The SMN-ribosome interplay: a new opportunity for Spinal Muscular Atrophy therapies.

The underlying cause of Spinal Muscular Atrophy (SMA) is in the reduction of survival motor neuron (SMN) protein levels due to mutations in the SMN1 gene. The specific effects of SMN protein loss and the resulting pathological alterations are not fully understood. Given the crucial roles of the SMN protein in snRNP biogenesis and its interactions with ribosomes and translation-related proteins and mRNAs, a decrease in SMN levels below a specific threshold in SMA is expected to affect translational control of gene expression. This review covers both direct and indirect SMN interactions across various translation-related cellular compartments and processes, spanning from ribosome biogenesis to local translation and beyond. Additionally, it aims to outline deficiencies and alterations in translation observed in SMA models and patients, while also discussing the implications of the relationship between SMN protein and the translation machinery within the context of current and future therapies.

Visualizing gene expression changes in time, space, and single cells with expressyouRcell.

The last decade has witnessed massive advancements in high-throughput techniques capable of producing increasingly complex gene expression datasets across time and space and at the resolution of single cells. Yet, the large volume of big data available and the complexity of experimental designs hamper an easy understanding and effective communication of the results. We present expressyouRcell, an easy-to-use R package to map the multi-dimensional variations of transcript and protein levels in dynamic cell pictographs. expressyouRcell visualizes gene expression variations as pictographic representations of cell-type thematic maps. expressyouRcell visually reduces the complexity of displaying gene expression and protein level changes across multiple measurements (time points or single-cell trajectories) by generating dynamic representations of cellular pictographs. We applied expressyouRcell to single cell, bulk RNA sequencing (RNA-seq), and proteomics datasets, demonstrating its flexibility and usability in the visualization of complex variations in gene expression. Our approach improves the standard quantitative interpretation and communication of relevant results.