Structural characterization of amyloid aggregates with spatially resolved infrared spectroscopy.

The self-assembly of proteins and peptides into ordered structures called amyloid fibrils is a hallmark of numerous diseases, impacting the brain, heart, and other organs. The structure of amyloid aggregates is central to their function and thus has been extensively studied. However, the structural heterogeneities between aggregates as they evolve throughout the aggregation pathway are still not well understood. Conventional biophysical spectroscopic methods are bulk techniques and only report on the average structural parameters. Understanding the structure of individual aggregate species in a heterogeneous ensemble necessitates spatial resolution on the length scale of the aggregates. Recent technological advances have led to augmentation of infrared (IR) spectroscopy with imaging modalities, wherein the photothermal response of the sample upon vibrational excitation is leveraged to provide spatial resolution beyond the diffraction limit. These combined approaches are ideally suited to map out the structural heterogeneity of amyloid ensembles. AFM-IR, which integrates IR spectroscopy with atomic force microscopy enables identification of the structural facets the oligomers and fibrils at individual aggregate level with nanoscale resolution. These capabilities can be extended to chemical mapping in diseased tissue specimens with submicron resolution using optical photothermal microscopy, which combines IR spectroscopy with optical imaging. This book chapter provides the basic premise of these novel techniques and provides the typical methodology for using these approaches for amyloid structure determination. Detailed procedures pertaining to sample preparation and data acquisition and analysis are discussed and the aggregation of the amyloid ß peptide is provided as a case study to provide the reader the experimental parameters necessary to use these techniques to complement their research efforts.

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60-100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.

Fluorescence in depth: integration of spectroscopy and imaging with Raman, IR, and CD for advanced research.

Fluorescence spectroscopy serves as a vital technique for studying the interaction between light and fluorescent molecules. It encompasses a range of methods, each presenting unique advantages and applications. This technique finds utility in various chemical studies. This review discusses Fluorescence spectroscopy, its branches such as Time-Resolved Fluorescence Spectroscopy (TRFS) and Fluorescence Lifetime Imaging Microscopy (FLIM), and their integration with other spectroscopic methods, including Raman, Infrared (IR), and Circular Dichroism (CD) spectroscopies. By delving into these methods, we aim to provide a comprehensive understanding of the capabilities and significance of fluorescence spectroscopy in scientific research, highlighting its diverse applications and the enhanced understanding it brings when combined with other spectroscopic methods. This review looks at each technique's unique features and applications. It discusses the prospects of their combined use in advancing scientific understanding and applications across various domains.

Probing Backbone Coupling within Hydrated Proteins with Two-Color 2D Infrared Spectroscopy.

The vibrational coupling between protein backbone modes and the role of water interactions are important topics in biomolecular spectroscopy. Our work reports the first study of the coupling between amide I and amide A modes within peptides and proteins with secondary structure and water contacts. We use two-color two-dimensional infrared (2D IR) spectroscopy and observe cross peaks between amide I and amide A modes. In experiments with peptides with different secondary structures and side chains, we observe that the spectra are sensitive to secondary structure. Water interactions affect the cross peaks, which may be useful as probes for the accessibility of protein sites to hydration water. Moving to two-color 2D IR spectra of proteins, the data demonstrate that the cross peaks integrate the sensitivities of both amide I and amide A spectra and that a two-color detection scheme may be a promising tool for probing secondary structures in proteins.

Development of Nontargeted Workflow of Occupational Exposure by Infrared Ion Spectroscopy and Silicone Wristbands' Passive Sampling.

A new approach using orthogonal analytical techniques is developed for chemical identification. High resolution mass spectrometry and infrared ion spectroscopy are applied through a 5-level confidence paradigm to demonstrate the effectiveness of nontargeted workflow for the identification of hazardous organophosphates. Triphenyl phosphate is used as a surrogate organophosphate for occupational exposure, and silicone wristbands are used to represent personal samplers. Spectral data of a target compound is combined with spectral data of the sodium adduct and quantum chemical calculations to achieve a confirmed identification. Here, we demonstrate a nontargeted workflow that identifies organophosphate exposure and provides a mechanism for selecting validated methods for quantitative analyses.

Spectroscopic insight into breast cancer: profiling small extracellular vesicles lipids via infrared spectroscopy for diagnostic precision.

