Biology subjects

Fuhrmann, F.

Publications and source records attributed to Fuhrmann, F..

7 recordsLinked to original sources

bioRxiv2026

OMIO: A policy-driven Python library for reproducible microscopy image I/O

Modern fluorescence and multiphoton microscopy workflows operate within a heterogeneous ecosystem of file formats, partially overlapping metadata standards, and reader-specific conventions. In practice, this frequently leads to silent axis misinterpretations, loss or corruption of physical voxel size information, and laboratory-specific glue code that is fragile, poorly documented, and difficult to reproduce. OMIO, short for Open Microscopy Image I/O, addresses these issues by providing a lightweight, policy-driven image I/O layer for Python that enforces a canonical, OME-compatible data representation at the API boundary. The central contribution of OMIO is the explicit separation of low-level format access from semantic normalization. Existing reader libraries are used as interchangeable backends for extracting pixel data and available metadata, while OMIO enforces axis conventions, metadata interpretation, and fallback decisions in a centralized and auditable policy layer. This design allows heterogeneous microscopy inputs to be converted into a stable representation without propagating backend-specific assumptions into downstream analysis code. The core design principles of OMIO include canonical axis semantics (TZCYX), robust metadata normalization with explicit and auditable fallbacks, memory-aware operation via optional Zarr-based backends, and workflow-level semantics that extend beyond individual files to folder stacks and BIDS-like project structures. This architecture allows OMIO to orchestrate existing reader libraries into a coherent and reproducible I/O pipeline without replacing or duplicating their functionality. OMIO is implemented as an open-source and community-oriented system in which support for additional file formats and metadata conventions can be added incrementally through modular reader backends. By encouraging the contribution of example datasets, backend extensions, and feature requests, OMIO is designed to evolve alongside emerging acquisition systems while preserving strict semantic guarantees at the interface level. The resulting standardized OME-TIFF outputs are immediately suitable for downstream quantitative analysis and interactive inspection in scientific Python workflows, including workflows based on ImageJ and Napari. By standardizing image data at the I/O boundary, OMIO supports FAIR-aligned data sharing and reproducible microscopy analysis while facilitating the development of interoperable downstream tools.

bioinformatics↗

bioRxiv2025

MotilA - A Python pipeline for the analysis of microglial fine process motility in 3D time-lapse multiphoton microscopy data

MotilA is a Python-based image analysis pipeline for quantifying fine process motility of microglia from 3D time-lapse two-channel fluorescence microscopy data. Developed for high-resolution multiphoton in vivo imaging datasets, MotilA enables both single-file and batch processing across multiple experimental conditions. It performs image preprocessing, segmentation, and motility quantification over time, using a pixel-based change detection strategy that yields biologically interpretable metrics such as the turnover rate (TOR) of microglial fine processes. While originally designed for microglial imaging, the pipeline can be extended to other cell types and imaging applications that require analysis of dynamic morphological changes. MotilA is openly available, platform-independent, and includes extensive documentation, tutorials, and example data to facilitate adoption by the broader scientific community. It is released under the GPL-3.0 open-source license.

neuroscience↗

bioRxiv2024

Dynamics of microglia-glioblastoma crosstalk at the far infiltration zone

The interaction of glioblastoma (GB) and microglia is critical due to its implications for tumor progression, immune response modulation, and potential therapeutic strategies. However, the role of microglia in GB pathogenesis remains unclear, especially regarding the in vivo dynamics of their interplay. Performing three-photon imaging in an autochthonous, immunocompetent mouse GB model, we examined tumor/microglia dynamics within previously inaccessible regions at the GB far infiltration zone in the corpus callosum. Initially, microglia increased tissue surveillance upon encountering GB-cells in sparsely infiltrated areas. In contrast, when GB-cell density increased, microglia reduced surveillance, suggesting a biphasic response to tumor invasion. Additionally, microglia were not uniformly attracted to infiltrating GB-cells; only a subset moved directionally toward them within a defined spatial range. This study provides insight into the heterogeneity of the immune response to tumor invasion and the dynamics of microglia-GB interactions in vivo. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/614016v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@24b1d8org.highwire.dtl.DTLVardef@1180609org.highwire.dtl.DTLVardef@346dbforg.highwire.dtl.DTLVardef@1144dd_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Three-photon in vivo imaging of neurons and glia in the medial prefrontal cortex with sub-cellular resolution

