Martel, S. | bioRxiv Science

bioRxiv2022

Toxicology assessment of manganese oxide nanomaterials with enhanced electrochemical properties using human in vitro models representing different exposure routes

In the present study, a comparative human toxicity assessment between newly developed Mn3O4 nanoparticles with enhanced electrochemical properties (GNA35) and their precursor material (Mn3O4) was performed, employing different in vitro cellular models representing alveolar, oral and dermal exposure routes, namely the human alveolar carcinoma epithelial cell line (A549), the human colorectal adenocarcinoma cell line (HT29), and the reconstructed 3D human epidermal model EpiDerm (RhE). The obtained results showed that Mn3O4 and GNA35 harbour similar morphological characteristics, while differences were observed in relation to their thermal stability and electrochemical properties. In regard to their toxicological properties, both nanomaterials induced oxidative stress in the A549 and HT29 cell lines that have demonstrated that manganese oxide nanoparticles reduce the cell viability in A549 cells while they do not produce any negative effect on that of HT29 cells. On the other hand, it was observed that Mn3O4 and GNA35 induce oxidative stress in both cell lines. Finally, it was noticed that none of the nanoparticles caused a reduction of the viability on the skin tissue so that they could not be classified as irritants. Our findings demonstrate that the evaluation of the toxicity of nanoparticles using different models is a critical aspect to increase the knowledge on their potential impact on human health.

cell biology↗

bioRxiv2022

Machine learning-based detection of label-free cancer stem-like cell fate

Most imaging methods rely on labelling biological samples in order to provide specific and easily detectable features. However, label-free imaging is a non-invasive and non-toxic alternative that requires accurate image analysis algorithm based on cell morphology. Such analysis has to deal with a high image variability while fewer features are extractable, so far, fast analysis of label-free brightfield microscopy (LFBM) images remains a challenging task. With the development of microfabricated devices during the last decades, high throughput image generation makes it possible to use machine learning-based algorithms in order to analyse LFBM images. Fast algorithms are also crucially needed to analyze high throughput experiments. In this paper, we provide a data-driven study in order to assess the complexity of LFBM time-lapses monitoring isolated cancer stem-like cells (CSCs) fate in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. Several image analysis algorithms of increasing processing capacities were tested: a classical computer vision algorithm (CCVA), a shallow learning-based algorithm (SLBA) and a deep learning-based algorithm (DLBA). We show that our optimized DLBA has by far the best accuracy compared to CCVA and SLBA, is at least as accurate as other state-of-the-art DLBAs while being faster. With this study, we demonstrate that optimizing our DLBA accordingly to the image analysis problem can overall provide better results than pretrained models. Such a fast and accurate DLBA is therefore compatible with the generation of high throughput data and opens the route for on-the-fly analysis of CSC fate from LFBM time-lapses.

bioinformatics↗