Standardization of complex biologically derived spectrochemical datasets.

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO2 levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

An imaging dataset of cervical cells using scanning near-field optical microscopy coupled to an infrared free electron laser.

Using a scanning near-field optical microscope coupled to an infrared free electron laser (SNOM-IR-FEL) in low-resolution transmission mode, we collected chemical data from whole cervical cells obtained from 5 pre-menopausal, non-pregnant women of reproductive age, and cytologically classified as normal or with different grades of cervical cell dyskaryosis. Imaging data are complemented by demography. All samples were collected before any treatment. Spectra were also collected using attenuated total reflection, Fourier-transform (ATR-FTIR) spectroscopy, to investigate the differences between the two techniques. Results of this pilot study suggests SNOM-IR-FEL may be able to distinguish cervical abnormalities based upon changes in the chemical profiles for each grade of dyskaryosis at designated wavelengths associated with DNA, Amide I/II, and lipids. The novel data sets are the first collected using SNOM-IR-FEL in transmission mode at the ALICE facility (UK), and obtained using whole cells as opposed to tissue sections, thus providing an 'intact' chemical profile. These data sets are suited to complementing future work on image analysis, and/or applying the newly developed algorithm to other datasets collected using the SNOM-IR-FEL approach.

Imaging cervical cytology with scanning near-field optical microscopy (SNOM) coupled with an IR-FEL.

Cervical cancer remains a major cause of morbidity and mortality among women, especially in the developing world. Increased synthesis of proteins, lipids and nucleic acids is a pre-condition for the rapid proliferation of cancer cells. We show that scanning near-field optical microscopy, in combination with an infrared free electron laser (SNOM-IR-FEL), is able to distinguish between normal and squamous low-grade and high-grade dyskaryosis, and between normal and mixed squamous/glandular pre-invasive and adenocarcinoma cervical lesions, at designated wavelengths associated with DNA, Amide I/II and lipids. These findings evidence the promise of the SNOM-IR-FEL technique in obtaining chemical information relevant to the detection of cervical cell abnormalities and cancer diagnosis at spatial resolutions below the diffraction limit (≥0.2 µm). We compare these results with analyses following attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy; although this latter approach has been demonstrated to detect underlying cervical atypia missed by conventional cytology, it is limited by a spatial resolution of ~3 µm to 30 µm due to the optical diffraction limit.

Classification of agents using Syrian hamster embryo (SHE) cell transformation assay (CTA) with ATR-FTIR spectroscopy and multivariate analysis.

The Syrian hamster embryo (SHE) cell transformation assay (pH 6.7) has a reported sensitivity of 87% and specificity of 83%, and an overall concordance of 85% with in vivo rodent bioassay data. To date, the SHE assay is the only in vitro assay that exhibits multistage carcinogenicity. The assay uses morphological transformation, the first stage towards neoplasm, as an endpoint to predict the carcinogenic potential of a test agent. However, scoring of morphologically transformed SHE cells is subjective. We treated SHE cells grown on low-E reflective slides with 2,6-diaminotoluene, N-nitroso-N-ethylnitroguanidine, N-nitroso-N-methylurea, N-nitroso-N-ethylurea, EDTA, dimethyl sulphoxide (DMSO; vehicle control), methyl methanesulfonate, benzo[e]pyrene, mitomycin C, ethyl methanesulfonate, ampicillin or five different concentrations of benzo[a]pyrene. Macroscopically visible SHE colonies were located on the slides and interrogated using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy acquiring five spectra per colony. The acquired IR data were analysed using Fisher's linear discriminant analysis (LDA) followed by principal component analysis (PCA)-LDA cluster vectors to extract major and minor discriminating wavenumbers for each treatment class. Each test agent vs. DMSO and treatment-induced transformed cells vs. corresponding non-transformed were classified by a unique combination of major and minor discriminating wavenumbers. Alterations associated with Amide I, Amide II, lipids and nucleic acids appear to be important in segregation of classes. Our findings suggest that a biophysical approach of ATR-FTIR spectroscopy with multivariate analysis could facilitate a more objective interrogation of SHE cells towards scoring for transformation and ultimately employing the assay for risk assessment of test agents.

