The characterization and identification of microorganisms by infrared or Raman spectroscopy is probably one of the best
developed and most frequent applications of biomedical vibrational spectroscopy. The serial types of dedicated
instruments for routine FT-IR characterizations of microorganisms are now available on the market and already used in
routine microbiological laboratories. The experiences gained to date, especially the necessity to define standards for
sampling and measurement procedures and the details of how data compatibility between different laboratories is achieve
will be discussed as well as the problem to establish validated reference data bases for objective species or strain
identifications.
Infrared transmission/absorption measurements of cells and biofluids in water are restricted to very short optical pathlengths. When the amide I and amide II bands of protein constituents have to be analysed, path-lengths of less than 8 μm are necessary. Infrared spectra of cancer cells were collected from physiological buffer solutions utilizing custom-made mid-infrared compatible IR-cuvettes. The technology permitted to obtain cell-type specific spectral signatures and probe biochemical changes induced by varying temperatures or cell-drug interaction. Optical path-lengths of 8-30 μm were used on a set of microbial test strains to evaluate, whether the methodology can also be used to discriminate and identify micro-organisms. A semi-automatic methodology was developed for the analysis of liquid serum samples, which combines simple sample handling with high sample throughput and extreme measurement reproducibility. The applicability of this infrared technology to the analysis of liquid serum samples from cattle and human beings suffering from various acute viral or bacterial infections was explored testing the interrelationship between α-helical and β-sheet specific spectral signatures in the amide I band contour and total albumin and globulin content in serum. The technical details, advantages, and limitations of the new technology are described in the context of developing a routine, IR-based biodiagnostic technique for biofluids and biological cells.
In this study we describe a semiautomatic Fourier transform infrared spectroscopic methodology for the analysis of liquid serum samples, which combines simple sample introduction with high sample throughput. The applicability of this new infrared technology to the analysis of liquid serum samples from a cohort of cattle naturally infected with bovine spongiform encephalopathy and from controls was explored in comparison to the conventional approach based on transmission infrared spectroscopy of dried serum films. Artifical neural network analysis of the infrared data was performed to differentiate between bovine spongiform encephalopathy-negative controls and animals in the late stage of the disease. After training of artifical neural network classifiers, infrared spectra of sera from an independent external validation data set were analyzed. In this way, sensitivities between 90 and 96% and specificities between 84 and 92% were achieved, respectively, depending upon the strategy of data collection and data analysis. Based on these results, the advantages and limitations of the liquid sample technique and the dried film approach for routine analysis of biofluids are discussed.
Syrian hamster nervous tissue was investigated by FTIR microspectroscopy with conventional and synchrotron infrared light sources. Various tissue structures from the cerebellum and medulla oblongata of scrapie-infected and control hamsters were investigated at a spatial resolution of 50 μm. Single neurons in dorsal root ganglia of scrapie-infected hamsters were analyzed by raster scan mapping at 6 μm spatial resolution. These measurements enabled us to (i) scrutinize structural differences between infected and non-infected tissue and (ii) analyze for the first time the distribution of different protein structures in situ within single nerve cells. Single nerve cells exhibited areas of increased β-sheet content, which co-localized consistently with accumulations of the pathological prion protein (PrPSc). Spectral data were also obtained from purified, partly proteinase K digested PrPSc isolated from scrapie-infected nervous tissue of hamsters to elucidate similarities/dissimilarities between prion structure in situ and ex vivo. A further comparison is drawn to the recombinant Syrian hamster prion protein SHaPrP90-232, whose in vitro transition from the predominantly a-helical isoform to β-sheet rich oligomeric structures was also investigated by FTIR spectroscopy.
In our former studies a diagnostic approach for the detection of transmissible spongiform encephalopaties (TSE) based on FT-IR spectroscopy in combination with artificial neural networks was described, based on a controlled animal study with terminally ill Syrian hamsters and control animals. As a consequence of the bovine spongiform encephalopathy (BSE) crisis in Europe, the development of a disgnostic ante mortem test for cattle has become a matter of great
scientific importance and public interest. Since 1986 more than 180,000 clinical cases of BSE have been observed in the UK alone. Most of these cases were confirmed by post mortem examination of brain tissue. However, BSE-related risk assessment and risk-management would greatly benefit from ante mortem testing on living animals. For example, a serum-based test could allow for screening of the cattle population, thus, even a BSE eradication program would be
conceivable. Here we report on a novel method for ante mortem BSE testing, which combines infrared spectroscopy of serum samples with multivariate pattern recognition analysis. A classification algorithm was trained using infrared spectra of sera from more than 800 animals from a field study (including BSE positive, healthy controls and animals suffering from viral or bacterial infections). In two validation studies sensitivities of 85% and 87% and specificities of
84% and 91% were achieved, respectively. The combination of classification algorithms increased sensitivity and specificity to 96% and 92%, respectively.
