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Mekki, I.

Publications and source records attributed to Mekki, I..

2 recordsLinked to original sources

Guided maturation of human neuromuscular organoids via electrical stimulation

Organoids derived from human pluripotent stem cells (hPSCs) are emerging as powerful models for studying development and disease. Despite their physiological relevance, the predictive power of organoids remains limited by the immature state of the constituent cells, posing a major challenge for mechanistic studies of adult physiology and late-onset diseases and disorders. Here, we establish a strategy for enhancing the maturation status of human neuromuscular organoids (NMOs) through chronic Electrical Pulse Stimulation (EPS). We demonstrate that low-frequency EPS, applied early on during NMO development and maintained over several weeks, promotes structural and functional maturation of neuromuscular junctions (NMJs). Independent of stimulation waveform dynamics, EPS-trained NMOs (EPS-NMOs) displayed stronger and more frequent spontaneous contractions that persisted long after stimulation ceased. Quantitative imaging and transcriptomic analyses revealed a robust improvement in EPS-NMO skeletal muscle and neural tissue morphology, coordinated regulation of lineage-specific biomarkers, and upregulation of gene programmes associated with mature neuromuscular function. Mechanobiological measurements further demonstrated increased EPS-NMO tissue stiffness and faster relaxation dynamics, consistent with advanced excitation-contraction coupling and force generation. Collectively, these findings establish EPS as a powerful, non-invasive, and on-demand modality for driving the morphological and functional maturation of complex organoid systems.

bioengineering↗

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