Trends in surface plasmon resonance biosensing: materials, methods, and machine learning.

Surface plasmon resonance (SPR) proves to be one of the most effective methods of label-free detection and has been integral for the study of biomolecular interactions and the development of biosensors. This trend delves into the latest SPR research and progress built upon the Kretschmann configuration, a pivotal platform, and highlights three key developments that have enhanced the capabilities of the technique. We will first cover a range of explorations of novel plasmonic materials that have shaped SPR performance. Innovative signal transduction and collection, which leverages traditional materials and emerging alternatives, will then be discussed. Finally, the evolving landscape of data analysis, including the integration of machine learning algorithms to navigate complex SPR datasets, will be reviewed. We will also discuss the implementation of these improvements that have enabled new biosensing functions. These advancements not only pave the way for enhanced biosensing in general but also open new avenues for the technique to play a more significant role in research concerning human health.

Curved Membrane Mimics for Quantitative Probing of Protein-Membrane Interactions by Surface Plasmon Resonance.

A majority of biomimetic membranes used for current biophysical studies rely on planar structures such as supported lipid bilayer (SLB) and self-assembled monolayers (SAMs). While they have facilitated key information collection, the lack of curvature makes these models less effective for the investigation of curvature-dependent protein binding. Here, we report the development and characterization of curved membrane mimics on a solid substrate with tunable curvature and ease in incorporation of cellular membrane components for the study of protein-membrane interactions. The curved membranes were generated with an underlayer lipid membrane composed of DGS-Ni-NTA and POPC lipids on the substrate, followed by the attachment of histidine-tagged cholera toxin (his-CT) as a capture layer. Lipid vesicles containing different compositions of gangliosides, including GA1, GM1, GT1b, and GQ1b, were anchored to the capture layer, providing fixation of the curved membranes with intact structures. Characterization of the curved membrane was accomplished with surface plasmon resonance (SPR), fluorescence recovery after photobleaching (FRAP), and nano-tracking analysis (NTA). Further optimization of the interface was achieved through principal component analysis (PCA) to understand the effect of ganglioside type, percentage, and vesicle dimensions on their interactions with proteins. In addition, Monte Carlo simulations were employed to predict the distribution of the gangliosides and interaction patterns with single point and multipoint binding models. This work provides a reliable approach to generate robust, component-tuning, and curved membranes for investigating protein interactions more pertinently than what a traditional planar membrane offers.

Label-Free Analysis of Binding and Inhibition of SARS-Cov-19 Spike Proteins to ACE2 Receptor with ACE2-Derived Peptides by Surface Plasmon Resonance.

SARS-CoV-2 has been shown to enter and infect human cells via interactions between spike protein (S glycoprotein) and angiotensin-converting enzyme 2 (ACE2). As such, it may be possible to suppress the infection of the virus via the blocking of this binding interaction through the use of specific peptides that can mimic the human ACE 2 peptidase domain (PD) α 1-helix. Herein, we report the use of competitive assays along with surface plasmon resonance (SPR) to investigate the effect of peptide sequence and length on spike protein inhibition. The characterization of these binding interactions helps us understand the mechanisms behind peptide-based viral blockage and develop SPR methodologies to quickly screen disease inhibitors. This work not only helps further our understanding of the important biological interactions involved in viral inhibition but will also aid in future studies that focus on the development of therapeutics and drug options. Two peptides of different sequence lengths, [30-42] and [22-44], based on the α 1-helix of ACE2 PD were selected for this fundamental investigation. In addition to characterizing their inhibitory behavior, we also identified the critical amino acid residues of the RBD/ACE2-derived peptides by combining experimental results and molecular docking modeling. While both investigated peptides were found to effectively block the RBD residues known to bind to ACE2 PD, our investigation showed that the shorter peptide was able to reach a maximal inhibition at lower concentrations. These inhibition results matched with molecular docking models and indicated that peptide length and composition are key in the development of an effective peptide for inhibiting biophysical interactions. The work presented here emphasizes the importance of inhibition screening and modeling, as longer peptides are not always more effective.

Probing Herbicide Toxicity to Algae (Selenastrum capricornutum) by Lipid Profiling with Machine Learning and Microchip/MALDI-TOF Mass Spectrometry.

Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS)-based lipid profiling is a powerful method to study the cytotoxicity of chemical exposure to microorganisms at the single cell level. We report here a combined approach of machine learning (ML) and microchip-based MALDI-time of flight (TOF) mass spectrometry to investigate the cytotoxic effect of herbicides on algae through single cell lipid profiling. Algal species Selenastrum capricornutum was chosen as the target system, and its exposure to different doses of common chemical herbicides and the resulting cytotoxic behaviors under various stress conditions were characterized. A lipid library for S. capricornutum has been established with 63 identified lipids that include glycosyldiacylglycerols and triacylglycerols. We demonstrated that major alternations occurred for lipids with functional groups of digalactosyldiacylglycerol (DGDG), triacylglycerol (TAG), and monogalactosyldiacylglycerol (MGDG). DGDG was shown to decrease upon exposure to herbicides of norflurazon and atrazine, while some MGDG and TAG lipids would increase for norflurazon. Compared to other algae, S. capricornutum was more strongly impacted by norflurazon than atrazine while the latter was observed to have a greater effect on C. reinhardtii. Machine learning algorithms have been applied to improve the classification of herbicide impact and help identify lipid species affected by the chemical exposure. A total of 69 machine learning models were trained and tested for the identification of ideal algorithms in the classification process, in which flexible discriminant analysis and support vector machine model were found to be the most accurate and consistent. The ML algorithms accurately differentiated herbicide impact and have identified cytotoxic differences that were previously hidden. The results suggest that herbicides express toxicity among different algae likely on the basis of metabolic differences. The ML-assisted method proves to be highly effective and can provide an advanced technological platform for probing cytotoxicity for bacterial species and in metabolic pathway analysis.

