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.

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.

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.

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.

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.

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.

In search of the correlation between nanomechanical and biomolecular properties of prostate cancer cells with different metastatic potential.

Nanomechanical properties of living cells, as measured with atomic force microscopy (AFM), are increasingly recognized as criteria that differentiate normal and pathologically altered cells. Locally measured cell elastic properties, described by the parameter known as Young's modulus, are currently proposed as a new diagnostic parameter that can be used at the early stage of cancer detection. In this study, local mechanical properties of normal human prostate (RWPE-1) cells and a range of malignant (22Rv1) and metastatic prostate cells (LNCaP, Du145 and PC3) were investigated. It was found that non-malignant prostate cells are stiffer than cancer cells while the metastatic cells are much softer than malignant cells from the primary tumor site. Next, the biochemical properties of the cells were measured using confocal Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopies to reveal these cells' biochemical composition as malignant transformation proceeds. Nanomechanical and biochemical profiles of five different prostate cell lines were subsequently analyzed using partial least squares regression (PLSR) in order to identify which spectral features of the RS and FT-IR spectra correlate with the cell's elastic properties. The PLSR-based model could predict Young's modulus values based on both RS and FT-IR spectral information. These outcomes show not only that AFM, RS and FT-IR techniques can be used for discrimination between normal and cancer cells, but also that a linear correlation between mechanical response and biomolecular composition of the cells that undergo malignant transformation can be found. This knowledge broadens our understanding of how prostate cancer cells evolve thorough the multistep process of tumor pathogenesis.

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.

Lipid droplets in prostate cancer cells and effect of irradiation studied by Raman microspectroscopy.

Lipid droplets (LDs) are key organelles in cancer cells proliferation, growth, and response to stress. These nanometric structures can aggregate to reach the size of microns becoming important cell components. Although it is known that LDs contain various lipids, their chemical composition is still under investigation. Moreover, their function in cell's response to exogenous factors is also not fully understood. Raman spectroscopy, together with chemometrics, has been shown to be a powerful tool for analytical analyses of cancer cell components on the subcellular level. It provides the opportunity to analyse LDs in a label-free manner in live cells. In the current study, this method was applied to investigate LDs composition in untreated and irradiated with X-ray beams prostate cancer cells. Raman mapping technique proved lipids accumulation in PC-3 cells and allowed visualization of LDs spatial distribution in cytoplasm. A heterogeneous composition of LDs was revealed by detailed analysis of Raman spectra. Interestingly, PC-3 cells were found to accumulate either triacylglycerols or cholesteryl esters. Finally, effect of X-ray radiation on the cells was investigated using Raman spectroscopy and fluorescence staining. Significant influence of LDs in the process of cell response was confirmed and time dependence of this phenomenon was determined.

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.

Influence of denoising on classification results in the context of hyperspectral data: High Definition FT-IR imaging.

Owing to the high information content about the biochemical composition of the sample, the implementation of Fourier-Transform Infrared Spectroscopy (FT-IR) in the clinic is currently under investigation by many researchers. Cancer biology with the use of histopathological models is one of the most explored application areas. Most of the publications show sensitivity of the method to be above 90%, however, it is still often not enough for clinical standards. Robust denoising techniques with an optimized classification model allow to shorten the experimental acquisition times which still are a bottleneck for FT-IR translation into the clinic. The main premise of this work is to evaluate denoising impact on classification results using spectral techniques: Savitzky Golay (SG), Wavelets (WV), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF); and spatial techniques: Deep Neural Network (DNN), Median Filter. Using denoising methods, especially MNF and PCA, gave significant improvement of the classification and prediction results. Moreover, the increase in pixel level accuracy for High Definition data (1.1 µm projected pixel size) was found to be dependent on the complexity of the histopathological class and reached even 43-44% level, while core level increase reached around 28%. Moreover, we investigated the impact of denoising methods on the spectral input to better understand the mechanism of such large improvement. The results presented here highlight the benefits and the importance of proper denoising for classification purposes of FT-IR imaging data.

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.

Exploring subcellular responses of prostate cancer cells to X-ray exposure by Raman mapping.

Understanding the response of cancer cells to ionising radiation is a crucial step in modern radiotherapy. Raman microspectroscopy, together with Partial Least Squares Regression (PLSR) analysis has been shown to be a powerful tool for monitoring biochemical changes of irradiated cells on the subcellular level. However, to date, the majority of Raman studies have been performed using a single spectrum per cell, giving a limited view of the total biochemical response of the cell. In the current study, Raman mapping of the whole cell area was undertaken to ensure a more comprehensive understanding of the changes induced by X-ray radiation. On the basis of the collected Raman spectral maps, PLSR models were constructed to elucidate the time-dependent evolution of chemical changes induced in cells by irradiation, and the performance of PLSR models based on whole cell averages as compared to those based on average Raman spectra of cytoplasm and nuclear region. On the other hand, prediction of X-ray doses for individual cellular components showed that cytoplasmic and nuclear regions should be analysed separately. Finally, the advantage of the mapping technique over single point measurements was verified by a comparison of the corresponding PLSR models.

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 challenging2,3. 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.

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.

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.