No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Vibrational spectroscopic changes of B-lymphocytes upon activation
Abstract
The first study interpreting B-lymphocyte activation in normal lymph nodes using vibrational micro-spectral imaging is reported. Lymphocyte activation indicates the presence and response against a pathogen, regardless of the inciting pathogen's etiology, whether a benign, reactive or malignant process. Understanding the biochemical makeup of lymphocyte activation during early stages of disease and immune response may offer significant aid in determining a tumor's origin without the presence of malignant metastatic cells but within lymph nodes that are reactive and displaying regions of hyperplasia. Infrared and Raman data scrutinized via unsupervised multivariate methods may provide a physical and reproducible method to determine the biochemical components and variances therein of activated lymph nodes with distinguishing characteristics depending on the malignancy present in the region or elsewhere in the body. The results reported here provide a proof-of-concept study that reveal a potential to screen lymph nodes for disease without the presence of metastatic cells. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
... In general, tumor growth is mainly recognized by an increase of DNA in neoplastic and highly proliferating cells whereas a lower level of lipids is associated with supplying nutrition and energy to malignant cells [24,[28][29][30]. The above-mentioned ECM remodeling highlighted by collagen bands and an increase of a carbohydrates level have been observed in lung and colon cancer as well as in lymph nodes with cancer metastasis [23,[31][32][33]. ...
... Fig. 4 displays only changes in FTIR spectra which were found to be statistically significant (at least p < 0.050). Changes in the DNA content were monitored using the 1082 cm −1 band of DNA because the other characteristic DNA band at ca. 1230 cm −1 overlaps with a collagen band of the lung parenchyma [31,47]. The intensity of the 1082 cm −1 band increases gradually upon the proliferation of cancer cells in the lungs and its values are specific for HC and each metastatic phase (Fig. 4A). ...
... A reverse trend to DNA changes is observed for a band at 1169 cm −1 and its intensity is specific for each experimental group (Fig. 4A). This band arises from stretches of the CO groups in phospholipids and cholesterol esters [31]. Alteration of lipid metabolism is often associated with neoplastic diseases since the lipid uptake by neoplastic cells and solid tumors is more pronounced than in healthy cells [54]. ...
An application of FTIR spectroscopic imaging for the identification and visualization of early micrometastasis from breast cancer to lungs in a murine model is shown. Spectroscopic and histological examination is focused on lung cross-sections derived from animals at the early phase of metastasis (early micrometastasis, EM) as compared to healthy control (HC) and late phase of metastasis (advanced macrometastasis, AM) using murine model of metastatic breast cancer with 4T1 cells orthotopically inoculated. FTIR imaging allows for a detailed, objective and label-free differentiation and visualization of EM foci including large and small micrometastases as well as single cancer cells grouped in clusters. An effect of the EM phase on the entire lung tissue matrix as well as characteristic biochemical profiles for HC and advanced macrometastasis were determined from morphological and spectroscopic points of view. The extraordinary sensitivity of FTIR imaging toward EM detection and discrimination of AM borders confirms its applicability as a complementary tool for the histopathological assessment of the metastatic cancer progression.
... So far, only few infrared studies about lymphocytes have been published. Two groups have worked on lymphocyte activation and two studies have shown that it was possible to observe differences induced by activation of B cells [66,67]. The first study showed that lymphocyte activation in vitro by different stimuli could be detected by FTIR after 90 min only. ...
... The first study showed that lymphocyte activation in vitro by different stimuli could be detected by FTIR after 90 min only. The second study showed that B cells in the germinal centers of activated lymph nodes were spectroscopically different from the non-activated B cells in the mantle area of the follicles [66] . Moreover, a study carried out in our laboratory reported that lymphocytes from secondary lymphoid organ (tonsils and lymph nodes) present different IR spectra based on their activation state [52] . ...
... All together these publications indicate that the observed differences could be associated with activation. The results presented in this paper and the ones previously published [66,67] suggest a potential use of FTIR in the characterization of the activation state of lymphocytes. Nonetheless , further characterization of the activation and differentiation state of the lymphocytes in these tissue sections would be required for a more in-depth understanding (e.g. through the characterization and quantification of cytokine production). ...
While early stages of melanoma are usually cured by surgery, metastatic melanomas are difficult to treat because the widely available options have low response rates. Careful and precise diagnosis and staging are essential to determine patient's risk and to select appropriate treatments. Fortunately, the recent progress in immunotherapy is very encouraging. In this context, it is important to characterize the intratumoral infiltration of immune cells in each patient, which is however not done routinely due to the lack of standardized methods. In this study, we used Fourier Transform Infrared (FTIR) imaging combined with multivariate statistical analyses to investigate non-metastatic and metastatic lymph nodes from melanoma patients. Our results show that the different cell types have different infrared spectral features allowing automated identification of these cell types. High recognition rates were obtained using a supervised Partial Least Square Discriminant Analysis (PLS-DA) model. Melanoma cells were recognized with 87.1% sensitivity and 85.7% specificity, showing that FTIR spectroscopy has similar detection power as immunohistochemistry. Besides, FTIR imaging could also distinguish lymphocyte subpopulations (B and T cells). Finally, we investigated the changes in lymphocytes due to the presence of metastases. Interestingly, specific features of spectra of lymphocytes present in metastatic or tumor-free lymph nodes could be evidenced by PCA. A PLS-DA model was capable of predicting whether lymphocytes originated from invaded or non-invaded lymph nodes. These data demonstrate that FITR imaging is capable to distinguish known and also novel biological features in human tissues, with potential practical relevance for histopathological diagnosis and biomarker assessment.
... 13,14 These techniques have also been demonstrated to be capable of differentiating between resting and activated lymphocytes, in addition to monocytes and macrophages. [15][16][17][18] These proof-of-concept studies demonstrate that both RS and FTIR are invaluable tools for PBMC analysis. Other studies have shown that RS and FTIR analysis of PBMCs and pure lymphocytes holds great promise for clinical real-world applications in the analysis of malignancies, infectious diseases, immune response, occupational contaminants, drug response, radiotherapeutic response, and for retrospective radiobiological dosimetry. ...
... Therefore, for each pre-analytical variable investigated, three peripheral blood samples were obtained and analysed. All sample collections were carried out in accordance with the 1964 Helsinki Declaration 36 and approved by the Technological University Dublin Research Ethics Committee (REC number [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Volunteer information was anonymised prior to analysis. ...
