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Spectral cytopathology: New aspects of data collection, manipulation and confounding effects
Abstract
This paper presents a short review on the improvements in data processing for spectral cytopathology, the diagnostic method developed for large scale diagnostic analysis of spectral data of individual dried and fixed cells. This review is followed by the analysis of the confounding effects introduced by utilizing reflecting "low-emissivity" (low-e) slides as sample substrates in infrared micro-spectroscopy of biological samples such as individual dried cells or tissue sections. The artifact introduced by these substrates, referred to as the "standing electromagnetic wave" artifact, indeed, distorts the spectra noticeably, as postulated recently by several research groups. An analysis of the standing wave effect reveals that careful data pre-processing can reduce the spurious effects to a level where they are not creating a major problem for spectral cytopathology and spectral histopathology.
... Some of the (spectroscopic) experimental procedures and the statistical methods and results have been summarized in two recent papers. 10,11 Furthermore, a detailed analysis of the biostatistical significance of the results was carried out, and their confidence intervals have been established. 12 Next, a short introduction to methods referred to in the past as 'optical diagnosis' will be presented. ...
... Subsequently, the effect of the standing wave on the observed spectra was analyzed by Wrobel et al. 27 and found to be much smaller than originally reported when microscope objectives with large numeric apertures are used; in addition, using the second derivative, rather than the absorption intensities, can further reduce the intensity distortions. 10 As the same tissue section was used for both infrared and white light imaging (after appropriate staining), the visible and infrared images could be accurately registered. This is necessary for annotation (see the section 'Annotation and data traceability') of spectral features. ...
... Summaries of data acquisition procedures and protocols have recently been published. 10,29 Spectral Preprocessing and Segmentation Each tissue spot produced B10 5 individual pixel spectra that were preprocessed as follows. First, the size of hyperspectral data cubes was reduced by a factor of four by co-adding four individual pixel spectra into a new spectrum with better signal-to-noise ratio, but larger pixel size, 12.5 mm on edge. ...
We report results of a study utilizing a novel tissue classification method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples on a tissue microarray. The spectral diagnostic method allows reproducible and objective classification of unstained tissue sections. This is accomplished by acquiring infrared data sets containing thousands of spectra, each collected from tissue pixels B6 mm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis that reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology is presented, suggesting that spectral histopathology can achieve levels of diagnostic accuracy that is comparable to that of multipanel immunohistochemistry.
... The amplitude of this wave depends strongly on the wavelength of the incident radiation and confounds the observed intensities. However, we have shown [16] that the errors for thin flat cells are relatively small, and can be further reduced by data preprocessing methods. ...
... This implementation uses the spectra of areas not occupied by cells to establish the noise characteristics of the instrument during data acquisition. Using a procedure known as noise-adjusted principal component analysis (NA-PCA), pixel spectra from areas occupied by cells can be corrected for instrument noise, thus reducing the noise components of the spectra by an order of magnitude [16,22,23]. This approach allowed for a much quicker data acquisition, co-adding only two interferograms for each pixel spectrum. ...
... However, as demonstrated theoretically by Bhargava and coworkers [11,12], any sample with strong gradients of the refractive index (for example, a straight edge of a particle boundary) will exhibit these effects as well. At present, there exist several computational approaches to correct these phenomena, based on either an iterative numerical process [14], a step-wise approximation method [15], or a phase-correction approach that was suggested as early as 2005 [9,16,24]. All these approaches are approximate in nature and some require extraordinarily long computation times. ...
Instrumental advances in vibrational microspectroscopy have made possible the observation of individual human cells and even subcellular structures. The observed spectra represent a snapshot of the biochemical composition of a cell; this composition varies subtly but reproducibly with cellular effects such as progression through the cell cycle, cell maturation and differentiation, and disease.The aim of this chapter is to summarize the progress achieved since the last edition of this book in using spectral cytopathology (SCP) – the combination of infrared (IR) microspectroscopy and multivariate methods of analysis – for the detection of abnormalities in exfoliated human cells. This work sets the stage for biomedical and diagnostic applications of this technology.
... These spectral changes can be attributed to the variations in the chemical composition of their corresponding cells [20]. Miljković et al. from the same group further trained an ANN to automatically distinguish the clinical oral disease cases from the normal cases, achieving sensitivity and specificity values of 96% and 94.3%, respectively [95]. Similar results (sensitivity of 95.5% and specificity of 94.7%) were achieved for exfoliated esophageal cells [96]. ...
... Part III: Cells, inoculated tissues and human tissues. [90] Infrared microspectroscopy of Oral Squamous Cell Carcinoma: Spectral signatures of cancer grading [91] Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma [92] Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer [93] FTIR-ATR and FT-Raman Spectroscopy for Biochemical Changes in Oral Tissue [94] Spectral cytopathology: new aspects of data collection, manipulation and confounding effects [95] Infrared micro-spectroscopy for cyto-pathological classification of esophageal cells [96] Oral cell studies Cancer Screening via Infrared Spectral Cytopathology (SCP): Results for the Upper Respiratory and Digestive Tracts [97] Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer [98] In vitro FTIR microspectroscopy analysis of primary oral squamous carcinoma cells treated with cisplatin and 5-fluorouracil: a new spectroscopic approach for studying the drug-cell interaction [99] Oral cancer diagnostics based on infrared spectral markers and wax physisorption kinetics [100] ...
Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.
... While cancerous cells can be collected from the surface, this does not constitute a diagnosis, which requires histological proof of breach of basement membrane. Despite this, brush biopsy may be very useful for patients with multiple oral lesions or for monitoring OPMDs A limited number of FTIR microspectroscopy studies have been reported, demonstrating the feasibility of diagnosis of oral cancers using single exfoliated cells prepared by cytocentrifugation onto low e microscope slides [13,[51][52][53][54]. Papamarkakis et al. demonstrated that FTIR microspectroscopy could classify oral cells according to anatomical site and that the compositional changes were attributed to the expression of keratins in the cells of the tongue and to the expression of collagen in the cells of the floor of the mouth [52]. ...
... Spectral differences in oral cells infected with the herpes simplex virus were also reported. Further studies from the same group showed that spectra from exfoliated cells from healthy volunteers could be discriminated from exfoliated cells from patients with oral dysplasia and cancer [13,53,54]. ...
Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.
... Different groups have used endometrial [19], cervical [20], brain [21,22], ovarian [23], intestine [22], lung [24], skin [25,26], prostate [27,28], breast [29,30], liver [31,32], colorectal [33], gastric [34], and esophagus [35] tissuesa method known as spectral histopathology (SHP) [36]. Various cell types and populations have also been examined, either fixed or live, derived from epithelial, nervous, muscle, or connective tissue, being either stem cells, transit-amplifying, or differentiated [4,[37][38][39] this method is known as spectral cytopathology (SCP) [40]. Some of the biofluids that have been employed for ATR-FTIR analysis are whole blood [41,42], blood plasma and serum [43][44][45], urine [9,42], sputum [46], saliva [47,48], tears [49], cerebrospinal fluid (CSF) [50], and amniotic fluid [17]. ...
... Numerous studies have suggested that, in case of very thin samples, these slides could generate an electric field standing wave (EFSW) effect coming from the metallic surface (Ag), urging careful consideration of the thickness of the samples (>2-3 μm) to avoid potential spectra distortion; therefore, ATR or transmission modes were thought preferable over transflection mode [68]. However, it was also pointed out that by using suitable preprocessing methods (e.g., second derivative), distortions from this phenomenon can be efficiently reduced [40,69]. Too-thin or too-sparse samples could also lead to low signal-to-noise (S/N) ratio [37]; thus, the samples should ideally be three-or fourfold thicker than the penetration depth, with no maximum thickness limitations. ...
Vibrational spectroscopic techniques are increasingly utilized in biomedical research. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has been applied extensively to investigate various diseases by determining the chemical and molecular differences coming with the disease. Being label-free, nondestructive, and inexpensive, biospectroscopy could potentially make a perfect diagnostic tool in the years to come.
... The methods of SHP, including data acquisition and data pre- processing, have been described in detail in the litera- ture. 2,20,21 A block diagram of the required steps will be dis- cussed in section 2.7. All spectroscopic studies reported here were carried out on 'low emissivity' (low-e) slides (Kevley Technologies, Chesterfield, OH) that are totally reflective toward infrared radiation, but are nearly totally transparent to visible light; thus, the same sample can be used both for infra- red data acquisition and, after appropriate staining, for classi- cal histopathology. ...
... The resulting set of ca. 25 000 pixels per tissue spot was corrected for confounding contributions such as noise, water vapor and resonance Mie (R-Mie) scattering (via a phase correction algorithm 24 ) using procedures developed and reported previously in the literature. 20 In order to enhance the sensitivity of spectral methods toward specific changes of protein abundance, the broad and often unstructured raw spectra were converted to 2 nd derivatives. This process is known to reduce the half width of spectral bands, thereby pro- viding better discriminatory power which provides for the ability to classify different tumor types. ...
