Background:The proinflammatory adipokine lipocalin-2 is associated with obesity-related complications, such as heart failure. Results: Lipocalin-2 induces cardiomyocyte apoptosis via elevating intracellular iron levels and mediates detrimental effects on cardiac function. Conclusion: Lipocalin-2 is an important mediator of cardiac remodeling. Significance: Regulation of cardiomyocyte apoptosis by lipocalin-2, and the mechanistic role of changes in intracellular iron, may contribute to the pathogenesis of obesity-related heart failure.

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

Background: Copper/zinc superoxide dismutase (SOD1) genetic mutants are associated with familial amyotrophic lateral sclerosis (ALS). Mutant proteins form abnormal aggregates. Results: We used imaging of live cells to observe SOD1 proteins harboring mutations associated with ALS. Conclusion: SOD1 mutations impair its dimerization, leading to subsequent aggregation. Significance: Analysis of the SOD1 quaternary structure in living human cells correlates with previous biochemical data.

High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV-host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step.