Correction: Vibrational dynamics and solvatochromism of the label SCN in various solvents and hemoglobin by time dependent IR and 2D-IR spectroscopy

Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model

Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine-induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Diagnosis of Ischemic Renal Failure Using Surface-Enhanced Raman Spectroscopy and a Machine Learning Algorithm

To diagnose renal function using a biochip capable of detecting SERS and to assess Raman measurements taken from a bilateral renal ischemia model and the feasibility of early diagnosis was done. After generating a bilateral renal ischemia rat model, blood and urine were collected. After confirming the presence of renal injury and function, liquid drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods. SERS biomarkers that diffused into the nanogaps were selectively amplified. Raman signals varied based on the severity of the renal function, and these differences were confirmed statistically. These results confirm that renal ischemia leads to renal dysfunction and that surface-enhanced Raman spectroscopy and a machine learning algorithm can be used to track signals in the urine from the release of SERS biomarkers.

Lessons from combined experimental and theoretical examination of the FTIR and 2D-IR spectroelectrochemistry of the amide I region of cytochrome c

Amide I difference spectroscopy is widely used to investigate protein function and structure changes. In this article, we show that the common approach of assigning features in amide I difference signals to distinct secondary structure elements in many cases may not be justified. Evidence comes from Fourier transform infrared (FTIR) and 2D-IR spectroelectrochemistry of the protein cytochrome c in the amide I range, in combination with computational spectroscopy based on molecular dynamics (MD) simulations. This combination reveals that each secondary structure unit, such as an alpha-helix or a beta-sheet, exhibits broad overlapping contributions, usually spanning a large part of the amide I region, which in the case of difference absorption experiments (such as in FTIR spectroelectrochemistry) may lead to intensity-compensating and even sign-changing contributions. We use cytochrome c as the test case, as this small electron-transferring redox-active protein contains different kinds of secondary structure units. Upon switching its redox-state, the protein exhibits a different charge distribution while largely retaining its structural scaffold. Our theoretical analysis suggests that the change in charge distribution contributes to the spectral changes and that structural changes are small. However, in order to confidently interpret FTIR amide I difference signals in cytochrome c and proteins in general, MD simulations in combination with additional experimental approaches such as isotope labeling, the insertion of infrared labels to selectively probe local structural elements will be required. In case these data are not available, a critical assessment of previous interpretations of protein amide I 1D- and 2D-IR difference spectroscopy data is warranted.

Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction

Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.

Exploring the 2D-IR repertoire of the -SCN label to study site-resolved dynamics and solvation in the calcium sensor protein calmodulin

The calcium sensor protein calmodulin is ubiquitous among eukaryotes. It translates intracellular Ca2+ influx (by a decrease of conformational flexibility) into increased target recognition affinity. Here we demonstrate that by using the IR reporter -SCN in combination with 2D-IR spectroscopy, global structure changes and local dynamics, degree of solvent exposure and protein-ligand interaction can be characterised in great detail. The long vibrational lifetime of the -SCN label allows for centerline slope analysis of the 2D-IR line shape up to 120 ps to deduce the frequency-frequency correlation function (FFCF) of the -SCN label in various states and label positions in the protein. Based on that we show clear differences between a solvent exposed site, the environment close to the Ca2+ binding motif and three highly conserved positions for ligand binding. Furthermore, we demonstrate how these dynamics are affected by conformational change induced by the addition of Ca2+ ions and by interaction with a short helical peptide mimicking protein binding. We show that the binding mode is strongly heterogeneous among the probed key binding methionine residues. SCN's vibrational relaxation is dominated by intermolecular contributions. Changes in the vibrational lifetime upon changing between H2O and D2O buffer therefore provide a robust measure for water accessibility of the label. Characterising -SCN's extinction coefficient, vibrational lifetime in light and heavy water and its FFCF we demonstrate the vast potential it has as a label especially for nonlinear spectroscopies, such as 2D-IR spectroscopy.

