Benoit, S. | bioRxiv Science

bioRxiv2023

Strain-specific changes in nucleus accumbens transcriptome and motivation for food reward in mice exposed to maternal separation

Adversity in childhood exerts enduring effects on brain and increases the vulnerability to psychiatric diseases. It also leads to a higher risk for eating disorders and obesity. We hypothesised that neonatal stress in mice affects motivation to obtain palatable food in adulthood and changes gene expression in reward system. Male and female pups from C57Bl/6J and C3H/HeN mice strains were subjected to a daily maternal separation (MS) protocol from PND2 to PND14. In adulthood, their motivation for palatable food reward was assessed in operant cages. Compared to control mice, male and female C3H/Hen mice exposed to MS significantly did more lever presses to obtain palatable food especially when the effort required to obtain the reward is high. Transcriptional analysis reveals 375 genes differentially expressed in the nucleus accumbens of male MS C3H/HeN mice compared to the control group, some of these being associated with the regulation of the reward system (e.g. Gnas, Pnoc). Interestingly, C57Bl/6J mice exposed to MS did not show any alteration in their motivation to obtain a palatable reward nor significant changes in gene expression in the nucleus accumbens. In conclusion, neonatal stress produces lasting changes in motivation for palatable food in C3H/HeN offspring but has no impact in C57Bl/6J offspring. These behavioural alterations are accompanied by drastic changes in gene expression specifically within the nucleus accumbens, a key structure in the regulation of motivational processes.

neuroscience↗

bioRxiv2022

Learning representations for image-based profiling of perturbations

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data that highlight phenotypic outcomes. Here, we present an optimized strategy for learning representations of treatment effects from high-throughput imaging data, which follows a causal framework for interpreting results and guiding performance improvements. We use weakly supervised learning (WSL) for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with Cell Painting images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a WSL model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN-1. We conducted a comprehensive evaluation of our strategy on three publicly available Cell Painting datasets, discovering that representations obtained by the Cell Painting CNN-1 can improve performance in downstream analysis for biological matching up to 30% with respect to classical features, while also being more computationally efficient.

bioinformatics↗