High-definition FT-IR reveals a synergistic effect on lipid accumulation in prostate cancer cells induced by a combination of X-rays and radiosensitizing drugs

Radiotherapy is one of the most commonly used cancer therapies with many benefits including low toxicity to healthy tissues. However, a major problem in radiotherapy is cancer radioresistance. To enhance the effect of this kind of therapy several approaches have been proposed such as the use of radiosensitizers. A combined treatment of radiotherapy and radiosensitizing drugs leads to a greater effect on cancer cells than anticipated from the addition of both responses (synergism). In this study, high-definition FT-IR imaging was applied to follow lipid accumulation in prostate cancer cells as a response to X-ray irradiation, radiosensitizing drugs, and a combined treatment of X-rays and the drugs. Lipid accumulation induced in the cells by an increasing X-ray dose and the presence of the drugs was analyzed using Principal Component Analysis and lipid staining. Finally, the synergistic effect of the combined therapy (X-rays and radiosensitizers) was confirmed by calculations of the integral intensity of the 2850 cm-1 band.

Pancreatic intraepithelial neoplasia detection and duct pathology grading using FT-IR imaging and machine learning

Pancreatic intraepithelial neoplasia (PanIN) is manifested by noninvasive lesions in the epithelium of smaller pancreatic ducts. Generally, cancer development risk from low-grade PanIN is minor, whereas, invasive pancreatic ductal adenocarcinoma (PDAC) development is highly related to high-grade PanINs. Therefore, in the case of high-grade PanIN detection, additional surgical resection may be recommended. However, even the low-grade PanINs can indicate possible progression to PDAC. The definition of PanIN is constantly changing and there is a need for new tools to better characterize and understand its behavior. We have recently developed a comprehensive pancreatic cancer classification model with biopsies collected from over 600 biopsies from 250 patients. Here, we take the next step and employ Infrared (IR) spectroscopy to build the first classification model for PanINs detection. Furthermore, we created a Partial Least Squares Regression (PLSR) model to characterize ducts from benign to cancerous. This model was then used to predict and grade PanINs accordingly to their malignancy level.

Fast cancer imaging in pancreatic biopsies using infrared imaging

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy

Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.

Comprehensive Histopathology Imaging in Pancreatic Biopsies: High Definition Infrared Imaging with Machine Learning Approach

Infrared (IR) based histopathology offers a new paradigm in looking at tissues and can provide a complimentary information source for more classical histopathology, which makes it a noteworthy tool given possible clinical application. This study aims to build a robust, pixel level machine learning model for pancreatic cancer detection using IR imaging. In this article, we report a pancreatic cancer classification model based on data from over 600 biopsies (coming from 250 patients) imaged with IR diffraction-limited spatial resolution. To fully research model's classification ability, we measured tissues using two optical setups, resulting in Standard and High Definitions data. This forms one of the largest IR datasets analyzed up to now, with almost 700 million spectra of different tissue types. The first six-class model created for comprehensive histopathology achieved pixel (tissue) level AUC values above 0.95, giving a successful technique for digital staining with biochemical information extracted from IR spectra.

Super-Resolved 3D Mapping of Molecular Orientation Using Vibrational Techniques

When a sample has an anisotropic structure, it is possible to obtain additional information controlling the polarization of incident light. With their straightforward instrumentation approaches, infrared (IR) and Raman spectroscopies are widely popular in this area. Single-band-based determination of molecular in-plane orientation, typically used in materials science, is here extended by the concurrent use of two vibration bands, revealing the orientational ordering in three dimension. The concurrent analysis was applied to IR spectromicroscopic data to obtain orientation angles of a model polycaprolactone spherulite sample. The applicability of this method spans from high-resolution, diffraction-limited Fourier transform infrared (FT-IR) and Raman imaging to super-resolved optical photothermal infrared (O-PTIR) imaging. Due to the nontomographic experimental approach, no image distortion is visible and nanometer scale orientation domains can be observed. Three-dimensional (3D) bond orientation maps enable in-depth characterization and consequently precise control of the sample’s physicochemical properties and functions.