We report results on a statistical analysis of an infrared spectral dataset comprising a total of 388 lung biopsies from 374 patients. The method of correlating classical and spectral results and analyzing the resulting data has been referred to as spectral histopathology (SHP) in the past. Here, we show that standard bio-statistical procedures, such as strict separation of training and blinded test sets, result in a balanced accuracy of better than 95% for the distinction of normal, necrotic and cancerous tissues, and better than 90% balanced accuracy for the classification of small cell, squamous cell and adenocarcinomas. Preliminary results indicate that further sub-classification of adenocarcinomas should be feasible with similar accuracy once sufficiently large datasets have been collected.
... In biomedical applications, it has been demonstrated that vibrational spectroscopy can differentiate normal and diseased (e.g. cancer, inflammation), and classify early stage diseased states, in tissue, cells, bodily fluids, and the term "spectropathology" has been coined [6][7][8]. ...
... Whole cell and tissue studies have been carried out on a range of pathologies [112]- [114] and in vivo studies [115], [116] have demonstrated the prospective for diagnostic applications. The potential of vibrational spectroscopy in conjunction with multivariate analysis techniques as a diagnostic tool has thus been well demonstrated and the concept of spectral cytopathology has been coined [117]. In this respect, Raman and infrared can be viewed as rival technologies, but to best advance the understanding of the potential of the techniques a combination of the two complementary techniques is recommended. ...
Alterations in biomolecular components in human blood are commonly used as an indication of disease states, namely differences in protein concentration. Unfortunately, conventional test kits currently employed in hospitals suffer from long time delays, meaning patients often have to wait anxiously for their test results. Vibrational spectroscopic techniques, such as infrared and Raman, have the ability to replace current practices, as they are label-free, cost-effective, easy to operate, and require minimal sample preparation. The sensitivity to subtle changes in biochemical composition makes them ideal diagnostic tools, and recent advances in technology and data analytics means bodily fluids can be analyzed rapidly and noninvasively to detect disease-related fluctuations in protein concentration. In this chapter, we outline the current clinical procedures for blood tests, examine the capability of biomedical vibrational spectroscopy for disease diagnostics and monitoring, and discuss the potentiality for the techniques to be successfully translated into the clinic.
... The use of low-emissivity slides has come under criticism in the literature, 34 but we have shown that they can be used without problems if certain precautions are followed. 35 The third section (A003) was mounted on glass slides and stained with a p40 antibody (Biocare Medical, Pacheco, California) for confirmation of SqCC. Section A004 was stained for programmed death ligand-1 (PD-L1), but the results obtained will not be discussed here. ...
This paper reports the results of a collaborative lung cancer study between City of Hope Cancer Center (Duarte, California) and CIRECA, LLC (Cambridge, Massachusetts), comprising 328 samples from 249 patients, that used an optical technique known as spectral histopathology (SHP) for tissue classification. Because SHP is based on a physical measurement, it renders diagnoses on a more objective and reproducible basis than methods based on assessing cell morphology and tissue architecture. This report demonstrates that SHP provides distinction of adenocarcinomas from squamous cell carcinomas of the lung with an accuracy comparable to that of immunohistochemistry and highly reliable classification of adenosquamous carcinoma. Furthermore, this report shows that SHP can be used to resolve interobserver differences in lung pathology. Spectral histopathology is based on the detection of changes in biochemical composition, rather than morphologic features, and is therefore more akin to methods such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging. Both matrix-assisted laser desorption ionization time-of-flight mass spectrometry and SHP imaging modalities demonstrate that changes in tissue morphologic features observed in classical pathology are accompanied by, and may be correlated to, changes in the biochemical composition at the cellular level. Thus, these imaging methods provide novel insight into biochemical changes due to disease.
... The most relevant bibliography involving FTIR cell analyses can be found in recent reviews [9,10]. Regarding the discrimination of cells from the diagnostic point of view, commonly referred to as Spectral Cytopathology (SCP), the most important works using FTIR microscopy are mainly related to Diem's collaborations [11][12][13][14][15][16][17][18]. All these studies are focused on smear cells directly extracted from different parts of the patients rather than in cell cultures. ...
Fourier transform infrared (FTIR) spectroscopy is a highly versatile tool for cell and tissue analysis. Modern commercial FTIR microspectroscopes allow the acquisition of good-quality hyperspectral images from cytopathological samples within relatively short times. This study aims at assessing the abilities of FTIR spectra to discriminate different types of cultured skin cell lines by different computer analysis technologies. In particular, 22700 single skin cells, belonging to two non-tumoral and two tumoral cell lines, were analysed. These cells were prepared in three different batches that included each cell type. Different spectral preprocessing and classification strategies were considered, including the current standard approaches to reduce Mie scattering artefacts. Special care was taken for the optimisation, training and evaluation of the learning models in order to avoid possible overfitting. Excellent classification performance (balanced accuracy between 0.85 and 0.95) was achieved when the algorithms were trained and tested with the cells from the same batch. When cells from different batches were used for training and testing the balanced accuracy reached values between 0.35 and 0.6, demonstrating the strong influence of sample preparation on the results and comparability of cell FTIR spectra. A deep study of the most optimistic results was performed in order to identify perturbations that influenced the final classification.
... The effect of water vapour is however more serious as it overlaps with the fingerprint region, has numerous sharp peaks (associated with the rotational structure of the vibrational bending mode) and is highly variable. Research is ongoing to explore methods to compensate for the presence of water vapour in the spectrum [133]- [135], and there have been some successful applications [136]. Perkin Elmer includes a patented method for Atmospheric Vapour Compensation (AVC) in the software for their FTIR systems that is based on a simulated water vapour spectrum with very high spectral resolution [137], [138]. ...
Mid-infrared (MIR) imaging has emerged as a valuable tool to investigate biological samples, such as tissue histological sections and cell cultures, by providing non-destructive chemical specificity without recourse to labels. While feasibility studies have shown the capabilities of MIR imaging approaches to address key biological and clinical questions, these techniques are still far from being deployable by non-expert users. In this review, we discuss the current state of the art of MIR technologies and give an overview on technical innovations and developments with the potential to make MIR imaging systems more readily available to a larger community. The most promising developments over the last few years are discussed here. They include improvements in MIR light sources with the availability of quantum cascade lasers and supercontinuum IR sources as well as the recently developed upconversion scheme to improve the detection of MIR radiation. These technical advances can substantially speed up data acquisition of multispectral or hyperspectral datasets thus providing the end user with vast amounts of data when imaging whole tissue areas of many mm². Therefore, effective data analysis is of tremendous importance, and progress in method development is discussed with respect to the specific biomedical context.
... A number of recent publications [23,52,53] have dealt with data preprocessing steps and methods, particularly for IR-based measurements, which will not be detailed here. These steps are necessary because any experimental data are contaminated by noise, unwanted spectral features, distortions, and so on, the source of which are well understood. ...
During the last 15years, vibrational spectroscopic methods have been developed, which can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome, and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for the detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors, and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction, or gene arrays. Because of the inherent sensitivity toward changes in the biomolecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that obtained by any one of the conventional techniques.
... It was further shown that samples from patients with reactive atypical changes or malignancy associated changes were more spectrally similar to a sample from a patient with squamous cell carcinoma than to samples from healthy volunteers [47]. A further IR study from the same group [5,48] showed that spectra from squamous cells collected from the tongues of healthy volunteers could be discriminated from squamous cells collected from the tongues of patients with oral disease, mostly dysplastic and cancer cases, using unsupervised principal components analysis. An artificial neural network was trained to automatically distinguish the clinical cases from the normal cases and sensitivity and specificity values of 96% and 94.3% were obtained. ...
Vibrational spectroscopy analyses vibrations within a molecule and can be used to characterise a molecular structure. Raman spectroscopy is one of the vibrational spectroscopic techniques, in which incident radiation is used to induce vibrations in the molecules of a sample, and the scattered radiation may be used to characterise the sample in a rapid and non-destructive manner. Infrared (IR) spectroscopy is a complementary vibrational spectroscopic technique based on the absorption of IR radiation by the sample. Molecules absorb specific frequencies of the incident light which are characteristic of their structure. IR and Raman spectroscopy are sensitive to subtle biochemical changes occurring at the molecular level allowing spectral variations corresponding to disease onset to be detected. Over the past 15 years, there have been numerous reports demonstrating the potential of IR and Raman spectroscopy together with multivariate statistical analysis techniques for the detection of a variety of cancers including, breast, lung, brain, colon, oral, oesophageal, prostate and cervical cancer. This paper discusses the recent advances and the future perspectives in relation to cancer screening applications, focussing on cervical and oral cancer.
