Electron spin resonance coupled with uranium-series dating (ESR/U-series) of carbonate hydroxyapatite in tooth enamel is the main technique used to obtain age determinations from Pleistocene fossils beyond the range of radiocarbon dating. This chronological information allows to better understand diachronic change in the palaeontological record, especially with regard to the evolution of the genus Homo. Given the relative paucity of human teeth at palaeontological and archaeological localities, ESR/U-series is widely applied to the teeth of ungulate species. However, the accuracy of ESR/U-series ages is greatly affected by the incorporation of uranium in the enamel during burial in sediments. It has been shown that uranium content is positively correlated with an increased degree of atomic order in carbonate hydroxyapatite crystals, the latter determined using infrared spectroscopy. Here we present a reference infrared spectral library of tooth enamel from African ungulates, based on the grinding curve method, which serves as baseline to track the diagenetic history of carbonate hydroxyapatite in different species and thus select the best-preserved specimens for dating.

The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980-1600 cm-1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common form of liver cancer, with cirrhosis being a major risk factor. Traditional blood markers like alpha-fetoprotein (AFP) demonstrate limited efficacy in distinguishing between HCC and cirrhosis, underscoring the need for more effective diagnostic methodologies. In this context, extracellular vesicles (EVs) have emerged as promising candidates; however, their practical diagnostic application is restricted by the current lack of label-free methods to accurately profile their molecular content. To address this gap, our study explores the potential of mid-infrared (mid-IR) spectroscopy, both alone and in combination with plasmonic nanostructures, to detect and characterize circulating EVs.
RESULTS: EVs were extracted from HCC and cirrhotic patients. Mid-IR spectroscopy in the Attenuated Total Reflection (ATR) mode was utilized to identify potential signatures for patient classification, highlighting significant changes in the Amide I-II region (1475-1700 cm-1). This signature demonstrated diagnostic performance comparable to AFP and surpassed it when the two markers were combined. Further investigations utilized a plasmonic metasurface suitable for ultrasensitive spectroscopy within this spectral range. This device consists of two sets of parallel rod-shaped gold nanoantennas (NAs); the longer NAs produced an intense near-field amplification in the Amide I-II bands, while the shorter NAs were utilized to provide a sharp reflectivity edge at 1800-2200 cm-1 for EV mass-sensing. A clinically relevant subpopulation of EVs was targeted by conjugating NAs with an antibody specific to Epithelial Cell Adhesion Molecule (EpCAM). This methodology enabled the detection of variations in the quantity of EpCAM-presenting EVs and revealed changes in the Amide I-II lineshape.
CONCLUSIONS: The presented results can positively impact the development of novel laboratory methods for the label-free characterization of EVs, based on the combination between mid-IR spectroscopy and plasmonics. Additionally, data obtained by using HCC and cirrhotic subjects as a model system, suggest that this approach could be adapted for monitoring these conditions.

The dynamics of lysozyme is probed by attaching -SCN to all alanine residues. The one-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption of 4 cm-1, which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution 12C → 13C leads to a redshift by -47 cm-1, which agrees quantitatively with experiments for CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contains label-specific information in the difference spectra when compared with the wild type protein. Depending on the position of the labels, local structural changes are observed. For example, introducing the -SCN label at Ala129 leads to breaking of the α-helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labeled alanine residues as a function of time can be related to the reorientation of the label. It is concluded that -SCN is potentially useful for probing protein dynamics, both in the high-frequency part (CN-stretch) and in the far-infrared part of the spectrum.

Vibrational spectroscopy of protein structure often utilizes 13C18O-labeling of backbone carbonyls to further increase structural resolution. However, sidechains such as arginine, aspartate, and glutamate absorb within the same spectral region, complicating the analysis of isotope-labeled peaks. In this study, we report that the waiting time between pump and probe pulses in two-dimensional infrared spectroscopy can be used to suppress sidechain modes in favor of backbone amide I\' modes based on differences in vibrational lifetimes. Furthermore, differences in the lifetimes of 13C18O-amide I\' modes can aid in the assignment of secondary structure for labeled residues. Using model disordered and β-sheet peptides, it was determined that while β-sheets exhibit a longer lifetime than disordered structures, amide I\' modes in both secondary structures exhibit longer lifetimes than sidechain modes. Overall, this work demonstrates that collecting 2D IR data at delayed waiting times, based on differences in vibrational lifetime between modes, can be used to effectively suppress interfering sidechain modes and further identify secondary structures.