Different cluster image reassembling methodologies have been used to generate infrared maps from FT-IR microspectra of human prostate tissue sections. Spectra were collected in transmission mode with high spatial resolution by the use of a HgCdTe focal plane array detector imaging system. While univariate imaging techniques such as chemical mapping often give unsatisfactory classification results, unsupervised multivariate data analysis techniques such as agglomerative hierarchical clustering, fuzzy C-means, or k-means clustering confirmed standard histopathological techniques and turned out to be helpful to identify and to discriminate tissues structures.
The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to chemical mapping. Among the cluster imaging methods, agglomerative hierarchical clustering (Ward's algorithm) turned out to be the best method in terms of tissue structure differentiation.
Transmissible spongiform encephalopathies (TSE), such as BSE in cattle, scrapie in sheep and goats, and Creutzfeldt-Jakob disease in man are a group of fatal infectious diseases of the central nervous system that are far from being fully understood. Presuming the pathological changes to originate from small disease-specific compositional and structural modifications at the molecular level, Fourier-transform infrared (FTIR) spectroscopy can be used to achieve insight into biochemical parameters underlying pathogenesis. We have developed an FTIR microspectroscopy-based strategy which, as a combination of image reconstruction and multivariate pattern recognition methods, permitted the comparison of identical substructures in the cerebellum of healthy and TSE-infected Syrian hamsters in the terminal stage of the disease. Here we present FTIR data about the pathological changes of scrapie-infected and normal tissue of the gray matter structures stratum granulosum and stratum moleculare. IR spectroscopy was also applied to tissue pieces of the medulla oblongata of infected and control Syrian hamsters. Mapping data were analyzed with cluster analysis and imaging methods. We found variations in the spectra of the infected tissue, which are due to changes in carbohydrates, nucleic acids, phospholipids, and proteins.
IR spectra of intact microbial cells are fingerprint-like signatures which provide multi-dimensional information on cell composition and structure. These spectral signatures are already used in practice to identify divers microbial species and strains, to characterize particular cell compounds in situ, and to monitor cell-drug interactions. New applications arise by means of a light microspace coupled to the IR spectrometer: IR-spectra of micro-colonies containing a few hundred cells can be obtained from colony replica by a stamping technique that transfers spatially accurate micro-colonies growing on solid culture plates to a special, IR-transparent stamping device. Using a computer controlled x, y stage together with spectral mapping and video techniques, detection, enumeration, and differentiation of micro-organisms are integrated in one single apparatus, providing diagnostic results within one working day. Additional items of the new are integrated in one single apparatus, providing diagnostic result within one working day. Additional items of the new approach are (i) rapid sensitivity and resistance testing against various antimicrobial drugs, and (ii) the conductance of spectral mapping analysis on single colonies enabling the spatially resolved characterization of growth heterogeneity within complex populations of micro-organisms.
The feasibility of characterizing human colorectal adenocarcinoma by IR microspectrometry is described. Carcinoma thin sections were analyzed by spatially resolved mid-IR FT microspectrometry, and for comparative purposes by conventional histological staining techniques. More than 2300 high quality FTIR reference spectra of 27 patient samples form 11 defined morphological structures such as crypts, tunica muscularis, submucosa and adenocarcinoma were recorded. The analysis of the spectral data included four steps: an initial test for spectral quality, data preprocessing, data reduction and classification of the tissue spectra by pattern recognition techniques. The overall classification accuracy attainted by an optimized ANN of about 95 percent highlights the great potential of FTIR microspectrometry as a diagnostic tool for the determination of a variety of tissue structures. Further improvements are necessary to make the new method applicable to routine and experimental clinical analysis in the future.
FT-IR microspectrometry, particularly in combination with digital imaging techniques shows great promise for in-vivo and ex-vivo medical diagnosis. The statement is based on the knowledge that this method delivers information of the chemical structure and composition of a sample and the fact that any disease is linked to changes in the molecular and structural composition of cells and tissues. Typically, these changes are highly specific for a given tissue structure and are therefore potentially detectable by FT-IR microspectrometry. In this paper we present several approaches for the representation of mid-infrared microspectroscopic data acquired with high spatial resolution by the use of a MCT focal plane array detector. The applicability of image reassembling methodologies like functional group analysis, image reconstruction based on factor analysis and artificial neural network analysis to the IR data is discussed.
FTIR microspectroscopic maps of unstained colon carcinoma thin sections were obtained on a conventional IR microscope equipped with an automatic x, y stage, or alternatively by using a MCT focal plane array detector system. IR data were analyzed by different image re-assembling techniques. One main goal of the present study was to test the influence of different spectra data compression approaches on the quality of the FTIR images. The images, re-assembled by Principal component analysis (PCA) on the basis of spectral information available from the fingerprint region exhibited an excellent image contrast confirming standard histo- pathological examinations. The second approach included a systematic search for spectral windows which were supposed to contain the relevant information, necessary for spectra classification and identification. Data from these spectral windows were analyzed by an ANN and output data were utilized for image construction. In contrast to the PCA approach, the image contrast was lower although the main morphological structures were exactly classified. From the spectroscopic point of view, the spectral feature selection method delivered useful information which could be discussed in terms of structural alternations upon carcinogenesis.