The medial prefrontal cortex (mPFC) is important for higher cognitive functions, including working memory, decision making, and emotional control. In vivo recordings of neuronal activity in the mPFC have been achieved via invasive electrical and optical approaches. Here we apply low invasive three-photon in vivo imaging in the mPFC of the mouse at unprecedented depth. Specifically, we measure neuronal and astrocytic Ca2+-transient parameters in awake head-fixed mice up to a depth of 1700 {micro}m. Furthermore, we longitudinally record dendritic spine density (0.41 {+/-}0.07 {micro}m-1) deeper than 1 mm for a week. Using 1650 nm wavelength to excite red fluorescent microglia, we quantify their processes motility (58.9 {+/-}2% turnover rate) at previously unreachable depths (1100 {micro}m). We establish three-photon imaging of the mPFC enabling neuronal and glial recordings with subcellular resolution that will pave the way for novel discoveries in this brain region.

neuroscience↗

bioRxiv2020

Hippocampal hyperactivity in a rat model of Alzheimers disease

Neuronal network dysfunction is a hallmark of Alzheimers disease (AD). However, the underlying pathomechanisms remain unknown. We analyzed the hippocampal micronetwork in a rat model of AD at an early disease stage at the beginning of extracellular amyloid beta (A{beta}) deposition. We established two-photon Ca2+-imaging in vivo in the hippocampus of rats and found hyperactivity of CA1 neurons. Patch-clamp recordings in brain slices in vitro revealed changes in the passive properties and intrinsic excitability of CA1 pyramidal neurons. Furthermore, we observed increased neuronal input resistance and prolonged action potential width in CA1 pyramidal neurons. Surprisingly, all parameters measured to quantify synaptic inhibition and excitation onto CA1 pyramidal neurons were intact suggesting a cell immanent deficit. Our data support the view that altered intrinsic excitability of CA1 neurons may precede inhibitory dysfunction at an early stage of disease progression.

neuroscience↗

bioRxiv2020

Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion

AO_SCPLOWBSTRACTC_SCPLOWQuantification and detection of the hierarchical organization of behavior is a major challenge in neuroscience. Recent advances in markerless pose estimation enable the visualization of highdimensional spatiotemporal behavioral dynamics of animal motion. However, robust and reliable technical approaches are needed to uncover underlying structure in these data and to segment behavior into discrete hierarchically organized motifs. Here, we present an unsupervised probabilistic deep learning framework that identifies behavioral structure from deep variational embeddings of animal motion (VAME). By using a mouse model of beta amyloidosis as a use case, we show that VAME not only identifies discrete behavioral motifs, but also captures a hierarchical representation of the motifs usage. The approach allows for the grouping of motifs into communities and the detection of differences in community-specific motif usage of individual mouse cohorts that were undetectable by human visual observation. Thus, we present a novel and robust approach for quantification of animal motion that is applicable to a wide range of experimental setups, models and conditions without requiring supervised or a-priori human interference.

neuroscience↗

bioRxiv2018

Restoring memory by optogenetic synchronization of hippocampal oscillations in an Alzheimer’s disease mouse model

Disrupted neural oscillations are a feature of Alzheimers disease (AD). We observed reduced frequency of theta oscillations in the hippocampal local field potential (LFP) in a mouse model of beta-amyloidosis. By restoring the temporal organization of theta oscillations using LFP-guided closed-loop optogenetic stimulation of parvalbumin-positive interneurons, we could rescue memory deficits of APP/PS1 mice in the novel object recognition test.

neuroscience↗