Fundamental developments in infrared spectroscopic imaging for biomedical applications

3, 5-Dihydroxy 4', 7-dimethoxyflavone-DNA interaction study for nucleic acid detection and differential cell staining

The present study is focused on application of a natural compound, 3, 5-dihydroxy 4', 7-dimethoxyflavone (DHDM) from a medicinal plant Alpinia nigra for nucleic acid detection and differential cell staining. DHDM was found to interact with nucleic acid and forms complex, which was investigated for various applications. It was successfully utilized to visualize plasmid, genomic, and ds-linear DNA in agarose gel electrophoresis without affecting the DNA mobility in the gel. Fluorescence of DHDM increased several fold upon binding to dsDNA. Photostability of the compound was assessed and showed photobleaching effect that decreased gradually over time. Application of the compound was further extended to differential cell staining. When observed in fluorescence microscope, DHDM stained the dead cells and differentiated them from live cells in the case of bacterial, yeast, and mammalian cells. Higher concentration of the compound was found to be less cytotoxic to cancerous cells. Nucleic acid staining dyes like Ethidium bromide (EtBr), Propidium iodide (PI), etc. are carcinogens and environmental pollutants and therefore DHDM a natural compound, is a major benefit and thus can serve as an alternative to the current dyes.

Wide-field mid-infrared hyperspectral imaging beyond video rate

Mid-infrared hyperspectral imaging has become an indispensable tool to spatially resolve chemical information in a wide variety of samples. However, acquiring three-dimensional data cubes is typically time-consuming due to the limited speed of raster scanning or wavelength tuning, which impedes real-time visualization with high spatial definition across broad spectral bands. Here, we devise and implement a high-speed, wide-field mid-infrared hyperspectral imaging system relying on broadband parametric upconversion of high-brightness supercontinuum illumination at the Fourier plane. The upconverted replica is spectrally decomposed by a rapid acousto-optic tunable filter, which records high-definition monochromatic images at a frame rate of 10 kHz based on a megapixel silicon camera. Consequently, the hyperspectral imager allows us to acquire 100 spectral bands over 2600-4085 cm-1 in 10 ms, corresponding to a refreshing rate of 100 Hz. Moreover, the angular dependence of phase matching in the image upconversion is leveraged to realize snapshot operation with spatial multiplexing for multiple spectral channels, which may further boost the spectral imaging rate. The high acquisition rate, wide-field operation, and broadband spectral coverage could open new possibilities for high-throughput characterization of transient processes in material and life sciences.

Tissue discrimination in head and neck cancer using image fusion of IR and optical microscopy

A regression-based fusion algorithm has been used to merge hyperspectral Fourier transform infrared (FTIR) data with an H&E image of oral squamous cell carcinoma metastases in cervical lymphoid nodal tissue. This provides insight into the success of the ratio of FTIR absorbances at 1252 cm-1 and 1285 cm-1 in discriminating between these tissue types. The success is due to absorbances at these two wavenumbers being dominated by contributions from DNA and collagen, respectively. A pixel-by-pixel fit of the fused spectra to the FTIR spectra of collagen, DNA and cytokeratin reveals the contributions of these molecules to the tissue at high spatial resolution.

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.

Room Temperature Broadband Bi2Te3/PbS Colloidal Quantum Dots Infrared Photodetectors

Lead sulfide colloidal quantum dots (PbS CQDs) are promising optoelectronic materials due to their unique properties, such as tunable band gap and strong absorption, which are of immense interest for application in photodetectors and solar cells. However, the tunable band gap of PbS CQDs would only cover visible short-wave infrared; the ability to detect longer wavelengths, such as mid- and long-wave infrared, is limited because they are restricted by the band gap of the bulk material. In this paper, a novel photodetector based on the synergistic effect of PbS CQDs and bismuth telluride (Bi₂Te₃) was developed for the detection of a mid-wave infrared band at room temperature. The device demonstrated good performance in the visible-near infrared band (i.e., between 660 and 850 nm) with detectivity of 1.6 × 10¹⁰ Jones at room temperature. It also exhibited photoelectric response in the mid-wave infrared band (i.e., between 4.6 and 5.1 μm). The facile fabrication process and excellent performance (with a response of up to 5.1 μm) of the hybrid Bi₂Te₃/PbS CQDS photodetector are highly attractive for many important applications that require high sensitivity and broadband light detection.

Compact and ultracompact spectral imagers: technology and applications in biomedical imaging

Significance

Spectral imaging, which includes hyperspectral and multispectral imaging, can provide images in numerous wavelength bands within and beyond the visible light spectrum. Emerging technologies that enable compact, portable spectral imaging cameras can facilitate new applications in biomedical imaging.

Aim

With this review paper, researchers will (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications.

Approach

We performed a comprehensive literature review in three databases (Scopus, PubMed, and Web of Science). We included only fully realized systems with definable dimensions. To best accommodate many different definitions of "compact," we included a table of dimensions and weights for systems that met our definition.

Results

There is a wide variety of contributions from industry, academic, and hobbyist spaces. A variety of new engineering approaches, such as Fabry-Perot interferometers, spectrally resolved detector array (mosaic array), microelectro-mechanical systems, 3D printing, light-emitting diodes, and smartphones, were used in the construction of compact spectral imaging cameras. In bioimaging applications, these compact devices were used for in vivo and ex vivo diagnosis and surgical settings.

Conclusions

Compact and ultracompact spectral imagers are the future of spectral imaging systems. Researchers in the bioimaging fields are building systems that are low-cost, fast in acquisition time, and mobile enough to be handheld.

Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images

Tissue structures, phenotypes, and pathology are routinely investigated based on histology. This includes chemically staining the transparent tissue sections to make them visible to the human eye. Although chemical staining is fast and routine, it permanently alters the tissue and often consumes hazardous reagents. On the other hand, on using adjacent tissue sections for combined measurements, the cell-wise resolution is lost owing to sections representing different parts of the tissue. Hence, techniques providing visual information of the basic tissue structure enabling additional measurements from the exact same tissue section are required. Here we tested unstained tissue imaging for the development of computational hematoxylin and eosin (HE) staining. We used unsupervised deep learning (CycleGAN) and whole slide images of prostate tissue sections to compare the performance of imaging tissue in paraffin, as deparaffinized in air, and as deparaffinized in mounting medium with section thicknesses varying between 3 and 20 μm. We showed that although thicker sections increase the information content of tissue structures in the images, thinner sections generally perform better in providing information that can be reproduced in virtual staining. According to our results, tissue imaged in paraffin and as deparaffinized provides a good overall representation of the tissue for virtually HE-stained images. Further, using a pix2pix model, we showed that the reproduction of overall tissue histology can be clearly improved with image-to-image translation using supervised learning and pixel-wise ground truth. We also showed that virtual HE staining can be used for various tissues and used with both 20× and 40× imaging magnifications. Although the performance and methods of virtual staining need further development, our study provides evidence of the feasibility of whole slide unstained microscopy as a fast, cheap, and feasible approach to producing virtual staining of tissue histology while sparing the exact same tissue section ready for subsequent utilization with follow-up methods at single-cell resolution.

The Impact of Tissue Preparation on Salivary Gland Tumors Investigated by Fourier-Transform Infrared Microspectroscopy

Due to the wide variety of benign and malignant salivary gland tumors, classification and malignant behavior determination based on histomorphological criteria can be difficult and sometimes impossible. Spectroscopical procedures can acquire molecular biological information without destroying the tissue within the measurement processes. Since several tissue preparation procedures exist, our study investigated the impact of these preparations on the chemical composition of healthy and tumorous salivary gland tissue by Fourier-transform infrared (FTIR) microspectroscopy. Sequential tissue cross-sections were prepared from native, formalin-fixed and formalin-fixed paraffin-embedded (FFPE) tissue and analyzed. The FFPE cross-sections were dewaxed and remeasured. By using principal component analysis (PCA) combined with a discriminant analysis (DA), robust models for the distinction of sample preparations were built individually for each parotid tissue type. As a result, the PCA-DA model evaluation showed a high similarity between native and formalin-fixed tissues based on their chemical composition. Thus, formalin-fixed tissues are highly representative of the native samples and facilitate a transfer from scientific laboratory analysis into the clinical routine due to their robust nature. Furthermore, the dewaxing of the cross-sections entails the loss of molecular information. Our study successfully demonstrated how FTIR microspectroscopy can be used as a powerful tool within existing clinical workflows.

Prediction of malignant transformation in oral epithelial dysplasia using machine learning

A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

Prediction of malignant transformation in oral epithelial dysplasia using infrared absorbance spectra

Oral epithelial dysplasia (OED) is a histopathologically-defined, potentially premalignant condition of the oral cavity. The rate of transformation to frank carcinoma is relatively low (12% within 2 years) and prediction based on histopathological grade is unreliable, leading to both over- and under-treatment. Alternative approaches include infrared (IR) spectroscopy, which is able to classify cancerous and non-cancerous tissue in a number of cancers, including oral. The aim of this study was to explore the capability of FTIR (Fourier-transform IR) microscopy and machine learning as a means of predicting malignant transformation of OED. Supervised, retrospective analysis of longitudinally-collected OED biopsy samples from 17 patients with high risk OED lesions: 10 lesions transformed and 7 did not over a follow-up period of more than 3 years. FTIR spectra were collected from routine, unstained histopathological sections and machine learning used to predict malignant transformation, irrespective of OED classification. PCA-LDA (principal component analysis followed by linear discriminant analysis) provided evidence that the subsequent transforming status of these 17 lesions could be predicted from FTIR data with a sensitivity of 79 ± 5% and a specificity of 76 ± 5%. Six key wavenumbers were identified as most important in this classification. Although this pilot study used a small cohort, the strict inclusion criteria and classification based on known outcome, rather than OED grade, make this a novel study in the field of FTIR in oral cancer and support the clinical potential of this technology in the surveillance of OED.

Who's Who? Discrimination of Human Breast Cancer Cell Lines by Raman and FTIR Microspectroscopy

(1) Breast cancer is presently the leading cause of death in women worldwide. This study aims at identifying molecular biomarkers of cancer in human breast cancer cells, in order to differentiate highly aggressive triple-negative from non-triple-negative cancers, as well as distinct triple-negative subtypes, which is currently an unmet clinical need paramount for an improved patient care. (2) Raman and FTIR (Fourier transform infrared) microspectroscopy state-of-the-art techniques were applied, as highly sensitive, specific and non-invasive methods for probing heterogeneous biological samples such as human cells. (3) Particular biochemical features of malignancy were unveiled based on the cells' vibrational signature, upon principal component analysis of the data. This enabled discrimination between TNBC (triple-negative breast cancer) and non-TNBC, TNBC MSL (mesenchymal stem cell-like) and TNBC BL1 (basal-like 1) and TNBC BL1 highly metastatic and low-metastatic cell lines. This specific differentiation between distinct TNBC subtypes-mesenchymal from basal-like, and basal-like 1 with high-metastatic potential from basal-like 1 with low-metastatic potential-is a pioneer result, of potential high impact in cancer diagnosis and treatment.

Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique

A meta-analysis of various multivariate/Machine Learning (ML) classifiers trained on IR Micro-spectroscopy tissue datasets for cancer classification are directly compared using a calculated F1-Score metric alongside study pre-processing techniques.

Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging

BACKGROUND

Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition.

SUMMARY

This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology.

KEY MESSAGES

In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.

Study of the Spatio-Chemical Heterogeneity of Tannin-Furanic Foams: From 1D FTIR Spectroscopy to 3D FTIR Micro-Computed Tomography

Tannin-furanic rigid foams are bio-based copolymers of tannin plant extract and furfuryl alcohol, promising candidates to replace synthetic insulation foams, as for example polyurethanes and phenolics, in eco-sustainable buildings thanks to their functional properties, such as lightness of the material and fire resistance. Despite their relevance as environmental-friendly alternatives to petroleum derivatives, many aspects of the polymerization chemistry still remain unclear. One of the open issues is on the spatial heterogeneity of the foam, i.e., whether the foam constituents prevalently polymerize in spatially segregated blocks or distribute almost homogenously in the foam volume. To address this matter, here we propose a multiscale FTIR study encompassing 1D FTIR spectroscopy, 2D FTIR imaging and 3D FTIR micro-tomography (FTIR-μCT) on tannin-furanic rigid foams obtained by varying the synthesis parameters in a controlled way. Thanks to the implementation of the acquisition and processing pipeline of FTIR-μCT, we were able for the first time to demonstrate that the polymer formulations influence the spatial organization of the foam at the microscale and, at the same time, prove the reliability of FTIR-μCT data by comparing 2D FTIR images and the projection of the 3D chemical images on the same plane.

High-Performance Flexible InAs Thin-Film Photodetector Arrays with Heteroepitaxial Growth Using an Abruptly Graded InxAl1-xAs Buffer

Current infrared thermal image sensors are mainly based on planar firm substrates, but the rigid form factor appears to restrain the versatility of their applications. For wearable health monitoring and implanted biomedical sensing, transfer of active device layers onto a flexible substrate is required while controlling the high-quality crystalline interface. Here, we demonstrate high-detectivity flexible InAs thin-film mid-infrared photodetector arrays through high-yield wafer bonding and a heteroepitaxial lift-off process. An abruptly graded In_xAl_1-xAs (0.5 < x < 1) buffer was found to drastically improve the lift-off interface morphology and reduce the threading dislocation density twice, compared to the conventional linear grading method. Also, our flexible InAs photodetectors showed excellent optical performance with high mechanical robustness, a peak room-temperature specific detectivity of 1.21 × 10⁹ cm-Hz^1/2/W at 3.4 μm, and excellent device reliability. This flexible InAs photodetector enabled by the heteroepitaxial lift-off method shows promise for next-generation thermal image sensors.

Regional differences in clonal Japanese knotweed revealed by chemometrics-linked attenuated total reflection Fourier-transform infrared spectroscopy

BACKGROUND

Japanese knotweed (R. japonica var japonica) is one of the world's 100 worst invasive species, causing crop losses, damage to infrastructure, and erosion of ecosystem services. In the UK, this species is an all-female clone, which spreads by vegetative reproduction. Despite this genetic continuity, Japanese knotweed can colonise a wide variety of environmental habitats. However, little is known about the phenotypic plasticity responsible for the ability of Japanese knotweed to invade and thrive in such diverse habitats. We have used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, in which the spectral fingerprint generated allows subtle differences in composition to be clearly visualized, to examine regional differences in clonal Japanese knotweed.

RESULTS

We have shown distinct differences in the spectral fingerprint region (1800-900 cm^- 1) of Japanese knotweed from three different regions in the UK that were sufficient to successfully identify plants from different geographical regions with high accuracy using support vector machine (SVM) chemometrics.

CONCLUSIONS

These differences were not correlated with environmental variations between regions, raising the possibility that epigenetic modifications may contribute to the phenotypic plasticity responsible for the ability of R. japonica to invade and thrive in such diverse habitats.

Image fusion of IR and optical microscopy for mapping of biomolecules in tissue

It is shown that a pixel-level image fusion technique can produce images that combine the spatial resolution of optical microscopy images of haematoxylin and eosin (H&E) stained tissue with the chemical information in Fourier transform infrared (FTIR) images. The fused images show minimal distortion and the higher spatial resolution of the H&E images overcomes the diffraction limit on the spatial resolution of the FTIR images. A consideration of the FTIR spectra of nucleic acids and collagen can explain the changes in contrast between non-cancerous oral epithelium and underlying stroma within fused images formed by combining an H&E stain of oral tissue with FTIR images of the tissue obtained at a number of wavenumbers.

Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm⁻¹ and 1285 cm⁻¹. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 μm. The image formed from the ratio of the SNOM images obtained at 1252 cm⁻¹ and 1285 cm⁻¹ shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm⁻¹ signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 μm wide and the SNOM topography shows that the core centre is raised by ∼1 μm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 μm wide, roughly the width of two cancer cells.

A SNOM image (a) provides chemical insight into a metastatic tumour identified by H&E staining (b).