Using Fourier transform IR spectroscopy to analyze biological materials.

IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.

Measuring similarity and improving stability in biomarker identification methods applied to Fourier-transform infrared (FTIR) spectroscopy.

FTIR spectroscopy is a powerful diagnostic tool that can also derive biochemical signatures of a wide range of cellular materials, such as cytology, histology, live cells, and biofluids. However, while classification is a well-established subject, biomarker identification lacks standards and validation of its methods. Validation of biomarker identification methods is difficult because, unlike classification, there is usually no reference biomarker against which to test the biomarkers extracted by a method. In this paper, we propose a framework to assess and improve the stability of biomarkers derived by a method, and to compare biomarkers derived by different method set-ups and between different methods by means of a proposed "biomarkers similarity index".

Histology verification demonstrates that biospectroscopy analysis of cervical cytology identifies underlying disease more accurately than conventional screening: removing the confounder of discordance.

BACKGROUND: Subjective visual assessment of cervical cytology is flawed, and this can manifest itself by inter- and intra-observer variability resulting ultimately in the degree of discordance in the grading categorisation of samples in screening vs. representative histology. Biospectroscopy methods have been suggested as sensor-based tools that can deliver objective assessments of cytology. However, studies to date have been apparently flawed by a corresponding lack of diagnostic efficiency when samples have previously been classed using cytology screening. This raises the question as to whether categorisation of cervical cytology based on imperfect conventional screening reduces the diagnostic accuracy of biospectroscopy approaches; are these latter methods more accurate and diagnose underlying disease? The purpose of this study was to compare the objective accuracy of infrared (IR) spectroscopy of cervical cytology samples using conventional cytology vs. histology-based categorisation. METHODS: Within a typical clinical setting, a total of n = 322 liquid-based cytology samples were collected immediately before biopsy. Of these, it was possible to acquire subsequent histology for n = 154. Cytology samples were categorised according to conventional screening methods and subsequently interrogated employing attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. IR spectra were pre-processed and analysed using linear discriminant analysis. Dunn's test was applied to identify the differences in spectra. Within the diagnostic categories, histology allowed us to determine the comparative efficiency of conventional screening vs. biospectroscopy to correctly identify either true atypia or underlying disease. RESULTS: Conventional cytology-based screening results in poor sensitivity and specificity. IR spectra derived from cervical cytology do not appear to discriminate in a diagnostic fashion when categories were based on conventional screening. Scores plots of IR spectra exhibit marked crossover of spectral points between different cytological categories. Although, significant differences between spectral bands in different categories are noted, crossover samples point to the potential for poor specificity and hampers the development of biospectroscopy as a diagnostic tool. However, when histology-based categories are used to conduct analyses, the scores plot of IR spectra exhibit markedly better segregation. CONCLUSIONS: Histology demonstrates that ATR-FTIR spectroscopy of liquid-based cytology identifies the presence of underlying atypia or disease missed in conventional cytology screening. This study points to an urgent need for a future biospectroscopy study where categories are based on such histology. It will allow for the validation of this approach as a screening tool.

Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients.

Despite numerous advances in "omics" research, early detection of ovarian cancer still remains a challenge. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy could characterise alterations in the biomolecular signatures of human blood plasma/serum obtained from ovarian cancer patients compared to non-cancer controls. Blood samples isolated from ovarian cancer patients (n = 30) and healthy controls (n = 30) were analysed using ATR-FTIR spectroscopy. For comparison, a smaller cohort of samples (n = 8) were analysed using an InVia Renishaw Raman spectrometer. Resultant spectra were pre-processed prior to being inputted into principal component analysis (PCA) and linear discriminant analysis (LDA). Statistically significant differences (P < 0.001) were observed between spectra of ovarian cancer versus control subjects for both biospectroscopy methods. Using a support vector machine classifier for Raman spectra of blood plasma, a diagnostic accuracy of 74% was achieved, while the same classifier showed 93.3% accuracy for IR spectra of blood plasma. These observations suggest that a biospectroscopy approach could be applied to identify spectral alterations associated with the presence of insidious ovarian cancer.