Breast cancer, a leading cause of female mortality due to delayed detection owing to asymptomatic nature and limited early diagnostic tools, was investigated using a multi-modal approach. Plasma-derived small EVs from breast cancer patients (BrCa, n = 74) and healthy controls (HC, n = 30) were analyzed. Small EVs (n = 104), isolated through chemical precipitation, underwent characterization via transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). Validation involved antibody-based tests (TSG101, CD9, CD81, CD63). Infrared spectra of small EVs were obtained, revealing significant differences in lipid acyl chains, particularly in the C-H stretching of CH3. The study focused on the lipid region (3050-2900 cm-1), identifying peaks (3015 cm-1, 2960 cm-1, 2929 cm-1) as distinctive lipid characteristics. Spectroscopic lipid-to-lipid ratios [(I3015/I2929), (I2960/I2929)] emerged as prominent breast cancer markers. Exploration of protein, nucleic acid, and carbohydrate ratios indicated variations in alpha helices, asymmetric C-H stretching vibrations, and C-O stretching at 1033 cm-1. Principal component analysis (PCA) successfully differentiated BrCa and HC small EVs, and heatmap analysis and receiver operating characteristic (ROC) curve evaluations underscored the discriminatory power of lipid ratios. Notably, (I2960/I2929) exhibited 100% sensitivity and specificity, highlighting its potential as a robust BrCa sEV marker for breast cancer detection.

Nanoscale colocalized thermal and chemical mapping of pharmaceutical powder aerosols.

Inhalation of pharmaceutical aerosol formulations is widely used to treat respiratory diseases. Spatially resolved thermal characterization offers promise for better understanding drug release rates from particles; however, this has been an analytical challenge due to the small particle size (from a few micrometers down to nanometers) and the complex composition of the formulations. Here, we employ nano-thermal analysis (nanoTA) to probe the nanothermal domain of a pharmaceutical aerosol formulation containing a mixture of fluticasone propionate (FP), salmeterol xinafoate (SX), and excipient lactose, which is widely used to treat asthma and chronic obstructive pulmonary disease (COPD). Furthermore, atomic force microscopy-infrared spectroscopy (AFM-IR) and AFM force measurements are performed to provide nanochemical and nanomechanical information to complement the nanothermal data. The colocalized thermal and chemical mapping clearly reveals the surface heterogeneity of the drugs in the aerosol particles and demonstrates the contribution of the surface chemical composition to the variation in the thermal properties of the particles. We present a powerful analytical approach for in-depth characterization of thermal/chemical/morphological properties of dry powder inhaler particles at micro- and nanometer scales. This approach can be used to facilitate the comparison between generics and reference inhalation products and further the development of high-performance pharmaceutical formulations.

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomarkers, lecithin (L) and sphingomyelin (S) with the potential for point of care (POC) diagnosis and monitoring. The effects of varying other lipid species present in lung surfactant on the mid IR spectra used to train machine learning models are explored. This study presents a lung lipid model of five lipids present in lung surfactant and varies each in a systematic approach to evaluate the ability of machine learning models to predict the lipid concentrations, the L/S ratio and to quantify the uncertainty in the predictions using the jackknife + -after-bootstrap and variant bootstrap methods. We establish the L/S ratio can be determined with an uncertainty of approximately ±0.3 mol/mol and we further identify the 5 most prominent wavenumbers associated with each machine learning model.

Inverted Transflection Spectroscopy of Live Cells Using Metallic Grating on Elevated Nanopillars.

Water absorption of mid-infrared (MIR) radiation severely limits the options for vibrational spectroscopy of the analytes-including live biological cells-that must be probed in aqueous environments. While internal reflection elements, such as attenuated total reflection prisms and metasurfaces, partially overcome this limitation, such devices have their own limitations: ATR prisms are difficult to integrate with multiwell cell culture workflows, while metasurfaces suffer from a limited spectral range and small penetration depth into analytes. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated on top of elevated dielectric pillars. For the MIR wavelengths that are significantly longer than the grating period, reflection-based spectroscopy enables broadband sensing of the analytes inside the trenches separating the dielectric pillars. Because the depth of the analyte twice-traversed by the MIR light excludes the highly absorbing thick water layer above the grating, we refer to the technique as inverted transflection spectroscopy (ITS). The analytic power of ITS is established by measuring a wide range of protein concentrations in solution, with the limit of detection in the single-digit mg mL-1. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is used to characterize their adhesion kinetic.