The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.
... This is not surprising, as cellular responses induce a swarm of transcriptional up-and down-regulation orchestrating changes to the transcriptomic and proteomic profile of the cell. Using multivariate analysis, spectral information enables classification of cell states or phenotypes of mammalian (Wood et al., 2000a,b;Brown et al., 2009;Smith et al., 2010;Zoladek et al., 2010;Pully et al., 2011;Ramoji et al., 2012;Mazur et al., 2013;Maguire et al., 2015;Hobro et al., 2016;Ichimura et al., 2016;Pavillon et al., 2018), bacterial, and yeast cells (Germond et al., 2018;Kobayashi-Kirschvink et al., 2018). ...
Large-scale intracellular signaling during developmental growth or in response to environmental alterations are largely orchestrated by chromatin within the cell nuclei. Chemical and conformational modifications of the chromatin architecture are critical steps in the regulation of differential gene expression and ultimately cell fate determination. Therefore, establishing chemical properties of the nucleus could provide key markers for phenotypic characterization of cellular processes on a scale of individual cells. Raman microscopy is a sensitive technique that is capable of probing single cell chemical composition—and sub-cellular regions—in a label-free optical manner. As such, it has great potential in both clinical and basic research. However, perceived limitations of Raman spectroscopy such as low signal intensity and the difficulty in linking alterations in vibrational signals directly with ensuing biological effects have hampered advances in the field. Here we use immune B lymphocyte development as a model to assess chromatin and transcriptional changes using confocal Raman microscopy in combination with microfluidic devices and correlative transcriptomics, thereby linking changes in chemical and structural properties to biological outcomes. Live B lymphocytes were assessed before and after maturation. Multivariate analysis was applied to distinguish cellular components within each cell. The spectral differences between non-activated and activated B lymphocytes were then identified, and their correlation with known intracellular biological changes were assessed in comparison to conventional RNA-seq analysis. Our data shows that spectral analysis provides a powerful tool to study gene activation that can complement conventional molecular biology techniques and opens the way for mapping the dynamics in the biochemical makeup of individual cells.
... [27][28][29] The necessary information for the differentiation between normal and abnormal cell and tissues is based on their biochemical content which generates small differences in their infrared spectra (i.e., intensity, bandwidth, and spectral position of the vibrational band). [30][31][32][33] On the other hand, FT-IR spectroscopy offers many advantages in the cancer diagnosis such as simplicity, reproducibility, short procedures, and is a relatively cost-effective process. [34][35][36] This method was also successfully used to distinguish other benign from malignant changes in organs such as colon, prostate, breast, cervix, stomach, oral, liver, skin, thyroid, and esophageus. ...
Histopathology, despite being the gold standard as a diagnostic tool, does not always provide a correct diagnosis for different pleural lesions. Although great progress was made in this field, the problem to differentiate between reactive and malignant pleural lesions still stimulates the search for additional diagnostic tools. Our research using vibrational spectroscopy and principal component analysis (PCA) statistical modeling represents a potentially useful tool to approach the problem. The objective method this paper explores is based on the correlation between different types of pleural lesions and their vibrational spectra. Obtained tissue spectra recorded by infrared spectroscopy allowed us to categorize spectra in different groups using a created PCA statistical model. The PCA model was built using tissues of known pathology as the model group. The validation samples were then used to confirm the functionality of our PCA model. Student's t-test was also used for comparing samples in paired groups. The PCA model was able to clearly differentiate the spectra of mesothelioma, metastasis and reactive changes (inflammation), and place them in discrete groups. Thus, we showed that Fourier transform infrared spectroscopy combined with PCA can differentiate pleural lesions with high sensitivity and specificity. This new approach could contribute in objectively differentiating specific pleural lesions, thus helping pathologists to better diagnose difficult pleural samples but also could shed additional light into the biology of malignant pleural mesothelioma.
... Indeed, preliminary studies examining the immune cell state of lymphocytes and macrophages using label-free spectroscopic techniques have been published in the last couple of decades (Wood et al., 2000;Mazur et al., 2013;Hobro et al., 2016;Ichimura et al., 2016;Pavillon et al., 2018). ...
The dynamic architecture of chromatin, the macromolecular complex comprised primarily of DNA and histones, is vital for eukaryotic cell growth. Chemical and conformational changes to chromatin are important markers of functional and developmental processes in cells. However, chromatin architecture regulation has not yet been fully elucidated. Therefore, novel approaches to assessing chromatin changes at the single-cell level are required. Here we report the use of FTIR imaging and microfluidic cell-stretcher chips to assess changes to chromatin architecture and its effect on the mechanical properties of the nucleus in immune cells. FTIR imaging enables label-free chemical imaging with subcellular resolution. By optimizing the FTIR methodology and coupling it with cell segmentation analysis approach, we have identified key spectral changes corresponding to changes in DNA levels and chromatin conformation at the single cell level. By further manipulating live single cells using pressure-driven microfluidics, we found that chromatin decondensation – either during general transcriptional activation or during specific immune cell maturation – can ultimately lead to nuclear auxeticity which is a new biological phenomenon recently identified. Taken together our findings demonstrate the tight and, potentially bilateral, link between extra-cellular mechanotransduction and intra-cellular nuclear architecture.
... The methods of SHP, including data acquisition and data preprocessing, have been described in detail in the literature. 24,25 All spectroscopic studies reported here were carried out on 'low emissivity' (low-e) slides (Kevley Technologies, Chesterfield, OH, USA) that are reflective toward infrared radiation, but are nearly totally transparent to visible light. The use of these sample substrates has been discouraged by some authors, 26 citing the distortion of spectral intensities by the standing electromagnetic wave that forms when radiation is reflected from a metallic surface. ...
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 required components can then be clearly distinguished by extracting particular cluster of interest from the total set of all clusters. An additional data processing (i.e., removal of cosmic rays, baseline correction, control of intensity thresholds and vector normalisation) was performed for all spectra [20,[78][79][80][81]. ...
