Phenotypic Discovery of SB1501, an Anti-obesity Agent, through Modulating Mitochondrial Activity

Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks

Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotype of LDs in these cells reflects their maturity, metabolic activity and function. Although LDs quantification in adipocytes is significant for understanding the origins of obesity and associated complications, it remains challenging and requires the implementation of computer science innovations. This article outlines a practical workflow for application of Segment.ai neural network from the commercial software NIS-Elements and the open-source StarDist Jupyter notebook from the ZeroCostDL4Mic platform for the analysis of LDs number and morphology. To generate a training dataset, 3T3-L1 cells were differentiated into adipocytes and stained with lipophilic dye BODIPY493/503. Subsequently, confocal live cell images were acquired, annotated and used for training. As an example task, deep learning models were tested on their ability to detect LDs enlargement on images of adipocytes with inhibited lipolysis. We demonstrated that both Segment.ai and StarDist models are capable of accurately recognising LDs on microphotographs, thereby significantly accelerating the processing of imaging data. The advantage of the Segment.ai model is its integration into NIS-Elements General Analysis 3, which performs quantitative and statistical data interpretation. Alternatively, StarDist is a more accessible and transparent tool, enabling precise model evaluation. In conclusion, both created approaches have the potential to accelerate the exploration of LDs dynamics, thus paving the way for further insights into how these organelles regulate energy homeostasis and contribute to the development of metabolic abnormalities.

Adipocyte-Targeted Nanotechnology and Cell-Based Therapy for Obesity Treatment

Obesity is a critical risk factor for the development of metabolic diseases and is often associated with dysfunctional adipocytes. Prevalent treatments such as lifestyle intervention, pharmacotherapy, and bariatric surgery are often accompanied by adverse side effects and poor patient compliance. Nanotechnology and cell-based therapy offer innovative approaches for targeted obesity treatments, as they can directly target adipocytes, regulate lipid metabolism, and minimize off-target effects. Here, we provide an overview of the intricate relationship between adipocytes and obesity, highlighting the potential of nanotechnology and cell-based therapy in obesity treatment. Additionally, we discuss the advancements of adipose-derived mesenchymal stem cells (ADMSCs) in obesity progression, including the latest challenges and considerations for developing adipose-targeted treatments for obesity. The objective is to provide a perspective on the design and development of nanotechnology and cell-based therapy for treating obesity and related comorbidities.

Autophagic Clearance of Lipid Droplets Alters Metabolic Phenotypes in a Genetic Obesity-Diabetes Mouse Model

Lipid droplets (LDs) are intracellular organelles that store neutral lipids, and their aberrant accumulation is associated with many diseases including metabolic disorders such as obesity and diabetes. Meanwhile, the potential pathological contributions of LDs in these diseases are unclear, likely due to a lack of chemical biology tools to clear LDs. We recently developed LD-clearance small molecule compounds, Lipid Droplets·AuTophagy TEthering Compounds (LD·ATTECs), that are able to induce autophagic clearance of LDs in cells and in the liver of db/db (C57BL/6J Leprdb/Leprdb) mouse model, which is a widely used genetic model for obesity-diabetes. Meanwhile, the potential effects on the metabolic phenotype remain to be elucidated. Here, using the metabolic cage assay and the blood glucose assay, we performed phenotypic characterization of the effects of the autophagic degradation of LDs by LD·ATTECs in the db/db mouse model. The study reveals that LD·ATTECs increased the oxygen uptake of mice and the release of carbon dioxide, enhanced the heat production of animals, partially enhanced the exercise during the dark phase, decreased the blood glucose level and improved insulin sensitivity. Collectively, the study characterized the metabolic phenotypes induced by LD·ATTECs in an obesity-diabetes mouse model, revealing novel functional impacts of autophagic clearance of LDs and providing insights into LD biology and obesity-diabetes pathogenesis from the phenotypic perspective.

Adipocyte-based high throughput screening for anti-obesity drug discovery: Current status and future perspectives

Drug discovery for obesity treatment, particularly bodily slimming, is a topic of timely importance that requires continued investigation, as the current therapies have limited efficacy with many adverse effects. Obesity is associated with adipose tissue expansion, where the size and number of adipocytes increase. Over the past few decades, high-throughput/content screening (HTS/HCS) has been carried out on morphological changes in adipose tissues and adipocytes for the development of anti-obesity therapies. Increased understating of current adipocyte-based HTS/HCS technology will facilitate drug screening for obesity and weight control.