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.

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.

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.

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.

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.

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.

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.

Mouse single oocyte imaging by MALDI-TOF MS for lipidomics.

Reproductive cells are a very special kind of material for the analysis. Depending on the species, their dimensions allow for the application of mass spectrometry imaging-based techniques to receive a reasonable data for interpretation of their condition without any additional sample preparation steps, except for typical sample preparation characteristic for IMS protocols. A comparison between lipid profiles of oocytes could answer the question of the overall quality of the cells in the function of time or conditions of storage. Even tiny differences in the lipid profiles, but still detectable by bioinformatic analysis, could be crucial for the estimation of the conditions of the cells in various stages of development or aging. In our study, MALDI-TOF/TOF MSI was used to analyze and visualize the single oocytes. We deposited the cells on the transparent indium-tin-oxide (ITO) glass and marked their positions, which allowed for the fast localization of the cells and precise laser targeting in the ion source. We also optimized the usage of different MALDI matrices and different approaches. The proposed way of measurement allows analyzing quite a significant quantity of oocytes in a reasonably short time. During the analysis, the lipid composition of the single cell was successfully estimated in a conventional usage of the MALDI ion source, and the localization of lipids was confirmed by imaging mass spectrometry (IMS) analysis. The observed quantity of the lipids allowed for the application of the LIFT™ technique to obtain MS/MS spectra sufficient for lipids' unambiguous identification. We hope that our idea of the oocyte analysis will help to elucidate chemical changes that accompany different processes in which oocytes are involved. There could be such fascinating phenomena as the oocyte maturation, changes in the lipid components during their storage, and much more.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.