... NOTE: There are multiple approaches available by which baseline correction, spectral normalization and noise reduction can be achieved, with most software having automated algorithms built in. In addition, there are an emerging number of approaches that will correct for spectral aberrations, which have been discussed in detail [29][30][31][32][33][34][35][36][37][38][39][40][41][42] ; however, the community does not yet agree on which of these are needed. 5. Observe a list of all the IR frequencies collected within the image (typically of a spectral range from 900 to 4,000 cm -1 ). ...
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.
... The problems reported with the use of the low-e sample substrates 16 were accounted for by methods reported in the literature. 17,18 The instrument bench, the IR microscope, and an external microscope enclosure box were purged with a continuous stream of dry air (−40°C dew point) to reduce atmospheric water vapor spectral contributions in the spectra. Data were acquired in reflectance mode with the following parameters: 4 cm −1 spectral resolution, 6.25 μm 2 pixel size, 2 scans per pixel, Norton-Beer apodization, and 1 level of zero filling. ...
We report results from a study utilizing infrared spectral cytopathology (SCP) to detect abnormalities in exfoliated esophageal cells. SCP has been developed over the past decade as an ancillary tool to classical cytopathology. In SCP, the biochemical composition of individual cells is probed by collecting infrared absorption spectra from each individual, unstained cell, and correlating the observed spectral patterns, and the variations therein, with against classical diagnostic methods to obtain an objective, machine-based classification of cells. In the past, SCP has been applied to the analysis and classification of cells exfoliated from the cervix and the oral cavity. In these studies, it was established that SCP can distinguish normal and abnormal cell types. Furthermore, SCP can differentiate between truly normal cells, and cells with normal morphology from the vicinity of abnormalities. Thus, SCP may be a valuable tool for the screening of early stages of dysplasia and pre-cancer.
... Hence, care should be taken on substrate choice 17,18 . Recently, topographical features of the sample and its effects have been shown to be minimized by inputting second derivative spectra in the classification model; better segregation of normal versus various disease categories facilitates potential spectral histopathological diagnosis 19 . Research by Cao et al. 20 has demonstrated that if this pre-processing data analysis approach is performed (e.g., after both transflection and transmission measurements on dried cellular monolayers), the resulting classification is the same. ...
IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.
... Other important effects such as the electric-field standing wave effect and aberrations that are associated with inconsistent section thickness specially using the IR-T mode, which employ low-e reflective substrates, have been elaborated in a few recent studies. 38,39 In ATR-IR, the penetration depth is wavelength dependent and is on the order of a few micrometers using the germanium internal reflection element, which decreases the optical path lengths; hence only the information from the superficial layers of the tissue is obtained. Although ATR-IR provided highcontrast images, the total sample area that can be measured is limited to 500 3 500 lm 2 per each image acquisition and can also induce tissue damage due to the pressure applied and may render the sample unusable for further analysis. ...
Complementary diagnostic methods to conventional histopathology are currently being investigated for developing rapid and objective molecular-level understanding of various disorders, especially cancers. Spectral histopathology using vibrational spectroscopic imaging has been put in the frontline as potentially promising in this regard as it provides a "spectral fingerprint" of the biochemical composition of cells and tissues. In order to ascertain the feasible conditions of vibrational spectroscopic methods for tissue-imaging analysis, vibrational multimodal imaging (infrared transmission, infrared-attenuated total reflection, and Raman imaging) of the same colon tissue has been implemented. The spectral images acquired were subjected to multivariate clustering analysis in order to identify on a molecular level the constituent histological organization of the colon tissue such as the epithelium, connective tissue, etc., by comparing the cluster images with the histological reference images. Based on this study, a comparative analysis of important factors involved in the vibrational multimodal imaging approaches such as image resolution, time constraints, their advantages and limitations, and their applicability to biological tissues has been carried out. Out of the three different vibrational imaging modalities tested, infrared-attenuated total reflection mode of imaging appears to provide a good compromise between the tissue histology and the time constraints in achieving similar image contrast to that of Raman imaging at an approximately 33-fold faster measurement time. The present study demonstrates the advantages, the limitations of the important parameters involved in vibrational multimodal imaging approaches, and their potential application toward imaging of biological tissues.
The investigation of pathological diseases largely relies on laboratory examinations. The ability to identify and characterise cells is an essential process for clinicians to reach an accurate diagnosis and inform appropriate treatments. There is currently a gap between the advancement of scientific knowledge on cellular and molecular pathways and the development of novel techniques capable of detecting subtle cellular changes associated with disease. Biospectroscopy is the use of spectroscopy techniques to investigate biological materials. Within a biological sample, important molecules such as lipids, carbohydrates, nucleic acids, and proteins are held together by chemical bonds; these bonds will vibrate following excitation with infrared light. By measuring the vibrational energy of each molecule present in a biological sample, a unique spectrum, known as the “molecular fingerprint” is generated. As disease-related changes in biological samples will be reflected in the molecular fingerprint, biospectroscopy is a well-placed candidate for the investigation of disease. Biospectroscopy has been gaining wider acceptance and application in the clinical setting over the past decade; however, it has yet to reach diagnostic laboratories and healthcare clinics as a routine platform for clinical assessment. Immunological disorders are complex, often demonstrating interaction across multiple molecular pathways which results in delayed diagnosis. Vibrational spectroscopy is being applied in many fields, and here we present a review of its use in cellular immunology. Potential benefits, including an enhanced definition of molecular processes and the use of spectroscopy in disease diagnosis, monitoring, and treatment response, are discussed. The translation of vibrational spectroscopic techniques into clinical practice offers rapid, noninvasive, and inexpensive methods to obtain information on the molecular composition of biological samples. The potential clinical benefits of biospectroscopy include providing a more prompt and accurate disease diagnosis, thus improving patient care and resulting in better health outcomes.
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.
Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a real concern in hospital environments. Nosocomial infections caused by P. aeruginosa are often hard to treat because of its intrinsic resistance and remarkable ability to acquire further resistance mechanisms to multiple groups of antimicrobial agents. Thus, rapid determination of the susceptibility of P. aeruginosa isolates to antibiotics is crucial for effective treatment. The current methods used for susceptibility determination are time-consuming; hence the importance of developing a new method. Fourier-transform infra-red (FTIR) spectroscopy is known as a rapid and sensitive diagnostic tool, with the ability to detect minor abnormal molecular changes including those associated with the development of antibiotic- resistant bacteria. The main goal of this study is to evaluate the potential of FTIR spectroscopy together with machine learning algorithms, to determine the susceptibility of P. aeruginosa to different antibiotics in a time span of ∼20 minutes after the first culture. For this goal, 590 isolates of P. aeruginosa, obtained from different infection sites of various patients, were measured by FTIR spectroscopy and analyzed by machine learning algorithms. We have successfully determined the susceptibility of P. aeruginosa to various antibiotics with an accuracy of 82-90%.
In this contribution, data will be presented that suggest that certain cancer markers or their surrogates can be detected by infrared spectral imaging methodology. In particular, the co-localization of certain regions in spectral images with PD-L1-postitive immunohistochemical response suggests that either the PD-L1 protein or a surrogate associated with its presence are detectable. In the case of Her2/neu, an analysis of mean spectral signatures of over forty thousand individual spectra revealed small, but statistically significant spectral differences between cancer marker positive and negative tissues in spectral regions associated with protein phosphorylation.
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.
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...
Raman spectroscopy can provide a rapid, label-free, nondestructive measurement of the chemical fingerprint of a sample and has shown potential for cancer screening and diagnosis. Here we report a protocol for Raman microspectroscopic analysis of different exfoliative cytology samples (cervical, oral and lung), covering sample preparation, spectral acquisition, preprocessing and data analysis. The protocol takes 2 h 20 min for sample preparation, measurement and data preprocessing and up to 8 h for a complete analysis. A key feature of the protocol is that it uses the same sample preparation procedure as commonly used in diagnostic cytology laboratories (i.e., liquid-based cytology on glass slides), ensuring compatibility with clinical workflows. Our protocol also covers methods to correct for the spectral contribution of glass and sample pretreatment methods to remove contaminants (such as blood and mucus) that can obscure spectral features in the exfoliated cells and lead to variability. The protocol establishes a standardized clinical routine allowing the collection of highly reproducible data for Raman spectral cytopathology for cancer diagnostic applications for cervical and lung cancer and for monitoring suspicious lesions for oral cancer. This protocol details how to use Raman spectroscopy for cancer cytopathology diagnosis. Detailed instructions are provided for sample preparation (cervical, oral and lung exfoliated cells), data acquisition, preprocessing and analysis.