Exploiting biospectroscopy as a novel screening tool for cervical cancer: towards a framework to validate its accuracy in a routine clinical setting.

Biospectroscopy is an emerging field that harnesses the platform of physical sciences with computational analysis in order to shed novel insights on biological questions. An area where this approach seems to have potential is in screening or diagnostic clinical settings, where there is an urgent need for new approaches to objectively interrogate large numbers of samples in an objective fashion with acceptable levels of sensitivity and specificity. This review outlines the benefits of biospectroscopy in screening for precancer lesions of the cervix due to its ability to separate different grades of dysplasia. It evaluates the feasibility of introducing this technique into cervical screening programs on the basis of its ability to identify biomarkers of progression within derived spectra ('biochemical­cell fingerprints').

Dose-related alterations of carbon nanoparticles in mammalian cells detected using biospectroscopy: potential for real-world effects.

Nanotechnologies generate a wide range of engineered nanomaterials that enter into our ecosystem, especially carbon-based nanoparticles (CNPs). As these novel materials acquire ever increasing numbers of applications, they may pose a risk to organisms, including humans. However, our knowledge of nanoparticle-induced effects remains limited. We are yet to understand the interaction between nanoparticles and organisms, and classical toxicology fails to provide models for risk assessment. Biospectroscopy techniques were employed to identify the effects induced by real-world levels of a panel of CNPs. MCF-7 cells concentrated in S-phase or G0/G1-phase were treated for 24 h with short or long multiwalled carbon nanotubes (MWCNTs) or Fullerene (C60) at the following concentrations: 0.0025 mg/L, 0.005 mg/L, 0.01 mg/L, 0.025 mg/L, 0.05 mg/L, and 0.1 mg/L. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with computational analysis was then applied to interrogate the cells and significant dose-related effects were detected. From derived infrared spectra, distinct spectral biomarkers of cell alteration induced by each CNP type were identified. Additionally, Raman spectroscopy was applied and allowed us to determine that reactive oxygen species (ROS) were generated by CNPs. These observations highlight the potential of biospectroscopy techniques to determine CNP-induced alterations in target mammalian cells at ppb levels.

Incorporation of deuterium oxide in MCF-7 cells to shed further mechanistic insights into benzo[a]pyrene-induced low-dose effects discriminated by ATR-FTIR spectroscopy.

This study evaluated the potential of deuteration to enhance the mechanistic information obtainable by biospectroscopy techniques in biological-cell models. These techniques were previously demonstrated to identify low-dose effects (≤nM) induced by test agents; this is of critical interest in terms of developing novel approaches to monitor environmentally-induced cell alterations. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was coupled with multivariate analysis to characterize a low-dose (10(-10) M) compared to a high-dose (10(-6) M) exposure of benzo[a]pyrene (B[a]P) in oestrogen-responsive MCF-7 cells; these results were used as a positive control for spectroscopic detection of B[a]P-induced effects. Deuterium oxide (D2O) was then applied as part of a fixative solution and/or at low levels incorporated into growth medium prior to ATR-FTIR spectrochemical analysis. The application of D2O as an alternative solvent in spectroscopy is widespread, but D2O has never before been applied to biospectroscopic analysis of in vitro toxicology assays. This allowed comparison between deuterated- and typically-derived IR spectra, facilitating significant insights into the effects of deuteration, and suggested that the addition of D2O to biospectroscopy assays could improve understanding of low-dose effects.

IRootLab: a free and open-source MATLAB toolbox for vibrational biospectroscopy data analysis.