New approaches for visualisation of silicon nanoparticles (SiNPs) in cancer cells are realised by means of the linear and nonlinear optics in vitro. Aqueous colloidal solutions of SiNPs with sizes of about 10–40 nm obtained by ultrasound grinding of silicon nanowires were introduced into breast cancer cells (MCF-7 cell line). Further, the time-varying nanoparticles enclosed in cell structures were visualised by high-resolution structured illumination microscopy (HR-SIM) and micro-Raman spectroscopy. Additionally, the nonlinear optical methods of two-photon excited fluorescence (TPEF) and coherent anti-Stokes Raman scattering (CARS) with infrared laser excitation were applied to study the localisation of SiNPs in cells. Advantages of the nonlinear methods, such as rapid imaging, which prevents cells from overheating and larger penetration depth compared to the single-photon excited HR-SIM, are discussed. The obtained results reveal new perspectives of the multimodal visualisation and precise detection of the uptake of biodegradable non-toxic SiNPs by cancer cells and they are discussed in view of future applications for the optical diagnostics of cancer tumours.
... 45 These algorithms for spectral pre-processing were selected according with previously published Raman data analysis. 33,[46][47][48] After the pre-processing step an unsupervised endmember extraction analysisvertex component analysis (VCA)was performed with the MatLab software. 49,50 This algorithm enabled to calculate the most dissimilar spectra from all measured spectral datasets and to combine these spectra in a number of well-defined clusters. ...
In-vitro Raman micro-spectroscopy was used for diagnostics of the processes of uptake and biodegradation of porous silicon nanoparticles (SiNPs) in breast cancer cells (MCF-7 cell line). Two types of nanoparticles, with and without photoluminescence in the visible spectral range, were investigated. The spatial distribution of photoluminescent SiNPs within the cells obtained by Raman imaging was verified by high-resolution structured-illumination optical microscopy. Nearly complete biodegradation of SiNPs inside the living cells was observed after 13 days of the incubation. The results reveal new prospects of multi-modal visualization of SiNPs inside cancer cells for theranostic applications.
... The intricate and varied nature of lymphoid tissue and the large image pixel sizes of commercial FT-IR imaging instruments have restricted studies performed on lymph nodes themselves to descriptions of multi-cellular tissue morphology [26][27][28][29] and major changes in lymph nodes due to breast micro-metastases [30][31][32][33], typically through the use of Hierarchical Cluster Analysis (HCA) or Fuzzy C-means Clustering (FCM). For example, IR imaging data with 6.25 μm-sized pixels combined with HCA and PCA using the fingerprint region of the spectrum delineated the structure of the lymph node and various lymphocyte populations [34]. This pixel size, however, was on the order of the diameter of a typical lymphocyte, which left the resolution and classification of individual lymphocytes out of reach. ...
Chemical imaging is a rapidly emerging field in which molecular information within samples can be used to predict biological function and recognize disease without the use of stains or manual identification. In Fourier transform infrared (FT-IR) spectroscopic imaging, molecular absorption contrast provides a large signal relative to noise. Due to the long mid-IR wavelengths and sub-optimal instrument design, however, pixel sizes have historically been much larger than cells. This limits both the accuracy of the technique in identifying small regions, as well as the ability to visualize single cells. Here we obtain data with micron-sized sampling using a tabletop FT-IR instrument, and demonstrate that the high-definition (HD) data lead to accurate identification of multiple cells in lymph nodes that was not previously possible. Highly accurate recognition of eight distinct classes - naïve and memory B cells, T cells, erythrocytes, connective tissue, fibrovascular network, smooth muscle, and light and dark zone activated B cells was achieved in healthy, reactive, and malignant lymph node biopsies using a random forest classifier. The results demonstrate that cells currently identifiable only through immunohistochemical stains and cumbersome manual recognition of optical microscopy images can now be distinguished to a similar level through a single IR spectroscopic image from a lymph node biopsy.
... Hence, distinction between classes is often a problem of finding clusters in which intra-cluster variation is smaller than inter-cluster variation. Unsupervised clustering approach has been applied previously to investigate tissue samples [37][38][39]. Since nothing is assumed known about the data classes, unsupervised processes can involve data reduction using the variance before applying a classification procedure. ...
Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.
... In this paper vertex component analysis (VCA) is introduced as a promising approach for analyzing Raman maps of wooden cell walls. So far VCA has been successfully applied in the analysis of biomedical Raman images (Hedegaard et al., 2011;Krafft et al., 2011Krafft et al., , 2012Bergner et al., 2012;Ashtikar et al., 2013;Lattermann et al., 2013;Mazur et al., 2013), but not on plant cell walls. VCA belongs to the group of multivariate curve resolution methods and projects the data to the identified orthogonal subspace in an interactive way and finds the endmember by repeated iteration (Nascimento and Dias, 2005;Zhang and Tauler, 2013). ...
At the molecular level the plant cell walls consist of a few nanometer thick semi-crystalline cellulose fibrils embedded in amorphous matrix polymers such as pectins, hemicelluloses, and lignins. The arrangement of these molecules within the cell wall in different plant tissues, cells and cell wall layers is of crucial importance for a better understanding and thus optimized utilization of plant biomass. During the last years Confocal Raman microscopy evolved as a powerful method in plant science by revealing the different molecules in context with the microstructure. In this study two-dimensional spectral maps have been acquired of micro-cross-sections of spruce (softwood) and beech (hardwood). Raman images have been derived by using univariate (band integration, height ratios) and multivariate methods [vertex component analysis (VCA)]. While univariate analysis only visualizes changes in selected band heights or areas, VCA separates anatomical regions and cell wall layers with the most different molecular structures. Beside visualization of the distinguished regions and features the underlying molecular structure can be derived based on the endmember spectra. VCA revealed that the lumen sided S3 layer has a similar molecular composition as the pit membrane, both revealing a clear change in lignin composition compared to all other cell wall regions. Within the S2 layer a lamellar structure was visualized, which was elucidated to derive from slight changes in lignin composition and content and might be due to successive but not uniform lignification during growth.