This study demonstrates the efficacy of Raman microspectroscopy of oral cytological samples for differentiating dysplastic, potentially malignant lesions from those of normal, healthy donors. Cells were collected using brush biopsy from healthy donors (n = 20) and patients attending a Dysplasia Clinic (n = 20). Donors were sampled at four different sites (buccal mucosa, tongue, alveolus, gingiva), to ensure matched normal sites for all lesions, while patient samples were taken from clinically‐evident, histologically‐verified dysplastic lesions. Spectra were acquired from the nucleus and cytoplasm of individual cells of all samples and subjected to partial least squares‐discriminant analysis. Discriminative sensitivities of 94% and 86% and specificity of 85% were achieved for the cytoplasm and nucleus, respectively, largely based on lipidic contributions of dysplastic cells. Alveolar/gingival samples were differentiated from tongue/buccal samples, indicating that anatomical site is potentially a confounding factor, while age, gender, smoking and alcohol consumption were confirmed not to be.
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Spectral cytopathology (SCP) is a promising label-free technique for diagnosing diseases and monitoring therapeutic outcomes using FTIR spectroscopy. In most cases, cells must be immobilized on a substrate prior to spectroscopic interrogation. This creates significant limitations for high throughput phenotypic whole-cell analysis, especially for the non-adherent cells. Here we demonstrate how metasurface-enhanced infrared reflection spectroscopy (MEIRS) can be applied to a continuous flow of live cell solution by applying AC voltage to metallic metasurfaces. By integrating metasurfaces with microfluidic delivery channels and attracting the cells to the metasurface via dielectrophoretic (DEP) force, we collect the infrared spectra of cells in real time within a minute, and correlate the spectra with simultaneously acquired images of the attracted cells. The resulting DEP-MEIRS technique paves the way for rapid SCP of complex cell-containing body fluids with low cell concentrations, and for the development of a wide range of label-free liquid biopsies.
This paper summarizes results from two large lung cancer studies comprising over 700 samples that demonstrate the ability of SHP to distinguish cancerous tissue regions from normal tissue, to differentiate benign lesions from normal tissue and cancerous lesions, and to classify lung cancer types. Furthermore, malignancy‐associated changes (MACs) can be identified in cancer‐adjacent normal tissue. The ability to differentiate a multitude of normal cells and tissue types allow SHP to identify tumor margins and immune cell infiltration. Finally, SHP easily distinguishes small cell lung cancer (SCLC) from non‐small cell lung cancer (NSCLC), and provides a further differentiation of NSCLC into adenocarcinomas (ADC) and squamous cell carcinomas (SqCC) with an accuracy comparable of classical histopathology combined with immuno‐histochemistry (IHC). Case studies are presented that demonstrates that SHP can resolve inter‐observer discrepancies in standard histopathology. This article is protected by copyright. All rights reserved.
FTIR or Raman spectroscopy of biological analytes are increasingly explored as screening tools for early detection of cancer. In the present study, integrated analysis of FTIR and Raman spectra obtained from exfoliated cells is adopted to improve discrimination performance among normal, pre-cancerous and cancerous conditions. Multiple spectra were obtained from 13 normal, 13 pre cancer and 10 cancer patients in both modes. Compared to normal, significant differences were observed at 1550, 1580, 1640, 2370, 2330, 2950-3000 and 3650-3750 cm-1 (FTIR) and 520, 640, 785, 827, 850, 935, 1003, 1175, 1311 cm-1 and 1606 cm-1 (Raman) vibrations of other two. Increase in DNA, protein and lipid content with malignancy was more clearly elucidated by examining both spectra. Principal component analysis (PCA)-linear discriminant analysis (LDA) with 10-fold cross validation of the FTIR and Raman spectral data sets showed effecient discrimination between normal and pathological conditions while overlapping was seen between the two pathologies. The PCA-LDA model of the dual spectra yiielded classification accuracy of 98% in comparison to either FTIR (85%) or Raman (82%) in a spectrum-wise comparison. In patient-wise approach (mean of all spectra from a patient), the overall classification efficiency was 73%, 80% and 87% for FTIR, Raman and integrated spectral approach respectively. Moreover, the efficiency of the integrated FTIR-Raman PCA-LDA model as a prediction tool was tested to screen susceptible individuals (11 cigarette smokers) using the dual spectra acquired from these individuals. The study presents proof-of-concept for adopting a large-scale, follow-up trial of the approach for mass screening purpose.
The search for disease markers in whole blood, or easily accessible blood components by spectral methods is a highly important aspect in the field of biophotonic research for disease diagnostics and screening, since it promises a minimally invasive approach to assess an individual's state of health. Fourier transform infrared spectroscopy, in particular, promises to be a fast, inexpensive method to search for markers of disease, since it detects variation in the proteome, lipidome and metabolome of biofluids, or activation of immune cells. However, the analysis of any materials by spectral methods is confounded by external factors such as those related to sample deposition and data acquisition, and by inherent variations in blood plasma concentration of small molecules (lactate, carbonate, phosphate, glucose) that varies between individual subjects and even for a given individual, as a function of time. Furthermore, observed differences in spectral patterns between patient samples and the control group may be due to the body's immune response (in particular, to the albumin to globulin ratio) and therefore, may not be specific to disease. These factors need to be accounted for in any effort to reliably detect much smaller variations in the concentration of disease‐specific markers.
Vibrational spectroscopies, based on Infrared absorption and/or Raman scattering provide a detailed fingerprint of a material, based on the chemical content. Diagnostic and prognostic tools based on these technologies have the potential to revolutionise our clinical systems leading to improved patient outcome, more efficient public services and significant economic savings. However, despite these strong drivers, there are many fundamental scientific and technological challenges which have limited the implementation of this technology in the clinical arena, although recent years have seen significant progress in addressing these challenges. This review examines (i) the state of the art of clinical applications of infrared absorption and Raman spectroscopy, and (ii) the outstanding challenges, and progress towards translation, highlighting specific examples in the areas of in vivo, ex vivo and in vitro applications. In addition, the requirements of instrumentation suitable for use in the clinic, strategies for pre-processing and statistical analysis in clinical spectroscopy and data sharing protocols, will be discussed. Emerging consensus recommendations are presented, and the future perspectives of the field are assessed, particularly in the context of national and international collaborative research initiatives, such as the UK EPSRC Clinical Infrared and Raman Spectroscopy Network, the EU COST Action Raman4Clinics, and the International Society for Clinical Spectroscopy.
This chapter presents a review of the methodology for analyzing large spectral data sets typically available from vibrational microspectral measurements. Focusing on supervised methods, it highlights that the spectral variance is correlated with prior knowledge of the outcome. In unsupervised methods, no input data except the spectral hypercube is provided to the algorithm, and the class membership is calculated from the variance of the data set. Several factor methods have been developed to decompose data sets into a bilinear model of variables. Methods and algorithms are derived in the chapter that allow for supervised classification, for example, to allow the construction of computer-based self-learning diagnostic algorithms. The correlation of spectral changes with continuously varying parameters is also introduced. This method is commonly employed in “two-dimensional” FTIR spectroscopy. In analogy to 2D-NMR spectroscopy, 2D-infrared (IR) spectroscopy is based on the cross-correlation function between variations in the time-dependent spectral intensities.
Background:
Development of a nonendoscopic test for Barrett's esophagus would revolutionize population screening and surveillance for patients with Barrett's esophagus. Swallowed cell collection devices have recently been developed to obtain cytology brushings from the esophagus: automated detection of neoplasia in such samples would enable large-scale screening and surveillance.
Methods:
Fourier transform infrared (FTIR) spectroscopy was used to develop an automated tool for detection of Barrett's esophagus and Barrett's neoplasia in esophageal cell samples. Cytology brushings were collected at endoscopy, cytospun onto slides and FTIR images were measured. An automated cell recognition program was developed to identify individual cells on the slide.
Results:
Cytology review and contemporaneous histology was used to inform a training dataset containing 141 cells from 17 patients. A classification model was constructed by principal component analysis fed linear discriminant analysis, then tested by leave-one-sample-out cross validation. With application of this training model to whole slide samples, a threshold voting system was used to classify samples according to their constituent cells. Across the entire dataset of 115 FTIR maps from 66 patients, whole samples were classified with sensitivity and specificity respectively as follows: normal squamous cells 79.0% and 81.1%, nondysplastic Barrett's esophagus cells 31.3% and 100%, and neoplastic Barrett's esophagus cells 83.3% and 62.7%.
Conclusions:
Analysis of esophageal cell samples can be performed with FTIR spectroscopy with reasonable sensitivity for Barrett's neoplasia, but with poor specificity with the current technique.
