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

Evaluation of a Sugarcane (Saccharum spp.) Hybrid F1 Population Phenotypic Diversity and Construction of a Rapid Sucrose Yield Estimation Model for Breeding

Sugarcane is the major sugar-producing crop worldwide, and hybrid F₁ populations are the primary populations used in breeding. Challenged by the sugarcane genome's complexity and the sucrose yield's quantitative nature, phenotypic selection is still the most commonly used approach for high-sucrose yield sugarcane breeding. In this study, a hybrid F₁ population containing 135 hybrids was constructed and evaluated for 11 traits (sucrose yield (SY) and its related traits) in a randomized complete-block design during two consecutive growing seasons. The results revealed that all the traits exhibited distinct variation, with the coefficient of variation (CV) ranging from 0.09 to 0.35, the Shannon-Wiener diversity index (H') ranging between 2.64 and 2.98, and the broad-sense heritability ranging from 0.75 to 0.84. Correlation analysis revealed complex correlations between the traits, with 30 trait pairs being significantly correlated. Eight traits, including stalk number (SN), stalk diameter (SD), internode length (IL), stalk height (SH), stalk weight (SW), Brix (B), sucrose content (SC), and yield (Y), were significantly positively correlated with sucrose yield (SY). Cluster analysis based on the 11 traits divided the 135 F₁ hybrids into three groups, with 55 hybrids in Group I, 69 hybrids in Group II, and 11 hybrids in Group III. The principal component analysis indicated that the values of the first four major components' vectors were greater than 1 and the cumulative contribution rate reached 80.93%. Based on the main component values of all samples, 24 F₁ genotypes had greater values than the high-yielding parent 'ROC22' and were selected for the next breeding stage. A rapid sucrose yield estimation equation was established using four easily measured sucrose yield-related traits through multivariable linear stepwise regression. The model was subsequently confirmed using 26 sugarcane cultivars and 24 F₁ hybrids. This study concludes that the sugarcane F₁ population holds great genetic diversity in sucrose yield-related traits. The sucrose yield estimation model, ySY=2.01xSN+8.32xSD+0.79xB+3.44xSH-47.64, can aid to breed sugarcane varieties with high sucrose yield.

Study on Droplet Impact and Spreading and Deposition Behavior of Harvest Aids on Cotton Leaves

The deposition and spreading of pesticide droplets on the surface of plants is a severe challenge to precise pesticide application, which directly affects the pesticide utilization rate and efficacy. Cotton harvest aids are widely used in machine-picked cotton but the effect of formulation and concentration on the droplet behavior and defoliation effect of cotton defoliants is not clear. To clarify the influence of formulation and concentration on the droplet behavior of cotton defoliants, four formulations (suspension concentrate (SC), water dispersible granule (WG), oil dispersion (OD), and wettable powder (WP)) of cotton defoliants were used to prepare different concentrations of harvest aid solutions, according to the spraying volume. The physicochemical properties, droplet impact, and spreading and deposition behavior were studied. The results indicated that the four kinds of harvest aids have good physicochemical properties and can be wet and spread on cotton leaves. The surface tension of the high-concentration harvest aid solution (the spraying volume was less than 1.2 L/667 m²) was increased, which increased the contact angle and reduced the adhesion tension, adhesion work, and the spreading area. Once the harvest aid solution systems impacted the cotton leaves, it could spread to the maximum in a short time (10 ms). The field experiment showed that the droplet spectrum of harvest aids changed slightly, the coefficient of variation (CV) did not exceed 50%, and the defoliation rate was better when the spraying volume was 1.5 L/667 m². The correlation and principal component analysis showed that the spraying volume (concentration) and coverage were negatively correlated with the defoliation rate, while the viscosity, diffusion factor, and spreading rate were positively correlated with the defoliation rate. Overall, the use of appropriate spraying volume application in cotton fields can improve the performance of spray, increase the effective deposition and wetting spread of defoliants on cotton leaves, further reduce the dosage of defoliants, and improve pesticide utilization. These results can provide a theoretical basis for the scientific preparation and spraying of cotton harvest aid solutions.