Label-free mapping of cetuximab in multi-layered tumor oral mucosa models by atomic force-microscopy-based infrared spectroscopy.

Sensitive mapping of drugs and drug delivery systems is pivotal for the understanding and improvement of treatment options. Since labeling alters the physicochemical and potentially the pharmacological properties of the molecule of interest, its label-free detection by photothermal expansion is investigated. We report on a proof-of-concept study to map the cetuximab distribution by atomic-force microscopy-based infrared spectroscopy (AFM-IR). The monoclonal antibody cetuximab was applied to a human tumor oral mucosa model, consisting of a tumor epithelium on a lamina propria equivalent. Hyperspectral imaging in the wavenumber regime between 903 cm-1 and 1312 cm-1 and a probing distance between the data points down to 10 × 10 nm are used for determining the local drug distribution. The local distinction of cetuximab from the tissue background is gained by linear combination modeling making use of reference spectra of the drug and untreated models. The results from this approach are compared to principal component analyses, yielding comparable results. Even single molecule detection appears feasible. The results indicate that cetuximab penetrates the cytosol of tumor cells but does not bind to structures in the cell membrane. In conclusion, AFM-IR mapping of cetuximab proved to sensitively determine drug concentrations at an unprecedented spatial resolution without the need for drug labeling.

Intermolecular Protein-Water Coupling Impedes the Coupling Between the Amide A and Amide I Mode in Interfacial Proteins.

The coupling between different vibrational modes in proteins is essential for chemical dynamics and biological functions and is linked to the propagation of conformational changes and pathways of allosteric communication. However, little is known about the influence of intermolecular protein-H2O coupling on the vibrational coupling between amide A (NH) and amide I (C═O) bands. Here, we investigate the NH/CO coupling strength in various peptides with different secondary structures at the lipid cell membrane/H2O interface using femtosecond time-resolved sum frequency generation vibrational spectroscopy (SFG-VS) in which a femtosecond infrared pump is used to excite the amide A band, and SFG-VS is used to probe transient spectral evolution in the amide A and amide I bands. Our results reveal that the NH/CO coupling strength strongly depends on the bandwidth of the amide I mode and the coupling of proteins with water molecules. A large extent of protein-water coupling significantly reduces the delocalization of the amide I mode along the peptide chain and impedes the NH/CO coupling strength. A large NH/CO coupling strength is found to show a strong correlation with the high energy transfer rate found in the light-harvesting proteins of green sulfur bacteria, which may understand the mechanism of energy transfer through a molecular system and assist in controlling vibrational energy transfer by engineering the molecular structures to achieve high energy transfer efficiency.

Using 2D-IR Spectroscopy to Measure the Structure, Dynamics, and Intermolecular Interactions of Proteins in H2O.

ConspectusInfrared (IR) spectroscopy probes molecular structure at the level of the chemical bond or functional group. In the case of proteins, the most informative band in the IR spectrum is the amide I band, which arises predominantly from the C═O stretching vibration of the peptide link. The folding of proteins into secondary and tertiary structures leads to vibrational coupling between peptide units, generating specific amide I spectral signatures that provide a fingerprint of the macromolecular conformation. Ultrafast two-dimensional IR (2D-IR) spectroscopy allows the amide I band of a protein to be spread over a second frequency dimension in a way that mirrors 2D-NMR methods. This means that amide I 2D-IR spectroscopy produces a spectral map that is exquisitely sensitive to protein structure and dynamics and so provides detailed insights that cannot be matched by IR absorption spectroscopy. As a result, 2D-IR spectroscopy has emerged as a powerful tool for probing protein structure and dynamics over a broad range of time and length scales in the solution phase at room temperature. However, the protein amide I band coincides with an IR absorption from the bending vibration of water (δHOH), the natural biological solvent. To circumvent this problem, protein IR studies are routinely performed in D2O solutions because H/D substitution shifts the solvent bending mode (δDOD) to a lower frequency, revealing the amide I band. While effective, this method raises fundamental questions regarding the impact of the change in solvent mass on the structural or solvation dynamics of the protein and the removal of the energetic resonance between solvent and solute.In this Account, a series of studies applying 2D-IR to study the spectroscopy and dynamics of proteins in H2O-rich solvents is reviewed. A comparison of IR absorption spectroscopy and 2D-IR spectroscopy of protein-containing fluids is used to demonstrate the basis of the approach before a series of applications is presented. These range from measurements of fundamental protein biophysics to recent applications of machine learning to gain insight into protein-drug binding in complex mixtures. An outlook is presented, considering the potential for 2D-IR measurements to contribute to our understanding of protein behavior under near-physiological conditions, along with an evaluation of the obstacles that still need to be overcome.