Exploring subcellular responses of prostate cancer cells to clinical doses of X-rays by Raman microspectroscopy

Modern techniques of radiotherapy such as fractioned radiotherapy require applications of low doses of ionizing radiation (up to 10 Gy) for effective patient treatment. It is, therefore, crucial to understand the response mechanisms in cancer cells irradiated with low (clinical) doses. The cell's response to irradiation depends on a dose and post-irradiation time. Both factors should be considered when studying the influence of ionizing radiation on cancer cells. Thus, in the present study, PC-3 prostate cancer cells were irradiated with clinical doses of X-rays to determine dose- and time-dependent response to the irradiation. Raman spectroscopy and biological methods (MTT and comet assays) were applied for the analysis of biochemical changes in the cells induced by low doses of X-ray irradiation at 0 h and 24 h post-irradiation timepoints. Due to a limited view of the biochemical changes at the subcellular level given by single spectrum Raman measurements, Raman mapping of the whole cell area was performed. The results were compared with those obtained for cell irradiation with high doses. The analysis was based on the Partial Least Squares Regression (PLSR) method for the cytoplasmic and nuclear regions separately. Additionally, for the first time, irradiation classification was performed to confirm Raman spectroscopy as a powerful tool for studies on cancer cells treated with clinical doses of ionizing radiation.

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

Physicochemical damage and early-stage biological response to X-ray radiation studied in prostate cancer cells by Raman spectroscopy

Exposure to ionizing radiation significantly affects biochemistry of cancer cells. The effect of irradiation can be divided into two stages, that is, the physicochemical stage and the biological response. Both effects induce different biochemical changes in the cells and should be analyzed as two separate phenomena. Thus, in the current study, Raman spectroscopy of prostate cancer cells fixed before (the physicochemical damage model) and just after (the biological response model) irradiation was undertaken to compare biochemical composition of irradiated cancer cells at both stages. Spectroscopic analysis of the cells was performed separately for cytoplasmic and nuclear regions. Biochemical changes of irradiated cells were analyzed using partial least squares regression (PLSR) method on the basis of the collected Raman spectra. Regression coefficients were therefore used to describe differences and similarities between biochemical composition of cancer cells undergoing the physicochemical stage and biological response. Additionally, PLSR models of both phenomena were compared for linear dose-dependence and a cross prediction.

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms. Transflection mode of measurements has the potential to be translated into the clinic due to economic reasons of large-scale imaging with the need for inexpensive substrates. Unfortunately, in this mode spectral distortions originating from light interference appear. Due to this fact transmission measurement mode is more frequently used in pathology recognition. Nevertheless, this measurement mode also is not devoid of spectral distortion effects like scattering. However, this effect is better understood and there are preprocessing algorithms to minimize it. In this work, we investigated the influence of interference effects on spectral quality of pancreatic tissues measured in transmission and transflection mode with Fourier tranform IR (FT-IR) microscopy using samples embedded with and without paraffin. The removal of paraffin leads to an altered magnitude of interference in transflection and provides a platform for a detailed analysis of its effect on the spectra of biological material, since the same sample is measured with different interference conditions. Moreover, the potential of transflection mode measurements in histological classification of analyzed samples was investigated and compared with classification results for transmission mode.

Macromolecular Orientation in Biological Tissues Using a Four-Polarization Method in FT-IR Imaging

Fourier transform infrared spectroscopy has emerged as a powerful tool for tissue specimen investigation. Its nondestructive and label-free character enables direct determination of biochemical composition of samples. Furthermore, the introduction of polarization enriches this technique by the possibility of molecular orientation study apart from purely quantitative analysis. Most of the molecular orientation studies focused on polymer samples with a well-defined molecular axis. Here, a four-polarization approach for Herman’s in-plane orientation function and azimuthal angle determination was applied to a human tissue sample investigation for the first time. Attention was focused on fibrous tissues rich in collagen because of their cylindrical shape and established amide bond vibrations. Despite the fact that the tissue specimen contains a variety of molecules, the presented results of molecular ordering and orientation agree with the theoretical prediction based on sample composition and vibration directions.

Translation of an esophagus histopathological FT-IR imaging model to a fast quantum cascade laser modality

The technical progress in fast quantum cascade laser (QCL) microscopy offers a platform where chemical imaging becomes feasible for clinical diagnostics. QCL systems allow the integration of previously developed FT-IR-based pathology recognition models in a faster workflow. The translation of such models requires a systematic approach, focusing only on the spectral frequencies that carry crucial information for discrimination of pathologic features. In this study, we optimize an FT-IR-based histopathological method for esophageal cancer detection to work with a QCL system. We explore whether the classifier's performance is affected by paraffin presence from tissue blocks compared to removing it chemically. Working with paraffin-embedded samples reduces preprocessing time in the lab and allows samples to be archived after analysis. Moreover, we test, whether the creation of a QCL model requires a preestablished FTIR model or can be optimized using solely QCL measurements.

