Staub R.; Goldstein D.; Cady R.; McGrath M.; Lucy M. Survey of retail milk composition as affected by label claims regarding farm-management practices. J. Am. Diet Assoc 2008, 108 (7), 1198–1203. 10.1016/j.jada.2008.04.021. [DOI] [PubMed] [Google Scholar]
  36. Jani D.; Allinson J.; Berisha F.; Cowan K. J.; Devanarayan V.; Gleason C.; Jeromin A.; Keller S.; Khan M. U.; Nowatzke B.; et al. Recommendations for Use and Fit-for-Purpose Validation of Biomarker Multiplex Ligand Binding Assays in Drug Development. AAPS Journal 2016, 18, 1–14. 10.1208/s12248-015-9820-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Chapman F.; Pour S. J.; Wieczorek R.; Trelles Sticken E.; Budde J.; Röwer K.; Otte S.; Mason E.; Czekala L.; Nahde T.; et al. Twenty-eight day repeated exposure of human 3D bronchial epithelial model to heated tobacco aerosols indicates decreased toxicological responses compared to cigarette smoke. Front. Toxicol. 2023, 5 10.3389/ftox.2023.1076752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wilkins D.; Yuan Y.; Chang Y.; Aksyuk A. A.; Núñez B. S.; Wählby-Hamrén U.; Zhang T.; Abram M. E.; Leach A.; Villafana T.; et al. Durability of neutralizing RSV antibodies following nirsevimab administration and elicitation of the natural immune response to RSV infection in infants. Nat. Med. 2023, 29 (5), 1172–1179. 10.1038/s41591-023-02316-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kenny G.; Negi R.; O’Reilly S.; Garcia-Leon A.; Alalwan D.; Gaillard C. M.; Saini G.; Inzitari R.; Feeney E. R.; Yousif O.; et al. Performance and validation of an adaptable multiplex assay for detection of serologic response to SARS-CoV-2 infection or vaccination. J. Immunol Methods 2022, 510, 113345 10.1016/j.jim.2022.113345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Haar J.; Blazevic D.; Strobel B.; Kreuz S.; Michelfelder S. MSD-based assays facilitate a rapid and quantitative serostatus profiling for the presence of anti-AAV antibodies. Mol. Ther Methods Clin Dev 2022, 25, 360–369. 10.1016/j.omtm.2022.04.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Abbatiello S. E.; Schilling B.; Mani D. R.; Zimmerman L. J.; Hall S. C.; MacLean B.; Albertolle M.; Allen S.; Burgess M.; Cusack M. P.; et al. Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma. Molecular & cellular proteomics: MCP 2015, 14 (9), 2357–2374. 10.1074/mcp.M114.047050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Houston N. L.; Lee D. G.; Stevenson S. E.; Ladics G. S.; Bannon G. A.; McClain S.; Privalle L.; Stagg N.; Herouet-Guicheney C.; MacIntosh S. C.; et al. Quantitation of soybean allergens using tandem mass spectrometry. J. Proteome Res. 2011, 10 (2), 763–773. 10.1021/pr100913w. [DOI] [PubMed] [Google Scholar]
  43. Hill R. C.; Oman T. J.; Wang X.; Shan G.; Schafer B.; Herman R. A.; Tobias R.; Shippar J.; Malayappan B.; Sheng L.; et al. Development, Validation, and Interlaboratory Evaluation of a Quantitative Multiplexing Method To Assess Levels of Ten Endogenous Allergens in Soybean Seed and Its Application to Field Trials Spanning Three Growing Seasons. J. Agric. Food Chem. 2017, 65, 5531. 10.1021/acs.jafc.7b01018. [DOI] [PubMed] [Google Scholar]