Identification and determination of different processed products and their extracts of Crataegi Fructus by infrared spectroscopy combined with two-dimensional correlation analysis.

The fruit of Crataegus sp. is known as "Shanzha (SZ)" in China and is widely used in the food, beverage, and traditional Chinese medicine (TCM) industries. SZ usually requires thermal processing to reduce the irritation of its acidity to the gastric mucosa. Different processed products of SZ resulting from thermal processing have different or even opposite functions in clinical applications. In addition, 5-hydroxymethylfurfural (5-HMF) intermediates produced during thermal processing are carcinogenic to humans. Therefore, the aim of this study was to explore a rapid and accurate method by Fourier transform infrared spectroscopy (FT-IR) for the identification of different processed products and the determination of 5-HMF in extracts. In qualitative identification, a three-stage infrared spectroscopy identification method (raw spectra, the second derivative spectra, and two-dimensional correlation (2DCOS) spectra) was developed to distinguish different processed products of SZ step by step. In quantitative determination, partial least squares regression combined with different variable selection methods, especially the 2DCOS method, was applied to determine the 5-HMF content. The results show that temperature-induced 2DCOS synchronous spectra can effectively identify different processed products of SZ by shape, intensity, and position of auto-peaks or cross-peaks, and the variables selected by power spectra from concentration-induced 2DCOS synchronous spectra have better prediction ability for 5-HMF compared to full variables. The above results demonstrate that 2D-COS analysis is a potential tool in qualitative and quantitative analysis, which can improve sample identification accuracy and determination capabilities. This study not only establishes a rapid and accurate method for the identification of different processed products but also provides a practical reference for food safety and the efficient use of TCM.

Does the age of milk affect its mid-infrared spectrum and predictions?

Milk of dairy species commonly undergo standardized official analyses, these that may require chemical preservation and transportation to a certified laboratory. In this context, storage duration is an important factor that can potential affect both milk chemical analyses and its mid-infrared spectrum. We analysed milk samples at different time points/ages to assess repeatability and reproducibility of mid-infrared predicted traits (e.g., fat and protein). Using spectral data, we also evaluated the ability of spectroscopy coupled with chemometrics to discriminate samples according to their age. Although the main components of milk remained consistently reproducible across age (days), changes in the spectrum due to sample aging and deterioration of the matrix were detectable. Using a discriminant analysis, we achieved a classification accuracy of 77% in validation. Predicting milk age using mid-infrared spectra is feasible, allowing for sample monitoring within circuits where maximum reliability is needed, e.g., bulk or individual milk samples for legal/official use or payment systems.

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Elucidating fungal decomposition of organic matter at sub-micrometer spatial scales using optical photothermal infrared (O-PTIR) microspectroscopy.