Developments and Ongoing Challenges for Analysis of Surface-Bound Proteins

Proteins at surfaces and interfaces play important roles in the function and performance of materials in applications ranging from diagnostic assays to biomedical devices. To improve the performance of these materials, detailed molecular structure (conformation and orientation) along with the identity and concentrations of the surface-bound proteins on those materials must be determined. This article describes radiolabeling, surface plasmon resonance, quartz crystal microbalance with dissipation, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, sum frequency generation spectroscopy, and computational techniques along with the information each technique provides for characterizing protein films. A multitechnique approach using both experimental and computation methods is required for these investigations. Although it is now possible to gain much insight into the structure of surface-bound proteins, it is still not possible to obtain the same level of structural detail about proteins on surfaces as can be obtained about proteins in crystals and solutions, especially for large, complex proteins. However, recent results have shown it is possible to obtain detailed structural information (e.g., backbone and side chain orientation) about small peptides (5-20 amino sequences) on surfaces. Current studies are extending these investigations to small proteins such as protein G B1 (∼6 kDa). Approaches for furthering the capabilities for characterizing the molecular structure of surface-bound proteins are proposed.

The Progress of Label-Free Optical Imaging in Alzheimer's Disease Screening and Diagnosis

As the major neurodegenerative disease of dementia, Alzheimer's disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer's disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for in vivo studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.

Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma

Therapeutic decisions in lung cancer critically depend on the determination of histologic types and oncogene mutations. Therefore, tumor samples are subjected to standard histologic and immunohistochemical analyses and examined for relevant mutations using comprehensive molecular diagnostics. In this study, an alternative diagnostic approach for automatic and label-free detection of mutations in lung adenocarcinoma tissue using quantum cascade laser-based infrared imaging is presented. For this purpose, a five-step supervised classification algorithm was developed, which was not only able to detect tissue types and tumor lesions, but also the tumor type and mutation status of adenocarcinomas. Tumor detection was verified on a data set of 214 patient samples with a specificity of 97% and a sensitivity of 95%. Furthermore, histology typing was verified on samples from 203 of the 214 patients with a specificity of 97% and a sensitivity of 94% for adenocarcinoma. The most frequently occurring mutations in adenocarcinoma (KRAS, EGFR, and TP53) were differentiated by this technique. Detection of mutations was verified in 60 patient samples from the data set with a sensitivity and specificity of 95% for each mutation. This demonstrates that quantum cascade laser infrared imaging can be used to analyze morphologic differences as well as molecular changes. Therefore, this single, one-step measurement provides comprehensive diagnostics of lung cancer histology types and most frequent mutations.

Perspectives on infrared spectroscopic imaging from cancer diagnostics to process analysis

This perspective paper discusses the recent and potential developments in the application of infrared spectroscopic imaging, with a focus on Fourier transform infrared (FTIR) spectroscopic imaging. The current state-of-the-art has been briefly reported, that includes recent trends and advances in applications of FTIR spectroscopic imaging to biomedical systems. Here, some new opportunities for research in the biomedical field, particularly for cancer diagnostics, and also in the engineering field of process analysis; as well as challenges in FTIR spectroscopic imaging are discussed. Current and future prospects that will bring spectroscopic imaging technologies to the frontier of advanced medical diagnostics and to process analytics in engineering applications will be outlined in this opinion paper.

Analysis of Fixed and Live Single Cells Using Optical Photothermal Infrared with Concomitant Raman Spectroscopy

This paper reports the first use of a novel completely optically based photothermal method (O-PTIR) for obtaining infrared spectra of both fixed and living cells using a quantum cascade laser (QCL) and optical parametric oscillator (OPO) laser as excitation sources, thus enabling all biologically relevant vibrations to be analyzed at submicron spatial resolution. In addition, infrared data acquisition is combined with concomitant Raman spectra from exactly the same excitation location, meaning the full vibrational profile of the cell can be obtained. The pancreatic cancer cell line MIA PaCa-2 and the breast cancer cell line MDA-MB-231 are used as model cells to demonstrate the capabilities of the new instrumentation. These combined modalities can be used to analyze subcellular structures in both fixed and, more importantly, live cells under aqueous conditions. We show that the protein secondary structure and lipid-rich bodies can be identified on the submicron scale.

Qualitative and quantitative vibrational spectroscopic analysis of macronutrients in breast milk

Raman and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy were used to analyze 208 breast milk samples as part of a larger research study. Comprehensive qualitative and quantitative analysis was carried out using chemometric methods: principal component analysis (PCA) and partial least squares (PLS) regression. The obtained information on the main macronutrients (protein, fat and carbohydrate) were primarily evaluated in relation to the available metadata of the samples, where study location and respective primary food sources revealed a stronger differentiation in fat composition than its absolute content. The limitations and challenges of using both spectroscopic techniques for the type of analysis are also highlighted.

Introduction to Infrared and Raman-Based Biomedical Molecular Imaging and Comparison with Other Modalities

Molecular imaging has rapidly developed to answer the need of image contrast in medical diagnostic imaging to go beyond morphological information to include functional differences in imaged tissues at the cellular and molecular levels. Vibrational (infrared (IR) and Raman) imaging has rapidly emerged among the molecular imaging modalities available, due to its label-free combination of high spatial resolution with chemical specificity. This article presents the physical basis of vibrational spectroscopy and imaging, followed by illustration of their preclinical in vitro applications in body fluids and cells, ex vivo tissues and in vivo small animals and ending with a brief discussion of their clinical translation. After comparing the advantages and disadvantages of IR/Raman imaging with the other main modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography/single-photon emission-computed tomography (PET/SPECT), ultrasound (US) and photoacoustic imaging (PAI), the design of multimodal probes combining vibrational imaging with other modalities is discussed, illustrated by some preclinical proof-of-concept examples.