In this study, we investigated the cytotoxic effect of highly soluble dextran-coated CeO2 nanoparticles on human fetal lung fibroblasts MRC-5. We examined individual nanoparticle-treated cells by Raman spectroscopy and analyzed Raman spectra using non-negative principal component analysis and k-means clustering. In this way, we determined dose-dependent differences between treated cells, which were reflected through the intensity change of lipid, phospholipid and RNA-related Raman modes. Performing standard biological tests for cell growth, viability and induction of apoptosis in parallel, these changes were correlated with nanoparticle-induced apoptotic processes. The cells with specific spectral characteristics, referring to non-apoptotic, but possibly autophagic cell death modality, were also detected. Additionally, Raman imaging combined with principal component and vertex component analysis was used to map the spatial distribution of biological molecules in treated and untreated cells. This work provided the description of different resulting states of the treated cells depending on the dextran-coated CeO2 nanoparticles dose, which can be later used in the design of the nanoparticles for industrial or medical applications. The wide content of information resulting from single-cell Raman spectroscopy has the potential to detect biochemical changes caused by nanoparticles that would otherwise require a series of expensive and time-consuming standard biological techniques.
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.
Nanotechnology is essential in the development of advanced technologies for medicine and healthcare. Advances depend on the ability to study these materials and nano-biotechnologies using advanced analytical techniques. Providing a comprehensive overview of analytical techniques for applications in biomedical nanotechnology at both the fundamental and applied level, this book provides a broad perspective of the development of analytical methods involved in materials science and electronics. It highlights the fundamentals and systematic developments of techniques to achieve better characterization, rapid diagnostics, cost-effective and user-friendly approaches, and state-of-the-art methodologies for personalized health management, and explains how this fundamental knowledge can be translated into applications in nano-enabled biomedical research. The multidisciplinary readership includes chemists, biologists, physicists, materials scientists, engineers and information technologists.Key features• Provides a comprehensive overview of analytical techniques for applications in biomedical nanotechnology at both the fundamental and applied level.• Explains how fundamental knowledge of the techniques can be translated to applications in nano-enabled biomedical research.• Covers state-of-art analytical techniques for biomedical nanotechnology.• Includes the prospects and challenges with each technique, including potential solutions.• Provides a multidisciplinary view of interest to chemists, biologists, physicists, materials scientists, engineers and information technologists.
Label-free Raman imaging is a noninvasive spectroscopic method for investigating the nature and distribution of molecular species within a sample. In this chapter, we describe the applications of conventional Raman imaging, as well as the related techniques of coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) imaging for medical, life sciences, and other biological applications.
We present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable a rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications. The potential of the new approach is illustrated by two applications: 1. HTS-RS-based differential white blood cell count. A classification model was trained using 52218 Raman spectra of lymphocytes, 48220 spectra of neutrophils and 7294 spectra of monocytes from four normal volunteers. The model was applied to white blood samples of two volunteers and three patients, with comparable results for white blood cell counts between HTS-RS and machine counting. 2. HTS-RS-based identification of circulating tumour cells (CTCs) in 1:1, 1:9 and 1:99 mixtures of Panc1 cells and leukocytes yielded ratios of 55:45, 10:90 and 3:97, respectively.
The great capability of label-free classification of tissue via vibrational spectroscopy, like Raman or infrared imaging, is shown in numerous publications (review: Diem et al. J. Biophotonics 2013, 6, 855-886). Here, we present a new approach, virtual staining, that improves the Raman spectral histopathology (SHP) images of colorectal cancer tissue by combining the integrated Raman intensity image in the C-H stretching region (2800-3050 cm-1) with the pseudo-colour Raman image. This allows the display of fine structures such as the filamentous composition of muscle tissue. The morphology of the virtually stained images is in agreement with the gold standard in medical diagnosis, the haematoxylin-eosin staining. The virtual stainig image also represents the whole biochemical fingerprint, and several tissue components including carcinoma were identified automatically with high sensitivity and specificity. For fast tissue classifications, a similar approach was applied on coherent anti-Stokes Raman scattering (CARS) spectral data which is faster and therefore potentially more suitable for clinical applications.
Raman spectroscopy has previously been used to identify eukaryotic and prokaryotic cells. While prokaryotic cells are small in size and can be assessed by a single Raman spectrum, the larger size of eukaryotic cells and their complex organization requires the acquisition of multiple Raman spectra to properly characterize them. A Raman spectrum from a diffraction-limited spot at an arbitrary location within a cell results in spectral variations that affect classification approaches. To probe whole cells with Raman imaging at high spatial resolution is time consuming, because a large number of Raman spectra need to be collected, resulting in low cell throughput and impairing statistical analysis due to low cell numbers. Here we propose a method to overcome the effects of cellular heterogeneity by acquiring integrated Raman spectra covering a large portion of a cell. The acquired spectrum represents the mean macromolecular composition of a cell with an exposure time that is comparable to acquisition of a single Raman spectrum. Data sets were collected from T lymphocyte Jurkat cells, and pancreatic cell lines Capan1 and MiaPaca2. Cell classification by support vector machines was compared for single spectra, spectra of images and integrated Raman spectra of cells. The integrated approach provides better and more stable prediction for individual cells, and in the current implementation, the mean macromolecular information of a cell can be acquired faster than with the acquisition of individual spectra from a comparable region. It is expected that this approach will have a major impact on the implementation of Raman based cell classification.
Unactivated lymphocytes are morphologically identical and biochemically are relatively similar, making them difficult to distinguish from one another with conventional light microscopy. Here, we use Raman spectroscopy to provide biochemical information on the composition of different lymphocyte cell lines. As could be expected, the biochemical differences measured with Raman between lymphocyte cell lines are small, but in combination with partial least squares discriminant analysis it is possible not only to distinguish between T- and B-cells, but also between individual T-cell and B-cell lines.
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.
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.
The use of predictive biomarkers can help to improve therapeutic options for the individual cancer patient. For the treatment of colon cancer patients with anti-EGFR-based drugs, the KRAS mutation status has to be determined to pre-select responders that will benefit from this medication. Amongst others, array-based tests have been established for profiling of the KRAS mutation status. Within this article we describe an on-chip hybridization technique to screen therapeutic relevant KRAS codon 12 mutations. The DNA chip-based platform enables the reliable discrimination of selected mutations by allele-specific hybridization. Here, silver deposits represent robust endpoint signals that allow for a simple naked eye rating. With the here presented assay concept a precise identification of heterozygous and homozygous KRAS mutations, even against a background of up to 95% wild-type DNA, was realizable. The applicability of the test was successfully proven for various cancer cell lines as well as clinical tumour samples. Thus, the chip-based DNA hybridization technique seems to be a promising tool for KRAS mutation analysis to further improve personalized cancer treatment.