Oral squamous cell carcinoma ranks as the 15th most common cancer worldwide. The present study was undertaken to standardise a protocol for the analysis of oral exfoliated cells using Raman microspectroscopy. For this purpose, samples were obtained from two different sites, based on prevalence of disease (ventral side of the tongue and buccal mucosa). Different oral rinsing agents were employed and it was concluded that non-alcoholic mouthwash adequately removes food debris. Samples were collected using various collection tools and compared. It was observed that endo-cervical brushes yielded cells from deeper layers of the epithelium. Furthermore, monolayer formation of cells was carried out adopting cytospin and ThinPrep techniques and only the ThinPrep method provided flat and separated cells on the glass slide. Raman spectra were acquired from the nuclear and cytoplasmic regions of the cell using an XploRA confocal Raman instrument (HORIBA Jobin Yvon) with a 532 nm laser as the source. Glass spectral contamination was removed using non negatively constrained least squares (NNLS) algorithms. Corrected spectra were subjected to principal components analysis (PCA) which was able to differentiate the nucleus and cytoplasm regions of the cell; based on nucleic acid and protein features, respectively. However, no classification of the two anatomically different sites was observed according to PCA or PCA-LDA (linear discriminant analysis) using either the nuclear or cytoplasmic spectra. Nevertheless, the study has developed a standardised protocol for sample collection, sample preparation, spectral acquisition and data processing for future studies of oral exfoliated cells based on Raman microspectroscopy.
Mid-infrared microscopy has become a key technique in the field of biomedical science and spectroscopy. This label-free, non-destructive technique permits the visualisation of a wide range of intrinsic biochemical markers in tissues, cells and biofluids by detection of the vibrational modes of the constituent molecules. Together, infrared microscopy and chemometrics is a widely accepted method that can distinguish healthy and diseased states with high accuracy. However, despite the exponential growth of the field and its research world-wide, several barriers currently exist for its full translation into the clinical sphere, namely sample throughput and data management. The advent and incorporation of quantum cascade lasers (QCLs) into infrared microscopes could help propel the field over these remaining hurdles. Such systems offer several advantages over their FT-IR counterparts, a simpler instrument architecture, improved photon flux, use of room temperature camera systems, and the flexibility of a tunable illumination source. In this current study we explore the use of a QCL infrared microscope to produce high definition, high throughput chemical images useful for the screening of biopsied colorectal tissue.
Biospectroscopic investigations have attracted attention of both the clinicians and basic sciences researchers in recent years. Scientists are discovering new areas for FTIR biospectroscopy applications in medicine. The aim of this study was to measure the possibility of FTIR-MSP application for the recognition and detection of fetus abnormalities after exposure of pregnant mouse to phenobarbital (PB) and levamisole (LEV) alone or in combination. PB is one of the most widely used antiepileptic drugs (AEDs), with sedative and hypnotic effects. When used by pregnant women, it is known to be a teratogenic agent. LEV is an antihelminthic drug with some applications in immune-deficiency as well as colon cancer therapy. Four groups of ten pregnant mice were selected for the experiments as follows: one control group received only standard diet, one group was injected with 120 mg/kg of BP, one group was injected with 10 mg/kg of LEV, and the last group was treated simultaneously with both BP and LEV at the above mentioned doses. Drugs administration was performed on gestation day 9 and fetuses were dissected on pregnancy day 15. Each dissected fetus was fixed, dehydrated and embedded in paraffin. Sections of liver (10 μm) were prepared from control and treated groups by microtome and deparaffinized with xylene. The spectra were taken by FTIR-MSP in the region of 4000–400 cm−1. All the spectra were normalized based on amide II band (1545 cm−1) after baseline correction of the entire spectrum, followed by classification using PCA, ANN and SVM. Both morphological and spectral changes were shown in the treated fetuses as compared to the fetuses in the control group. While cleft palate and C-R elongation were seen in PB injected fetuses, developmental retardation was mostly seen in the LEV injected group. Biospectroscopy revealed that both drugs mainly affected the cellular lipids and proteins, with LEV causing more changes in amide I and lipid regions than PB. Application of PCA, ANN and SVM methods were able to successfully classify these FTIR spectroscopic data and discriminate between control and treated groups of fetuses, making it a new potential tool for drugs teratogenic investigations.
Infrared chemical imaging is a rapidly emerging field with new advances in instrumentation, data acquisition and data analysis. These developments have had significant impact in biomedical applications and numerous studies have now shown that this technology offers great promise for the improved diagnosis of the diseased state. Relying on purely biochemical signatures rather than contrast from exogenous dyes and stains, infrared chemical imaging has the potential to revolutionise histopathology for improved disease diagnosis. In this review we discuss the recent advances in infrared spectroscopic imaging specifically related to spectral histopathology (SHP) and consider the current state of the field. Finally we consider the practical application of SHP for disease diagnosis and consider potential barriers to clinical translation highlighting current directions and the future outlook.
This article summarizes the methods employed, and the progress achieved over the past two decades in applying vibrational (Raman and IR) micro-spectroscopy to problems of medical diagnostics and cellular biology. During this time, several research groups have verified the enormous information content of vibrational spectra; in fact, information on protein, lipid and metabolic composition of cells and tissues can be deduced by decoding the observed vibrational spectra. This decoding process is aided by the availability of computer workstations and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared micro-spectral data has enabled the collection of images of cells and tissues based solely on vibrational spectroscopic data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational spectroscopy in the biological and biomedical arenas.
Instrumental advances in infrared micro-spectroscopy have made possible the observation of individual human cells and even subcellular structures. The observed spectra represent a snapshot of the biochemical composition of a cell; this composition varies subtly but reproducibly with cellular effects such as progression through the cell cycle, cell maturation and differentiation, and disease. The aim of this summary is to provide a synopsis of the progress achieved in infrared spectral cytopathology (SCP) - the combination of infrared micro-spectroscopy and multivariate methods of analysis - for the detection of abnormalities in exfoliated human cells of the upper respiratory and digestive tract, namely the oral and nasopharyngeal cavities, and the esophagus.
This chapter emphasizes results from all vibrational techniques, in particular those from vibrational circular dichroism (VCD) measurements. It reviews the basic results for peptides, proteins, nucleic acids, and lipids, and also explores some results that demonstrate how imaginative application of these techniques can reveal information that is not available from any other techniques. Owing to their formidable size, low solubility, and structural complexity, proteins were realized early on to be poor subjects for initial studies using vibrational spectroscopy. A plethora of vibrational spectroscopic data exists for various small linear and cyclic peptides. The chapter reviews the basic principles, including the transition dipole coupling model to explain the conformational sensitivity of IR spectroscopy; furthermore, it contains the most comprehensive compilation of amino acid side group vibrations in peptides and proteins, and a thorough discussion of the amide I and II manifolds.
In the present paper, we compared the histopathological and vibrational analyses of different tissue sections of Oral Squamous Cell Carcinoma (OSCC) at various malignancy grades, in order to unambiguously identify them. To achieve reliable results, healthy and dysplastic samples were also taken into account. FTIR microspectroscopy is considered an effective tool for studying different molecular structures occurring in tumoral tissues and offers an interesting alternative to detect biochemical changes in a non-subjective way. In particular, on an adequate number of tissue sections affected by three different grades of OSSC (well G1, moderately G2, and poorly G3 differentiated), as well as on dysplastic and healthy tissues (all obtained from surgical resection), the chemical maps were acquired on meaningful areas containing both epithelial and connective structures. The multivariate analysis (Hierarchical Cluster Analysis, HCA, and Principal Component Analysis, PCA), performed separately on epithelial and connective spectral data, afforded to a good segregation for the different morphological structures. By analysing the representative spectra of healthy, dysplastic and tumoral epithelia and connectives, modifications were pin-pointed in the position of bands and absorbance band ratios usually associated with carcinogenesis. Above all, the changes in the protein pattern (with modifications in the length of side chains and in secondary structures), and in carbohydrates and nucleic acids moieties were associated with specific spectral markers of this pathology. The vibrational investigation led to a satisfactory understanding of these lesions so contributing to an early diagnosis, when the sole morphological inspection may result troublesome.
FTIR microscopy is a powerful technique which has become popular due to its ability to provide complementary information during histopathological assessment of biomedical tissue samples. Recently however, questions have been raised on the suitability of the transflection mode of operation for clinical diagnosis due to the so called Electric Field Standing Wave (EFSW) effect. In this paper we compare chemical images measured in transmission and transflection from prostate tissue obtained from five different patients, and discuss the variability of the spectra acquired with each sampling modality. We find that spectra obtained in transflection undergo a non-linear distortion, i.e. non-linear variations in absorption band strength across the spectra, and that there are significant differences in spectra measured from the same area of tissue depending on the mode of operation. Principal Component Analysis (PCA) is used to highlight that poorer discrimination between benign and cancerous tissue is obtained in transflection mode. In addition we show that use of second derivatives, while qualitatively improves spectral discrimination, does not completely alleviate the underlying problem.
We report results of a study utilizing a novel tissue classification method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples on a tissue microarray. The spectral diagnostic method allows reproducible and objective classification of unstained tissue sections. This is accomplished by acquiring infrared data sets containing thousands of spectra, each collected from tissue pixels ∼6 μm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis that reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology is presented, suggesting that spectral histopathology can achieve levels of diagnostic accuracy that is comparable to that of multipanel immunohistochemistry.Laboratory Investigation advance online publication, 9 February 2015; doi:10.1038/labinvest.2015.1.
