Hyperspectral Mapping of Human Primary and Stem Cells at Cell-Matrix Interfaces.

Extracellular matrices interface with cells to promote cell growth and tissue development. Given this critical role, matrix mimetics are introduced to enable biomedical materials ranging from tissue engineering scaffolds and tumor models to organoids for drug screening and implant surface coatings. Traditional microscopy methods are used to evaluate such materials in their ability to support exploitable cell responses, which are expressed in changes in cell proliferation rates and morphology. However, the physical imaging methods do not capture the chemistry of cells at cell-matrix interfaces. Herein, we report hyperspectral imaging to map the chemistry of human primary and embryonic stem cells grown on matrix materials, both native and artificial. We provide the statistical analysis of changes in lipid and protein content of the cells obtained from infrared spectral maps to conclude matrix morphologies as a major determinant of biochemical cell responses. The study demonstrates an effective methodology for evaluating bespoke matrix materials directly at cell-matrix interfaces.

Artificial intelligence for neurodegenerative experimental models.

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Linking environmental risk factors with epigenetic mechanisms in Parkinson's disease.

Sporadic Parkinson's disease (PD) is a progressive neurodegenerative disease, with a complex risk structure thought to be influenced by interactions between genetic variants and environmental exposures, although the full aetiology is unknown. Environmental factors, including pesticides, have been reported to increase the risk of developing the disease. Growing evidence suggests epigenetic changes are key mechanisms by which these environmental factors act upon gene regulation, in disease-relevant cell types. We present a systematic review critically appraising and summarising the current body of evidence of the relationship between epigenetic mechanisms and environmental risk factors in PD to inform future research in this area. Epigenetic studies of relevant environmental risk factors in animal and cell models have yielded promising results, however, research in humans is just emerging. While published studies in humans are currently relatively limited, the importance of the field for the elucidation of molecular mechanisms of pathogenesis opens clear and promising avenues for the future of PD research. Carefully designed epidemiological studies carried out in PD patients hold great potential to uncover disease-relevant gene regulatory mechanisms. Therefore, to advance this burgeoning field, we recommend broadening the scope of investigations to include more environmental exposures, increasing sample sizes, focusing on disease-relevant cell types, and recruiting more diverse cohorts.