Lipidomic Profiling of Algae with Microarray MALDI-MS toward Ecotoxicological Monitoring of Herbicide Exposure.

Misuse of agrochemicals has a long-lasting negative impact on aquatic systems. Mismanagement of herbicides in agri-food sectors is often linked to a simultaneous decline in the health of downstream waterways. However, monitoring the herbicide levels in these areas is a laborious task, and modern analytical approaches, such as solid-phase extraction-liquid chromatography-mass spectrometry (SPE-LC-MS) and enzyme-linked immunosorbent assay, are low-throughput and require significant sample preparation. We report here the use of microchip technology in combination with matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) for the assessment of the ecotoxicological effect of agrochemicals on aquatic species at the single-cell level. This approach quantifies the fluctuations in lipid content in sentinel organisms and targets the microalga, Chlamydomonas reinhardtii (C. reinhardtii), as the model system. Specifically, we investigated the cytotoxicity of three herbicides (atrazine, clomazone, and norflurazon) on C. reinhardtii by analyzing the lipid component variation upon assorted herbicide exposure. Lipidomic profiling reveals a significantly altered lipid content at >EC50 in atrazine-exposed cells. The response for norflurazon showed similar trends but diminished in magnitude, while the result for clomazone was near muted. At lower herbicide concentrations, digalactosyldiacylglycerols showed a rapid decrease in abundance, while several other lipids displayed a moderate increase. The microchip-based MALDI technique demonstrates the ability to achieve lipidomic profiling of aquatic species exposed to different stressors, proving effective for high-throughput screening and single-cell analysis in ecotoxicity studies.

Detection of Multiple Sclerosis Biomarkers in Serum by Ganglioside Microarrays and Surface Plasmon Resonance Imaging.

Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheaths of nerve cells in the central nervous system. An individual suffering from MS produces increased levels of antibodies that target cell membrane components, such as phospholipids, gangliosides, and membrane proteins. Among them, anti-ganglioside antibodies are considered as important biomarkers to differentiate MS from other diseases that exhibit similar symptoms. We report here a label-free method for detecting a series of antibodies against gangliosides in serum by surface plasmon resonance imaging (SPRi) in combination with a carbohydrate microarray. The ganglioside array was fabricated with a plasmonically tuned, background-free biochip, and coated with a perfluorodecyltrichlorosilane (PFDTS) layer for antigen attachment as a self-assembled pseudo-myelin sheath. The chip was characterized with AFM and matrix-assisted laser desorption ionization mass spectrometry, demonstrating effective functionalization of the surface. SPRi measurements of patients' mimicking blood samples were conducted. A multiplexed detection of antibodies for anti-GT1b, anti-GM1, and anti-GA1 in serum was demonstrated, with a working range of 1 to 100 ng/mL, suggesting that it is well suited for clinical assessment of antibody abnormality in MS patients. Statistical analyses, including PLS-DA and PCA show the array allows comprehensive characterization of cross reactivity patterns between the MS specific antibodies and can generate a wide range of information compared to traditional end point assays. This work uses PFDTS surface functionalization and enables direct MS biomarker detection in serum, offering a powerful alternative for MS assessment and potentially improved patient care.

Plasmonic Gold Templates Enhancing Single Cell Lipidomic Analysis of Microorganisms.

Single cell lipid profiling is a powerful tool to connect membrane composition and its changes within individual cells to specific biochemical functions or stimuli, but current approaches are inadequate due to the complex nature of the cells and technical limitation in analysis. Herein we report a new method with plasmonic substrates capable of cell localization and enhanced lipid ionization through thin-gold-film MALDI-MS. We performed lipidomic profiling of algae single cells with a 120-well microarray and identified more than 50 lipids in C. reinhardtii without an extraction process. The substrate was used for probing toxicological effect of herbicide atrazine on the algae's lipidome, demonstrating molecular changes in glycerol lipid profiles. Fast location of cells with metal-enhanced fluorescence (MEF) and subsequent precise and direct ionization of the LDI process contribute to the enhanced performance, allowing for assessment of lipid changes concurrent with atrazine affected populations. This method that combines microarrays, MEF, and MALDI-MS presents an effective platform for lipidomic study of single cells and for environmental toxicity study with microorganisms.