  44. Colgrave M. L.; Byrne K.; Pillai S. V.; Dong B.; Leonforte A.; Caine J.; Kowalczyk L.; Scoble J. A.; Petrie J. R.; Singh S.; et al. Quantitation of seven transmembrane proteins from the DHA biosynthesis pathway in genetically engineered canola by targeted mass spectrometry. Food Chem. Toxicol. 2019, 126, 313–321. 10.1016/j.fct.2019.02.035. [DOI] [PubMed] [Google Scholar]
  45. Hu X. T.; Owens M. A. Multiplexed protein quantification in maize leaves by liquid chromatography coupled with tandem mass spectrometry: an alternative tool to immunoassays for target protein analysis in genetically engineered crops. J. Agric. Food Chem. 2011, 59 (8), 3551–3558. 10.1021/jf104516r. [DOI] [PubMed] [Google Scholar]
  46. Hu X. T.; Owens M. A. Multiplexed Protein Quantification in Maize Leaves by Liquid Chromatography Coupled with Tandem Mass Spectrometry: An Alternative Tool to Immunoassays for Target Protein Analysis in Genetically Engineered Crops. J. Agric. Food Chem. 2011, 59 (8), 3551–3558. 10.1021/jf104516r. [DOI] [PubMed] [Google Scholar]
  47. Liebler D. C.; Zimmerman L. J. Targeted quantitation of proteins by mass spectrometry. Biochemistry-Us 2013, 52 (22), 3797–3806. 10.1021/bi400110b. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Schacherer L. J.; Owens M. A.; Hu T. X. Quantification of intractable membrane proteins in genetically engineered crops by liquid chromatography coupled with tandem mass spectrometry. Anal Methods-Uk 2017, 9 (19), 2821–2829. 10.1039/C7AY00161D. [DOI] [Google Scholar]
  49. Schacherer L. J.; Xie W.; Owens M. A.; Alarcon C.; Hu T. X. Rapid detection of proteins in transgenic crops without protein reference standards by targeted proteomic mass spectrometry. J. Sci. Food Agric 2016, 96 (12), 4116–4125. 10.1002/jsfa.7612. [DOI] [PubMed] [Google Scholar]
  50. Valaskovic G. A.; Kelleher N. L.; McLafferty F. W. Attomole Protein Characterization by Capillary Electrophoresis-Mass Spectrometry. Science 1996, 273 (5279), 1199–1202. 10.1126/science.273.5279.1199. [DOI] [PubMed] [Google Scholar]
  51. Rauniyar N. Parallel Reaction Monitoring: A Targeted Experiment Performed Using High Resolution and High Mass Accuracy Mass Spectrometry. International journal of molecular sciences 2015, 16 (12), 28566–28581. 10.3390/ijms161226120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Ramachandran B.; Yang C. T.; Downs M. L. Parallel Reaction Monitoring Mass Spectrometry Method for Detection of Both Casein and Whey Milk Allergens from a Baked Food Matrix. J. Proteome Res. 2020, 19 (8), 2964–2976. 10.1021/acs.jproteome.9b00844. [DOI] [PubMed] [Google Scholar]
  53. Chen S.; Yang C. T.; Downs M. L. Detection of Six Commercially Processed Soy Ingredients in an Incurred Food Matrix Using Parallel Reaction Monitoring. J. Proteome Res. 2019, 18 (3), 995–1005. 10.1021/acs.jproteome.8b00689. [DOI] [PubMed] [Google Scholar]
  54. Ronsein G. E.; Pamir N.; von Haller P. D.; Kim D. S.; Oda M. N.; Jarvik G. P.; Vaisar T.; Heinecke J. W. Parallel reaction monitoring (PRM) and selected reaction monitoring (SRM) exhibit comparable linearity, dynamic range and precision for targeted quantitative HDL proteomics. J. Proteomics 2015, 113, 388–399. 10.1016/j.jprot.2014.10.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Codex . Foods derived from modern biotechnology. Codex Alimentarius Commission, Joint FAO/WHO Food Standards Programme, Rome. 2009.