Results of a study comparing infrared imaging data sets collected on different instruments or instrument platforms are reported, along with detailed methods developed to permit such comparisons. It was found that different instrument platforms, although employing different detector technologies and pixel sizes, produce highly similar and reproducible spectral results. However, differences in the absolute intensity values of the reflectance data sets were observed that were cause by heterogeneity of the sample sub-strate in terms of reflectivity and planarity.
The vibrational spectroscopy techniques of Raman spectroscopy and Fourier-transform infrared spectroscopy offer a number of potential advantages as tools for clinical diagnosis. The ability of these methods to detect subtle biochemical changes relating to pathology opens the possibility of their use in tissue diagnosis. Potential applications include use as an 'optical biopsy' technique for in vivo tissue diagnosis or to guide therapy, as a 'digital staining' method to assist a histopathologist in analysing a sample, or as an entirely automated process for histopathology classification. To date, much work has been undertaken in applying these spectroscopic methods to discriminate between disease states across a wide range of pathologies and organ systems, but as yet none have entered routine clinical practice. There is a pressing clinical need for real-time, accurate tissue diagnosis, especially in malignant conditions for which rapid diagnosis and comprehensive identification and treatment of diseased tissue are of paramount importance. Cancer diagnostics remains reliant on analysis of tissue samples by histopathologists to confirm malignancy, based on morphological tissue changes and immunohistochemical staining techniques. There is increasing evidence that vibrational spectroscopy, in combination with chemometric data analysis, is a powerful and accurate technique for detecting cancerous and pre-cancerous biochemical changes both in vitro and in vivo, for a range of malignant conditions. This review examines the progress of vibrational spectroscopy towards selected clinical applications, with a particular focus on cancer diagnostics.
Rapid and sensitive methods for identifying stem cell differentiation state are required for facilitating future stem cell therapies. We aimed to evaluate the capability of focal plane array-Fourier transform infrared (FPA-FTIR) microspectroscopy for characterising the differentiation of chondrocytes from human mesenchymal stem cells (hMSCs). Successful induction was validated by reverse transcription polymerase chain reaction (RT-PCR) and Western blot analysis for collagen and aggrecan expression as chondrocyte markers in parallel with the spectroscopy. Spectra derived from chondrocyte-induced cells revealed strong IR absorbance bands attributed to collagen near 1338 and 1234cm(-1) and proteoglycan at 1245 and 1175-960cm(-1) compared to the non-induced cells. In addition, spectra from control and induced cells are segregated into separate clusters in partial least squares discriminant analysis score plots at the very early stages of induction and discrimination of an independent set of validation spectra with 100% accuracy. The predominant bands responsible for this discrimination were associated with collagen and aggrecan protein concordant with those obtained from RT-PCR and Western blot techniques. Our findings support the capability of FPA-FTIR microspectroscopy as a label-free tool for stem cell characterization allowing rapid and sensitive detection of macromolecular changes during chondrogenic differentiation.
Fourier-Transform Infrared microspectroscopy, a largely used
spectroscopic technique in basic and industrial researches, offers the
possibility to analyze the vibrational features of molecular groups
within a variety of environments. In the bioclinical field, and, in
particular, in the study of cells, tissues and biofluids, it could be
considered a supporting objective technique able to characterize the
biochemical processes involved in relevant pathologies, such as tumoral
diseases, highlighting specific spectral markers associable with the
principal biocomponents (proteins, lipids and carbohydrates). In this
article, we review the applications of infrared spectroscopy to the
study of tumoral diseases of oral cavity compartments with the aim to
improve understanding of biological processes involved during the onset
of these lesions and to afford to an early diagnosis. Spectral studies
on mouth, salivary glands and oral cystic lesions, objectively
discriminate normal from dysplastic and cancer states characterizing
also the grading.
During the last 15 years, vibrational spectroscopic methods have been developed that can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa, and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction or gene arrays. Due to the inherent sensitivity toward changes in the bio-molecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that of any one of the conventional techniques. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
Raman spectroscopy is a non-destructive, non-invasive, rapid and economical technique which has the potential to be an excellent method for the diagnosis of cancer and understanding disease progression through retrospective studies of archived tissue samples. Historically, biobanks are generally comprised of formalin fixed paraffin preserved tissue and as a result these specimens are often used in spectroscopic research. Tissue in this state has to be dewaxed prior to Raman analysis to reduce paraffin contributions in the spectra. However, although the procedures are derived from histopathological clinical practice, the efficacy of the dewaxing procedures that are currently employed is questionable. Ineffective removal of paraffin results in corruption of the spectra and previous experiments have shown that the efficacy can depend on the dewaxing medium and processing time. The aim of this study was to investigate the influence of commonly used spectroscopic substrates (CaF2, Spectrosil quartz and low-E slides) and the influence of different histological tissue types (normal, cancerous and metastatic) on tissue preparation and to assess their use for spectral histopathology. Results show that CaF2 followed by Spectrosil contribute the least to the spectral background. However, both substrates retain paraffin after dewaxing. Low-E substrates, which exhibit the most intense spectral background, do not retain wax and resulting spectra are not affected by paraffin peaks. We also show a disparity in paraffin retention depending upon the histological identity of the tissue with abnormal tissue retaining more paraffin than normal.
The so-called electric field standing wave effect (EFSW) has recently been demonstrated to significantly distort FT-IR spectra acquired in a transflection mode, both experimentally and in simulated models, bringing into question the appropriateness of the technique for sample characterization, particularly in the field of spectroscopy of biological materials. The predicted effects are most notable in the regime where the sample thickness is comparable to the source wavelength. In this work, the model is extended to sample thicknesses more representative of biological tissue sections and to include typical experimental factors which are demonstrated to reduce the predicted effects. These include integration over the range of incidence angles, varying degrees of coherence of the source and inhomogeneities in sample thickness. The latter was found to have the strongest effect on the spectral distortions and, with inhomogeneities as low as 10% of the sample thickness, the predicted distortions due to the standing wave effect are almost completely averaged out. As the majority of samples for biospectroscopy are prepared by cutting a cross section of tissue resulting in a high degree of thickness variation, this finding suggests that the standing wave effect should be a minor distortion in FT-IR spectroscopy of tissues. The study has important implications not only in optimization of protocols for future studies, but notably for the validity of the extensive studies which have been performed to date on tissue samples in the transflection geometry.
FT-IR Microspectroscopic maps of unstained thin sections from human melanoma and colon carcinoma tissues were obtained on a conventional infrared microscope equipped with an automatic xy stage. Mapped infrared data were analyzed by different image re-assembling techniques, namely functional group mapping ("chemical mapping", CM) and, for the first time by cluster analysis (CA), principal component analysis (PCA) and artificial neural networks (ANN). The output values of the different classifiers were recombined with the original spatial information to construct IR-images whose color or gray tones were based on the spatial distribution of individual spectral patterns. While the functional group mapping technique could not reliably differentiate between the different tissue regions, the approach based on pattern recognition yielded images with a high contrast that confirmed standard histopathological techniques. The new technique turned out to be particularly helpful to improve discrimination between different types of tissue structures in general, and to increase image contrast between normal and cancerous regions of a given tissue sample in particular.
The study of individual cells with infrared (IR) microspectroscopy often requires living cells to be cultured directly onto a suitable substrate. The surface effect of the specific substrates on the cell growth—viability and associated biochemistry—as well as on the IR analysis—spectral interference and optical artifacts—is all too often ignored. Using the IR beamline, MIRIAM (Diamond Light Source, UK), we show the importance of the substrate used for IR absorption spectroscopy by analyzing two different cell lines cultured on a range of seven optical substrates in both transmission and reflection modes. First, cell viability measurements are made to determine the preferable substrates for normal cell growth. Successively, synchrotron radiation IR microspectroscopy is performed on the two cell lines to determine any genuine biochemically induced changes or optical effect in the spectra due to the different substrates. Multivariate analysis of spectral data is applied on each cell line to visualize the spectral changes. The results confirm the advantage of transmission measurements over reflection due to the absence of a strong optical standing wave artifact which amplifies the absorbance spectrum in the high wavenumber regions with respect to low wavenumbers in the mid-IR range. The transmission spectra reveal interference from a more subtle but significant optical artifact related to the reflection losses of the different substrate materials. This means that, for comparative studies of cell biochemistry by IR microspectroscopy, it is crucial that all samples are measured on the same substrate type.
Figure
Cell separation by PCA due to the refractive index of the substrate used, revealing transmission artifact.
