Biology subjects

Schröter, M.

Publications and source records attributed to Schröter, M..

6 recordsLinked to original sources

bioRxiv2026

A perturbation atlas of mouse neural organoids resolves macroglial reactivity and nominates drivers of degeneration

Understanding neuro-glial roles in homeostasis and disease requires scalable central nervous system (CNS) models, particularly as macroglia like astrocytes and Muller glia remain challenging to study in slow-developing human systems. Here, we generate and map the cellular diversity of mouse neural organoids spanning multiple brain regions and the retina. Benchmarking transcriptomic maturation against in vivo references captures postnatal neuro-macroglial states within a month. We show that macroglia are highly responsive to inflammatory cues and adopt specific transcriptomic states also found in vivo. Through a single-cell perturbation screen targeting 13 Muller-glia-specific pathways, we map how certain inflammatory cytokines drive degeneration-like stress programs in photoreceptors and distinct reactive states in Muller glia, including an Oncostatin M-driven chaperone-associated state. Mechanistically, we identify gp130-STAT3 signaling as a targetable driver of macroglial reactivity and photoreceptor distress. Together, these single-cell atlases provide a foundation for dissecting cellular crosstalk and neuro-glial pathology in the mature CNS.

neuroscience↗

bioRxiv2026

In vitro programming and pseudounipolarization of human iPSC-derived sensory neurons

Ectopic expression of NGN1, BRN3A and ISLET1 (NBI) from a safe harbor locus in induced pluripotent stem cells (iPSCs) yielded robust differentiation into functional sensory neurons (iNBI-SNs) within seven days. Single nucleus transcriptomics identified peripheral sensory neuron profiles of nociceptors and mechanoreceptors. Electrophysiological studies showed that more than 98 % of iNBI-SNs display TTX-resistant sodium currents and that they establish functional connections to excitatory CNS neurons. iNBI-SNs derived from patients with inherited erythromelalgia, a pain disorder associated with gain-of-function mutations in the Nav1.7 sodium channel showed pathologically increased firing rates which could be partially rescued with a Nav1.7 inhibitor. Notably, iNBI-SNs acquire a characteristic pseudounipolar morphology upon co-culture with embryonic rodent DRG cells. Taken together, NBI-based forward programming of iPSCs represents a robust approach for the generation of human sensory neurons suitable for developmental, disease- and therapy-related studies.

neuroscience↗

bioRxiv2024

Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing

Glioblastomas are invasive brain tumors with high therapeutic resistance. Neuron-to-glioma synapses have been shown to promote glioblastoma progression. However, a characterization of tumor-connected neurons has been hampered by a lack of technologies. Here, we adapted retrograde tracing using rabies viruses to investigate and manipulate neuron-tumor networks. Glioblastoma rapidly integrated into neural circuits across the brain engaging in widespread functional communication, with acetylcholinergic neurons driving glioblastoma invasion. We uncovered patient-specific and tumor cell state-dependent differences in synaptogenic gene expression associated with neuron-tumor connectivity and subsequent invasivity. Importantly, radiotherapy enhanced neuron-tumor connectivity by increased neuronal activity. In turn, simultaneous neuronal activity inhibition and radiotherapy showed increased therapeutic effects, indicative of a role for neuron-to-glioma synapses in contributing to therapeutic resistance. Lastly, rabies-mediated genetic ablation of tumor-connected neurons halted glioblastoma progression, offering a viral strategy to tackle glioblastoma. Together, this study provides a framework to comprehensively characterize neuron-tumor networks and target glioblastoma.

cancer biology↗

bioRxiv2023

Scalable covariance-based connectivity inference for synchronous neuronal networks

We present a novel method for inferring connectivity from large-scale neuronal networks with synchronous activity. Our approach leverages Dynamic Differential Covariance to address the associated computational challenges. First, we analyze spike trains generated from Leaky Integrate-and-Fire network simulations and evaluate the performance of several off-the-shelf multivariate connectivity inference methods. Next, we introduce a new approach, Fractional Dynamic Differential Covariance (FDDC), and demonstrate that it consistently outperforms the other methods. Finally, we apply FDDC to experimental data to assess the topological organization of inferred graphs of in vitro neural network recordings obtained using high-density microelectrode arrays (HD-MEAs). Our results indicate that FDDC-derived graphs exhibit a significant negative correlation between small-worldness and measures of network synchrony. In contrast, graphs inferred through the well-established pairwise correlation method do not show such a correlation. This finding implies that the graphs obtained through FDDC provide stronger evidence in support of the theoretical notion that networks with clustered connections tend to exhibit higher levels of synchronizability. We hypothesize that our findings have implications for the development of scalable connectivity inference methods for large-scale neural network data.

neuroscience↗

bioRxiv2022

Homophilic wiring principles underpin neuronal network topology in vitro

Economic efficiency has been a popular explanation for how networks self-organize within the developing nervous system. However, the precise nature of the economic negotiations governing this putative organizational principle remains unclear. Here, we address this question further by combining large-scale electrophysiological recordings, to characterize the functional connectivity of developing neuronal networks in vitro, with a generative modeling approach capable of simulating network formation. We find that the best fitting model uses a homophilic generative wiring principle in which neurons form connections to other neurons which are spatially proximal and have similar connectivity patterns to themselves. Homophilic generative models outperform more canonical models in which neurons wire depending upon their spatial proximity either alone or in combination with the extent of their local connectivity. This homophily-based mechanism for neuronal network emergence accounts for a wide range of observations that are described, but not sufficiently explained, by traditional analyses of network topology. Using rodent and human monolayer and organoid cultures, we show that homophilic generative mechanisms can accurately recapitulate the topology of emerging cellular functional connectivity, representing an important wiring principle and determining factor of neuronal network formation in vitro.

neuroscience↗

bioRxiv2020

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Existing labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescence indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended times. We report on a novel dual-mode high-density microelectrode array, which can simultaneously record in i) full-frame mode with 19,584 recording sites and ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we developed reliable analysis tools with drastically increased throughput for extracting axonal morphology and conduction parameters.

neuroscience↗