Synchrotron Radiation-Based FTIR Microspectroscopic Imaging of Traumatically Injured Mouse Brain Tissue Slices

Traumatic brain injury (TBI) is a health problem of global concern because of its serious adverse effects on public health and social economy. A technique that can be used to precisely detect TBI is highly demanded. Here, we report on a synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopic imaging technique that can be exploited to identify TBI-induced injury by examining model mouse brain tissue slices. The samples were first examined by conventional histopathological techniques including hematoxylin and eosin (H&E) staining and 2,3,5-triphenyltetrazolium chloride staining and then spectroscopically imaged by SR-FTIR. SR-FTIR results show that the contents of protein and nucleic acid in the injured region are lower than their counterparts in the normal region. The injured and normal regions can be unambiguously distinguished from each other by the principle component analysis of the SR-FTIR spectral data corresponding to protein or nucleic acid. The images built from the spectral data of protein or nucleic acid clearly present the injured region of the brain tissue, which is in good agreement with the H&E staining image and optical image of the sample. Given the label-free and fingerprint features, the demonstrated method suggests potential application of SR-FTIR spectroscopic mapping for the digital and intelligent diagnosis of TBI by providing spatial and chemical information of the sample simultaneously.

Optical nanosensors for biofilm detection in the food industry: principles, applications and challenges

Biofilms are the universal lifestyle of bacteria enclosed in extracellular polymeric substances (EPS) on the contact surfaces of food processing facilities. The EPS-encapsulated foodborne bacterial pathogens are the main food contaminant sources, posing a serious threat to human health. The microcrystalline, sophisticated and dynamic biofilms necessitate the development of conventional microscopic imaging and spectral technology. Nanosensors, which can transfer the biochemical information into optical signals, have recently emerged for biofilm optical detection with high sensitivity and high spatial resolution at nanoscale scopes. Therefore, the aim of this review is to clarify the main detection scope in biofilms and the detection principles of optical nanosensors arousing Raman enhancement, fluoresce conversion and color change. The difficulties and challenges of biofilm characterization including the secretion and variation of main biochemical components are first discussed, the details about the principles and application examples of bioassays targeting foodborne pathogens based on optical nanosensors are then summarized. Finally, the challenges and future trends in developing optical nanosensors are also highlighted. The current review indicates that optical nanosensors have taken the challenges of detecting biofilm in complex food samples, including the characterization of biofilm formation mechanism, identification of microbial metabolic activities, diagnosis of potential food pathogens and sanitation monitoring of food processing equipment. Numerous in-depth explorations and various trials have proven that the bioassays based on multifunctional optical nanosensors are promising to ensure and promote food safety and quality. However, there still remains a daunting challenge to structure reproducible, biocompatible and applicable nano-sensors for biofilm characterization, identification, and imaging.

A three-dimensional discriminant analysis approach for hyperspectral images

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques.

Applications of atomic force microscopy in immunology

Cellular mechanics, a major regulating factor of cellular architecture and biological functions, responds to intrinsic stresses and extrinsic forces exerted by other cells and the extracellular matrix in the microenvironment. Cellular mechanics also acts as a fundamental mediator in complicated immune responses, such as cell migration, immune cell activation, and pathogen clearance. The principle of atomic force microscopy (AFM) and its three running modes are introduced for the mechanical characterization of living cells. The peak force tapping mode provides the most delicate and desirable virtues to collect high-resolution images of morphology and force curves. For a concrete description of AFM capabilities, three AFM applications are discussed. These applications include the dynamic progress of a neutrophil-extracellular-trap release by neutrophils, the immunological functions of macrophages, and the membrane pore formation mediated by perforin, streptolysin O, gasdermin D, or membrane attack complex.

Identifying muscle hemorrhage in rat cadavers with advanced decomposition by FT-IR microspectroscopy combined with chemometrics

The identification of muscle hemorrhage in a cadaver that is in an advanced stage of decomposition is an important but challenging task. Our study investigated whether Fourier transform infrared (FT-IR) microspectroscopy in conjunction with chemometrics could identify muscle hemorrhage using rat cadavers with advanced decomposition. In this study, an intramuscular blood injection method, instead of a mechanical injury method, was used to construct a muscle hemorrhage model, and the modeling idea of muscle hemorrhage identification was to discriminate and classify hemoglobin-leaking myofibrils from negative myofibrils. First, the optical images of hematoxylin/eosin (H&E) stained hemorrhagic muscle at different postmortem intervals (PMIs) were observed and showed that the morphological features of whole erythrocytes disappeared since the PMI of 4 d. Subsequently, principle component analysis (PCA) was performed and indicated that the biochemical differences in protein structures between fresh erythrocytes and myofibrils can be detected by the IR spectroscopic method. Ultimately, several classification models based on the partial least square discriminant analysis (PLS-DA) algorithm were successfully constructed for different PMIs and PMI ranges and achieved great prediction performances in external validations. This preliminary study demonstrates the feasibility of using FT-IR microspectroscopy combined with chemometrics as a potential approach for identifying muscle hemorrhage in cadavers with advanced decomposition for offering vital evidences in judicial process.