Comparison between high definition FT-IR, Raman and AFM-IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells

The family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones, especially in the case of demanding biological samples. Three vibrational spectroscopy techniques (high definition Fourier-transform infrared [FT-IR], Raman and atomic force microscopy infrared [AFM-IR]) were applied for subcellular chemical imaging of cholesteryl esters in PC-3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern and chemical information. All tested techniques were found to be useful in chemical imaging of cholesterol derivatives in cancer cells. The results obtained from FT-IR and Raman imaging showed to be comparable, whereas those achieved from AFM-IR study exhibited higher spectral heterogeneity. It confirms AFM-IR method as a powerful tool in local chemical imaging of cells at the nanoscale level. Furthermore, due to polarization effect, p-polarized AFM-IR spectra showed strong enhancement of lipid bands when compared to FT-IR.

Noise-free simulation of an FT-IR imaging hyperspectral dataset of pancreatic biopsy core bound by experiment

A noise-free hyperspectral FT-IR imaging dataset of a pancreatic tissue core was simulated based on experimental data that allows to test the performance of various data analysis and processing algorithms. A set of experimental noise levels was also added and used for denoising approaches comparison, which due to the noise-free reference signal enables to truly observe signal distortion caused by different approaches.

Nanoscale AFM-IR spectroscopic imaging of lipid heterogeneity and effect of irradiation in prostate cancer cells

The recent development of the AFM-IR technique, which combines nanoscale imaging with chemical contrast through infrared spectroscopy, opened up new fields for exploration, which were out of reach for other modalities, e.g. Raman spectroscopy. Lipid droplets (LDs) are key organelles, which are associated with stress response mechanisms in cells and their size falls into that niche. LDs composition is heterogeneous and varies depending on cancer cell type and the tumor microenvironment. Prostate cancer cells show a unique lipid metabolism manifested by an increased requirement for lipid accumulation in cytosolic LDs. In the current work, AFM-IR nanoimaging was undertaken to analyze lipids in untreated and x-ray irradiated PC-3 prostate cancer cells. Cells poor in LDs showed slightly increased lipid signal in cytoplasm close to the nucleus. On the other hand, high lipid signal coming from LDs accumulation could be found in any part of the cytoplasmic region. The observed behavior was found to be independent from irradiation and its dose. According to the band assignment of the collected AFM-IR spectra, the main components of LDs were assigned to cholesteryl esters. The size of LDs present in cells poor in lipids was found to be of less than 1 μm, whereas LDs aggregates spread out over a few microns. Analysis of AFM-IR spectra shows relative homogeneity of LDs composition in single cells and heterogeneity of LDs content within the PC-3 cell population.

Bulk Protein and Oil Prediction in Soybeans Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy

Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15 ± 0.04 (R²= 0.87) and 0.80 ± 0.02 (R²= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to threefold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ∼5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models; using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.

An ultra-stable gold-coordinated protein cage displaying reversible assembly

Symmetrical protein cages have evolved to fulfil diverse roles in nature, including compartmentalization and cargo delivery¹, and have inspired synthetic biologists to create novel protein assemblies via the precise manipulation of protein-protein interfaces. Despite the impressive array of protein cages produced in the laboratory, the design of inducible assemblies remains challenging^2,3. Here we demonstrate an ultra-stable artificial protein cage, the assembly and disassembly of which can be controlled by metal coordination at the protein-protein interfaces. The addition of a gold (I)-triphenylphosphine compound to a cysteine-substituted, 11-mer protein ring triggers supramolecular self-assembly, which generates monodisperse cage structures with masses greater than 2 MDa. The geometry of these structures is based on the Archimedean snub cube and is, to our knowledge, unprecedented. Cryo-electron microscopy confirms that the assemblies are held together by 120 S-Auⁱ-S staples between the protein oligomers, and exist in two chiral forms. The cage shows extreme chemical and thermal stability, yet it readily disassembles upon exposure to reducing agents. As well as gold, mercury(II) is also found to enable formation of the protein cage. This work establishes an approach for linking protein components into robust, higher-order structures, and expands the design space available for supramolecular assemblies to include previously unexplored geometries.

