Spectroscopic techniques, such as Fourier-transform infrared (FTIR) spectroscopy, are used to study the interaction of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening and diagnosis, microbiological studies, forensic and environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need 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. In order 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 the aging of instrumental parts.Thus, it is evident that spectral standardization is crucial for the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, where the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variations 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. In this paper, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step towards the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present. 4 worldwide are developing spectrochemical approaches for diagnosis, discrimination and monitoring of diseases, as well as for other uses. Combination of multiple datasets would facilitate the conduction of large-scale studies which are still lacking in the field of bio-spectroscopy. Sensor-based technologiesSensor-based technologies are an integral part of daily life ranging from locating sensorbased technology, such as global positioning system (GPS) 6 , to image biosensors, such as X-rays 7-10 and γ-rays [11][12][13] , which are used extensively for medical applications. Other powerful approaches that make use of sensor-based technologies toward medical disease examination and diagnostics include circular dichroism (CD) spectroscopy 14-17 , ultraviolet (UV) or visible spectroscopy 18,19 , fluorescence 20-24 , nuclear magnetic resonance (NMR) spectroscopy 25-29 and ultrasound (US) 7,30- .Over the last two decades, optical biosensors employing vibrational spectroscopy, particularly IR spectroscopy, have seen tremendous progress in biomedical and biological research. A number of studies using the above-mentioned methods ha...

Abstract:The importance of stroma as a rich diagnostic region in tissue biopsies is growing as there is an increasing understanding that disease processes in multiple organs can affect the composition of adjacent connective tissue regions. This may be especially true in the liver, since this organ's central metabolic role exposes it to multiple disease processes. We use quantum cascade laser infrared spectroscopic imaging to study changes in the chemical status of hepatocytes and fibrotic regions of liver tissue that result from the progression of liver cirrhosis to hepatocellular carcinoma and the potentially confounding effects of diabetes mellitus.

High-definition Fourier Transform Infrared (FT-IR) spectroscopic imaging is an emerging approach to obtain detailed images that have associated biochemical information. FT-IR imaging of tissue is based on the principle that different regions of the mid-infrared are absorbed by different chemical bonds (e.g., C=O, C-H, N-H) within cells or tissue that can then be related to the presence and composition of biomolecules (e.g., lipids, DNA, glycogen, protein, collagen). In an FT-IR image, every pixel within the image comprises an entire Infrared (IR) spectrum that can give information on the biochemical status of the cells that can then be exploited for cell-type or disease-type classification. In this paper, we show: how to obtain IR images from human tissues using an FT-IR system, how to modify existing instrumentation to allow for high-definition imaging capabilities, and how to visualize FT-IR images. We then present some applications of FT-IR for pathology using the liver and kidney as examples. FT-IR imaging holds exciting applications in providing a novel route to obtain biochemical information from cells and tissue in an entirely label-free non-perturbing route towards giving new insight into biomolecular changes as part of disease processes. Additionally, this biochemical information can potentially allow for objective and automated analysis of certain aspects of disease diagnosis.

Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease-states can be directly assessed by analyzing the mid-IR spectra of different cell-types (e.g. epithelial cells) and subcellular components (e.g. nuclei), provided we can accurately classify the pixels belonging to these components. The challenge is to extract information from hundreds of noisy mid-IR bands at each pixel, where each band is not very informative in itself, making annotations of unstained tissue HSI images particularly tricky. Because the tissue structure is not necessarily identical between the two sections, only a few regions in unstained HSI image can be annotated with high confidence, even when serial (or adjacent) H&E stained section is used as a visual guide. In order to completely use both labeled and unlabeled pixels in training images, we have developed an HSI pixel classification method that uses semi-supervised learning for both spectral dimension reduction and hierarchical pixel clustering. Compared to supervised classifiers, the proposed method was able to account for the vast differences in spectra of sub-cellular components of the same cell-type and achieve an F1-score of 71.18% on twofold cross-validation across 20 tissue images. To generate further interest in this promising modality we have released our source code and also showed that disease classification is straightforward after HSI image segmentation.

Infrared spectroscopic tissue imaging is a potentially powerful adjunct tool to current histopathology techniques. By coupling the biochemical signature obtained through infrared spectroscopy to the spatial information offered by microscopy, this technique can selectively analyze the chemical composition of different features of unlabeled, unstained tissue sections. In the past, the tissue features that have received the most interest were parenchymal and epithelial cells, chiefly due to their involvement in dysplasia and progression to carcinoma; however, the field has recently turned its focus toward stroma and areas of fibrotic change. These components of tissue present an untapped source of biochemical information that can shed light on many diverse disease processes, and potentially hold useful predictive markers for these same pathologies. Here we review the recent applications of infrared spectroscopic imaging to stromal and fibrotic regions of diseased tissue, and explore the potential of this technique to advancing current capabilities for tissue analysis.

Tissue fibrosis is a progressive and destructive disease process that can occur in many different organs including the liver, kidney, skin, and lungs. Fibrosis is typically initiated by inflammation as a result of chronic insults such as infection, chemicals and autoimmune diseases. Current approaches to examine organ fibrosis are limited to radiological and histological analyses. Infrared spectroscopic imaging offers a potential alternative approach to gain insight into biochemical changes associated with fibrosis progression. In this study, we demonstrate that IR imaging of a mouse model of pulmonary fibrosis can identify biochemical changes observed with fibrosis progression and the beginning of resolution using K-means analysis, spectral ratios and multivariate data analysis. This study demonstrates that IR imaging may be a useful approach to understand the biochemical events associated with fibrosis initiation, progression and resolution for both the clinical setting and for assessing novel anti-fibrotic drugs in a model system.

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Fourier transform infrared (FT-IR) microscopy was used to image tissue samples from twenty patients diagnosed with thyroid carcinoma. The spectral data were then used to differentiate between follicular thyroid carcinoma and follicular variant of papillary thyroid carcinoma using principle component analysis coupled with linear discriminant analysis and a Naïve Bayesian classifier operating on a set of computed spectral metrics. Classification of patients’ disease type was accomplished by using average spectra from a wide region containing follicular cells, colloid, and fibrosis; however, classification of disease state at the pixel level was only possible when the extracted spectra were limited to follicular epithelial cells in the samples, excluding the relatively uninformative areas of fibrosis. The results demonstrate the potential of FT-IR microscopy as a tool to assist in the difficult diagnosis of these subtypes of thyroid cancer, and also highlights the importance of selectively and separately analyzing spectral information from different features of a tissue of interest.