Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS.

Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological samples. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the samples that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1-300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological samples. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.

Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, providing instruments with growing capabilities and resolution. The data sets generated by these instruments are likewise increasing dramatically in size and complexity. Paradoxically, methods for efficient analysis of these large, rich data sets have not improved at the same rate. Clearly, more effective computational methods for analysis of ToF-SIMS data are becoming essential. Several research groups are customizing standard multivariate analytical tools to decrease computational demands, provide user-friendly interfaces, and simplify identification of trends and features in large ToF-SIMS data sets. We previously applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. Self-organizing maps (SOMs), a type of artificial neural network (ANN), classified the polymers based on their molecular composition and primary ion probe type more effectively than simple PCA. The effectiveness of this approach led us to question whether it would be useful in distinguishing polymers that were very similar. How sensitive is the technique to changes in polymer chemical structure and composition? To address this question, we generated ToF-SIMS ion peak signatures for seven nylon polymers with similar chemistries and used our up-binning and SOM approach to classify and cluster the polymers. The widely used linear PCA method failed to separate the samples. Supervised and unsupervised training of SOMs using positive or negative ion mass spectra resulted in effective classification and separation of the seven nylon polymers. Our SOM classification method has proven to be tolerant of minor sample irregularities, sample-to-sample variations, and inherent data limitations including spectral resolution and noise. We have demonstrated the potential of machine learning methods to analyze ToF-SIMS data more effectively than traditional methods. Such methods are critically important for future complex data analysis and provide a pipeline for rapid classification and identification of features and similarities in large data sets.

Determining the limit of detection of surface bound antibody.

Determination of a limit of detection (LoD) for surface bound antibodies is crucial for the development and deployment of sensitive bioassays. The measurement of very low concentrations of surface bound antibodies is also important in the manufacturing of pharmaceutical products such as antibody-conjugated pharmaceuticals. Low concentrations are required to avoid an immune response from the target host. Enzyme-linked immunosorbent assay (ELISA), x-ray photoelectron spectroscopy (XPS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were used to determine the LoD for the surface bound antibody (antiepidermal growth factor receptor antibody) on silicon substrates. Antibody solution concentrations between 10 µg/ml and 1 ng/ml and a control (antibody-free buffer solution) were employed, and the detection performance of each technique was compared. For this system, the ELISA LoD was 100 ng/ml and the XPS LoD was 1 µg/ml, corresponding to an estimated surface concentration of 49 ± 7 ng/cm2 using a 1 µg/ml solution. Due to the multivariate complexity of ToF-SIMS data, analysis was carried out using three different methods, peak ratio calculations, principal component analysis, and artificial neural network analysis. The use of multivariate analysis with this dataset offers an unbiased analytical approach based on the peaks selected from ToF-SIMS data. The results estimate a ToF-SIMS LoD between applied antibody concentrations of 10 and 100 ng/mL. For surface bound antibodies on a silicon substrate, the LoD is below an estimated surface concentration of 49 ng/cm2. The authors have determined the LoD for this system using ELISA, XPS, and ToF-SIMS with multivariate analyses, with ToF-SIMS offering an order of magnitude better detection over ELISA and 2 orders of magnitude better detection over XPS.

Surface immobilized antibody orientation determined using ToF-SIMS and multivariate analysis.

Antibody orientation at solid phase interfaces plays a critical role in the sensitive detection of biomolecules during immunoassays. Correctly oriented antibodies with solution-facing antigen binding regions have improved antigen capture as compared to their randomly oriented counterparts. Direct characterization of oriented proteins with surface analysis methods still remains a challenge however surface sensitive techniques such as Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) provide information-rich data that can be used to probe antibody orientation. Diethylene glycol dimethyl ether plasma polymers (DGpp) functionalized with chromium (DGpp+Cr) have improved immunoassay performance that is indicative of preferential antibody orientation. Herein, ToF-SIMS data from proteolytic fragments of anti-EGFR antibody bound to DGpp and DGpp+Cr are used to construct artificial neural network (ANN) and principal component analysis (PCA) models indicative of correctly oriented systems. Whole antibody samples (IgG) test against each of the models indicated preferential antibody orientation on DGpp+Cr. Cross-reference between ANN and PCA models yield 20 mass fragments associated with F(ab')2 region representing correct orientation, and 23 mass fragments associated with the Fc region representing incorrect orientation. Mass fragments were then compared to amino acid fragments and amino acid composition in F(ab')2 and Fc regions. A ratio of the sum of the ToF-SIMS ion intensities from the F(ab')2 fragments to the Fc fragments demonstrated a 50% increase in intensity for IgG on DGpp+Cr as compared to DGpp. The systematic data analysis methodology employed herein offers a new approach for the investigation of antibody orientation applicable to a range of substrates. STATEMENT OF SIGNIFICANCE: Controlled orientation of antibodies at solid phases is critical for maximizing antigen detection in biosensors and immunoassays. Surface-sensitive techniques (such as ToF-SIMS), capable of direct characterization of surface immobilized and oriented antibodies, are under-utilized in current practice. Selection of a small number of mass fragments for analysis, typically pertaining to amino acids, is commonplace in literature, leaving the majority of the information-rich spectra unanalyzed. The novelty of this work is the utilization of a comprehensive, unbiased mass fragment list and the employment of principal component analysis (PCA) and artificial neural network (ANN) models in a unique methodology to prove antibody orientation. This methodology is of significant and broad interest to the scientific community as it is applicable to a range of substrates and allows for direct, label-free characterization of surface bound proteins.

