Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks

Application of mid-infrared microscopic imaging for the diagnosis and classification of human lymphomas

Mid-infrared (MIR) microscopic imaging of indolent and aggressive lymphomas was performed including formalin-fixed and paraffin-embedded samples of six follicular lymphomas and 12 diffuse large B-cell-lymphomas as well as reactive lymph nodes to investigate benefits and challenges for lymphoma diagnosis. MIR images were compared to defined pathological characteristics such as indolent versus aggressive versus reactive, germinal centre versus activated cell-of-origin (COO) subtypes, or a low versus a high proliferative index and level of PD-L1 expression. We demonstrated that MIR microscopic imaging can differentiate between reactive lymph nodes, indolent and aggressive lymphoma samples. Also, it has potential to be used in the subtyping of lymphomas, as shown with the differentiation between COO subtypes, the level of proliferation and PD-L1 expression. MIR microscopic imaging is a promising tool for diagnosis and subtyping of lymphoma and further evaluation is needed to fully explore the advantages and disadvantages of this method for pathological diagnosis.

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models.

Infrared Spectroscopic Imaging Visualizes a Prognostic Extracellular Matrix-Related Signature in Breast Cancer

Molecular analysis techniques such as gene expression analysis and proteomics have contributed greatly to our understanding of cancer heterogeneity. In prior studies, gene expression analysis was shown to stratify patient outcome on the basis of tumor-microenvironment associated genes. A specific gene expression profile, referred to as ECM3 (Extracellular Matrix Cluster 3), indicated poorer survival in patients with grade III tumors. In this work, we aimed to visualize the downstream effects of this gene expression profile onto the tissue, thus providing a spatial context to altered gene expression profiles. Using infrared spectroscopic imaging, we identified spectral patterns specific to the ECM3 gene expression profile, achieving a high spectral classification performance of 0.87 as measured by the area under the curve of the receiver operating characteristic curve. On a patient level, we correctly identified 20 out of 22 ECM3 group patients and 19 out of 20 non-ECM3 group patients by using this spectroscopic imaging-based classifier. By comparing pixels that were identified as ECM3 or non-ECM3 with H&E and IHC images, we were also able to observe an association between tissue morphology and the gene expression clusters, showing the ability of our method to capture broad outcome associated features from infrared images.

Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology

Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-microscopy-based cytopathology. Conceptually, DCNNs facilitate a flexible combination of spectral and spatial information for classifying cellular images as healthy or cancer-affected cells. As we demonstrate, this conceptual advantage translates into practice, where DCNNs exceed the accuracy of both conventional classifiers based on pixel spectra as well as classifiers based on morphological features extracted from Raman microscopic images. Remarkably, accuracies exceeding those of all previously proposed classifiers are obtained while using only a small fraction of the spectral information provided by the dataset. Overall, our results indicate a high potential for DCNNs in medical applications of not just Raman, but also infrared microscopy.

Gene expression data and FTIR spectra provide a similar phenotypic description of breast cancer cell lines in 2D and 3D cultures

Gene expression patterns and FTIR spectral data are strongly correlated. Both identified the genotypes and phenotypes of breast cancer cell lines.

Application of mid-infrared (MIR) microscopy imaging for discrimination between follicular hyperplasia and follicular lymphoma in transgenic mice

Mid-infrared (MIR) microscopy imaging is a vibrational spectroscopic technique that uses infrared radiation to image molecules of interest in thin tissue sections. A major advantage of this technology is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue. Therefore, this technology has become an essential tool for the detection and characterization of the molecular components of many biological processes. Using this method, it is possible to investigate the spatial distribution of proteins and small molecules within biological systems by in situ analysis. In this study, we have evaluated the potential of mid-infrared microscopy imaging to study biochemical changes which distinguish between reactive lymphadenopathy and cancer in genetically modified mice with different phenotypes. We were able to demonstrate that MIR microscopy imaging and multivariate image analyses of different mouse genotypes correlated well with the morphological tissue features derived from HE staining. Using principal component analyses, we were also able to distinguish spectral clusters from different phenotype samples, particularly from reactive lymphadenopathy (follicular hyperplasia) and cancer (follicular lymphoma).

