Andrew D. Scott

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 practitioners 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.

Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers.

Biospectroscopy is employed to derive absorbance spectra representative of biomolecules present in biological samples. The mid-infrared region (λ = 2.5 μm-25 μm) is absorbed to give a biochemical-cell fingerprint (v = 1800-900 cm(-1)). Cellular material produces complex spectra due to the variety of chemical bonds present. The complexity and size of spectral data sets warrant multivariate analysis for data reduction, interpretation, and classification. Various multivariate analyses are available including principal component analysis (PCA), partial least-squares (PLS), linear discriminant analysis (LDA), and evolving fuzzy rule-based classifier (eClass). Interpretation of both visual and numerical results facilitates biomarker identification, cell-type discrimination, and predictive and mechanistic understanding of cellular behavior. Biospectroscopy is a high-throughput nondestructive technology. A comparison of biomarkers/mechanistic knowledge determined from conventional approaches to biospectroscopy coupled with multivariate analysis often provides complementary answers and a novel approach for diagnosis of disease and cell biology.

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.

Cell transformation assays for prediction of carcinogenic potential: state of the science and future research needs

Cell transformation assays (CTAs) have long been proposed as in vitro methods for the identification of potential chemical carcinogens. Despite showing good correlation with rodent bioassay data, concerns over the subjective nature of using morphological criteria for identifying transformed cells and a lack of understanding of the mechanistic basis of the assays has limited their acceptance for regulatory purposes. However, recent drivers to find alternative carcinogenicity assessment methodologies, such as the Seventh Amendment to the EU Cosmetics Directive, have fuelled renewed interest in CTAs. Research is currently ongoing to improve the objectivity of the assays, reveal the underlying molecular changes leading to transformation and explore the use of novel cell types. The UK NC3Rs held an international workshop in November 2010 to review the current state of the art in this field and provide directions for future research. This paper outlines the key points highlighted at this meeting.

Integrated in silico approaches for the prediction of Ames test mutagenicity

The bacterial reverse mutation assay (Ames test) is a biological assay used to assess the mutagenic potential of chemical compounds. In this paper approaches for the development of an in silico mutagenicity screening tool are described. Three individual in silico models, which cover both structure activity relationship methods (SARs) and quantitative structure activity relationship methods (QSARs), were built using three different modelling techniques: (1) an in-house alert model: which uses SAR approach where alerts are generated based on experts judgements; (2) a kNN approach (k-Nearest Neighbours), which is a QSAR model where a prediction is given based on outcomes of its k chemical neighbours; (3) a naive Bayesian model (NB), which is another QSAR model, where a prediction is derived using a Bayesian formula through preselected identified informative chemical features (e.g., physico-chemical, structural descriptors). These in silico models, were compared against two well-known alert models (DEREK and ToxTree) and also against three different consensus approaches (Categorical Bayesian Integration Approach (CBI), Partial Least Squares Discriminate Analysis (PLS-DA) and simple majority vote approach). By applying these integration methods on the validation sets it was shown that both integration models (PLS-DA and CBI) achieved better performance than any of the individual models or consensus obtained by simple majority rule. In conclusion, the recommendation of this paper is that when obtaining consensus predictions for Ames mutagenicity, approaches like PLS-DA or CBI should be the first choice for the integration as compared to a simple majority vote approach.

A comparison of the genotoxicity of benzo[a]pyrene in four cell lines with differing metabolic capacity

Benzo[a]pyrene (B[a]P) is a known genotoxin and carcinogen, yet its genotoxic response at low level exposure has not been determined. This study was conducted to examine the interplay of dose and metabolic capacity on genotoxicity of B[a]P. Investigating and better understanding the biological significance of low level chemical exposures will help improve human health risk assessments. The genotoxic and mutagenic effects of B[a]P were investigated using human cell lines (AHH-1, MCL-5, TK6 and HepG2) with differential expression of the CYP450 enzymes CYP1A1, 1B1 and1A2 involved in B[a]P metabolism. MCL-5 and HepG2 cells showed detectable basal expression and activity of CYP1A1, 1B1 and 1A2 than AHH-1 which only show CYP1A1 basal expression and activity. TK6 cells showed negligible expression levels of all three CYP450 enzymes. In vitro micronucleus and HPRT assays were conducted to determine the effect of B[a]P on chromosome damage and point mutation induction. After 24h exposure, linear increases in micronucleus (MN) frequency were observed in all cell lines except TK6. After 4h exposure, only the metabolically competent cell lines MCL-5 and HepG2 showed MN induction (with a threshold concentration at 25.5μM from MCL-5 cells) indicating the importance of exposure time for genotoxicity. The HPRT assay also displayed linear increases in mutant frequency in MCL-5 cells, after 4h and 24h treatments. Mutation spectra analysis of MCL-5 and AHH-1 HPRT mutants revealed frequent B[a]P induced G to T transversion mutations (72% and 44% of induced mutations in MCL-5 and AHH-1 respectively). This study therefore demonstrates a key link between metabolic capability, B[a]P exposure time and genotoxicity.

