A data-derived reference mixture representative of European wastewater treatment plant effluents to complement mixture assessment

Aquatic environments are polluted with a multitude of organic micropollutants, which challenges risk assessment due the complexity and diversity of pollutant mixtures. The recognition that certain source-specific background pollution occurs ubiquitously in the aquatic environment might be one way forward to approach mixture risk assessment. To investigate this hypothesis, we prepared one typical and representative WWTP effluent mixture of organic micropollutants (EWERBmix) comprised of 81 compounds selected according to their high frequency of occurrence and toxic potential. Toxicological relevant effects of this reference mixture were measured in eight organism- and cell-based bioassays and compared with predicted mixture effects, which were calculated based on effect data of single chemicals retrieved from literature or different databases, and via quantitative structure-activity relationships (QSARs). The results show that the EWERBmix supports the identification of substances which should be considered in future monitoring efforts. It provides measures to estimate wastewater background concentrations in rivers under consideration of respective dilution factors, and to assess the extent of mixture risks to be expected from European WWTP effluents. The EWERBmix presents a reasonable proxy for regulatory authorities to develop and implement assessment approaches and regulatory measures to address mixture risks. The highlighted data gaps should be considered for prioritization of effect testing of most prevalent and relevant individual organic micropollutants of WWTP effluent background pollution. The here provided approach and EWERBmix are available for authorities and scientists for further investigations. The approach presented can furthermore serve as a roadmap guiding the development of archetypic background mixtures for other sources, geographical settings and chemical compounds, e.g. inorganic pollutants.

FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array®. The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 s per 96-well plate, and achieves this result even in heavily downsampled data (∼30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.

From protein and its hydration water dynamics to controlling mechano-elasticity of cellular lipid membranes and cell migration via ionic liquids

In this invited Commentary, as requested, I will walk the reader through my research path starting from my first works on proteins and their hydration water dynamics to my most recent activity on the use of ionic liquids (ILs) as molecular handles to control and manipulate cell membrane mechano-elasticity and cell migration. In doing so I will comment on my research activity on polymers, proteins, natural bioprotectants, phospholipid bilayers, amyloids and cells, which I have carried out by combining several different experimental and computational approaches including neutron scattering, atomic force microscopy, classical molecular dynamics and ab initio calculations, used in tandem with several biological assays and a palette of complementary techniques ranging from calorimetry to static and dynamic light scattering. In parallel to this biophysical/chemical-physical core activity, a smaller portion of my interest and effort has been-I may say always-dedicated to the development of a new neutron scattering method/spectroscopy for dynamics based on "elastic" scattering. I will comment on this instrumental side of my research as well and show its relationship with the biophysical core of my activity. The overall picture that emerges is, from my personal prospective, of a coherent 13-year research path based on curiosity and a problem-solving approach, in which the fundamental importance of inter- and trans-disciplinary approaches and collaborations is emerging on the way, forecasting a prosper and intriguing future for physics in biology and in nanomedicine and bionanotechnology applications.

Techniques to Enable 1536-Well Phenotypic Screening

Adaptation of phenotypic cell assays to 1536-well format brings major challenges in liquid handling for high-content assays requiring washing steps and coating of plates. In addition, problematic edge effects and reduced assay quality are frequently encountered. In this chapter, we describe the novel application of a centrifugal plate washer to facilitate miniaturization of 1536-well cell assays and a combination of techniques to reduce edge effects, all of which improved throughput and data quality. Cell assays currently limited in throughput because of cost and complex protocols may be enabled by the techniques presented in this chapter.

A novel measure and significance testing in data analysis of cell image segmentation

Background

Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed.

Results

We propose the total error rate (TER), a novel performance measure for segmenting all cells in the supervised evaluation. The TER statistically aggregates all misclassification error rates (MER) by taking cell sizes as weights. The MERs are for segmenting each single cell in the population. The TER is fully supported by the pairwise comparisons of MERs using 106 manually segmented ground-truth cells with different sizes and seven CIS algorithms taken from ImageJ. Further, the SE and 95% confidence interval (CI) of TER are computed based on the SE of MER that is calculated using the bootstrap method. An algorithm for computing the correlation coefficient of TERs between two CIS algorithms is also provided. Hence, the 95% CI error bars can be used to classify CIS algorithms. The SEs of TERs and their correlation coefficient can be employed to conduct the hypothesis testing, while the CIs overlap, to determine the statistical significance of the performance differences between CIS algorithms.

Conclusions

A novel measure TER of CIS is proposed. The TER’s SEs and correlation coefficient are computed. Thereafter, CIS algorithms can be evaluated and compared statistically by conducting the significance testing.

Imaging of Protein Secretion from a Single Cell Using Plasmonic Substrates

Detecting, imaging, and monitoring cell function on a single cell basis is very important in the field of immunology research where many molecules are secreted from cells in response to external stimuli including immunization. Here we introduce substrates with plasmonic nanoparticles and fluorescence microscopy as promising imaging methods for studies on molecular processes controlling cell behavior, particularly secretion of cytokines. We developed unique composition of silver and silica layers of plasmonic nanostructures which resulted in fluorescence enhancement of more than 200-fold for ensemble of molecules in the immunoassay. For the proof of concept demonstration, we used primary mouse macrophages and imaged tumor necrosis alpha (TNF-α) secretion after stimulation of the cells with lipopolysaccharide (LPS). We demonstrate that metal-enhanced fluorescence assay provides imaging capability detection of cytokine secretion from a single cell without extensive biochemical procedures as required with standard methods. In addition it is demonstrated that cell viability can be controlled during secretion.