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.
It is now widely accepted that the immune microenvironment of tumors and more precisely Tumor Infiltrating Lymphocytes (TIL) play an important role in cancer development and outcome. TILs are considered to be important prognostic and predictive factors based on a growing body of clinical evidence; however, their presence at the tumor site is not currently assessed routinely. FTIR (Fourier transform infrared) imaging has proven it has value in studying a range of tumors, particularly for characterizing tumor cells. Currently, very little is known about the potential for FTIR imaging to characterize TIL. The present proof of concept study investigates the ability of FTIR imaging to identify the principal lymphocyte subpopulations present in human peripheral blood (PB). A negative cell isolation method was employed to select pure, label-free, helper T cells (CD4(+)), cytotoxic T cells (CD8(+)) and B cells (CD19(+)) from the Peripheral Blood (PB) of six Healthy Donors (HD) by Fluorescence Activated Cell Sorting (FACS). Cells were centrifuged onto Barium Fluoride windows and ten infrared images were recorded for each lymphocyte subpopulation from all six donors. After spectral pre-treatment, statistical analyses were performed. Unsupervised Principal Component Analyses (PCA) revealed that in the absence of donor variability, CD4(+) T cells, CD8(+) T cells and B cells each display distinct IR spectral features. Supervised Partial Least Square Discriminant Analyses (PLS-DA) demonstrated that the differences between the three lymphocyte subpopulations are reflected in their IR spectra, permitting their individual identification even when significant donor variability is present. Our results also show that a distinct spectral signature is associated with antibody binding. To our knowledge this is the first study reporting that FTIR imaging can effectively identify T and B lymphocytes and differentiate helper T cells from cytotoxic T cells. This proof of concept study demonstrates that FTIR imaging is a reliable tool for the identification of lymphocyte subpopulations and has the potential for use in characterizing TIL.
Discrimination of nodular lesions in cirrhotic liver is a challenge in the histopathologic diagnostics. For this reason, there is an urgent need for new detection methods to improve the accuracy of the diagnosis of liver cancer. Raman imaging allows to determine spatially distribution of variety of molecules in label-free cells or tissue and to correlate this molecular information with the morphological structures at the very same sample location. This study reports about investigations of two liver cancer cell lines - HepG2 and SK-Hep1 - as well as HepG2 cells in different cellular growth phases by using Raman imaging. Spectral data of all cells were recorded, assembled as a color-coded image and analyzed by hierarchical cluster analysis and principal component analysis. A performed support vector machine-based classification algorithm reliably predicts previously unknown cancer cells and cell cycle phases. By including only the Raman spectra of the cytoplasmatic lipids in the classifier, the accuracy has been improved. The main spectral differences that were found in the comparative analyses can be attributed to a higher expression of unsaturated fatty acids in the hepatocellular carcinoma cells and during proliferation phase. This corresponds to the already examined de novo lipogenesis in cells of liver cancer.
Purpose:
Patients with hepatocellular carcinoma (HCC) can only be treated curatively at early stages and then have a favorable prognosis of this often fatal disease. For this reason, an early detection and diagnostic confirmation are crucial. Raman imaging spectroscopy is a promising technology for high-resolution visualization of the spatial distribution of molecular composition in tissue sections. The aim of this study was to investigate molecular information of liver tissue by Raman imaging for classification and diagnostic prediction.
Methods:
Unstained cryosections of human hepatic tissues (23 patients) were measured by Raman spectroscope in the regions of HCC (n = 12) and fibrosis (n = 17). The acquired data set was used to generate a random forest classification model with 101 iterations of sevenfold cross-validation. The models obtained during cross-validation were also used to predict regions of tumor margin (n = 8) aside from independent testing.
Results:
Raman spectra differed between malignant and non-malignant tissue regions. Based on these spectral data, a random forest classification model calculated a prediction accuracy of 86 % (76 % sensitivity and 93 % specificity). The ten most important variables were identified at 2895, 2856, 1439, 1298, 1080, 1063, 1023, 937, 920, and 719 cm(-1).
Conclusions:
In this study, Raman imaging spectroscopy was applied successfully for liver tissue to differentiate, classify, and predict with high accuracy malignant and non-malignant tissue regions. Furthermore, the most important differences were identified as the Raman signature of fatty acids. The demonstrated results highlight the enormous potential which vibrational spectroscopy techniques have for the future diagnostics and prognosis estimation of HCC.
Current evaluation of histological sections of breast cancer samples remains unsatisfactory. The search for new predictive and prognostic factors is ongoing. Infrared spectroscopy and its potential to probe tissues and cells at the molecular level without requirement for contrast agents could be an attractive tool for clinical and diagnostic analysis of breast cancer. In this study, we report the successful application of FTIR (Fourier transform infrared) imaging for breast tissue component characterization. We show that specific FTIR spectral signatures can be assigned to the major tissue components of breast tumor samples. We demonstrate that a tissue component classifier can be built based on a spectral database of well-annotated tissues and successfully validated on independent breast samples. We also demonstrate that spectral features can reveal subtle differences within a tissue component, capturing for instance lymphocytic and stromal activation. By investigating in parallel lymph nodes, tonsils and wound healing tissues, we prove the uniqueness of the signature of both lymphocytic infiltrate and tumor microenvironment in the breast disease context. Finally, we demonstrate that the biochemical information reflected in the epithelial spectra might be clinically relevant for the grading purpose, suggesting potential to improve breast cancer management in the future.
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).
Spectral histopathology (SHP) is an emerging tool for label free annotation of tissue. While FTIR based SHP provides fast annotation of larger tissue sections, Raman based SHP is slower but achieves a 10 times higher spatial resolution as compared to FTIR. Usually NIR excitation is used for Raman measurements on biological samples. Here, for the first time 532 nm excitation is used to annotate colon tissue by Raman SHP. Excellent data quality is obtained, which resolves for example erythrocytes and lymphocytes. In addition to Raman scattering auto-fluorescence is observed. We found that this auto-fluorescence overlaps spatially with the fluorescence of antibodies against p53 used in routine immunohistochemistry in surgical pathology. This fluorescence indicates nuclei of cancer cells with mutated p53 and allows new label free assignment of cancer cells. These results open new avenues for optical diagnosis by Raman spectroscopy and autofluorescence.