The pituitary gland under infrared light – in search of a representative spectrum for homogeneous regions

This work focuses on obtaining unique representative FTIR spectrum characteristic for one type of cells architecture. Presented idea is based on using of HCA for data evaluation to search for uniform patterns within samples from the perspective of FTIR spectra.

A FTIR imaging characterization of fibroblasts stimulated by various breast cancer cell lines

It is well known that the microenvironment plays a major role in breast cancer progression. Yet, the mechanism explaining the transition from normal fibroblasts to cancer-stimulated fibroblasts remains to be elucidated. Here we report a FTIR imaging study of the effects of three different breast cancer cell lines on normal fibroblasts in culture. Fibroblast activation process was monitored by FTIR imaging and spectra compared by multivariate statistical analyses. Principal component analysis evidenced that the fibroblasts stimulated by these cancer cell lines grouped together and remained distinctly separated from normal fibroblasts indicating a modified different chemical composition in the cancer-stimulated fibroblasts. Similar changes in fibroblasts were induced by the various breast cancer cell lines belonging to different sub-types. Most significant changes were observed in the region of 2950 and 1230 cm⁻¹, possibly related to changes in lipids and in the 1230 cm⁻¹ area assigned to phosphate vibrations (nucleotides). Interestingly, the cancer-cell induced changes in the fibroblasts also occurred when there was no possible direct contact between the two cell lines in the co-culture. When contact was possible, the spectral changes were similar, suggesting that soluble factors but not direct cell-cell interactions were responsible for fibroblast activation. Overall, the results indicate that IR imaging could be used in the future for analyzing the microenvironment of breast tumors.

Detection and identification of cancerous murine fibroblasts, transformed by murine sarcoma virus in culture, using Raman spectroscopy and advanced statistical methods

BACKGROUND

Cancer is one of the leading worldwide causes of death. It may be induced by a variety of factors, including carcinogens, radiation, genetic factors, or DNA and RNA viruses. The early detection of cancer is critical for its successful therapy, which can result in complete recovery from some types of cancer.

METHODS

Raman spectroscopy has been widely used in medicine and biology. It is a noninvasive, nondestructive, and water-insensitive technique that can detect changes in cells and tissues that are caused by different disorders, such as cancer. In this study, Raman spectroscopy was used for the identification and characterization of murine fibroblast cell lines (NIH/3T3) and malignant fibroblast cells transformed by murine sarcoma virus (NIH-MuSV) cells.

RESULTS

Using principal component analysis and LDA it was possible to differentiate between the NIH/3T3 and NIH-MuSV cells with an 80-85% success rate based on their Raman shift spectra.

CONCLUSIONS

The best results for differentiation were achieved from spectra that were obtained from the rich membrane sites.

GENERAL SIGNIFICANCE

Because of its homogeneity and complete control of most factors affecting its growth, cell culture is a preferred model for the detection and identification of specific biomarkers related to cancer transformation or other cellular modifications.

Fourier transform infrared imaging analysis in discrimination studies of St. John's wort (Hypericum perforatum)

In the present study, Fourier transform infrared (FTIR) imaging and data analysis methods were combined to study morphological and molecular patterns of St. John's wort (Hypericum perforatum) in detail. For interpretation, FTIR imaging results were correlated with histological information gained from light microscopy (LM). Additionally, we tested several evaluation processes and optimized the methodology for use of complex FTIR microscopic images to monitor molecular patterns. It is demonstrated that the combination of the used spectroscopic method with LM enables a more distinct picture, concerning morphology and distribution of active ingredients, to be gained. We were able to obtain high-quality FTIR microscopic imaging results and to distinguish different tissue types with their chemical ingredients.