FTIR and FT-NIR Raman spectra of intact microbial cells are highly specific, fingerprint-like signatures which can be used to (i) discriminate between diverse microbial species and strains, (ii) detect in situ intracellular components or structures such as inclusion bodies, storage materials or endospores, (iii) detect and quantify metabolically released CO2 in response to various different substrate, and (iv) characterize growth-dependent phenomena and cell-drug interactions. The characteristic information is extracted from the spectral contours by applying resolution enhancement techniques, difference spectroscopy, and pattern recognition methods such as factor-, cluster-, linear discriminant analysis, and artificial neural networks. Particularly interesting applications arise by means of a light microscope coupled to the spectrometer. FTIR spectra of micro-colonies containing less than 103 cells can be obtained from colony replica by a stamping technique that transfers micro-colonies growing on culture plates to a special IR-sample holder. Using a computer controlled x, y- stage together with mapping and video techniques, the fundamental tasks of microbiological analysis, namely detection, enumeration, and differentiation of micro- organisms can be integrated in one single apparatus. FTIR and NIR-FT-Raman spectroscopy can also be used in tandem to characterize medically important microorganisms. Currently novel methodologies are tested to take advantage of the complementary information of IR and Raman spectra. Representative examples on medically important microorganisms will be given that highlight the new possibilities of vibrational spectroscopies.
Biomedical applications of vibrational spectroscopy developed for routine analysis require methods for data evaluation. Artificial neural networks open a new perspective for the spectra differentiation and identification of biological samples with their small spectra variance. In the present study, the stacked spectral data processing and the following use of neural networks for spectral identification was investigated. 6 different neural network architectures were tested in their capability to built spectral libraries for different bacterial genera and for yeasts, using FTIR and FT-Raman spectra. After developing these libraries, they were connected to a large library, what we called 'multilayered neural networks'. This combines the advantages that the wavelength can be chosen more selective for a given differentiation problem and the network architecture and training function can be more adapted to a special task.
IR spectra of breast tumor cell lines and breast tumor tissues have been measured. IR measurements of tumor cells revealed that approximately 15 cells are necessary to obtain spectra of good signal-to-noise ratio using an IR microspectrometer equipped with a conventional IR thermal source. Comparative studies of human breast tumor cell line suspensions demonstrated that MCF-7 cells and drug-resistant NCI/ADR cells can be differentiated based on their IR spectra. The most striking differences between MCF-7 and NCI/ADR were found in features assigned to CH2 and CH3 stretching vibrations of lipid acyl chains and PO2 stretching vibrations of nucleic acids. To assess the potential of IR spectroscopy for the diagnosis of breast tumor tissues, thin sections of tissue were mapped by FTIR microspectroscopy. The spectra of these maps were analyzed using functional group mapping techniques and cluster analysis, and the output values of the different approaches were then reassembled into IR images of the tissue. A comparison of the IR images with the standard light microscopic images of the corresponding areas suggested that: (i) chemical mapping based on single band intensities is an easy way to detect microscopic fat droplets within tissue; (ii) the comparison of IR images based on band intensities at 1054 and 1339 cm-1 provides information on tissue areas containing tumor cells; (iii) cluster analysis of the spectra is superior to the single band approach and more appropriate for differentiation between tissue types.
The FT-IR-microspectroscopic mapping technique in combination with image construction methods has been used to characterize thin tissue sections from human melanoma. IR mapping spectroscopy as a spatially two-dimensional working technique is a non-invasive analytical method. Up to now IR-imaging has been based on distinct spectral parameters such as frequency, intensity or fullwidth of half maximum, ratio of single wavelengths and so on. We decided to compare this technique with other chemometric methods and to secure that these parameters will not give unsatisfactory results. For this purpose pattern recognition analysis (PRA) e.g. principal component analysis (PCA) or artificial neural networks (ANN) of IR-data, confirmed with standard histopathological techniques, has turned out to be helpful to discriminate also between different tissues.
Being interested in the effects of base sequence and environmental conditions on the DNA structure, we have analyzed the solution and crystal structures of some G/C-rich DNA oligomers by vibrational spectroscopy. Here, we report on the analysis of the octamer d(GGGATCCC) and the decamer d(CCAGGCCTGG). Raman spectra were recorded on a Raman spectrometer as in, infrared spectra were measured with a Fourier transform infrared spectrophotometer Bruker IFS-66, for infrared measurements on crystals, the IFS-66 was coupled to an IR-microscope.
The Fourier-transform infrared spectra of intact procaryotic cells (bacteria) have already been used in the past to characterize (differentiate, classify and identify) a variety of bacterial strains and taxa. In this paper the essential features of a methodology are described which extend the FT-IR pattern recognition approach to intact eucaryotic cells (yeasts/fungi). Basically, the characteristic information pertaining to microbial FT-IR patterns is explored by applying multivariate statistics and cluster analysis to both the time and frequency domain of the mid-ir spectral data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.