Transflection-mode FTIR spectroscopy has become a popular method of measuring spectra from biomedical and other samples due to the relative low cost of substrates compared to transmission windows, and a higher absorbance due to a double pass through the same sample approximately doubling the effective path length. In this publication we state an optical description of samples on multilayer low-e reflective substrates. Using this model we are able to explain in detail the so-called electric-field standing wave effect and rationalise the non-linear change in absorbance with sample thickness. The ramifications of this non-linear change, for imaging and classification systems, where a model is built from tissue sectioned at a particular thickness and compared with tissue of a different thickness are discussed. We show that spectra can be distorted such that classification fails leading to inaccurate tissue segmentation which may have subsequent implications for disease diagnostics applications.
Infrared spectroscopic cytology is potentially a powerful clinical tool. However, in order for it to be successful, practitioners must be able to extract reliably a pure absorption spectrum from a measured spectrum that often contains many confounding factors. The most intractable problem to date is the, so called, dispersion artefact which most prominently manifests itself as a sharp decrease in absorbance on the high wavenumber side of the amide I band in the measured spectrum, exhibiting a derivative-like line shape. In this paper we use synchrotron radiation FTIR micro-spectroscopy to record spectra of mono-dispersed poly(methyl methacrylate) (PMMA) spheres of systematically varying size and demonstrate that the spectral distortions in the data can be understood in terms of resonant Mie scattering. A full understanding of this effect will enable us to develop strategies for deconvolving the scattering contribution and recovering the pure absorption spectrum, thus removing one of the last technological barriers to the development of clinical spectroscopic cytology.
Aim
Spectral Cytopathology (SCP) is a novel spectroscopic method for objective and unsupervised classification of individual exfoliated cells. The limitations of conventional cytopathology are well-recognized within the pathology community. In SCP, cellular differentiation is made by observing molecular changes in the nucleus and the cytoplasm, which may or may not produce morphological changes detectable by conventional cytopathology. This proof of concept study demonstrates SCP’s potential as an enhancing tool for cytopathologists by aiding in the accurate and reproducible diagnosis of cells in all states of disease.
Method
Infrared spectra are collected from cervical cells deposited onto reflectively coated glass slides. Each cell has a corresponding infrared spectrum that describes its unique biochemical composition. Spectral data are processed and analyzed by an unsupervised chemometric algorithm, Principal Component Analysis (PCA).
Results
In this blind study, cervical samples are classified by analyzing the spectra of morphologically normal looking squamous cells from normal samples and samples diagnosed by conventional cytopathology with low grade squamous intraepithelial lesions (LSIL). SCP discriminated cytopathological diagnoses amongst twelve different cervical samples with a high degree of specificity and sensitivity. SCP also correlated two samples with abnormal spectral changes: these samples had a normal cytopathological diagnosis but had a history of abnormal cervical cytology. The spectral changes observed in the morphologically normal looking cells are most likely due to an infection with human papillomavirus, HPV. HPV DNA testing was conducted on five additional samples, and SCP accurately differentiated these samples by their HPV status.
Conclusions
SCP tracks biochemical variations in cells that are consistent with the onset of disease. HPV has been implicated as the cause of these changes detected spectroscopically. SCP does not depend on identifying the sparse number of morphologically abnormal cells within a large sample in order to make an accurate classification, as does conventional cytopathology. These findings suggest that the detection of cellular biochemical variations by SCP can serve as a new enhancing screening method that can identify earlier stages of disease.
Spectral cytopathology (SCP) is a novel approach for diagnostic differentiation of disease in individual exfoliated cells. SCP is carried out by collecting information on each cell's biochemical composition through an infrared micro-spectral measurement, followed by multivariate data analysis. Deviations from a cell's natural composition produce specific spectral patterns that are exclusive to the cause of the deviation or disease. These unique spectral patterns are reproducible and can be identified and used through multivariate statistical methods to detect cells compromised at the molecular level by dysplasia, neoplasia, or viral infection. In this proof of concept study, a benchmark for the sensitivity of SCP is established by classifying healthy oral squamous cells according to their anatomical origin in the oral cavity. Classification is achieved by spectrally detecting cells with unique protein expressions: for example, the squamous cells of the tongue are the only cell type in the oral cavity that have significant amounts of intracytoplasmic keratin, which allows them to be spectrally differentiated from other oral mucosa cells. Furthermore, thousands of cells from a number of clinical specimens were examined, among them were squamous cell carcinoma, malignancy-associated changes including reactive atypia, and infection by the herpes simplex virus. Owing to its sensitivity to molecular changes, SCP often can detect the onset of disease earlier than is currently possible by cytopathology visualization. As SCP is based on automated instrumentation and unsupervised software, it constitutes a diagnostic workup of medical samples devoid of bias and inconsistency. Therefore, SCP shows potential as a complementary tool in medical cytopathology.
Infrared spectra of single biological cells often exhibit the 'dispersion artefact' observed as a sharp decrease in intensity on the high wavenumber side of absorption bands, in particular the Amide I band at approximately 1655 cm(-1), causing a downward shift of the true peak position. The presence of this effect makes any biochemical interpretation of the spectra unreliable. Recent theory has shed light on the origins of the 'dispersion artefact' which has been attributed to resonant Mie scattering (RMieS). In this paper a preliminary algorithm for correcting RMieS is presented and evaluated using simulated data. Results show that the 'dispersion artefact' appears to be removed; however, the correction is not perfect. An iterative approach was subsequently implemented whereby the reference spectrum is improved after each iteration, resulting in a more accurate correction. Consequently the corrected spectra become increasingly more representative of the pure absorbance spectra. Using this correction method reliable peak positions can be obtained.
In this report the applicability of an improved method of image segmentation of infrared microspectroscopic data from histological specimens is demonstrated. Fourier transform infrared (FT-IR) microspectroscopy was used to record hyperspectral data sets from human colorectal adenocarcinomas and to build up a database of spatially resolved tissue spectra. This database of colon microspectra comprised 4120 high-quality FT-IR point spectra from 28 patient samples and 12 different histological structures. The spectral information contained in the database was employed to teach and validate multilayer perceptron artificial neural network (MLP-ANN) models. These classification models were then employed for database analysis and utilised to produce false colour images from complete tissue maps of FT-IR microspectra. An important aspect of this study was also to demonstrate how the diagnostic sensitivity and specificity can be specifically optimised. An example is given which shows that changes of the number of teaching patterns per class can be used to modify these two interrelated test parameters. The definition of ANN topology turned out to be crucial to achieve a high degree of correspondence between the gold standard of histopathology and IR spectroscopy. Particularly, a hierarchical scheme of ANN classification proved to be superior for the reliable classification of tissue spectra. It was found that unsupervised methods of clustering, specifically agglomerative hierarchical clustering (AHC), were helpful in the initial phases of model generation. Optimal classification results could be achieved if the class definitions for the ANNs were carried out by considering the classification information provided by cluster analysis.
Fourier transform infrared spectra of a single cell in transflection geometry are seen to vary significantly with position on the cell, showing a distorted derivative-like lineshape in the region of the optically dense nucleus. A similar behaviour is observable in a model system of the protein albumin doped in a potassium bromide disk. It is demonstrated that the spectrum at any point is a weighted sum of the sample reflection and transmission and that the dominance of the reflection spectrum in optically dense regions can account for some of the spectral distortions previously attributed to dispersion artefacts. Rather than being an artefact, the reflection contribution is ever present in transflection spectra and it is further demonstrated that the reflection characteristics can be used for cellular mapping.
Fourier-transform IR (FT-IR) spectra of pelleted exfoliated cervical cells from patients with cervical cancer or dysplasia differ from those from normal women. To study the origin of these spectral changes, we obtained the FT-IR spectra of individual cervical cells from normal, dysplastic, and malignant cervical samples. Ninety five percent of normal superficial and intermediate cells displayed two distinct spectral patterns designated A and B, and 5% displayed an intermediate pattern, suggesting extensive structural heterogeneity among these cells. Parabasal and endocervical cells showed pattern B spectra. The spectra of malignant, dysplastic, and other abnormal cells also were characterized. Analysis of FT-IR spectra of over 2, 000 individual cells from 10 normal females, 7 females with dysplasia, and 5 females with squamous cell carcinoma revealed that the spectra of normal-appearing intermediate and superficial cells of the cervix from women with either dysplasia or cancer differed from those of normal women. Chemometric and classical spectroscopic analysis showed a continuum of changes paralleling the transition from normalcy to malignancy. These findings suggest that (i) the structural changes underlying the spectroscopic changes are involved in or are a product of cervical carcinogenesis and (ii) the neoplastic process may be more extensive than currently recognized with morphological criteria. This approach may be useful for the structural study of neoplasia and also may be of help in the diagnosis or classification of cervical disorders.
Recent results suggest that infrared microspectroscopy, coupled to methods of multivariate analysis, can distinguish normal from virally infected cells. This was established for infection of the oral cavity by the herpes simplex virus, which is associated with the outbreak of cold sores, and for infection of the cervix uteri by the human papillomavirus (HPV). In both cases, subtle (but different) spectral changes are observed that could be used for fast and inexpensive viral screening.