Denoising influence on discrete frequency classification results for quantum cascade laser based infrared microscopy

Currently, there is great interest in bringing the application of IR spectroscopy into the clinic. This however will require a significant reduction in measurement time as Fourier Transform Infrared (FT-IR) imaging takes hours to days to scan a clinically relevant specimen. A potential remedy for this issue is the use of Quantum Cascade Laser Infrared (QCL IR) microscopy performed in Discrete Frequency (DF) mode for maximum speed gain. This gain could be furthermore improved by applying a proper denoising algorithm that takes into account the specific data structure. We have recently compared spectral and spatial denoising techniques in the context of Fourier Transform IR (FT-IR) imaging and showed that the optimal methods depend heavily on the exact data structure. In general multivariate denoising methods such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) are the most effective for a dataset containing multiple bands. Histologic classification of QCL IR images of pancreatic tissue using Random Forest was therefore performed to investigate which denoising schemes are the most optimal for such experimental data structure. This work is the first to show the effects of denoising on classification accuracy of QCL data and is likely to be transferable to other QCL microscopes and other modalities using DF imaging, e.g. AFM-IR or CARS/SRS imaging.

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

The recent emergence of High Definition (HD) FT-IR and Quantum Cascade Laser (QCL) Microscopes elevated the IR imaging field very close to clinical timescales. However, the speed of acquisition and data quality are still the critical factors in reaching the clinic. Denoising offers aide in both aspects if performed properly. However, there is a lack of a direct comparison of the efficiency of denoising techniques in IR imaging in general. To achieve such comparison within a rigorous framework and obtaining the critical information about signal loss, a simulated dataset strongly bound by experimental parameters was created. Using experimental structural and spectral information and experimental noise levels data as an input for the simulation, a direct comparison of spatial (Fourier transform, Mean Filter, Weighted Mean Filter, Gauss Filter, Median Filter, spatial Wavelets and Deep Neural Networks) and spectral (Savitzky-Golay, Fourier transform, Principal Component Analysis, Minimum Noise Fraction and spectral Wavelets) denoising schemes was enabled. All of these techniques were compared on the simulated dataset, taking into account SNR gain, signal distortion and sensitivity to tuning parameters as comparison metrics. Later, the best techniques were applied to experimental data for validation. The results presented here clearly show the benefit of using hyperspectral denoising schemes such as PCA and MNF which outperform other methods.

Erythrocyte heme‑oxygenation status indicated as a risk factor in prehypertension by Raman spectroscopy

Raman spectroscopy of erythrocytes provides detailed information about the structure and status of heme moiety, which can be used to provide new insights into molecular pathogenesis of several diseases. In this study, we present the first Raman spectroscopy investigations of the effect of hemoglobin oxygenation in the context of hypertensive disease. The experimental data was subjected to Logistic Regression, which indicated heme‑oxygenation status as an important risk factor alongside other clinical parameters. The 1605/1621 cm^-1 band ratio was selected as an optimal Raman metric for risk assessment and along with other band ratios (1583, 1639, 1310 cm^-1) related to heme status and when combined with clinical data via logistic regression gave an Area Under the Curve (AUC) >0.95 for prehypertension risk prediction. The work demonstrates the feasibility of Raman spectroscopy to distinguish between prehypertensive and normotensive states. Simultaneously, it is implied that the etiology of the high blood pressure progression may be connected with the changes in hemoglobin oxygenation.

Rapid visualization of macromolecular orientation by discrete frequency mid-infrared spectroscopic imaging

Infrared (IR) spectroscopic imaging has been used to measure the composition and orientation of polymeric systems for decades. IR microscopy can provide detailed views of microscopic regions, allowing the observation of both morphology and molecular properties of a sample, but involves a trade-off between the spatial extent and details of molecular content. Here we describe an approximately two orders of magnitude faster approach to measure the spherulitic structure and molecular orientation in large semi-crystalline polymer samples compared to extant Fourier transform infrared (FT-IR) spectroscopic imaging. This discrete frequency approach utilizes individual narrowband emission lines of a quantum cascade laser (QCL) source to spectrally image large areas rapidly. The inherent polarization of the laser beam is employed to measure orientation, enabling calculation of Hermans in-plane orientation function along with molecular chain angles distribution.

Anti-atherosclerotic effects of pravastatin in brachiocephalic artery in comparison with en face aorta and aortic roots in ApoE/LDLR-/- mice

BACKGROUND

Cholesterol-dependent and independent mechanisms were proposed to explain anti-atherosclerotic action of statins in humans. However, their effects in murine models of atherosclerosis have not been consistently demonstrated. Here, we studied the effects of pravastatin on atherosclerosis in ApoE/LDLR^-/- mice fed a control and atherogenic diet.

