Grouping of chemicals into mode of action classes by automated effect pattern analysis using the zebrafish embryo toxicity test.

A central element of high throughput screens for chemical effect assessment using zebrafish is the assessment and quantification of phenotypic changes. By application of an automated and more unbiased analysis of these changes using image analysis, patterns of phenotypes may be associated with the mode of action (MoA) of the exposure chemical. The aim of our study was to explore to what extent compounds can be grouped according to their anticipated toxicological or pharmacological mode of action using an automated quantitative multi-endpoint zebrafish test. Chemical-response signatures for 30 endpoints, covering phenotypic and functional features, were generated for 25 chemicals assigned to 8 broad MoA classes. Unsupervised clustering of the profiling data demonstrated that chemicals were partially grouped by their main MoA. Analysis with a supervised clustering technique such as a partial least squares discriminant analysis (PLS-DA) allowed to identify markers with a strong potential to discriminate between MoAs such as mandibular arch malformation observed for compounds interfering with retinoic acid signaling. The capacity for discriminating MoAs was also benchmarked to an available battery of in vitro toxicity data obtained from ToxCast library indicating a partially similar performance. Further, we discussed to which extent the collected dataset indicated indeed differences for compounds with presumably similar MoA or whether other factors such as toxicokinetic differences could have an important impact on the determined response patterns.

Analysis of multiple physical parameters for mechanical phenotyping of living cells.

Since the cytoskeleton is known to regulate many cell functions, an increasing amount of effort to characterize cells by their mechanical properties has occured. Despite the structural complexity and dynamics of the multicomponent cytoskeleton, mechanical measurements on single cells are often fit to simple models with two to three parameters, and those parameters are recorded and reported. However, different simple models are likely needed to capture the distinct mechanical cell states, and additional parameters may be needed to capture the ability of cells to actively deform. Our new approach is to capture a much larger set of possibly redundant parameters from cells' mechanical measurement using multiple rheological models as well as dynamic deformation and image data. Principal component analysis and network-based approaches are used to group parameters to reduce redundancies and develop robust biomechanical phenotyping. Network representation of parameters allows for visual exploration of cells' complex mechanical system, and highlights unexpected connections between parameters. To demonstrate that our biomechanical phenotyping approach can detect subtle mechanical differences, we used a Microfluidic Optical Cell Stretcher to mechanically stretch circulating human breast tumor cells bearing genetically-engineered alterations in c-src tyrosine kinase activation, which is known to influence reattachment and invasion during metastasis.