Biology subjects

Laubli, N. F.

Publications and source records attributed to Laubli, N. F..

4 recordsLinked to original sources

bioRxiv2025

Impairment of neuronal activity occurs at the early stages of the aggregation cascade of Ab1-42 and mutant Tau

Alzheimers disease (AD) is a progressive neurodegenerative disease that is characterized by the accumulation of amyloid-{beta} (A{beta}) plaques and neurofibrillary Tau tangles, ultimately leading to brain atrophy and death. To elucidate the relationship between the aberrant folding and aggregation of A{beta} and mutant Tau and neuronal function, we monitored neuronal activity in C. elegans AD models across age. Our findings reveal that expression of both A{beta} and Tau lead to significant reductions in neuronal activity and function in young adult animals preceding the accumulation of amyloid aggregates. Notably, A{beta} expression and aggregation in muscle tissue produced comparable detrimental effects on neuronal activity as its expression in neurons, suggesting that proteotoxic stress in muscle can influence neuronal function. This may occur through the propagation of A{beta} from muscle to neurons or through retrograde signaling pathways. Further, our new sub-stoichiometrically labeled Tau strains highlight that TauP301L,V337M has a significant impact on neuronal activity throughout aging. These results enhance our understanding of the early functional effects of amyloid aggregation in Alzheimers disease.

neuroscience↗

bioRxiv2024

Graphene microelectrode arrays, 4D structured illumination microscopy, and a machine learning-based spike sorting algorithm permit the analysis of ultrastructural neuronal changes during neuronal signalling in a model of Niemann-Pick disease type C

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use graphene microelectrode arrays (G-MEAs) to address these challenges, as they are compatible with high spatial resolution imaging across various scales as well as high temporal resolution electrophysiological recordings. Furthermore, alongside G-MEAs, we deploy an easy-to-implement machine learning-based algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML)-based spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables the monitoring of the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC) and show that synaptic boutons, compared to untreated controls, significantly increase in size, which leads to a loss in neuronal signalling capacity.

neuroscience↗

bioRxiv2022

ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology for video-rate super-resolution imaging

The topology of endoplasmic reticulum (ER) network is highly regulated by various cellular and environmental stimuli and affects major functions such as protein quality control and the cells response to metabolic changes. The ability to quantify the dynamical changes of the ER structures in response to cellular perturbations is crucial for the development of novel therapeutic approaches against ER associated diseases, such as hereditary spastic paraplegias and Niemann Pick Disease type C. However, the rapid movement and small spatial dimension of ER networks make this task challenging. Here, we combine video-rate super-resolution imaging with a state-of-the-art semantic segmentation method capable of automatically classifying sheet and tubular ER domains inside individual cells. Data are skeletonised and represented by connectivity graphs to enable the precise and efficient quantification and comparison of the network connectivity from different complex ER phenotypes. The method, called ERnet, is powered by a Vision Transformer architecture, and integrates multi-head self-attention and channel attention into the model for adaptive weighting of frames in the time domain. We validated the performance of ERnet by measuring different ER morphology changes in response to genetic or metabolic manipulations. Finally, as a means to test the applicability and versatility of ERnet, we showed that ERnet can be applied to images from different cell types and also taken from different imaging setups. Our method can be deployed in an automatic, high-throughput, and unbiased fashion to identify subtle changes in cellular phenotypes that can be used as potential diagnostics for propensity to ER mediated disease, for disease progression, and for response to therapy.

bioengineering↗

bioRxiv2021

Growth and immunolocalisation of the brown alga Ectocarpus in a microfluidic environment

PDMS chips have proven to be suitable environments for the growth of several filamentous organisms. However, depending on the specimen, the pattern of growth and cell differentiation has been rarely investigated. We monitored the developmental pattern of the brown alga Ectocarpus inside a PDMS lab-on-chip. Two main methods of inoculation of the lab-on-chip were tested, i.e. by injection of spores or by insertion of sporophyte filaments into the chamber. Growth rate, growth trajectory, cell differentiation, and branching were the main development steps that were monitored for 20 days inside 25 m or 40 m parallel channels under standard light and temperature conditions. They were shown to be similar to those observed in non-constrained in-vitro conditions. Labelling of Ectocarpus cell wall polysaccharides - both with calcofluor for cellulose, and by immunolocalisation for alginates with monoclonal antibodies-showed expected patterns when compared to open space growth using either epifluorescence or confocal microscopy. Overall this article describes the experimental conditions for observing and studying the basic unaltered processes of brown algal growth using microfluidic technology, which provides the basis for future biochemical and biological research.

cell biology↗