Contact sites between mitochondria and endoplasmic reticulum (ER) are points in which the two organelles are in close proximity. Due to their structural and functional complexity, their exploitation as pharmacological targets has never been considered so far. Notwithstanding, the number of compounds described to target proteins residing at these interfaces either directly or indirectly is rising. Here we provide original insight into mitochondria–ER contact sites (MERCs), with a comprehensive overview of the current MERCs pharmacology. Importantly, we discuss the considerable potential of MERCs to become a druggable target for the development of novel therapeutic strategies.

Neuroblastoma (NB) is the most common extracranial tumor of early childhood and accounts for 15% of all pediatric cancer mortalities. However, the precise pathways and genes underlying its progression are unknown. Therefore, we performed a differential gene expression analysis of neuroblastoma stage 1 and stage 4 + 4S to discover biological processes associated with NB progression. From this preliminary analysis, we found that NB samples (stage 4 + 4S) are characterized by altered expression of some proteins involved in mitochondria function and mitochondria–ER contact sites (MERCS). Although further analyses remain necessary, this review may provide new hints to better understand NB molecular etiopathogenesis, by suggesting that MERCS alterations could be involved in the progression of NB.

Recent proteomic, metabolomic, and transcriptomic studies have highlighted a connection between changes in mitochondria physiology and cellular pathophysiological mechanisms. Secondary assays to assess the function of these organelles appear fundamental to validate these -omics findings. Although mitochondrial membrane potential is widely recognized as an indicator of mitochondrial activity, high-content imaging-based approaches coupled to multiparametric to measure it have not been established yet. In this paper, we describe a methodology for the unbiased high-throughput quantification of mitochondrial membrane potential in vitro, which is suitable for 2D to 3D models. We successfully used our method to analyze mitochondrial membrane potential in monolayers of human fibroblasts, neural stem cells, spheroids, and isolated muscle fibers. Moreover, by combining automated image analysis and machine learning, we were able to discriminate melanoma cells from macrophages in co-culture and to analyze the subpopulations separately. Our data demonstrated that our method is a widely applicable strategy for large-scale profiling of mitochondrial activity.