METHODS

ApoE/LDLR^-/- mice were fed a control (CHOW) or an atherogenic (Low Carbohydrate High Protein, LCHP) diet. Two doses of pravastatin (40mg/kg and 100mg/kg) were used. The anti-atherosclerotic effects of pravastatin in en face aorta, cross-sections of aortic roots and brachiocephalic artery (BCA) were analysed. The lipid profile was determined. Fourier Transform Infrared Spectroscopy followed by Fuzzy C-Means (FCM) clustering was used for the quantitative assessment of plaque composition.

RESULTS

Treatment with pravastatin (100mg/kg) decreased total and LDL cholesterol only in the LCHP group, but displayed a pronounced anti-atherosclerotic effect in BCA and abdominal aorta. The anti-atherosclerotic effect of pravastatin (100mg/kg) in BCA was associated with significant alterations of the chemical plaque composition, including a fall in cholesterol and cholesterol esters contents independently on total cholesterol and LDL concentration in plasma.

CONCLUSIONS

Pravastatin at high (100mg/kg), but not low dose displayed a pronounced anti-atherosclerotic effect in ApoE/LDLR^-/- mice fed a CHOW or LCHP diet that was remarkable in BCA, visible in en face aorta, whereas it was not observed in aortic roots, suggesting that previous inconsistencies might have been due to the various sites of atherosclerotic plaque analysis.

Measuring and Predicting the Internal Structure of Semiconductor Nanocrystals through Raman Spectroscopy

Nanocrystals composed of mixed chemical domains have diverse properties that are driving their integration in next-generation electronics, light sources, and biosensors. However, the precise spatial distribution of elements within these particles is difficult to measure and control, yet profoundly impacts their quality and performance. Here we synthesized a unique series of 42 different quantum dot nanocrystals, composed of two chemical domains (CdS:CdSe), arranged in 7 alloy and (core)shell structural classes. Chemometric analyses of far-field Raman spectra accurately classified their internal structures from their vibrational signatures. These classifications provide direct insight into the elemental arrangement of the alloy as well as an independent prediction of fluorescence quantum yield. This nondestructive, rapid approach can be broadly applied to greatly enhance our capacity to measure, predict and monitor multicomponent nanomaterials for precise tuning of their structures and properties.

Micro-Attenuated Total Reflection Fourier Transform Infrared (Micro ATR FT-IR) Spectroscopic Imaging with Variable Angles of Incidence

The control of the angle of incidence in attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy allows for the probing of the sample at different depths of penetration of the evanescent wave. This approach has been recently coupled with macro-imaging capability using a diamond ATR accessory. In this paper, the design of optical apertures for the micro-germanium (Ge) ATR objective is presented for an FT-IR spectroscopic imaging microscope, allowing measurements with different angles of incidence. This approach provides the possibility of three-dimensional (3D) profiling in micro-ATR FT-IR imaging mode. The proof of principle results for measurements of polymer laminate samples at different angles of incidence confirm that controlling the depth of penetration is possible using a Ge ATR objective with added apertures.

Characterisation of Atherogenic Effects of Low Carbohydrate, High Protein Diet (LCHP) in ApoE/LDLR-/- Mice

INTRODUCTION

Low Carbohydrate High Protein diet represents a popular strategy to achieve weight loss.

OBJECTIVE

The aim of this study was to characterize effects of low carbohydrate, high protein diet (LCHP) on atherosclerotic plaque development in brachiocephalic artery (BCA) in apoE/LDLR-/- mice and to elucidate mechanisms of proatherogenic effects of LCHP diet.

MATERIALS AND METHODS

Atherosclerosis plaques in brachiocephalic artery (BCA) as well as in aortic roots, lipoprotein profile, inflammation biomarkers, expression of SREBP-1 in the liver as well as mortality were analyzed in Control diet (AIN-93G) or LCHP (Low Carbohydrate High Protein) diet fed mice.

RESULTS

Area of atherosclerotic plaques in aortic roots or BCA from LCHP diet fed mice was substantially increased as compared to mice fed control diet and was characterized by increased lipids and cholesterol contents (ORO staining, FT-IR analysis), increased macrophage infiltration (MOMA-2) and activity of MMPs (zymography). Pro-atherogenic phenotype of LCHP fed apoE/LDLR-/- mice was associated with increased plasma total cholesterol concentration, and in LDL and VLDL fractions, increased TG contents in VLDL, and a modest increase in plasma urea. LCHP diet increased SCD-1 index, activated SREBP-1 transcription factor in the liver and triggered acute phase response as evidence by an increased plasma concentration of haptoglobin, CRP or AGP. Finally, in long-term experiment survival of apoE/LDLR-/- mice fed LCHP diet was substantially reduced as compared to their counterparts fed control diet suggesting overall detrimental effects of LCHP diet on health.