A de-waxing methodology for scanning probe microscopy

A de-waxing protocol that successfully removes paraffin from tissue microarray (TMA) cores of fixed tissue obtained from oral cancer is described. The success of the protocol is demonstrated by the comparison of Fourier transform infrared (FTIR) results obtained on paraffin-embedded and de-waxed tissue and the absence of any significant correlations between infrared scanning near-field optical microscopy (SNOM) images of de-waxed tissue obtained at the three main paraffin IR peaks. The success of the protocol in removing paraffin from tissue is also demonstrated by images obtained with scanning electron microscopy (SEM) and by energy dispersive spectra (EDS) of a de-waxed CaF2 disc which shows no significant contribution from carbon. The FTIR spectra of the de-waxed TMA core overlaps that obtained from OE19 oesophageal cancer cells which had never been exposed to paraffin.

Mid-infrared metabolic imaging with vibrational probes

Understanding metabolism is indispensable in unraveling the mechanistic basis of many physiological and pathological processes. However, in situ metabolic imaging tools are still lacking. Here we introduce a framework for mid-infrared (MIR) metabolic imaging by coupling the emerging high-information-throughput MIR microscopy with specifically designed IR-active vibrational probes. We present three categories of small vibrational tags including azide bond, ¹³C-edited carbonyl bond and deuterium-labeled probes to interrogate various metabolic activities in cells, small organisms and mice. Two MIR imaging platforms are implemented including broadband Fourier transform infrared microscopy and discrete frequency infrared microscopy with a newly incorporated spectral region (2,000-2,300 cm^-1). Our technique is uniquely suited to metabolic imaging with high information throughput. In particular, we performed single-cell metabolic profiling including heterogeneity characterization, and large-area metabolic imaging at tissue or organ level with rich spectral information.

Label-free, automated classification of microsatellite status in colorectal cancer by infrared imaging

Challenging histopathological diagnostics in cancer include microsatellite instability-high (MSI-H) colorectal cancer (CRC), which occurs in 15% of early-stage CRC and is caused by a deficiency in the mismatch repair system. The diagnosis of MSI-H cannot be reliably achieved by visual inspection of a hematoxylin and eosin stained thin section alone, but additionally requires subsequent molecular analysis. Time- and sample-intensive immunohistochemistry with subsequent fragment length analysis is used. The aim of the presented feasibility study is to test the ability of quantum cascade laser (QCL)-based infrared (IR) imaging as an alternative diagnostic tool for MSI-H in CRC. We analyzed samples from 100 patients with sporadic CRC UICC stage II and III. Forty samples were used to develop the random forest classifier and 60 samples to verify the results on an independent blinded dataset. Specifically, 100% sensitivity and 93% specificity were achieved based on the independent 30 MSI-H- and 30 microsatellite stable (MSS)-patient validation cohort. This showed that QCL-based IR imaging is able to distinguish between MSI-H and MSS for sporadic CRC - a question that goes beyond morphological features - based on the use of spatially resolved infrared spectra used as biomolecular fingerprints.

Origin of micro-scale heterogeneity in polymerisation of photo-activated resin composites

Photo-activated resin composites are widely used in industry and medicine. Despite extensive chemical characterisation, the micro-scale pattern of resin matrix reactive group conversion between filler particles is not fully understood. Using an advanced synchrotron-based wide-field IR imaging system and state-of-the-art Mie scattering corrections, we observe how the presence of monodispersed silica filler particles in a methacrylate based resin reduces local conversion and chemical bond strain in the polymer phase. Here we show that heterogeneity originates from a lower converted and reduced bond strain boundary layer encapsulating each particle, whilst at larger inter-particulate distances light attenuation and monomer mobility predominantly influence conversion. Increased conversion corresponds to greater bond strain, however, strain generation appears sensitive to differences in conversion rate and implies subtle distinctions in the final polymer structure. We expect these findings to inform current predictive models of mechanical behaviour in polymer-composite materials, particularly at the resin-filler interface.

Resin-based-composites are widely used in industry and medicine but the influence of the filler particles on the reactive group conversion in photocurable resins is yet to be elucidated. Here the authors observe reduced local conversion and chemical bond strain in silica filler acrylate composite using synchrotron-based wide-field IR imaging.

Biomolecular and bioanalytical applications of infrared spectroscopy - A review

Infrared (IR; or mid-infrared, MIR; 4000-400 cm^-1; 2500-25,000 nm) spectroscopy has become one of the most powerful and versatile tools at the disposal of modern bioscience. Because of its high molecular specificity, applicability to wide variety of samples, rapid measurement and non-invasivity, IR spectroscopy forms a potent approach to elucidate qualitative and quantitative information from various kinds of biological material. For these reasons, it became an established bioanalytical technique with diverse applications. This work aims to be a comprehensive and critical review of the recent accomplishments in the field of biomolecular and bioanalytical IR spectroscopy. That progress is presented on a wider background, with fundamental characteristics, the basic principles of the technique outlined, and its scientific capability directly compared with other methods being used in similar fields (e.g. near-infrared, Raman, fluorescence). The article aims to present a complete examination of the topic, as it touches the background phenomena, instrumentation, spectra processing and data analytical methods, spectra interpretation and related information. To suit this goal, the article includes a tutorial information essential to obtain a thorough perspective of bio-related applications of the reviewed methodologies. The importance of the fundamental factors to the final performance and applicability of IR spectroscopy in various areas of bioscience is explained. This information is interpreted in critical way, with aim to gain deep understanding why IR spectroscopy finds extraordinarily intensive use in this remarkably diverse and dynamic field of research and utility. The major focus is placed on the diversity of the applications in which IR biospectroscopy has been established so far and those onto which it is expanding nowadays. This includes qualitative and quantitative analytical spectroscopy, spectral imaging, medical diagnosis, monitoring of biophysical processes, and studies of physicochemical properties and dynamics of biomolecules. The application potential of IR spectroscopy in light of the current accomplishments and the future prospects is critically evaluated and its significance in the progress of bioscience is comprehensively presented.