Chromium functionalized diglyme plasma polymer coating enhances enzyme-linked immunosorbent assay performance.

Ensuring the optimum orientation, conformation, and density of substrate-bound antibodies is critical for the success of sandwich enzyme-linked immunosorbent assays (ELISAs). In this work, the authors utilize a diethylene glycol dimethyl ether plasma polymer (DGpp) coating, functionalized with chromium within a 96 well plate for the enhanced immobilization of a capture antibody. For an equivalent amount of bound antibody, a tenfold improvement in the ELISA signal intensity is obtained on the DGpp after incubation with chromium, indicative of improved orientation on this surface. Time-of-flight secondary-ion-mass-spectrometry (ToF-SIMS) and principal component analysis were used to probe the molecular species at the surface and showed ion fragments related to lysine, methionine, histidine, and arginine coupled to chromium indicating candidate antibody binding sites. A combined x-ray photoelectron spectroscopy and ToF-SIMS analysis provided a surface molecular characterization that demonstrates antibody binding via the chromium complex. The DGpp+Cr surface treatment holds great promise for improving the efficacy of ELISAs.

ToF-SIMS and Principal Component Analysis Investigation of Denatured, Surface-Adsorbed Antibodies.

Antibody denaturation at solid-liquid interfaces plays an important role in the sensitivity of protein assays such as enzyme-linked immunosorbent assays (ELISAs). Surface immobilized antibodies must maintain their native state, with their antigen binding (Fab) region intact, to capture antigens from biological samples and permit disease detection. In this work, two identical sample sets were prepared with whole antibody IgG, F(ab')2 and Fc fragments, immobilized to either a silicon wafer or a diethylene glycol dimethyl ether plasma polymer surface. Analysis was conducted on one sample set at day 0, and the second sample set after 14 days in vacuum, with time-of-flight secondary ion mass spectrometry (ToF-SIMS) for molecular species representative of denaturation. A 1003 mass fragment peak list was compiled from ToF-SIMS data and compared to a 35 amino acid mass fragment peak list using principal component analysis. Several ToF-SIMS secondary ions, pertaining to disulfide and thiol species, were identified in the 14 day (presumably denatured) samples. A substrate and primary ion independent marker for denaturation (aging) was then produced using a ratio of mass peak intensities according to denaturation ratio: [I61.9534 + I62.9846 + I122.9547 + I84.9609 + I120.9461]/[I30.9979 + I42.9991 + I73.0660 + I147.0780]. The ratio successfully identifies denaturation on both the silicon and plasma polymer substrates and for spectra generated with Mn+, Bi+, and Bi3+ primary ions. We believe this ratio could be employed to as a marker of denaturation of antibodies on a plethora of substrates.

Surface Adsorbed Antibody Characterization Using ToF-SIMS with Principal Component Analysis and Artificial Neural Networks.

Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality of large data sets and highlight key characteristics. Herein, we discuss the strengths and weaknesses of PCA and ANNs as methods for investigation and interpretation of a complex multivariate sample set. Using time-of-flight secondary ion mass spectrometry (ToF-SIMS) we acquired spectra from an antibody and its proteolysis fragments with three primary-ion sources to obtain a panel of 72 spectra and a characteristic peak list of 775 fragment ions. We describe the use of ANNs as a means to interpret the ToF-SIMS spectral data, highlight the optimal neural network design and computational parameters, and discuss the technique limitations. Further, employing Bi3(+) as the primary-ion source, ANNs can accurately classify antibody fragments from the parent antibody based on ToF-SIMS spectra.