CONCLUSIONS

The pro-atherogenic effect of LCHP diet in apoE/LDLR-/- mice is associated with profound increase in LDL and VLDL cholesterol, VLDL triglicerides, liver SREBP-1 upregulation, and systemic inflammation.

High definition infrared spectroscopic imaging for lymph node histopathology

Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.

Visualization of the biochemical markers of atherosclerotic plaque with the use of Raman, IR and AFM

In this work, we describe a methodology to visualize the biochemical markers of atherosclerotic plaque in cross sections of brachiocephalic arteries (BCA) taken from ApoE/LDLR(-/-) mice. The approach of the visualization of the same area of atherosclerotic plaque with the use of Raman, IR and AFM imaging enables the parallel characterisation of various features of atherosclerotic plaques. This support to the histochemical staining is utilized mainly in studies on mice models of atherosclerotic plaques, where micro and sub-micro resolutions are required. This work presents the methodology of the measurement and visualization of plaque features important for atherosclerosis development and plaques vulnerability analysis. Label-free imaging of cholesterol, cholesteryl esters, remodeled media, heme, internal elastic lamina, fibrous cap and calcification provides additional knowledge to previously presented quantitative measurements of average plaque features. AFM imaging enhanced the results obtained with the use of vibrational microspectroscopies with additional topographical information of the sample. To the best of our knowledge, this is the first work which demonstrates that co-localized measurement of atherosclerotic plaque with Raman, IR and AFM imaging provides a comprehensive insight into the biochemical markers of atherosclerotic plaques, and can be used as an integrated approach to assess vulnerability of the plaque.

Protein profile in vascular wall of atherosclerotic mice analyzed ex vivo using FT-IR spectroscopy

The structure of proteins in a tissue can undergo changes on account of disease state such as diabetes or atherosclerosis. In this work the protein profile in atherosclerotic tissue is monitored by FT-IR imaging coupled with Hierarchical Cluster Analysis (HCA). Additionally, a model for prediction of secondary structure of proteins content based on amide I and II range is used to show the distribution of analyzed proteins. A new protein class emerged in atherosclerotic tissue in the region of the plaque and additionally the plaque was found to be strongly mixed with smooth muscle cell. The calculated secondary structure contents of proteins in atherosclerotic tissue in comparison to healthy tissue showed an increase of structures related to beta-sheet (E and T) and a decrease of helical (H) and unassigned arrangements.

Imaging of lipids in atherosclerotic lesion in aorta from ApoE/LDLR-/- mice by FT-IR spectroscopy and Hierarchical Cluster Analysis

Spectroscopy-based approaches can provide an insight into the biochemical composition of a tissue sample. In the present work Fourier transform infrared (FT-IR) spectroscopy was used to develop a reliable methodology to study the content of free fatty acids, triglycerides, cholesteryl esters as well as cholesterol in aorta from mice with atherosclerosis (ApoE/LDLR(-/-) mice). In particular, distribution and concentration of palmitic, oleic and linoleic acid derivatives were analyzed. Spectral analysis of pure compounds allowed for clear discrimination between free fatty acids and other similar moieties based on the carbonyl band position (1699-1710 cm(-1) range). In order to distinguish cholesteryl esters from triglycerides a ratio of carbonyl band to signal at 1010 cm(-1) was used. Imaging of lipids in atherosclerotic aortic lesions in ApoE/LDLR(-/-) mice was followed by Hierarchical Cluster Analysis (HCA). The aorta from C57Bl/6J control mice (fed with chow diet) was used for comparison. The measurements were completed with an FT-IR spectrometer equipped with a 128 × 128 FPA detector. In cross-section of aorta from ApoE/LDLR(-/-) mice a region of atherosclerotic plaque was clearly identified by HCA, which was later divided into 2 sub-regions, one characterized by the higher content of cholesterol, while the other by higher contents of cholesteryl esters. HCA of tissues deposited on normal microscopic glass, hence limited to the 2200-3800 cm(-1) spectral range, also identified a region of atherosclerotic plaque. Importantly, this region correlates with the area stained by standard histological staining for atherosclerotic plaque (Oil Red O). In conclusion, the use of FT-IR and HCA may provide a novel tool for qualitative and quantitative analysis of contents and distribution of lipids in atherosclerotic plaque.