SUMMARY: IRootLab is a free and open-source MATLAB toolbox for vibrational biospectroscopy (VBS) data analysis. It offers an object-oriented programming class library, graphical user interfaces (GUIs) and automatic MATLAB code generation. The class library contains a large number of methods, concepts and visualizations for VBS data analysis, some of which are introduced in the toolbox. The GUIs provide an interface to the class library, including a module to merge several spectral files into a dataset. Automatic code allows developers to quickly write VBS data analysis scripts and is a unique resource among tools for VBS. Documentation includes a manual, tutorials, Doxygen-generated reference and a demonstration showcase. IRootLab can handle some of the most popular file formats used in VBS. License: GNU-LGPL. AVAILABILITY: Official website: http://irootlab.googlecode.com/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer.

Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva), with their potential application in clinical practice.

Biospectroscopy insights into the multi-stage process of cervical cancer development: probing for spectral biomarkers in cytology to distinguish grades.

Cervical cancer screening programmes have greatly reduced the burden associated with this disease. However, conventional cervical cytology screening still lacks sensitivity and specificity. There is an urgent need for the development of a low-cost robust screening technique. By generating a spectral "biochemical-cell fingerprint", Fourier-transform infrared (FTIR) spectroscopy has been touted as a tool capable of segregating grades of dysplasia. A total of 529 specimens were collected over a period of one year at two colposcopy centres in Dublin, Ireland. Of these, n = 128 were conventionally classed as high-grade, n = 186 as low-grade and n = 215 as normal. Following FTIR spectroscopy, derived spectra were examined for segregation between classes in scores plots generated with subsequent multivariate analysis. A degree of crossover between classes was noted and this could be associated with imperfect conventional screening resulting in an inaccurate diagnosis or an incomplete transition between classes. Maximal crossover associated with n = 102 of 390 specimens analyzed was found between normal and low-grade specimens. However, robust spectral differences (P≤ 0.0001) were still observed at 1512 cm(-1), 1331 cm(-1) and 937 cm(-1). For high-grade vs. low-grade specimens, spectral differences (P≤ 0.0001) were observed at Amide I (1624 cm(-1)), Amide II (1551 cm(-1)) and asymmetric phosphate stretching vibrations (νasPO2(-); 1215 cm(-1)). Least crossover (n = 50 of 343 specimens analyzed) was seen when comparing high-grade vs. normal specimens; significant inter-class spectral differences (P≤ 0.0001) were noted at Amide II (1547 cm(-1)), 1400 cm(-1) and 995 cm(-1). Deeper understanding of the underlying changes in the transition between cervical cytology classes (normal vs. low-grade vs. high-grade) is required in order to develop biospectroscopy tools as a screening approach. This will then allow for the development of blind classification algorithms.

Sub-cellular spectrochemical imaging of isolated human corneal cells employing synchrotron radiation-based Fourier-transform infrared microspectroscopy.

Understanding stem cell (SC) biology remains challenging and one of the few human tissues within which their in situ location is well characterized is the cornea. Individual human corneal epithelial cells were isolated from biopsies of live tissues using fluorescence-activated cell sorting (FACS); these were divided into putative SCs, transit-amplifying (TA) cells and terminally-differentiated (TD) cells. Employing synchrotron radiation-based Fourier-transform infrared (SR-FTIR) microspectroscopy with a focal plane array (FPA), sub-cellular spatial resolution analysis of unstained isolated cells was achieved as a consequence of the brilliance of a 12 collimated beams arrangement allowing rapid spectral acquisition. Infrared (IR) spectra were extracted and pre-processed. Subsequent categorization with multivariate analysis of IR spectra derived from FPA images was used to investigate biomolecular changes between classes. A progressive segregation in cell-specific spectral categories with differentiation from SC to TA cell to TD cell was noted. Multiple different absorption peaks that discriminated putative SCs, TA cells and TD cells across DNA, protein and lipid spectral regions were identified. DNA regions (1080 and 1225 cm(-1)) and some protein regions (1443 cm(-1)) primarily segregated SCs from TA cells and TD cells, whilst amide regions and lipids (1,550, 1650 and 1740 cm(-1)) segregated TA cells and TD cells. Scanning electron microscopy images verified the external phenotypic characteristics of the different isolated cell types. These findings highlight the applicability of SR-FTIR microspectroscopy towards distinguishing SCs, TA cells and TD cells, and suggest that cellular classification via traditional methods of immunolabelling can be greatly aided by the use of spectral biomarkers.