The Syrian hamster embryo (SHE) assay (pH 6.7): mechanisms of cell transformation and application of vibrational spectroscopy to objectively score endpoint alterations

Using morphological transformation as an endpoint, the Syrian hamster embryo (SHE) cell transformation assay (pH 6.7) is an in vitro system with a high sensitivity and specificity for testing the carcinogenic potential of test agents. Advantages of the assay are that SHE cells are metabolically competent, genetically stable and acquire spontaneous transformation with a low frequency; additionally, it detects both genotoxic and non-genotoxic carcinogens. However, in comparison with other short-term mammalian cell assays, it is time consuming, laborious and, most importantly, the visual scoring of morphological transformation might be subjective. In this review, we examine the background to the test and why it has the potential for use in safety risk assessment. Additionally, we propose a novel approach to objectively interrogate and classify SHE colonies using vibrational spectroscopy coupled to a mathematical framework for high-throughput screening. It is our view that this alternative approach has the potential to improve the sensitivity and specificity of the in vitro SHE assay.

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 Fishers 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.

Syrian Hamster Embryo (SHE) cell transformation assay with and without X-ray irradiation of feeder cells using Di(2-ethylhexyl)phthalate (DEHP) and N-nitroso-N-methylnitroguanidine (MNNG).

The SHE cell transformation assay has traditionally been conducted with a feeder layer of X-ray irradiated cells to provide growth support to the target cells seeded in low numbers. The need for an X-ray irradiated feeder cell layer necessitates the maintenance of an X-ray machine and the additional step to seed feeder cells prior to plating target cells. This laboratory has previously reported a method allowing target cells to be seeded in conditioned media prepared from the stock culture flasks in lieu of plating them on a feeder layer (Pant et al. [1,2,4]). In order to expand the data base for chemicals tested using this method, we describe in this paper the results obtained testing Di(2-ethylhexyl)phthalate (DEHP) and N-nitroso-N-methylnitroguanidine (MNNG) which are known to give positive responses in the standard SHE cell transformation assay. With freshly prepared conditioned medium (used within 2 weeks of preparation), there was essentially no difference in the number of target cell colonies in the conditioned medium and in the plates with the X-ray irradiated feeder cell layer. The plating efficiencies of the vehicle controls were within the historical range for the standard SHE cell transformation assay. In more than ten experiments the positive control benzo(a)pyrene [B(a)P] elicited a significant increase in morphological transformation frequency (MTF), with or without X-ray irradiated feeder cells. Compounds, DEHP and MNNG, were tested in the SHE cell transformation assay with and without an X-ray irradiated feeder layer and using a 7-day exposure regimen. The results were comparable between experiments performed using either method. These results demonstrate the feasibility of conducting the SHE cell transformation assay without the use of an X-ray irradiated feeder layer, thereby simplifying the test procedure and assisting the scoring of morphologically transformed colonies.

Where will genetic toxicology testing be in 30 years’ time? Summary report of the 25th Industrial Genotoxicity Group Meeting, Royal Society of Medicine, London, November 9, 2011

A number of influences including legislation, industry and academia have encouraged advances in computational toxicology and high-throughput testing to probe more broadly putative toxicity pathways. The aim of the 25th United Kingdom Mutagen Society (UKEMS) Industrial Genotoxicity Group Annual Meeting 2011 was to explore current and upcoming research tools that may provide new cancer risk estimation approaches and discuss the genotoxicity testing paradigm of the future. The meeting considered whether computer modelling, molecular biology systems and/or adverse outcome pathway approaches can provide more accurate toxicity predictions and whether high-content study data, pluripotent stem cells or new scientific disciplines, such as epigenetics and adductomics, could be integrated into the risk assessment process. With close collaboration between industry, academia and regulators next generation predictive models and high-content tools have the potential to transform genetic toxicology testing in the 21st century.