Biology subjects

Gerstner, N.

Publications and source records attributed to Gerstner, N..

5 recordsLinked to original sources

bioRxiv2026

Tension TRAAKer: a chemigenetic fluorescent membrane tension reporter

Mechanical force transduction is essential to survival, underlying biological processes as fundamental as morphogenesis, somatosensation, audition, and interoception; and driving pathologies as diverse as hypertension and cancer metastasis. Exogenous forces are translated to intracellular signals through transient changes in membrane tension which are currently not possible to directly monitor in situ. To remedy this, we have designed and validated Tension TRAAKer, a chemigenetic fluorescent membrane tension reporter for the visualization of tension induction, propagation, and dissipation in living cells. Tension TRAAKer is derived from inserting a tension-sensitive nonconductive variant of the mechanosensitive potassium ion channel TRAAK into a self-labelling HaloTag. Increasing membrane tensions effect conformational changes in the TRAAK channel that are optically monitored by a HaloTag-conjugated fluorogenic (environment-sensitive) dye. EGFP incorporation C-terminal to the HaloTag enables unambiguous tension reporting in mobile membranes via dual-color ratiometric imaging that controls for variations in sensor density. Tension TRAAKer reports membrane tension changes rapidly, reversibly, and with spatiotemporal precision--its fluorescence scaling to both stimulus magnitude and area, with consistent effect sizes observed between diverse cell types. It better distinguishes among elevated membrane tensions than do available indirect chemical reporters, with the additional advantage of being readily genetically targetable. We thus expect Tension TRAAKer to be a powerful tool for the study of membrane tension across biological systems and disease states.

molecular biology↗

bioRxiv2024

Astrocytic glutamate regulation is implicated in the development of stress-related psychiatric disorders

Astrocytes are a brain cell type vulnerable to the effects of stress and the development of psychiatric-like phenotypes in animals, yet how this translates to humans is unclear. Here, we probed the diversity of [~]145,000 total human cortical astrocytes with single nucleus and spatial transcriptomics, showing that human astrocytes comprise a molecularly and anatomically diverse cell population. In individuals with psychiatric disorders and high adversity exposure, we identified distinct alterations to glutamate-related synaptic functions, supported by histological quantification of >20,000 astrocytes. Early-life adversity exposure produced more pronounced cellular changes than adversity experienced later in life, and female cases displayed stronger transcriptomic associations than males with adversity exposure. Human pluripotent stem cell-derived astrocytes from both two- and three-dimensional models confirmed that glutamate signalling is directly impacted by glucocorticoid activation. Our findings highlight astrocytes as crucial players in how exposure to severe adversity raises risk to psychopathology and position them as strategic pharmacological targets for future intervention strategies.

neuroscience↗

bioRxiv2022

DiffBrainNet: differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions

Genome-wide gene expression analyses are invaluable tools for increasing our knowledge of biological and disease processes, allowing a hypothesis-free comparison of gene expression profiles across experimental groups, tissues and cell types. Traditionally, transcriptomic data analysis has focused on gene-level effects found by differential expression. In recent years, network analysis has emerged as an important additional level of investigation, providing information on molecular connectivity, especially for diseases associated with a large number of linked effects of smaller magnitude, like neuropsychiatric disorders and their risk factors, including stress. In this manuscript, we describe how combined differential expression and prior-knowledge-based differential network analysis can be used to explore complex datasets. As an example, we analyze the transcriptional responses following administration of the glucocorticoid/stress hormone receptor agonist dexamethasone in C57Bl/6 mice, in 8 brain regions important for stress processing: the prefrontal cortex, the amygdala, the paraventricular nucleus of the hypothalamus, the cerebellar cortex, and sub regions of the hippocampus: the dorsal and ventral Cornu Ammonis 1, the dorsal and ventral dentate gyrus. By applying a combination of differential network- and differential expression-analyses, we find that these explain distinct but complementary aspects and biological mechanisms of the responses to the stimulus. In addition, network analysis identifies new differentially connected partners of important genes and can be used to generate hypotheses on specific molecular pathways affected. With this work, we provide an analysis framework and a publicly available resource for the study of the transcriptional landscape of the mouse brain: DiffBrainNet (http://diffbrainnet.psych.mpg.de), which can identify molecular pathways important for basic functioning and response to glucocorticoids in a brain-region specific manner.

genomics↗

bioRxiv2019

Systematic evaluation of normalization methods for glycomics data based on performance of network inference

Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, we here quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms (LC-ESI-MS, UHPLC-FLD and MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the Probabilistic Quotient method followed by log-transformation, irrespective of the measurement platform.

bioinformatics↗

bioRxiv2019

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are commonly used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the significance of the underlying correlation coefficients. A statistical cutoff, however, is not guaranteed to capture biological reality, and heavily depends on dataset properties such as sample size. We here propose an alternative, innovative approach to address the problem of network reconstruction. Specifically, we developed a cutoff selection algorithm that maximizes the agreement to a given ground truth. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. The optimal network outperforms networks obtained with statistical cutoffs and is robust with respect to sample size. Importantly, we can show that even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach on an untargeted metabolomics and a transcriptomics dataset from The Cancer Genome Atlas (TCGA). For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for the optimization. Overall, this paper shows that using prior information for correlation network inference is superior to using regular statistical cutoffs, even if the prior information is incomplete or partially inaccurate.

bioinformatics↗