Label-free characterization of cancer-activated fibroblasts using infrared spectroscopic imaging

Glandular tumors arising in epithelial cells comprise the majority of solid human cancers. Glands are supported by stroma, which is activated in the proximity of a tumor. Activated stroma is often characterized by the molecular expression of α-smooth muscle actin (α-SMA) within fibroblasts. However, the precise spatial and temporal evolution of chemical changes in fibroblasts upon epithelial tumor signaling is poorly understood. Here we report a label-free method to characterize fibroblast changes by using Fourier transform infrared spectroscopic imaging and comparing spectra with α-SMA expression in primary normal human fibroblasts. We recorded the fibroblast activation process by spectroscopic imaging using increasingly tissue-like conditions: 1), stimulation with the growth factor TGFβ1; 2), coculture with MCF-7 human breast cancerous epithelial cells in Transwell coculture; and 3), coculture with MCF-7 in three-dimensional cell culture. Finally, we compared the spectral signatures of stromal transformation with normal and malignant human breast tissue biopsies. The results indicate that this approach reveals temporally complex spectral changes and thus provides a richer assessment than simple molecular imaging based on α-SMA expression. Some changes are conserved across culture conditions and in human tissue, providing a label-free method to monitor stromal transformations.

Spectral signatures of colonic malignancies in the mid-infrared region: from basic research to clinical applicability

The process of carcinogenesis in the colon progresses through several overlapping stages, making the evaluation process challenging, as well as subjective. Owing to the complexity of colonic tissues and the search for a technique that is rapid and foolproof for precise grading and evaluation of biopsies, many spectroscopic techniques have been evaluated in the past few decades for their efficiency and clinical compatibility. Fourier-transform infrared spectroscopy, being quantitative and objective, has the capacity for automation and relevance to cancer diagnosis. This article highlights investigations on the application of Fourier-transform infrared spectroscopy (particularly microscopy) in colon cancer diagnosis and parallel developments in data analysis techniques for the characterization of spectral signatures of malignant tissues in the colon.

Cluster analysis of infrared spectra of rabbit cortical bone samples during maturation and growth

Bone consists of an organic and an inorganic matrix. During development, bone undergoes changes in its composition and structure. In this study we apply three different cluster analysis algorithms [K-means (KM), fuzzy C-means (FCM) and hierarchical clustering (HCA)], and discriminant analysis (DA) on infrared spectroscopic data from developing cortical bone with the aim of comparing their ability to correctly classify the samples into different age groups. Cortical bone samples from the mid-diaphysis of the humerus of New Zealand white rabbits from three different maturation stages (newborn (NB), immature (11 days-1 month old), mature (3-6 months old)) were used. Three clusters were obtained by KM, FCM and HCA methods on different spectral regions (amide I, phosphate and carbonate). The newborn samples were well separated (71-100% correct classifications) from the other age groups by all bone components. The mature samples (3-6 months old) were well separated (100%) from those of other age groups by the carbonate spectral region, while by the phosphate and amide I regions some samples were assigned to another group (43-71% correct classifications). The greatest variance in the results for all algorithms was observed in the amide I region. In general, FCM clustering performed better than the other methods, and the overall error was lower. The discriminate analysis results showed that by combining the clustering results from all three spectral regions, the ability to predict the correct age group for all samples increased (from 29-86% to 77-91%). This study is the first to compare several clustering methods on infrared spectra of bone. Fuzzy C-means clustering performed best, and its ability to study the degree of memberships of samples to each cluster might be beneficial in future studies of medical diagnostics.

Typing SNP based on the near-infrared spectroscopy and artificial neural network

Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G>A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg-Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to 100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.