In microbiological studies, a common goal is to link environmental factors to microbial activities. Both environmental factors and microbial activities are typically derived from bulk samples. It is becoming increasingly clear that such bulk environmental parameters poorly represent the microscale environments microorganisms experience. Using infrared (IR) microspectroscopy, the spatial distribution of chemical compound classes can be visualized, making it a useful tool for studying the interactions between microbial cells and their microenvironments. The spatial resolution of conventional IR microspectroscopy has been limited by the diffraction limit of IR light. The recent development of optical photothermal infrared (O-PTIR) microspectroscopy has pushed the spatial resolution of IR microspectroscopy beyond this diffraction limit, allowing the distribution of chemical compound classes to be visualized at sub-micrometer spatial scales. To examine the potential and limitations of O-PTIR microspectroscopy to probe the interactions between fungal cells and their immediate environments, we imaged the decomposition of cellulose films by cells of the ectomycorrhizal fungus Paxillus involutus and compared O-PTIR results using conventional IR microspectroscopy. Whereas the data collected with conventional IR microspectroscopy indicated that P. involutus has only a very limited ability to decompose cellulose films, O-PTIR data suggested that the ability of P. involutus to decompose cellulose was substantial. Moreover, the O-PTIR method enabled the identification of a zone located outside the fungal hyphae where the cellulose was decomposed by oxidation. We conclude that O-PTIR can provide valuable new insights into the abilities and mechanisms by which microorganisms interact with their surrounding environments.IMPORTANCEInfrared (IR) microspectroscopy allows the spatial distribution of chemical compound classes to be visualized. The use of conventional IR microspectroscopy in microbiological studies has been restricted by limited spatial resolution. Recent developments in laser technology have enabled a new class of IR microspectroscopy instruments to be developed, pushing the spatial resolution beyond the diffraction limit of IR light to approximately 500 nm. This improved spatial resolution now allows microscopic observations of changes in chemical compounds to be made, making IR microspectroscopy a useful tool to investigate microscale changes in chemistry that are caused by microbial activity. We show these new possibilities using optical photothermal infrared microspectroscopy to visualize the changes in cellulose substrates caused by oxidation by the ectomycorrhizal fungus Paxillus involutus at the interface between individual fungal hyphae and cellulose substrates.

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check.

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Integrated, Selective, Simultaneous Multigas Sensing Based on Nondispersive Infrared Spectroscopy-Type Photoacoustic Spectroscopy.

Most chemical sensing scenarios require the selective and simultaneous determination of the concentrations of multiple gas species. In order to enable large-scale monitoring, reliability, robustness, and the potential for integration and miniaturization are key parameters that next-generation sensing technologies must comply with. Due to their superior sensitivity and selectivity as compared to standard NDIR-type systems, photoacoustic NDIR-approaches offer a means for selective detection at much reduced system dimensions such that microintegration becomes feasible. This contribution presents an acoustic frequency multiplexing method to integrate sensing capabilities for the parallel analysis of multiple gases in a single device without loss in selectivity via sound frequency separation. The approach is demonstrated using mid-infrared light emitting diodes and a multigas photoacoustic detector to monitor some of the most important greenhouse gases: carbon dioxide and methane. The number of gas species the sensor concept is able to detect simultaneously can be expanded without increasing the size of the system or its complexity. Additionally, the results demonstrate that the integrated device features the same selectivity and sensitivity as the currently used single gas photoacoustic NDIR systems. Furthermore, the possibility of an extension to any number of gas species is argued.

Refining protein amide I spectrum simulations with simple yet effective electrostatic models for local wavenumbers and dipole derivative magnitudes.

Analysis of the amide I band of proteins is probably the most wide-spread application of bioanalytical infrared spectroscopy. Although highly desirable for a more detailed structural interpretation, a quantitative description of this absorption band is still difficult. This work optimized several electrostatic models with the aim to reproduce the effect of the protein environment on the intrinsic wavenumber of a local amide I oscillator. We considered the main secondary structures - α-helices, parallel and antiparallel ß-sheets - with a maximum of 21 amide groups. The models were based on the electric potential and/or the electric field component along the CO bond at up to four atoms in an amide group. They were bench-marked by comparison to Hessian matrices reconstructed from density functional theory calculations at the BPW91, 6-31G** level. The performance of the electrostatic models depended on the charge set used to calculate the electric field and potential. Gromos and DSSP charge sets, used in common force fields, were not optimal for the better performing models. A good compromise between performance and the stability of model parameters was achieved by a model that considered the electric field at the positions of the oxygen, nitrogen, and hydrogen atoms of the considered amide group. The model describes also some aspects of the local conformation effect and performs similar on its own as in combination with an explicit implementation of the local conformation effect. It is better than a combination of a local hydrogen bonding model with the local conformation effect. Even though the short-range hydrogen bonding model performs worse, it captures important aspects of the local wavenumber sensitivity to the molecular surroundings. We improved also the description of the coupling between local amide I oscillators by developing an electrostatic model for the dependency of the dipole derivative magnitude on the protein environment.