Real-time redox monitoring of a nitrosyl ruthenium complex acting as NO-donor agent in a single A549 cancer cell with multiplex Fourier-transform infrared microscopy

Multiplex Fourier-transform infrared microscopy (μFT-IR) helped to monitor trans-[Ru(NO) (NH₃)₄ (isn)]³⁺(I), uptake by A549 lung carcinoma cell, as well as the generation of its product, nitric oxide (NO), inside the cell. Chronoamperometry with NO-sensor and μFT-IR showed that exogenous NADH and the A549 cell induced the NO release redox mechanism. Chemical imaging confirmed that (I) was taken up by the cell, and that its localization coincided with its consumption in the cellular environment within 15 min of exposure. The Ru-NO absorption band in the IR spectrum shifted from 1932 cm^-1, when NO was coordinated to Ru as {Ru^II-NO⁺}³⁺, to 1876 cm^-1, due the formation of reduced species {Ru^II-NO⁰}²⁺, a precursor of NO release. Futhermore, the μFT-IR spectral profile demonstrated that, as a result of the NO action on the target, NO interacted with nucleic acids, which provided a biochemical response that is detectable in living cells.

All-digital histopathology by infrared-optical hybrid microscopy

Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical workflows. Here we conceptualize an IR-optical hybrid (IR-OH) approach that sensitively measures molecular composition based on an optical microscope with wide-field interferometric detection of absorption-induced sample expansion. We demonstrate that IR-OH exceeds state-of-the-art IR microscopy in coverage (10-fold), spatial resolution (fourfold), and spectral consistency (by mitigating the effects of scattering). The combined impact of these advances allows full slide infrared absorption images of unstained breast tissue sections on a visible microscope platform. We further show that automated histopathologic segmentation and generation of computationally stained (stainless) images is possible, resolving morphological features in both color and spatial detail comparable to current pathology protocols but without stains or human interpretation. IR-OH is compatible with clinical and research pathology practice and could make for a cost-effective alternative to conventional stain-based protocols for stainless, all-digital pathology.

Is Infrared Spectroscopy Ready for the Clinic?

Fourier transform-infrared spectroscopy (FT-IR) represents an attractive molecular diagnostic modality for translation to the clinic, where comprehensive chemical profiling of biological samples may revolutionize a myriad of pathways in clinical settings. Principally, FT-IR provides a rapid, cost-effective platform to obtain a molecular fingerprint of clinical samples based on vibrational transitions of chemical bonds upon interaction with infrared light. To date, considerable research activities have demonstrated competitive to superior performance of FT-IR strategies in comparison to conventional techniques, with particular promise for earlier, accessible disease diagnostics, thereby improving patient outcomes. However, amidst the changing healthcare landscape in times of aging populations and increased prevalence of cancer and chronic disease, routine adoption of FT-IR within clinical laboratories has remained elusive. Hence, this perspective shall outline the significant clinical potential of FT-IR diagnostics and subsequently address current barriers to translation from the perspective of all stakeholders, in the context of biofluid, histopathology, cytology, microbiology, and biomarker discovery frameworks. Thereafter, future perspectives of FT-IR for healthcare will be discussed, with consideration of recent technological advances that may facilitate future clinical translation.

Volumetric stimulated Raman scattering imaging of cleared tissues towards three-dimensional chemical histopathology

Thin tissue slice based histology has been used as a gold standard for disease diagnosis since over a hundred years ago. However, histopathological evaluation on two-dimensional slides suffers from large variations due to limited sampling. To improve the diagnostic accuracy, three-dimensional (3D) histology is performed through serial sectioning, staining, imaging and reconstruction of individual slices, which is highly time-consuming and labor intensive. We developed a volumetric stimulated Raman scattering (SRS) imaging method, which provides histology-like information in 3D context without the need for staining with dyes. Using a small molecule clearing agent, formamide, we performed tissue clearing within 30 min and achieved an imaging depth up to 500 µm in highly scattered tissues, including brain, kidney, liver and lung. Through a two-color SRS imaging scheme, we obtained histology-like images in cleared brain tissue slices. Our method has the potential for 3D tissue histopathology to improve the accuracy of histopathological examination.

Modern Scattering-Type Scanning Near-Field Optical Microscopy for Advanced Material Research

Infrared and optical spectroscopy represents one of the most informative methods in advanced materials research. As an important branch of modern optical techniques that has blossomed in the past decade, scattering-type scanning near-field optical microscopy (s-SNOM) promises deterministic characterization of optical properties over a broad spectral range at the nanoscale. It allows ultrabroadband optical (0.5-3000 µm) nanoimaging, and nanospectroscopy with fine spatial (<10 nm), spectral (<1 cm^-1 ), and temporal (<10 fs) resolution. The history of s-SNOM is briefly introduced and recent advances which broaden the horizons of this technique in novel material research are summarized. In particular, this includes the pioneering efforts to study the nanoscale electrodynamic properties of plasmonic metamaterials, strongly correlated quantum materials, and polaritonic systems at room or cryogenic temperatures. Technical details, theoretical modeling, and new experimental methods are also discussed extensively, aiming to identify clear technology trends and unsolved challenges in this exciting field of research.