Fourier transform infrared (FTIR) imaging was applied on histopathological specimens of breast cancer of different tumor histological grades. Focus was given to the extracellular matrix. FTIR spectral changes were observed when examining the extracellular matrix close to and far from carcinoma. Major changes were observed, in particular in the relative intensities of the collagen bands at 1640 and 1630 cm(-1). PCA analysis and global fitting indicate a continuous progression in collagen spectral features when moving away from the tumor. These preliminary results suggest FTIR spectral features present in the 1700-1600 cm(-1) spectral range could be used as spectral markers to identify cancer-induced modifications in collagen. This chemical imaging approach to analyze the breast cancer microenvironment could be used in the future for improving diagnostics of breast cancer.
The Her2/neu proto-oncogene is amplified in 25 to 30 percent of human primary breast carcinomas. The roles of Her2/neu have been reported before in literature, showing different relations to intracellular lipid composition. Here, we use Raman microspectroscopic imaging to reveal the chemical composition of single live cells from breast carcinoma cell lines MDA-MB-231, MDA-MB-435s and SK-BR-3, which express Her2/neu receptor in different extent. Average Raman spectra of the different cell populations show prominent lipid presence in all cell lines. With high significance, Raman difference spectra reveal increased lipid contents, as well as a lower degree of fatty acid saturation in the MDA-MB cell lines with respect to the SK-BR-3 cells. These results are confirmed by hierarchical cluster analysis of single cells. High internal consistency of the chemical compositions in the cell lines is shown by hierarchical cluster analysis on a single matrix composed of the data of different cells from a single cell line. Although Her2/neu expression is highest for SK-BR-3 cells, their lipid contents are lower than that of the MDA-MB cell lines, which express less to no Her2/neu receptors. Rather than metabolic rate or senescence, the degree of metastaticity of the cells appears to be related to the polyunsaturated fatty acid contents of the cells.
We report the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes and detect metastatic breast cancer cells. The acquisition of spectral data from tissue embedded in paraffin provided spectra free of dispersive artefacts that may be observed for infrared microscopic measurements using a 'reflection/absorption' methodology. As a consequence, superior tissue classification and identification of cellular abnormality unattainable for deparaffinised tissue was achieved.
The first detection of breast cancer micrometastases in lymph nodes by infrared spectral imaging and methods of multivariate analysis is reported. Micrometastases are indicators of early spread of cancer from the organ originally affected by disease, and their detection is of prime importance for the staging and treatment of cancer. Infrared spectral imaging, at a spatial resolution of ca. 10-12 mum, can detect small metastases down to the level of a few cancerous cells. The results presented here add to a rapidly growing database of infrared spectral imaging results for cancer diagnostics.
Histopathologic evaluation of surgical specimens is a well established technique for disease identification, and has remained relatively unchanged since its clinical introduction. Although it is essential for clinical investigation, histopathologic identification of tissues remains a time consuming and subjective technique, with unsatisfactory levels of inter- and intra-observer discrepancy. A novel approach for histological recognition is to use Fourier Transform Infrared (FT-IR) micro-spectroscopy. This non-destructive optical technique can provide a rapid measurement of sample biochemistry and identify variations that occur between healthy and diseased tissues. The advantage of this method is that it is objective and provides reproducible diagnosis, independent of fatigue, experience and inter-observer variability.
We report a method for analysing excised lymph nodes that is based on spectral pathology. In spectral pathology, an unstained (fixed or snap frozen) tissue section is interrogated by a beam of infrared light that samples pixels of 25 mum x 25 mum in size. This beam is rastered over the sample, and up to 100,000 complete infrared spectra are acquired for a given tissue sample. These spectra are subsequently analysed by a diagnostic computer algorithm that is trained by correlating spectral and histopathological features.
We illustrate the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes. By correlating spectral and histopathological features, a diagnostic algorithm was trained that allowed both accurate and rapid classification of benign and malignant tissues composed within different lymph nodes. This approach was successfully applied to both deparaffinised and frozen tissues and indicates that both intra-operative and more conventional surgical specimens can be diagnosed by this technique.
This paper provides strong evidence that automated diagnosis by means of infrared micro-spectral imaging is possible. Recent investigations within the author's laboratory upon lymph nodes have also revealed that cancers from different primary tumours provide distinctly different spectral signatures. Thus poorly differentiated and hard-to-determine cases of metastatic invasion, such as micrometastases, may additionally be identified by this technique. Finally, we differentiate benign and malignant tissues composed within axillary lymph nodes by completely automated methods of spectral analysis.
The potential problems associated with the use of formalin in histology, such as health hazards, degradation of RNA and cross-linking of proteins are well recognized. We describe the utilization of a formalin-free fixation and processing system for tissue detection of two important biopredictors in breast cancer - estrogen receptor and HER2 - at the RNA and protein levels.
Parallel sections of 62 cases of breast cancer were fixed in an alcohol-based molecular fixative and in formalin. Molecular fixative samples were processed by a novel formalin-free microwave-assisted processing system that preserves DNA, RNA and proteins. Formalin-fixed samples were processed using the conventional method. Estrogen receptor was assessed by immunohistochemistry and real-time PCR. HER2 was assessed by immunohistochemistry, FISH, CISH and real-time PCR.
The immunohistochemical reaction for estrogen receptor was similar in molecular- and formalin-fixed samples (Spearman Rank R = 0.83, p < 0.05). Also HER2 result was similar to that of formalin-fixed counterparts after elimination of antigen retrieval step (Spearman Rank R = 0.84, p < 0.05). The result of HER2 amplification by FISH and CISH was identical in the molecular fixative and formalin-fixed samples; although a shorter digestion step was required when using the former fixative. Real-time PCR for both estrogen receptor and HER2 were successful in all of the molecular fixative specimens.
The formalin-free tissue fixation and processing system is a practical platform for evaluation of biomolecular markers in breast cancer and it allows reliable DNA and RNA and protein studies.
A transformation known as the maximum noise fraction (MNF)
transformation, which always produces new components ordered by image
quality, is presented. It can be shown that this transformation is
equivalent to principal components transformations when the noise
variance is the same in all bands and that it reduces to a multiple
linear regression when noise is in one band only. Noise can be
effectively removed from multispectral data by transforming to the MNF
space, smoothing or rejecting the most noisy components, and then
retransforming to the original space. In this way, more intense
smoothing can be applied to the MNF components with high noise and low
signal content than could be applied to each band of the original data.