  56. EC E. C. Commission Implementing Regulation (EU); No 503/2013 on applications for authorisation of genetically modified food and feed in accordance with Regulation (EC) No 1829/2003 of the European Parliament and of the Council and amending Commission Regulations (EC) No 641/2004 and (EC) No 1981/2006. In Off. J. Eur. Union, 2013; Vol. 157, pp 1–48. [Google Scholar]
  57. Guidance on allergenicity assessment of genetically modified plants. EFSA J. 2017, 15 (6), e04862. 10.2903/j.efsa.2017.4862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Hill R. C.; Fast B. J.; Herman R. A. Transgenesis affects endogenous soybean allergen levels less than traditional breeding. Regulatory toxicology and pharmacology: RTP 2017, 89, 70–73. 10.1016/j.yrtph.2017.07.013. [DOI] [PubMed] [Google Scholar]
  59. Swatkoski S. J.; Croley T. R. Screening of Processed Foods for Transgenic Proteins from Genetically Engineered Plants Using Targeted Mass Spectrometry. Anal. Chem. 2020, 92 (4), 3455–3462. 10.1021/acs.analchem.9b05577. [DOI] [PubMed] [Google Scholar]
  60. Hill R. C.; Oman T. J.; Shan G.; Schafer B.; Eble J.; Chen C. Development and Validation of a Multiplexed Protein Quantitation Assay for the Determination of Three Recombinant Proteins in Soybean Tissues by Liquid Chromatography with Tandem Mass Spectrometry. J. Agric. Food Chem. 2015, 63 (33), 7450–7461. 10.1021/acs.jafc.5b03083. [DOI] [PubMed] [Google Scholar]
  61. Kuster D. W.; Barefield D.; Govindan S.; Sadayappan S.. A sensitive and specific quantitation method for determination of serum cardiac myosin binding protein-C by electrochemiluminescence immunoassay. J. Visualized Exp. 2013, (78) 10.3791/50786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Yeaman G. R.; Paul S.; Nahirna I.; Wang Y.; Deffenbaugh A. E.; Liu Z. L.; Glenn K. C. Development and Validation of a Fluorescent Multiplexed Immunoassay for Measurement of Transgenic Proteins in Cotton (Gossypium hirsutum). J. Agric. Food Chem. 2016, 64 (24), 5117–5127. 10.1021/acs.jafc.6b01441. [DOI] [PubMed] [Google Scholar]
  63. Bastarache J. A.; Koyama T.; Wickersham N. E.; Ware L. B. Validation of a multiplex electrochemiluminescent immunoassay platform in human and mouse samples. J. Immunol Methods 2014, 408, 13–23. 10.1016/j.jim.2014.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Settlage S. B.; Eble J. E.; Bhanushali J. K.; Cheever M. L.; Gao A.; Goldstrohm D. A.; Hill R. C.; Hu T. X.; Powley C. R.; Unger A.; Shan G. Validation Parameters for Quantitating Specific Proteins Using ELISA or LC-MS/MS: Survey Results. Food Anal. Methods 2017, 10, 1339–1348. 10.1007/s12161-016-0689-x. [DOI] [Google Scholar]
  65. ICH . Bioanalytical Method Validation and Study Sample Analysis; 2022. (accessed 2023 August 8).
  66. DeSilva B.; Smith W.; Weiner R.; Kelley M.; Smolec J.; Lee B.; Khan M.; Tacey R.; Hill H.; Celniker A. Recommendations for the bioanalytical method validation of ligand-binding assays to support pharmacokinetic assessments of macromolecules. Pharm. Res. 2003, 20 (11), 1885–1900. 10.1023/B:PHAM.0000003390.51761.3d. [DOI] [PubMed] [Google Scholar]
  67. Andreasson U.; Perret-Liaudet A.; van Waalwijk van Doorn L. J. C.; Blennow K.; Chiasserini D.; Engelborghs S.; Fladby T.; Genc S.; Kruse N.; Kuiperij H. B.; et al. A Practical Guide to Immunoassay Method Validation. Front. Neurol. 2015, 6, Technology Report 10.3389/fneur.2015.00179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Findlay J. W.; Smith W. C.; Lee J. W.; Nordblom G. D.; Das I.; DeSilva B. S.; Khan M. N.; Bowsher R. R. Validation of immunoassays for bioanalysis: a pharmaceutical industry perspective. J. Pharm. Biomed Anal 2000, 21 (6), 1249–1273. 10.1016/S0731-7085(99)00244-7. [DOI] [PubMed] [Google Scholar]
  69. Schmidt J.; Alarcon C.. Immunoassay Method Validation. In Immunoassays in Agricultural Biotechnology; 2011; pp 115–138.
  70. Naegeli H.; Bresson J.-L.; Dalmay T.; Dewhurst I. C.; Epstein M. M.; Firbank L. G.; Guerche P.; Hejatko J.; Moreno F. J.; Mullins E.; et al. Assessment of genetically modified soybean MON 87751 × MON 87701 × MON 87708 × MON 89788 for food and feed uses, under Regulation (EC) No 1829/2003 (application EFSA-GMO-NL-2016–128). EFSA Journal 2019, 17 (11), e05847 10.2903/j.efsa.2019.5847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. FDA . M10 Bioanalytical Method Validation and Study Sample Analysis. http://www.fda.gov/regulatory-information/search-fda-guidance-documents/m10-bioanalytical-method-validation-and-study-sample-analysis, 2022.
  72. Naegeli H.; Birch A. N.; Casacuberta J.; De Schrijver A.; Gralak M. A.; Guerche P.; Jones H.; Manachini B.; Messéan A.; Nielsen E. E.; et al. Guidance on allergenicity assessment of genetically modified plants. EFSA J. 2017, 15 (6), e04862 10.2903/j.efsa.2017.4862. [DOI] [PMC free article] [PubMed] [Google Scholar]

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