Classification of fixed urological cells using Raman tweezers

In this paper we report on preliminary investigations into using Raman tweezers to classify urological cell lines. This builds on earlier work within the group, whereby Raman tweezer methodologies were developed, and the application of this technique to differentiate between live prostate cancer (CaP) and bladder cells lines (PC-3 and MGH-U1 respectively) was demonstrated.In this present study we analysed chemically fixed cells using two different fixative methods; SurePath (a commercial available liquid based cytology media) and 4% v/v formalin/PBS fixatives. The study has been expanded from our previous live cell study to include the androgen sensitive CaP cell line LNCaP, primary benign prostate hyperplasia (BPH) cells as well as primary urethral cells. Raman light from the cells was collected using a 514.5 nm Ar-ion laser excitation source in back-scattering configuration mode.Principal component-linear discriminate analysis (PC-LDA) models of resulting cell spectra were generated and these were validated using a blind comparison. Sensitivities and specificities of > 72% and 90% respectively, for SurePath fixed cells, and > 93% and 98% respectively for 4% v/v formalin/PBS fixed cells was achieved. The higher prediction results for the formalin fixed cells can be attributed to a better signal-to-noise ratio for spectra obtained from these cells.Following on from this work, urological cell lines were exposed to urine for up to 12 hours to determine the effect of urine on the ability to classify these cells. Results indicate that urine has no detrimental effect on prediction results.

Shedding new light on the molecular architecture of oocytes using a combination of synchrotron Fourier transform-infrared and Raman spectroscopic mapping

Synchrotron Fourier transform-infrared (FT-IR) and Raman microspectroscopy were applied to investigate changes in the molecular architecture of mouse oocytes and demonstrate the overall morphology of the maturing oocyte. Here we show that differences were identified between immature mouse oocytes at the germinal vesicle (GV) and mature metaphase II (MII) stage when using this technology, without the introduction of any extrinsic markers, labels, or dyes. GV mouse oocytes were found to have a small, centrally located lipid deposit and another larger polar deposit of similar composition. MII oocytes have very large, centrally located lipid deposits. Each lipid deposit for both cell types contains an inner and outer lipid environment that differs in composition. To assess interoocyte variability, line scans were recorded across the diameter of the oocytes and compared from three independent trials (GV, n = 91; MII, n = 172), and the data were analyzed with principal component analysis (PCA). The average spectra and PCA loading plots show distinct and reproducible changes in the CH stretching region that can be used as molecular maturation markers. The method paves the way for developing an independent assay to assess oocyte status during maturation providing new insights into lipid distribution at the single cell level.

Discrimination between drug-resistant and non-resistant human melanoma cell lines by FTIR spectroscopy

We investigated the ability of FTIR-microscopy to define spectral changes between drug-sensitive and drug-resistant human melanoma cells. As a model system, a resistant melanoma cell line (GAC) was selected with cisplatin from parental (GA) cells. Using Fourier transform infrared spectroscopy (FTIR) we investigated the ability to differentiate between the resistant variant derived from the sensitive parental cell line, in the absence of cisplatin. We determined and validated spectral parameters (biomarkers) that differentiated between the two cell lines. By applying the principal component analysis (PCA) model, we reduced the original data size to six principal components. We detected a significant and consistent increase in the cell's DNA/RNA ratio as well as an increase in the lipid/protein ratio in the resistant cells. These results strongly support the potential of developing FTIR microspectroscopy as a simple, reagent-free method for the identification of drug-resistant cells. Rapid detection of tumors resistant to a particular drug, should contribute to the ability of the physician to choose an effective treatment protocol.

Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology

Background

Histopathologic evaluation of surgical specimens is a well established technique for disease identification, and has remained relatively unchanged since its clinical introduction. Although it is essential for clinical investigation, histopathologic identification of tissues remains a time consuming and subjective technique, with unsatisfactory levels of inter- and intra-observer discrepancy. A novel approach for histological recognition is to use Fourier Transform Infrared (FT-IR) micro-spectroscopy. This non-destructive optical technique can provide a rapid measurement of sample biochemistry and identify variations that occur between healthy and diseased tissues. The advantage of this method is that it is objective and provides reproducible diagnosis, independent of fatigue, experience and inter-observer variability.

Methods

We report a method for analysing excised lymph nodes that is based on spectral pathology. In spectral pathology, an unstained (fixed or snap frozen) tissue section is interrogated by a beam of infrared light that samples pixels of 25 μm × 25 μm in size. This beam is rastered over the sample, and up to 100,000 complete infrared spectra are acquired for a given tissue sample. These spectra are subsequently analysed by a diagnostic computer algorithm that is trained by correlating spectral and histopathological features.