The MNF transformation requires knowledge of both the signal and noise
covariance matrices. Except when the noise is in one band only, the
noise covariance matrix needs to be estimated. One procedure for doing
this is discussed and examples of cleaned images are presented
Chemometrics in Analytical Spectroscopy provides students and practising analysts with a tutorial guide to the use and application of the more commonly encountered techniques used in processing and interpreting analytical spectroscopic data. In detail the book covers the basic elements of univariate and multivariate data analysis, the acquisition of digital data and signal enhancement by filtering and smoothing, feature selection and extraction, pattern recognition, exploratory data analysis by clustering, and common algorithms in use for multivariate calibration techniques. An appendix is included which serves as an introduction or refresher in matrix algebra. The extensive use of worked examples throughout gives Chemometrics in Analytical Spectroscopy special relevance in teaching and introducing chemometrics to undergraduates and post-graduates undertaking analytical science courses. It assumes only a very moderate level of mathematics, making the material far more accessible than other publications on chemometrics. The book is also ideal for analysts with little specialist background in statistics or mathematical methods, who wish to appreciate the wealth of material published in chemometrics.
While a pattern approach to diagnosis is taught and practiced with almost every other tissue or organ in the body, the lymph node remains a mystery to most residents starting out in pathology and those pathologists with limited experience in the area. A Pattern Approach to Lymph Node Diagnosis demonstrates that a systematic approach to lymph node examination can be achieved through recognition of morphological patterns produced by different disease processes. It presents a combination of knowledge-based assessment and pattern recognition for diagnosis covering the major primary neoplastic and non neoplastic diseases and metastatic tumors in lymph nodes. This volume demonstrates that lymph node compartments can be recognized histologically especially with the aid of immunohistological markers and how this knowledge can be employed effectively to localize and identify pathological changes in the different compartments in order to facilitate histological diagnosis. It also defines histological features that, because of their pathological occurrence in lymph nodes, are useful pointers to specific diagnoses or disease processes. The volume is organized in accordance with the primary pattern of presentation of each diagnostic entity. Differential diagnosis is discussed and each diagnostic entity is accompanied by color illustrations that highlight the diagnostic features. Immunohistochemistry, clinical aspects, relevant cytogenetics and molecular information of each entity is provided by an author who is an expert in lymphoproliferative diseases. An algorithmic approach to diagnosis is adopted at the end of each section by listing a set of questions that help to consider diagnostic entities that can present with the morphological features observed. A Pattern Approach to Lymph Node Diagnosis is an essential text for residents and fellows in pathology and general pathologists making first hand lymph node diagnoses as well as to hematologists and physicians who treat patients with lymphoprolifeative diseases.
Raman microspectroscopy-based, label-free imaging methods for human cells at sub-micrometre spatial resolution are presented. Since no dyes or labels are used in this imaging modality, the pixel-to-pixel spectral variations are small and multivariate methods of analysis need to be employed to convert the hyperspectral datasets to spectral images. Thus, the main emphasis of this paper is the introduction and comparison of a number of multivariate image reconstruction methods. The resulting Raman spectral imaging methodology directly utilizes the spectral contrast provided by small (bio)chemical compositional changes over the spatial dimension of the sample to construct images that can rival fluorescence images in terms of spatial information, yet without the use of any external dye or label.
The formulation of histologic diagnosis in lymph nodes, as in other tissue/organ sites, is knowledge-based and is applied in combination with visual recognition. These processes are not mutually exclusive. The pathologist firstly must be acquainted with the entities that can occur at the specific tissue site with an appreciation of their relative frequencies, as “common things occur commonly.” In addition, the pathologist should be fully cognizant of the spectrum of histologic features that are manifested by each pathological entity and be able to recognize them, the latter skill commonly known as “pattern recognition.” While a specific entity may display a number of diagnostic features, they all may not be present in every case, and very few are specific or pathognomonic. The relative importance of each morphologic feature, like the minimum number or most important set of morphologic features that permits a definite diagnosis, is determined by published data, the skills and experience of the pathologist, and the clinical setting. The availability of ancillary diagnostic techniques lends greater objectivity to the outcome. The diagnostic process is therefore extremely complicated.
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 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.
A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.
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.
In this manuscript, we report the application of EMSC to correct infrared micro-spectral data recorded from tissue that describe resonant Mie scattering contributions. Small breast micro-metastases previously undetectable using the raw measured spectra were provided clear contrast from the surrounding tissue after signal correction. The technique also proved transferrable, successfully correcting imaging data sets recorded from multiple patients. It is envisaged more robust methods of supervised analysis can now be constructed to automatically classify and diagnose tissue spectra.
Novel optical imaging methods, such as Raman microspectroscopy, have been gaining recognition in their ability to obtain noninvasively the distribution of biochemical components of a sample. Raman spectroscopy in combination with optical microscopy provides a label-free method to assess and image cellular processes, without the use of extrinsic fluorescent dyes. The submicrometer resolution of the confocal Raman instrumentation allows us to image cellular organelles on the scale of conventional microscopy. We used the technique to monitor subcellular degradation patterns of two biodegradable nanocarrier systems-poly(epsilon-caprolactone) (PCL) and poly(lactic-co-glycolic acid) (PLGA). Our results suggest that both drug-delivery systems eventually are incorporated into Golgi-associated vesicles of late endosomes. These processes were monitored via the decrease of the molecule-characteristic peaks of PCL and PLGA. As the catabolic pathways proceed, shifts and variations in peak intensities and intensity ratios in the rendered Raman spectra unequivocally delineate their degradation patterns.
In this contribution, we discuss state-of-the-art methodology for the collection and analysis of hyperspectral images of tissue that will become useful in complementing classical histopathology. In particular, we discuss sampling strategies, data collection methods, and computational approaches to produce pseudo-color maps of large tissue sections of lymph nodes, up to about 100 mm(2) in size. The latter efforts include methods to reduce the presence of dispersion artifacts in IR transflection micro-spectra which can greatly impact the statistical analyzes performed on the data, such as hierarchical cluster analysis and principal components analysis.