Local dynamics of the photo-switchable protein PYP in ground and signalling state probed by 2D-IR spectroscopy of –SCN labels

We employ 2D-IR spectroscopy of the protein label –SCN to describe the local dynamics in the photo-switchable protein PYP in its dark state (pG) and after photoactivation, concomitant with vast structural rearrangements, in its signalling state (pB).

Vibrational Lifetime of the SCN Protein Label in H2O and D2O Reports Site-Specific Solvation and Structure Changes During PYP's Photocycle

The application of vibrational labels such as thiocyanate  (-S-C≡N) for studying protein structure and dynamics is thriving. Absorption spectroscopy is usually employed to obtain wavenumber and line shape of the label. An observable of great significance might be the vibrational lifetime, which can be obtained by pump probe or 2D-IR spectroscopy. Due to the insulating effect of the heavy sulfur atom in the case of the SCN label, the lifetime of the C≡N oscillator is expected to be particularly sensitive to its surrounding as it is not dominated by through-bond relaxation. We therefore investigate the vibrational lifetime of the SCN label at various positions in the blue light sensor protein Photoactive Yellow Protein (PYP) in the ground state and signaling state of the photoreceptor. We find that the vibrational lifetime of the C≡N stretching mode is strongly affected both by its protein environment and by the degree of exposure to the solvent. Even for label positions where the line shape and wavenumber observed by FTIR are barely changing upon activation of the photoreceptor, we find that the lifetime can change considerably. To obtain an unambiguous measure for the solvent exposure of the labeled site, we show that it is imperative to compare the lifetimes in H₂O and D₂O. Importantly, the lifetimes shorten in H₂O as compared to D₂O for water exposed labels, while they stay largely the same for buried labels. We quantify this effect by defining a solvent exclusion coefficient (SEC). The response of the label's vibrational lifetime to its solvent exposure renders it a suitable universal probe for protein investigations. This applies even to systems that are otherwise hard to address, such as transient or short-lived states, which could be created during a protein's working cycle (as here in PYP) or during protein folding. It is also applicable to flexible systems (intrinsically disordered proteins), protein-protein and protein-membrane interactions.

Following local light-induced structure changes and dynamics of the photoreceptor PYP with the thiocyanate IR label

Photoactive Yellow Protein (PYP) is a bacterial blue light receptor that enters a photocycle after excitation. The intermediate states are formed on time scales ranging from femtoseconds up to hundreds of milliseconds, after which the signaling state with a lifetime of about 1 s is reached. To investigate structural changes and dynamics, we incorporated the SCN IR label at distinct positions of the photoreceptor via cysteine mutation and cyanylation. FT-IR measurements of the SCN label at different sites of the well-established dark state structure of PYP characterized the spectral response of the label to differences in the environment. Under constant blue light irradiation, we observed the formation of the signaling state with significant changes of wavenumber and lineshape of the SCN bands. Thereby we deduced light-induced structural changes in the local environment of the labels. These results were supported by molecular dynamics simulations on PYP providing the solvent accessible surface area (SASA) at the different positions. To follow protein dynamics via the SCN label during the photocycle, we performed step-scan FT-IR measurements with a time resolution of 10 μs. Global analysis yielded similar time constants of τ1 = 70 μs, τ2 = 640 μs, and τ3 > 20 ms for the wild type and τ1 = 36 μs, τ2 = 530 μs, and τ3 > 20 ms for the SCN-labeled mutant PYP-A44C*, a mutant which provided a sufficiently large SCN difference signal to measure step-scan FT-IR spectra. In comparison to the protein (amide, E46) and chromophore bands the dynamics of the SCN label show a different behavior. This result indicates that the local kinetics sensed by the label are different from the global protein kinetics.

Cyano-tryptophans as dual infrared and fluorescence spectroscopic labels to assess structural dynamics in proteins

By moving the cyano group position on the indole ring, both artificial amino acids report differently to their microscopic environment.