Glycans are oligosaccharides attached to proteins or lipids and affect their functions, such as drug efficacy, structural contribution, metabolism, immunogenicity, and molecular recognition. Conventional glycosylation analysis has relied on destructive, slow, system-sensitive methods, including enzymatic reactions, chromatography, fluorescence labeling, and mass spectrometry. Herein, we propose quantum cascade laser (QCL) infrared (IR) spectroscopy as a rapid, nondestructive method to quantify glycans and their monosaccharide composition. Previously, we demonstrated high-sensitivity IR spectroscopy of protein solution using solvent absorption compensation (SAC) and double-beam modulation (DBM) techniques. However, the SAC-DBM approach suffered a limited frequency scanning range (<400 cm-1) due to the light dispersion by acousto-optic modulators (AOMs). Here, we implemented a mirror-based double-pass AOM in the SAC-DBM scheme and successfully extended the frequency range to (970 to 1840 cm-1), which encompasses the vibrational fingerprint of biomolecules. The extended frequency range allowed the simultaneous observation of monosaccharide ring bands (1000 to 1200 cm-1) and protein amide bands (1500 to 1700 cm-1). We compared the IR spectra of six glycoproteins and two nonglycosylated proteins with the results from intact mass spectrometry. The IR absorbance ratios of the ring band to the amide band of glycoproteins in solutions showed a linear correlation with the ratios of glycan to protein backbone masses. Furthermore, a multivariate analysis produced monosaccharide compositions consistent with the reported database for the glycoproteins, and the monosaccharide compositions were used to improve the predictability of the glycan-protein mass ratio from the IR-absorbance ratio. This nondestructive, high-sensitivity QCL-IR spectroscopy could be used as a standard method to monitor batch-to-batch comparability during drug manufacturing and quantify the glycosylation and monosaccharide composition of new glycoproteins and other glycosylated biosystems.

In this perspective, gas-phase studies of group 1 monocations and group 12 dications with amino acids and small peptides are highlighted. Although the focus is on two experimental techniques, threshold collision-induced dissociation and infrared multiple photon dissociation action spectroscopy, these methods as well as complementary approaches are summarized. The synergistic interplay with theory, made particularly powerful by the small sizes of the systems explored and the absence of solvent and support, is also elucidated. Importantly, these gas-phase methods permit quantitative insight into the structures and thermodynamics of metal cations interacting with biological molecules. Periodic trends in how these interactions vary as the metal cations get heavier are discussed as are quantitative trends with changes in the amino acid side chain and effects of hydration. Such trends allow these results to transcend the limitations associated with the biomimetic model systems.

In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm-1 and 1285 cm-1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist\'s interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.

BACKGROUND: Inclusion bodies (IBs) are well-known subcellular structures in bacteria where protein aggregates are collected. Various methods have probed their structure, but single-cell spectroscopy remains challenging. Atomic Force Microscopy-based Infrared Spectroscopy (AFM-IR) is a novel technology with high potential for the characterisation of biomaterials such as IBs.
RESULTS: We present a detailed investigation using AFM-IR, revealing the substructure of IBs and their variation at the single-cell level, including a rigorous optimisation of data collection parameters and addressing issues such as laser power, pulse frequency, and sample drift. An analysis pipeline was developed tailored to AFM-IR image data, allowing high-throughput, label-free imaging of more than 3500 IBs in 12,000 bacterial cells. We examined IBs generated in Escherichia coli under different stress conditions. Dimensionality reduction analysis of the resulting spectra suggested distinct clustering of stress conditions, aligning with the nature and severity of the applied stresses. Correlation analyses revealed intricate relationships between the physical and morphological properties of IBs.
CONCLUSIONS: Our study highlights the power and limitations of AFM-IR, revealing structural heterogeneity within and between IBs. We show that it is possible to perform quantitative analyses of AFM-IR maps over a large collection of different samples and determine how to control for various technical artefacts.

Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested by seabirds and other marine fauna, including methods for identifying different types of plastic. This study presents a robust method for the rapid, reliable chemical characterisation of ingested plastics in the 1-50 mm size range using infrared and Raman spectroscopy. We analysed 246 objects ingested by Flesh-footed Shearwaters (Ardenna carneipes) from Lord Howe Island, Australia, and compared the data yielded by each technique: 92 % of ingested objects visually identified as plastic were confirmed by spectroscopy, 98 % of those were low density polymers such as polyethylene, polypropylene, or their copolymers. Ingested plastics exhibit significant spectral evidence of biological contamination compared to other reports, which hinders identification by conventional library searching. Machine learning can be used to identify ingested plastics by their vibrational spectra with up to 93 % accuracy. Overall, we find that infrared is the more effective technique for identifying ingested plastics in this size range, and that appropriately trained machine learning models can be superior to conventional library searching methods for identifying plastics.