Results

We illustrate the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes. By correlating spectral and histopathological features, a diagnostic algorithm was trained that allowed both accurate and rapid classification of benign and malignant tissues composed within different lymph nodes. This approach was successfully applied to both deparaffinised and frozen tissues and indicates that both intra-operative and more conventional surgical specimens can be diagnosed by this technique.

Conclusion

This paper provides strong evidence that automated diagnosis by means of infrared micro-spectral imaging is possible. Recent investigations within the author's laboratory upon lymph nodes have also revealed that cancers from different primary tumours provide distinctly different spectral signatures. Thus poorly differentiated and hard-to-determine cases of metastatic invasion, such as micrometastases, may additionally be identified by this technique. Finally, we differentiate benign and malignant tissues composed within axillary lymph nodes by completely automated methods of spectral analysis.

Fourier transform infrared spectroscopy in cancer detection

The rapid developments in the field of infrared spectroscopy in the past decade have demonstrated a potential for disease diagnosis using noninvasive technologies. Several earlier studies have highlighted the advantage of using infrared spectroscopy both in the near- and mid-infrared regions for diagnostic purposes at clinical levels. The areas of focus have been the distinction of premalignant and malignant cells and tissues from their normal state using specific parameters obtained from Fourier transform infrared spectra, making it a rapid and reagent-free method. While it still requires pilot studies and designed clinical trials to ensure the applicability of such systems for cancer diagnosis, substantial progress has been made in incorporating advances in computational methods into the system to increase the sensitivity of the entire setup, making it an objective and sensitive technique suitable for automation to suit the demands of the medical community. The development of fiber-optics systems for infrared spectroscopy have further opened up new and modern avenues in medical diagnosis at various levels of cells, tissues and organs under laboratory and clinical conditions.

Early spectral changes of cellular malignant transformation using Fourier transform infrared microspectroscopy

Fourier transform infrared microspectroscopy (FTIR-MSP) is potentially a powerful analytical method for identifying the spectral properties of biological activity in cells. The goal of the present research is the implementation of FTIR-MSP to study early spectral changes accompanying malignant transformation of cells. As a model system, cells in culture are infected by the murine sarcoma virus (MuSV), which induces malignant transformation. The spectral measurements are taken at various postinfection time intervals. To follow up systematically the progress of the spectral changes at early stages of cell transformation, it is essential first to determine and validate consistent and significant spectral parameters (biomarkers), which can evidently discriminate between normal and cancerous cells. Early stages of cell transformation are classified by an array of spectral biomarkers utilizing cluster analysis and discriminant classification function techniques. The classifications indicate that the first spectral changes are detectable much earlier than the first morphological signs of cell transformation. Our results point out that the first spectral signs of malignant transformation are observed on the first and third day of postinfection (PI) (for NIH/3T3 and MEF cell cultures, respectively), while the first visible morphological alterations are observed only on the third and seventh day, respectively. These results strongly support the potential of developing FTIR microspectroscopy as a simple, reagent-free method for early detection of malignancy.

A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections

Background

Three-dimensional (3D) multivariate Fourier Transform Infrared (FTIR) image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique.

Methods

Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB^® routine and placed in a single data matrix for multivariate analysis using Cytospec™. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software.

Results

The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components.

Conclusion

3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.

A Fourier transform infrared microspectroscopic imaging investigation into an animal model exhibiting glioblastoma multiforme

Glioblastoma multiforme (GBM) is a highly malignant human brain tumour for which no cure is available at present. Numerous clinical studies as well as animal experiments are under way with the goal being to understand tumour biology and develop potential therapeutic approaches. C6 cell glioma in the adult rat is a frequently used and well accepted animal model for the malignant human glial tumour. By combining standard analytical methods such as histology and immunohistochemistry with Fourier Transform Infrared (FTIR) microspectroscopic imaging and multivariate statistical approaches, we are developing a novel approach to tumour diagnosis which allows us to obtain information about the structure and composition of tumour tissues that could not be obtained easily with either method alone. We have used a "Stingray" FTIR imaging spectrometer to analyse and compare the compositions of coronal brain tissue sections of a tumour-bearing animal and those from a healthy animal. We have found that the tumour tissue has a characteristic chemical signature, which distinguishes it from tumour-free brain tissue. The physical-chemical differences, determined by image and spectral comparison are consistent with changes in total protein absorbance, phosphodiester absorbance and physical dispersive artefacts. The results indicate that FTIR imaging analysis could become a valuable analytic method in brain tumour research and possibly in the diagnosis of human brain tumours.

