High-content screening technology combined with a human granuloma model as a new approach to evaluate the activities of drugs against Mycobacterium tuberculosis. | LitMetric
Tuberculosis remains a major health problem due to the emergence of drug-resistant strains of Mycobacterium tuberculosis. Some models have provided valuable information about drug resistance and efficacy; however, the translation of these results into effective human treatments has mostly proven unsuccessful. In this study, we adapted high-content screening (HCS) technology to investigate the activities of antitubercular compounds in the context of an in vitro granuloma model. We observed significant shifts in the MIC50s between the activities of the compounds under extracellular and granuloma conditions.
Applying artificial intelligence (AI) to image-based morphological profiling cells offers significant potential for identifying disease states and drug responses in high-content imaging (HCI) screens. When differences between populations (e.g.
Human induced pluripotent stem cells (iPSCs) provide powerful cellular models of Alzheimer's disease (AD) and offer many advantages over non-human models, including the potential to reflect variation in individual-specific pathophysiology and clinical symptoms. Previous studies have demonstrated that iPSC-neurons from individuals with Alzheimer's disease (AD) reflect clinical markers, including β-amyloid (Aβ) levels and synaptic vulnerability. However, despite neuronal loss being a key hallmark of AD pathology, many risk genes are predominantly expressed in glia, highlighting them as potential therapeutic targets.
Transcripts produced by RNA polymerase II (RNAPII) are fundamental for cellular responses to environmental changes. It is therefore no surprise that there exist multiple avenues for the regulation of this process. To explore the regulation mediated by RNAPII-interacting proteins, we used a small interfering RNA (siRNA)-based screen to systematically evaluate their influence on RNA synthesis.
Background: The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.
Inflammasomes are protein complexes that mediate innate immune responses whose dysregulation has been linked to a spectrum of acute and chronic human conditions, which dictates therapeutic development that is aligned with disease variability. We designed a scalable, physiologic high-content imaging assay in human PBMCs that we analyzed using a combination of machine-learning and cell biology methods. This resulted in a set of biologically interpretable readouts that can resolve a spectrum of cellular states associated with inflammasome activation and inhibition.
