Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging

Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens

The read-across hypothesis and environmental risk assessment of pharmaceuticals

This article is made available through the Brunel Open Access Publishing Fund. Copyright © 2013 American Chemical Society.Pharmaceuticals in the environment have received increased attention over the past decade, as they are ubiquitous in rivers and waterways. Concentrations are in sub-ng to low μg/L, well below acute toxic levels, but there are uncertainties regarding the effects of chronic exposures and there is a need to prioritise which pharmaceuticals may be of concern. The read-across hypothesis stipulates that a drug will have an effect in non-target organisms only if the molecular targets such as receptors and enzymes have been conserved, resulting in a (specific) pharmacological effect only if plasma concentrations are similar to human therapeutic concentrations. If this holds true for different classes of pharmaceuticals, it should be possible to predict the potential environmental impact from information obtained during the drug development process. This paper critically reviews the evidence for read-across, and finds that few studies include plasma concentrations and mode of action based effects. Thus, despite a large number of apparently relevant papers and a general acceptance of the hypothesis, there is an absence of documented evidence. There is a need for large-scale studies to generate robust data for testing the read-across hypothesis and developing predictive models, the only feasible approach to protecting the environment.BBSRC Industrial Partnership Award BB/ I00646X/1 and BBSRC Industrial CASE Partnership Studentship BB/I53257X/1 with AstraZeneca Safety Health and Environment Research Programme

Development and Analysis of an Adverse Outcome Pathway Network for Human Neurotoxicity

An adverse outcome pathway (AOP) network is an attempt to represent the complexity of systems toxicology. This study illustrates how an AOP network can be derived and analysed in terms of its topological features to guide research and support chemical risk assessment. A four-step workflow describing general design principles and applied design principles were established and implemented. An AOP network linking nine linear AOPs was mapped and made available in AOPXplorer. The resultant AOP network was modelled and analysed in terms of its topological features, including level of degree, eccentricity and betweenness centrality. Several well connected KEs were identified, and cell injury/death was established as the most hyperlinked KE across the network. The derived network expands the utility of linear AOPs to better understand signalling pathways involved in developmental and adult/aging neurotoxicity. The results provide a solid basis to guide the development of in vitro test method batteries, as well as further quantitative modelling of key events (KEs) and key event relationships (KERs) in the AOP network, with an eventual aim to support hazard characterisation and chemical risk assessment

Weight of evidence evaluation of a network of adverse outcome pathways linking activation of the nicotinic acetylcholine receptor in honey bees to colony death

Ongoing honey bee (Apis mellifera) colony losses are of significant international concern because of the essential role these insects play in pollinating crops. Both chemical and non-chemical stressors have been implicated as possible contributors to colony failure; however, the potential role(s) of commonly-used neonicotinoid insecticides has emerged as particularly concerning. Neonicotinoids act on the nicotinic acetylcholine receptors (nAChRs) in the central nervous system to eliminate pest insects. However, mounting evidence indicates that neonicotinoids also may adversely affect beneficial pollinators, such as the honey bee, via impairments on learning and memory, and ultimately foraging success. The specificmechanisms linking activation of the nAChR to adverse effects on learning and memory are uncertain. Additionally, clear connections between observed impacts on individual bees and colony level effects are lacking. The objective of this review was to develop adverse outcome pathways (AOPs) as a means to evaluate the biological plausibility and empirical evidence supporting (or refuting) the linkage between activation of the physiological target site, the nAChR, and colony level consequences. Potential for exposure was not a consideration in AOP development and therefore this effort should not be considered a risk assessment. Nonetheless, development of the AOPs described herein has led to the identification of research gaps which, for example,may be of high priority in understanding how perturbation of pathways involved in neurotransmission can adversely affect normal colony functions, causing colony instability and subsequent bee population failure. A putative AOP networkwas developed, laying the foundation for further insights as to the role of combined chemical and non-chemical stressors in impacting bee populations. Insights gained from the AOP network assembly, which more realistically represents multi-stressor impacts on honey bee colonies, are promising toward understanding common sensitive nodes in key biological pathways and identifying where mitigation strategies may be focused to reduce colony losses

Accuracy assessment of CT-based 3D bone surface reconstruction

3D reconstruction of human anatomy from cross-sectional imaging has recently gained increasing importance in several medical fields thus designating the 3D bones reconstruction accuracy, critical for the success of the whole surgical intervention. The 3D anatomic model quality depends on the quality of the reconstructed image, on the quality of the images segmentation step and on the error introduced by the iso-surface triangulation algorithm. The influence of image processing procedures and relative parametrization has been largely studied in the scientific literature; however, the analysis of the direct impact of the quality of the reconstructed medical images is still lacking. In this paper, a comparative study on the influence of both image reconstruction algorithm (standard and iterative) and applied kernel is reported. Research was performed on the 3D reconstruction of a pig tibia, by using Philips Brilliance 64 CT scanner. At the stage of scanning and at the stage of 3D reconstruction, the same procedures were followed, while only image reconstruction algorithm and kernel were changed. The influence of such selection on the accuracy of bone geometry was assessed by comparing it against the 3D model obtained with a professional 3D scanner. Results show an average error in reconstructing the geometry of around 0.1 mm with a variance of 0.08 mm. The presented study highlights new opportunities to control the deviations on the geometry accuracy of the bones structures at the stage of cross sectional imaging generation