Mechanistic insights into nanotoxicity determined by synchrotron radiation-based Fourier-transform infrared imaging and multivariate analysis.

Our ability to identify the mechanisms by which carbon-based nanomaterials (CBNs) exert toxicity in cells is constrained by the lack of standardized methodologies to assay endpoint effects. Herein we describe a method of mechanistically identifying the effects of various CBN types in both prokaryotic and eukaryotic cells using multi-beam synchrotron radiation-based Fourier-transform infrared imaging (SR-FTIRI) at diffraction-limited resolution. This technique overcomes many of the inherent difficulties of assaying nanotoxicity and demonstrates exceptional sensitivity in identifying the effects of CBNs in cells at environmentally-relevant concentrations. We identify key mechanisms of nanotoxicity as the alteration of Amide and lipid biomolecules, but propose more specific bioactivity of CBNs occurs as a result of specific interactions between CBN structural conformation and cellular characteristics.

Alterations in the infrared spectral signature of avian feathers reflect potential chemical exposure: a pilot study comparing two sites in Pakistan.

Chemical contamination of ecosystems is a global issue with evidence that pollutants impact on living organisms in a harmful fashion. Developing sensor approaches that would allow the derivation of biomarkers or signatures of effect in target sentinel organisms and monitor environmental chemical contamination in a high throughput manner is of utmost importance. As biomolecules absorb infrared (IR), signature vibrational spectra related to structure and function can be derived. In light of this, we tested the notion that IR spectra of bird feathers might reflect environmental chemical contaminant exposure patterns. Feathers were collected from monospecific heronries of cattle egret based in two independent locations (Trimu vs. Mailsi) in the Punjab province of Pakistan; these sites were found to differ in their chemical contamination patterns. Feather samples were chemically analyzed for polychlorinated biphenyls, polybrominated diphenyl ethers, organochlorines and heavy metals. Attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy was employed to derive a spectral signature of individual feathers. Resultant IR spectra were then subjected to canonical correspondence analysis (CAA) to determine whether feather spectral signatures correlate to chemical exposure. Additionally, we explored if principal component analysis (PCA) and linear discriminant analysis (LDA) could be applied to distinguish site-specific differences; linear discriminant function (LDF) was also applied to classify sites. The sampled feathers varied in their chemical exposure patterns depending on whether they were sourced from one site associated with heavy metal exposure or the other which suggested high organic pollutant exposures. CCA of chemical and spectral data showed a correlation between spectral signatures and chemical exposure. PCA-LDA readily distinguished feathers from the two different sites. Discriminating alterations were identified and these were associated with protein and lipid regions in IR spectra. Additionally, LDF showed that the classification rate of spectral categories correlated well with the two chemical exposure patterns (93.6% for Trimu feathers and 91.77% for Mailsi feathers). This pilot study suggests that IR spectra derived from feathers reflect background chemical exposure and points to a novel monitoring tool for contamination.

Extracting biological information with computational analysis of Fourier-transform infrared (FTIR) biospectroscopy datasets: current practices to future perspectives.