FTIR-microspectroscopy of prion-infected nervous tissue

The family of transmissible spongiform encephalopathies (TSE), also termed prion diseases, is a group of fatal, neurodegenerative diseases characterized by the accumulation of a misfolded protein, the disease-associated prion protein PrPSc. This glycoprotein differs in secondary structure from its normal, cellular isoform PrPC, which is physiologically expressed mostly by neurons. Scrapie is a prion disease first described in the 18th century in sheep and goats, and has been established as a model in rodents to study the pathogenesis and pathology of prion diseases. Assuming a multitude of molecular parameters change in the tissue in the course of the disease, FTIR microspectroscopy has been proposed as a valuable new method to study and identify prion-affected tissues due to its ability to detect a variety of changes in molecular structure and composition simultaneously. This paper reviews and discusses results from previous FTIR microspectroscopic studies on nervous tissue of scrapie-infected hamsters in the context of histological and molecular alterations known from conventional pathogenesis studies. In particular, data from studies reporting on disease-specific changes of protein structure characteristics, and also results of a recent study on hamster dorsal root ganglia (DRG) are discussed. These data include an illustration on how the application of a brilliant IR synchrotron light source enables the in situ investigation of localized changes in protein structure and composition in nervous cells or tissue due to PrPSc deposition, and a demonstration on how the IR spectral information can be correlated with results of complementary studies using immunohistochemistry and x-ray fluorescence techniques. Using IR microspectroscopy, some neurons exhibited a high accumulation of disease-associated prion protein evidenced by an increased amount of beta-sheet at narrow regions in or around the infected nervous cells. However, not all neurons from terminally diseased hamsters showed PrPSc deposition. Generally, the average spectral differences between all control and diseased DRG spectra are small but consistent as demonstrated by independent experiments. Along with studies on the purified misfolded prion protein, these data suggest that synchrotron FTIR microspectroscopy is capable of detecting the misfolded prion protein in situ without the necessity of immunostaining or purification procedures.

Fourier transform infrared vibrational spectroscopic imaging: integrating microscopy and molecular recognition

The recent development of Fourier transform infrared (FTIR) spectroscopic imaging has enhanced our capability to examine, on a microscopic scale, the spatial distribution of vibrational spectroscopic signatures of materials spanning the physical and biomedical disciplines. Recent activity in this emerging area has concentrated on instrumentation development, theoretical analyses to provide guidelines for imaging practice, novel data processing algorithms, and the introduction of the technique to new fields. To illustrate the impact and promise of this spectroscopic imaging methodology, we present fundamental principles of the technique in the context of FTIR spectroscopy and review new applications in various venues ranging from the physical chemistry of macromolecular systems to the detection of human disease.

Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis

In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation.

Fourier Transform Infrared Imaging and Unsupervised Hierarchical Clustering Applied to Cervical Biopsies

FTIR images of cervical tissue from patient biopsies were processed with an unsupervised hierarchical clustering algorithm and compared with hematoxylin- and eosin-stained adjacent sections. Anatomical and potential histopathological features were clearly resolved in the resultant cluster maps. The mean extracted spectra assigned to each cluster indicate that the major spectral differences between the different cells in tissue predictably occur in the amide I region (1700–1570 cm−1) and the phosphodiester/glycogen region (1200–1000 cm−1). FTIR imaging in which a focal plane array mercury–cadmium–telluride detector and unsupervised hierarchical clustering is used shows potential as a rapid, non-subjective diagnostic tool in cervical pathology.