Biology subjects

Strasser, S.

Publications and source records attributed to Strasser, S..

3 recordsLinked to original sources

bioRxiv2026

Human iPSC Models of Ganglioside Deficiency Reveal a Sialylated Lipid Requirement for Plasma-Membrane Organization and Neuronal Activity

Gangliosides are abundant neuronal glycosphingolipids, yet their roles in organizing the plasma membrane and supporting neuronal function remain poorly defined. Mutations in the biosynthetic enzymes ST3GAL5 or B4GALNT1 cause severe neurodevelopmental disorders, yet their cellular consequences are unclear. Using isogenic human iPSC-derived cortical neurons, we show that loss of these enzymes eliminates major neuronal gangliosides but produces strikingly divergent outcomes. ST3GAL5 deficiency reprograms the glycosphingolipid repertoire toward non-neuronal species and abolishes network-level electrical activity. In contrast, B4GALNT1-deficient neurons retain near-normal excitability, supported by accumulation of simple sialylated precursors (GM3/GD3). Quantitative proteomics reveals a profound loss of plasma membrane proteins, including ion channels and synaptic organizers, only in ST3GAL5-deficient neurons. These findings identify sialylated glycosphingolipids as essential scaffolds for plasma membrane organization and neuronal excitability, providing a mechanistic basis for the severe phenotype caused by loss of GM3 synthase in humans. TEASERHuman neuronal models reveal why loss of GM3 synthase causes severe neurodevelopmental disease

cell biology↗

bioRxiv2026

miR-940 suppresses ferroptosis by controlling expression of key regulatory genes

Ferroptosis is a form of regulated cell death that is characterized by iron-dependent lipid peroxidation. This process is regulated by specific metabolites, the lipid composition of the cells, redox-active iron, and antioxidant mechanisms. Although numerous regulators have been identified over the past decade, exploring other mechanisms, particularly from non-coding genomic regions, can build a thorough understanding of the multifaceted regulatory processes underlying ferroptosis. MicroRNAs (miRNAs) play a crucial role in gene regulation and cellular functions. Through a CRISPR KO screen, we identified miR-940 as a negative regulator of ferroptosis. Overexpression of miR-940 in several cell lines consistently suppressed ferroptosis induced by system xc- inhibition. Notably, multiple cancer patient cohorts with elevated miR-940 levels exhibit reduced survival. Integrated bioinformatic, transcriptomic, and proteomic analyses revealed that miR-940 decreases the expression of ACSL4, LPCAT3, DMT1, and NCOA4, and simultaneously increases levels of GPX4. Pharmacological inhibition of GPX4 attenuated the protective effect of miR-940, indicating that its primary anti-ferroptotic activity is mediated through GPX4. Overall, this gene rewiring is associated with reduced levels of redox-active iron and diminished lipid peroxidation, consistent with ferroptosis suppression. These findings suggest that miR-940 coordinates ferroptosis inhibition, which presents a novel regulatory layer for therapeutic exploration in susceptible cancers.

cell biology↗

bioRxiv2023

CellDeathPred: A Deep Learning framework for Ferroptosis and Apoptosis prediction based on cell painting

Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep learning framework that uses high-content-imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy datasets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an averaged accuracy of 95% on non-confocal datasets, supporting the capacity of the CellDeathPred framework for cell death discovery.

cell biology↗