Applying Fourier-transform infrared (FTIR) spectroscopy (or related technologies such as Raman spectroscopy) to biological questions (defined as biospectroscopy) is relatively novel. Potential fields of application include cytological, histological and microbial studies. This potentially provides a rapid and non-destructive approach to clinical diagnosis. Its increase in application is primarily a consequence of developing instrumentation along with computational techniques. In the coming decades, biospectroscopy is likely to become a common tool in the screening or diagnostic laboratory, or even in the general practitioner's clinic. Despite many advances in the biological application of FTIR spectroscopy, there remain challenges in sample preparation, instrumentation and data handling. We focus on the latter, where we identify in the reviewed literature, the existence of four main study goals: Pattern Finding; Biomarker Identification; Imaging; and, Diagnosis. These can be grouped into two frameworks: Exploratory; and, Diagnostic. Existing techniques in Quality Control, Pre-processing, Feature Extraction, Clustering, and Classification are critically reviewed. An aspect that is often visited is that of method choice. Based on the state-of-art, we claim that in the near future research should be focused on the challenges of dataset standardization; building information systems; development and validation of data analysis tools; and, technology transfer. A diagnostic case study using a real-world dataset is presented as an illustration. Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.

Identification of benzo[a]pyrene-induced cell cycle-associated alterations in MCF-7 cells using infrared spectroscopy with computational analysis.

Chemical contaminants, such as benzo[a]pyrene (B[a]P), may modulate transcriptional responses in cells via the activation of aryl hydrocarbon receptor (AhR) or through responses to DNA damage following adduct formation. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy can be employed in a non-destructive fashion to interrogate the biochemical signature of cells via generation of infrared (IR) spectra. By applying to generated spectral datasets subsequent computational approaches such as principal component analysis plus linear discriminant analysis (PCA-LDA), derived data reduction is achieved to facilitate the visualization of wavenumber-related alterations in target cells. Discriminating spectral variables might be associated with lipid or glycogen content, conformational protein changes and phosphorylation, and structural alterations in DNA/RNA. Using this approach, we investigated the dose-related effects of B[a]P in MCF-7 cells concentrated in S- or G0/G1-phase. Our findings identified that in PCA-LDA scores plots a clear segregation of IR spectra was evident, with the major spectral alterations associated with DNA/RNA, secondary protein structure and lipid. Dose-related effects were observed and even with exposures as low as 10⁻9 M B[a]P, significant (P ≤ 0.001) separation of B[a]P-treated vs. vehicle control cells was noted. ATR-FTIR spectroscopy with computational analysis is a novel approach to identify the effects of environmental contaminants in target cells.

Classification of test agent-specific effects in the Syrian hamster embryo assay (pH 6.7) using infrared spectroscopy with computational analysis.

The Syrian hamster embryo (SHE) cell transformation assay (pH 6.7) has utility in the assessment of potential chemical carcinogenicity (both genotoxic and non-genotoxic mechanisms of action). The assay uses morphological transformation as an end point and has a reported sensitivity of 87%, specificity of 83% and overall concordance of 85% with in vivo rodent bioassay data. However, the scoring of morphologically transformed SHE cells is subjective. We treated SHE cells grown on low-E reflective slides with benzo[a]pyrene, 3-methylcholanthrene, anthracene, N-nitroso-N-methylnitroguanidine, ortho-toluidine HCl, 2,4-diaminotoluene or D-mannitol for 7 days before fixation with methanol. Identified colonies were interrogated by acquiring a minimum of five infrared (IR) spectra per colony using attenuated total reflection Fourier-transform IR spectroscopy. Individual IR spectra were acquired over a spatial area of approximately 250 × 250 µm. Resultant data were analysed using Fisher's linear discriminant analysis and feature histogram algorithms to extract classifying biomarkers of test agent-specific effects or transformation in SHE cells. Clustering of spectral points suggested co-segregation or discrimination of test agent categories based on mechanism of action. Towards transformation, unifying alterations were associated with alterations in the Amide I and Amide II peaks; these were consistently major classifying biomarkers for transformed versus non-transformed SHE cells. Our approach highlights a novel method towards objectively screening and classifying SHE cells, be it to ascertain test agent treatment based on mechanism of action or transformation.