Developments and Ongoing Challenges for Analysis of Surface-Bound Proteins

Proteins at surfaces and interfaces play important roles in the function and performance of materials in applications ranging from diagnostic assays to biomedical devices. To improve the performance of these materials, detailed molecular structure (conformation and orientation) along with the identity and concentrations of the surface-bound proteins on those materials must be determined. This article describes radiolabeling, surface plasmon resonance, quartz crystal microbalance with dissipation, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, sum frequency generation spectroscopy, and computational techniques along with the information each technique provides for characterizing protein films. A multitechnique approach using both experimental and computation methods is required for these investigations. Although it is now possible to gain much insight into the structure of surface-bound proteins, it is still not possible to obtain the same level of structural detail about proteins on surfaces as can be obtained about proteins in crystals and solutions, especially for large, complex proteins. However, recent results have shown it is possible to obtain detailed structural information (e.g., backbone and side chain orientation) about small peptides (5-20 amino sequences) on surfaces. Current studies are extending these investigations to small proteins such as protein G B1 (∼6 kDa). Approaches for furthering the capabilities for characterizing the molecular structure of surface-bound proteins are proposed.

Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping

The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox^™ multilayer standard sample was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled sample, in which the mass spectral similarity of individual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and sample degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.

Understanding mass spectrometry images: complexity to clarity with machine learning

The application of artificial intelligence and machine learning to hyperspectral mass spectrometry imaging (MSI) data has received considerable attention over recent years. Various methodologies have shown great promise in their ability to handle the complexity and size of MSI data sets. Advances in this area have been particularly appealing for MSI of biological samples, which typically produce highly complicated data with often subtle relationships between features. There are many different machine learning approaches that have been applied to MSI data over the past two decades. In this review, we focus on a subset of non-linear machine learning techniques that have mostly only been applied in the past 5 years. Specifically, we review the use of the self-organizing map (SOM), SOM with relational perspective mapping (SOM-RPM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). While not their only functionality, we have grouped these techniques based on their ability to produce what we refer to as similarity maps. Similarity maps are color representations of hyperspectral data, in which spectral similarity between pixels-that is, their distance in high-dimensional space-is represented by relative color similarity. In discussing these techniques, we describe, briefly, their associated algorithms and functionalities, and also outline applications in MSI research with a strong focus on biological sample types. The aim of this review is therefore to introduce this relatively recent paradigm for visualizing and exploring hyperspectral MSI, while also providing a comparison between each technique discussed.

Controlling orientation, conformation, and biorecognition of proteins on silane monolayers, conjugate polymers, and thermo-responsive polymer brushes: investigations using TOF-SIMS and principal component analysis

Control over orientation and conformation of surface-immobilized proteins, determining their biological activity, plays a critical role in biointerface engineering. Specific protein state can be achieved with adjusted surface preparation and immobilization conditions through different types of protein-surface and protein-protein interactions, as outlined in this work. Time-of-flight secondary ion mass spectroscopy, combining surface sensitivity with excellent chemical specificity enhanced by multivariate data analysis, is the most suited surface analysis method to provide information about protein state. This work highlights recent applications of the multivariate principal component analysis of TOF-SIMS spectra to trace orientation and conformation changes of various proteins (antibody, bovine serum albumin, and streptavidin) immobilized by adsorption, specific binding, and covalent attachment on different surfaces, including self-assembled monolayers on silicon, solution-deposited polythiophenes, and thermo-responsive polymer brushes. Multivariate TOF-SIMS results correlate well with AFM data and binding assays for antibody-antigen and streptavidin-biotin recognition. Additionally, several novel extensions of the multivariate TOF-SIMS method are discussed.

Graphical abstract

ToF-SIMS and Machine Learning for Single-Pixel Molecular Discrimination of an Acrylate Polymer Microarray

Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties-such as surface chemistry and properties like cell attachment or protein adsorption-in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the data set.

Secondary ion mass spectrometry: The application in the analysis of atmospheric particulate matter

Currently, considerable attention has been paid to atmospheric particulate matter (PM) investigation due to its importance in human health and global climate change. Surface characterization, single particle analysis and depth profiling of PM is important for a better understanding of its formation processes and predicting its impact on the environment and human being. Secondary ion mass spectrometry (SIMS) is a surface technique with high surface sensitivity, high spatial resolution chemical imaging and unique depth profiling capabilities. Recent research shows that SIMS has great potential in analyzing both surface and bulk chemical information of PM. In this review, we give a brief introduction of SIMS working principle and survey recent applications of SIMS in PM characterization. Particularly, analyses from different types of PM sources by various SIMS techniques were discussed concerning their advantages and limitations. The future development and needs of SIMS in atmospheric aerosol measurement are proposed with a perspective in broader environmental sciences.

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.

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: [I_61.9534 + I_62.9846 + I_122.9547 + I_84.9609 + I_120.9461]/[I_30.9979 + I_42.9991 + I_73.0660 + I_147.0780]. 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.