A fully automated, faster noise rejection approach to increasing the analytical capability of chemical imaging for digital histopathology

Chemical hyperspectral imaging (HSI) data is naturally high dimensional and large. There are thus inherent manual trade-offs in acquisition time, and the quality of data. Minimum Noise Fraction (MNF) developed by Green et al. [1] has been extensively studied as a method for noise removal in HSI data. It too, however entails a manual speed-accuracy trade-off, namely the process of manually selecting the relevant bands in the MNF space. This process currently takes roughly around a month’s time for acquiring and pre-processing an entire TMA with acceptable signal to noise ratio. We present three approaches termed ‘Fast MNF’, ‘Approx MNF’ and ‘Rand MNF’ where the computational time of the algorithm is reduced, as well as the entire process of band selection is fully automated. This automated approach is shown to perform at the same level of accuracy as MNF with now large speedup factors, resulting in the same task to be accomplished in hours. The different approximations produced by the three algorithms, show the reconstruction accuracy vs storage (50×) and runtime speed (60×) trade-off. We apply the approach for automating the denoising of different tissue histology samples, in which the accuracy of classification (differentiating between the different histologic and pathologic classes) strongly depends on the SNR (signal to noise ratio) of recovered data. Therefore, we also compare the effect of the proposed denoising algorithms on classification accuracy. Since denoising HSI data is done unsupervised, we also use a metric that assesses the quality of denoising in the image domain between the noisy and denoised image in the absence of ground truth.

Standardization of complex biologically derived spectrochemical datasets

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO₂ levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

Research on a Dual-Mode Infrared Liquid-Crystal Device for Simultaneous Electrically Adjusted Filtering and Zooming

A new dual-mode liquid-crystal (LC) micro-device constructed by incorporating a Fabry⁻Perot (FP) cavity and an arrayed LC micro-lens for performing simultaneous electrically adjusted filtering and zooming in infrared wavelength range is presented in this paper. The main micro-structure is a micro-cavity consisting of two parallel zinc selenide (ZnSe) substrates that are pre-coated with ~20-nm aluminum (Al) layers which served as their high-reflection films and electrodes. In particular, the top electrode of the device is patterned by 44 × 38 circular micro-holes of 120 μm diameter, which also means a 44 × 38 micro-lens array. The micro-cavity with a typical depth of ~12 μm is fully filled by LC materials. The experimental results show that the spectral component with needed frequency or wavelength can be selected effectively from incident micro-beams, and both the transmission spectrum and the point spread function can be adjusted simultaneously by simply varying the root-mean-square value of the signal voltage applied, so as to demonstrate a closely correlated feature of filtering and zooming. In addition, the maximum transmittance is already up to ~20% according the peak-to-valley value of the spectral transmittance curves, which exhibits nearly twice the increment compared with that of the ordinary LC-FP filtering without micro-lenses.

Integrated Fourier Transform Infrared Imaging and Proteomics for Identification of a Candidate Histochemical Biomarker in Bladder Cancer

Histopathological differentiation between severe urocystitis with reactive urothelial atypia and carcinoma in situ (CIS) can be difficult, particularly after a treatment that deliberately induces an inflammatory reaction, such as intravesical instillation of Bacillus Calmette-Guèrin. However, precise grading in bladder cancer is critical for therapeutic decision making and thus requires reliable immunohistochemical biomarkers. Herein, an exemplary potential biomarker in bladder cancer was identified by the novel approach of Fourier transform infrared imaging for label-free tissue annotation of tissue thin sections. Identified regions of interest are collected by laser microdissection to provide homogeneous samples for liquid chromatography-tandem mass spectrometry-based proteomic analysis. This approach afforded label-free spatial classification with a high accuracy and without interobserver variability, along with the molecular resolution of the proteomic analysis. Cystitis and invasive high-grade urothelial carcinoma samples were analyzed. Three candidate biomarkers were identified and verified by immunohistochemistry in a small cohort, including low-grade urothelial carcinoma samples. The best-performing candidate AHNAK2 was further evaluated in a much larger independent verification cohort that also included CIS samples. Reactive urothelial atypia and CIS were distinguishable on the basis of the expression of this newly identified and verified immunohistochemical biomarker, with a sensitivity of 97% and a specificity of 69%. AHNAK2 can differentiate between reactive urothelial atypia in the setting of an acute or chronic cystitis and nonmuscle invasive-type CIS.

Deep UV dispersion and absorption spectroscopy of biomolecules

Owing to the high precision and sensitivity of optical systems, there is an increasing demand for optical methods that quantitatively characterize the physical and chemical properties of biological samples. Information extracted from such quantitative methods, through phase and/or amplitude variations of light, can be crucial in the diagnosis, treatment and study of disease. In this work we apply a recently developed quantitative method, called ultraviolet hyperspectral interferometry (UHI), to characterize the dispersion and absorbing properties of various important biomolecules. Our system consists of (1) a broadband light source that spans from the deep-UV to the visible region of the spectrum, and (2) a Mach-Zehnder interferometer to gain access to complex optical properties. We apply this method to characterize (and tabulate) the dispersive and absorptive properties of hemoglobin, beta nicotinamide adenine dinucleotide (NAD), flavin adenine dinucleotide (FAD), elastin, collagen, cytochrome c, tryptophan and DNA. Our results shed new light on the complex properties of important biomolecules.