In attempting to analyze, on digital computers, data from basically continuous physical experiments, numerical methods of performing familiar operations must be developed. The operations of differentiation and filtering are especially important both as an end in themselves, and as a prelude to further treatment of the data. Numerical counterparts of analog devices that perform these operations, such as RC filters, are often considered. However, the method of least squares may be used without additional computational complexity and with considerable improvement in the information obtained. The least squares calculations may be carried out in the computer by convolution of the data points with properly chosen sets of integers. These sets of integers and their normalizing factors are described and their use is illustrated in spectroscopic applications. The computer programs required are relatively simple. Two examples are presented as subroutines in the FORTRAN language.
This paper describes the methods used in this laboratory to measure absolute infrared absorption intensities of liquids and their applications in analytical and physical chemistry. The applications include the measurement of 43 bands of benzene, toluene, chlorobenzene and dichloromethane as secondary standards of infrared absorption for the International Union of Pure and Applied Chemistry, the use of these standards to calibrate transmission cells, measurement of the absolute intensity spectra of liquid benzene, the comparison of absorption intensities and transition moments in liquid and gaseous benzene, the separation of the integrated intensity of benzene into contributions from different transitions by curve fitting, and the determination of intensities and transition moments of the OH, OD, CH and CD stretching bands of CH3OH, CH3OD, CD3OH and CD3OD. The accuracy of the absolute intensities is impressively shown by the close similarity of the intensities of corresponding bands in the spectra of different isotopomers of methanol. One key to the measurement of accurate intensities is the determination of the baseline absorption.
The authors of this review will attempt to present the major advances (up to April 1980) in laser spectrometry, particularly those since the excellent review of Steinfeld1 in 1975. However, in order to have a consistent treatment, some of the significant articles published prior to 1975 will also be referenced; no attempt will be made to be exhaustive. In addition, we apologize because some of the significant papers, due to oversight, will undoubtedly be omitted. The authors, however, do not intend this review to be a tutorial in laser principles or operation or even on spectroscopic principles or instrumentation, but rather a review of the analytical uses of lasers, including some of the critical aspects of instrumentation and analytical figures of merit.
This paper explores different phenomena that cause distortions of infrared absorption spectra by mixing of reflective and absorptive band shape components of infrared spectra, and the resulting distortion of observed band shapes. In the context of this paper, we refer to the line shape of the variations of the refractive index in spectral regions of an absorption maximum (i.e., in regions of "anomalous dispersion") as "dispersive" or "reflective" line shape contributions, in analogy to previous spectroscopic literature. These distortions usually result in asymmetric bands with a negative intensity contribution at the high wavenumber of the band, accompanied by a shift toward lower wavenumber, and confounded band intensities. In extreme cases of band distortions caused by the "resonance Mie" (RMie) mechanism, spectral peaks may be split into doublets of peaks, change from positive to negative peaks, or appear as derivative-shaped features.
In this paper we describe the advantages of collecting infrared microspectral data in imaging mode opposed to point mode. Imaging data are processed using the PapMap algorithm, which co-adds pixel spectra that have been scrutinized for R-Mie scattering effects as well as other constraints. The signal-to-noise quality of PapMap spectra will be compared to point spectra for oral mucosa cells deposited onto low-e slides. Also the effects of software atmospheric correction will be discussed. Combined with the PapMap algorithm, data collection in imaging mode proves to be a superior method for spectral cytopathology. (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
We have optimized an imaging methodology capable of monitoring individual live HeLa cells using non-synchrotron FTIR in an aqueous environment. This methodology, in combination with MATLAB based pre-processing techniques, allows fast and efficient collection of data with high signal-to-noise ratio in comparison with previous methods using point mode data collection, which required manual operation and more collection time. Also, presented are early results that illustrate interpretable spectral differences from live cells treated with chemotherapeutic drugs, demonstrating the potential of this methodology to develop more desirable modes of treatment for patients in their diagnoses and treatments for disease.
FTIR absorption micro-spectroscopy is a widely used, powerful technique for analysing biological materials. In principle it is a straightforward linear absorption spectroscopy, but it can be affected by artefacts that complicate the interpretation of the data. In this article, artefacts produced by the electric-field standing-wave (EFSW) in micro-reflection-absorption (transflection) spectroscopy are investigated. An EFSW is present at reflective metallic surfaces due to the interference of incident and reflected light. The period of this standing wave is dependent on the wavelength of the radiation and can produce non-linear changes in absorbance with increasing sample thickness (non-Beer-Lambert like behaviour). A protein micro-structure was produced as a simple experimental model for a biological cell and used to evaluate the differences between FTIR spectra collected in transmission and transflection. By varying the thickness of the protein samples, the relationship between the absorbance and sample thickness in transflection was determined, and shown to be consistent with optical interference due to the EFSW coupled with internal reflection from the sample top surface. FTIR spectral image data from MCF 7 breast adenocarcinoma cells was then analysed to determine the severity of the EFSW artefact in data from a real sample. The results from these measurements confirmed that the EFSW artefact has a profound effect on transflection spectra, and in this case the main spectral variations were related to the sample thickness rather than any biochemical differences.
Fourier Transform Infrared (FT-IR) spectroscopic imaging is emerging as an automated alternative to human examination in studying development and disease in tissue. The technology's speed and accuracy, however, are limited by the trade-off with signal-to-noise ratio (SNR). Signal processing approaches to reduce noise have been suggested but often involve manual decisions, compromising the automation benefits of using spectroscopic imaging for tissue analysis. In this manuscript, we describe an approach that utilizes the spatial information in the data set to select parameters for noise reduction without human input. Specifically, we expand on the Minimum Noise Fraction (MNF) approach in which data are forward transformed, eigenimages that correspond mostly to signal selected and used in inverse transformation. Our unsupervised eigenimage selection method consists of matching spatial features in eigenimages with a low-noise gold standard derived from the data. An order of magnitude reduction in noise is demonstrated using this approach. We apply the approach to automating breast tissue histology, in which accuracy in classification of tissue into different cell types is shown to strongly depend on the SNR of data. A high classification accuracy was recovered with acquired data that was ∼10-fold lower SNR. The results imply that a reduction of almost two orders of magnitude in acquisition time is routinely possible for automated tissue classifications by using post-acquisition noise reduction.
Patients with a head and neck squamous cell carcinoma (HNSCC) often develop multiple (pre)malignant lesions. This finding led to the field cancerization theory, which hypothesizes that the entire epithelial surface of the upper aerodigestive tract has an increased risk for the development of (pre)malignant lesions because of multiple genetic abnormalities in the whole tissue region. Demonstration of alterations in histologically normal tumor-adjacent mucosa from HNSCC patients supported this hypothesis. Currently, the question has been raised whether multiple lesions develop independently from each other or from migrated malignant or progenitor cells. The majority of the mucosal alterations appear to be related to the exposure to alcohol and/or tobacco. Moreover, almost all primary remote tumors from HNSCC patients appear to be clonally unrelated. Therefore, there is more evidence that field cancerization is due to multiple independent events than to migration of genetically altered cells.
We report infrared microspectral features of nuclei in a completely inactive and contracted (pyknotic) state, and of nuclei of actively dividing cells. For pyknotic nuclei, the very high local concentration of DNA leads to opaqueness of the chromatin and, consequently, the absence of DNA signals in the IR spectra of very small nuclei. However, these nuclei can be detected by their scattering properties, which can be described by the Mie theory of scattering from dielectric spheres. This scattering depends on the size of the nucleus; consequently, quite different scattering cross-sections are calculated and observed for pyknotic and mitotic nuclei.
Fourier transform infrared (FT-IR) spectroscopy is a valuable technique for characterization of biological samples, providing a detailed fingerprint of the major chemical constituents. However, water vapor and CO(2) in the beam path often cause interferences in the spectra, which can hamper the data analysis and interpretation of results. In this paper we present a new method for removal of the spectral contributions due to atmospheric water and CO(2) from attenuated total reflection (ATR)-FT-IR spectra. In the IR spectrum, four separate wavenumber regions were defined, each containing an absorption band from either water vapor or CO(2). From two calibration data sets, gas model spectra were estimated in each of the four spectral regions, and these model spectra were applied for correction of gas absorptions in two independent test sets (spectra of aqueous solutions and a yeast biofilm (C. albicans) growing on an ATR crystal, respectively). The amounts of the atmospheric gases as expressed by the model spectra were estimated by regression, using second-derivative transformed spectra, and the estimated gas spectra could subsequently be subtracted from the sample spectra. For spectra of the growing yeast biofilm, the gas correction revealed otherwise hidden variations of relevance for modeling the growth dynamics. As the presented method improved the interpretation of the principle component analysis (PCA) models, it has proven to be a valuable tool for filtering atmospheric variation in ATR-FT-IR spectra.
Spectral Cytology of Human Oral and Cervical Samples
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