Resting B lymphocytes can be activated and induced to proliferate by antibodies against their antigen receptors (anti-lg). We demonstrate an early increase in the level of [3H]inositol trisphosphate in [3H]inositol-labeled murine B cells, which suggests breakdown of phosphatidylinositol bisphosphate by phospholipase C. In line with this, the level of [3H]1,2-diacylglycerol was also elevated after incubation of [3H]arachidonic-acid-labeled B cells with anti-Ig. Anti-lg also caused a rapid increase in the level of cytosolic Ca2+ in B cells. In contrast, two other polyclonal B cell activators, lipopolysaccharide and phorbol myristate acetate, failed to induce any of these effects. Our results suggest that anti-lg may induce B cell growth via phosphoinositide degradation and Ca2+ mobilization, and that phorbol myristate acetate, and possibly lipopolysaccharide, bypass these initial events.
Infrared spectroscopy probes the chemical composition and molecular structure of complex systems such as tissue and cells. Infrared spectroscopic imaging combines this spectral information with lateral resolution near the single-cell level. We analyzed whether this method is competitive with classic immunohistochemical methods for immunologic tissue and cells.
We recorded infrared microspectroscopic mapping datasets with a 90- x 90-microm2 aperture from a 3- x 3-mm2 unstained tissue area of human spleen. A secondary follicle containing a germinal center and a T zone were studied in more detail by infrared microspectroscopic imaging with lateral resolution near 5 mum. The results were compared with consecutive sections stained by immunoglobulin D antibodies. T and B lymphocytes were extracted from human blood and served as independent test samples.
Cluster analysis of infrared datasets produced images that distinguished anatomical features such as primary and secondary follicles, T zones, arteries, and spleen red pulp. The assignments could be confirmed in consecutive sections by immunohistochemical staining. Main spectral variances between T and B lymphocytes in high-resolution measurements were attributed to specific spectral contributions of DNA and cytosol.
Sensitivity and specificity of the infrared based methods are comparable to those of standard staining procedures for identification of B and T cells. However, infrared spectroscopic imaging can offer advantages in velocity, data throughput, and standardization because of minimal sample preparation. The results emphasize the potential of infrared spectroscopy as an innovative tool for the distinction of cell types, in particular in immunologic tissue.
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.
- B Bird
- M Romeo
- N Laver
- M Diem
B. Bird, M. Romeo, N. Laver, and M. Diem, J. Biophotonics 2, 37-46 (2009).
- S W Bruun
- A Kohler
- I Adt
- G D Sockalingum
- M Manfait
- H Martens
S. W. Bruun, A. Kohler, I. Adt, G. D. Sockalingum,
M. Manfait, and H. Martens, Appl. Spectrosc. 60,
1029–1039 (2006).
- T Chernenk
- C Matthaus
- L Milane
- L Quintero
- M Amiji
- M Diem
T. Chernenk, C. Matthaus, L. Milane, L. Quintero, M.
Amiji, and M. Diem, ACS Nano 3, 3552-3559 (2009).
- M J Romeo
- M Diem
M. J. Romeo and M. Diem, Vib. Spectrosc. 38, 115–
119 (2005).
- A Savitzky
- M J E Golay
A. Savitzky and M. J. E. Golay, Anal. Chem. 36,
1627-1639 (1964).
- L Hartsuiker
- N J Zeijen
- L W Terstappen
- C Otto
L. Hartsuiker, N. J. Zeijen, L. W. Terstappen, and
C. Otto, Analyst 135, 3220-3226 (2010).
- D Codrich
D. Codrich, Ph.D. thesis, Univerista' Degli Studi di
Trieste, Italy (2007).
- C Krafft
- R Salzer
- G Soff
- M Meyer-Hermann
C. Krafft, R. Salzer, G. Soff, and M. Meyer-Hermann,
Cytommetry 64A, 53-61 (2005).
- R K Reddy
- R Bhargava
R. K. Reddy and R. Bhargava, Analyst 135, 2818-2825 (2010).
- B Bird
- M Miljković
- M J Romeo
- J Smith
- N Stone
- M W George
- M Diem
B. Bird, M. Miljković, M. J. Romeo, J. Smith,
N. Stone, M. W. George, and M. Diem, BMC Clin.
Pathol. 8, 1-14 (2008).
- B Bird
- K Bedrossian
- N Laver
- M Miljković
- M J Romeo
- M Diem
B. Bird, K. Bedrossian, N. Laver, M. Miljković, M. J.
Romeo, and M. Diem, Analyst 134, 1067-1076
(2009).
- A A Green
- M Berman
- P Switzer
- M D Craig
A. A. Green, M. Berman, P. Switzer, and M. D. Craig,
IEEE Trans. Geosci. Remote Sens. 26, 65-74 (1988).
Phase correction to compensate for reflective distortions of optical spectra
- M Diem
- B Bird
- M Miljković
M. Diem, B. Bird, and M. Miljković, Phase correction
to compensate for reflective distortions of optical
spectra, U.P. Office, 2011.
- M Miljković
- B Bird
- M Diem
M. Miljković, B. Bird, and M. Diem, Analyst 137,
3954-3964 (2012).
- M Miljković
- T Chernenko
- M J Romeo
- B Bird
- C Matthaus
- M Diem
M. Miljković, T. Chernenko, M. J. Romeo, B. Bird,
C. Matthaus, and M. Diem, Analyst 135, 2002-2013
(2010).
- J H Ward
J. H. Ward, JASA 58, 236-244 (1963).
- M Diem
- K Papamarkakis
- J Schubert
- B Bird
- M J Romeo
- M Miljković
M. Diem, K. Papamarkakis, J. Schubert, B. Bird, M. J.
Romeo, and M. Miljković, Appl. Spectrosc. 63,
307A-318A (2009).
- B Bird
- M Miljković
- M Diem
B. Bird, M. Miljković, and M. Diem, J. Biophotonics 3, 597-608 (2010).
- M K Bijsterbosch
- C J Meade
- G A Turner
- G G Klaus
M. K. Bijsterbosch, C. J. Meade, G. A. Turner, and
G. G. Klaus, Cell 41, 999-1006 (1985).
Miljković Phase correction to compensate for reflective distortions of optical spectra
- M Diem
- B Bird