Martin Fuhrmann

Structural time-lapse microscopy experiments aim to follow cells or subcellular structures over time, typically by detecting objects in each frame and linking them into trajectories. In practice, this process yields sequences of spatial coordinates derived from segmentation, while identity information across time is often absent or unreliable. In addition, the observed coordinates may be affected by systematic geometric drift. As a result, two coupled inference problems arise: geometric drift must be corrected, and object identities must be reconstructed from unlabeled point clouds. Here, we formulate both drift correction and identity reconstruction within a unified point-cloud framework and evaluate them jointly using controlled synthetic benchmarks and biological datasets. For drift correction, we compare rigid, local, and nonparametric models across spatially varying and non-rigid drift regimes. For identity reconstruction, we analyze a family of Hungarian assignment methods with constant-velocity refinement and different gating strategies, including innovation-based normalization. Across benchmarks, we find that increased model flexibility does not necessarily improve drift correction. A simple rigid stepwise model provides the most robust performance across a wide range of conditions, with a local community model as a competitive alternative under structured spatial heterogeneity. On the association side, innovation-based gating emerges as the key determinant of performance. Hungarian constant-velocity assignment with normalized innovation gating consistently outperforms coordinate-space variants, particularly under heterogeneous motion scales, birth and death processes, and density-induced ambiguity. Application to biological datasets confirms that the combined pipeline improves trajectory consistency and restores interpretable motion statistics, while highlighting the role of proxy references when ground truth is unavailable. Together, these results establish a practical and robust workflow for post hoc trajectory analysis in point-cloud-based time-lapse data, and clarify the methodological trade-offs between model expressivity and robustness in both drift correction and identity reconstruction. Author Summary: Time-lapse microscopy is widely used to study how cells and subcellular structures move and change over time. A common analysis goal is to track these objects by detecting them in each frame and linking them into trajectories. In practice, however, this process often produces only their spatial coordinates, while consistent identity information across time is missing or unreliable. In addition, the entire field of view may shift or rotate during imaging, a problem known as drift. We developed and systematically evaluated a computational framework that addresses both challenges using coordinate-based data alone. By combining controlled synthetic benchmarks with real biological datasets, we show that simple and robust approaches often outperform more complex ones. In particular, a rigid drift correction model remains effective even when drift varies across space, and trajectory reconstruction improves substantially when candidate matches are evaluated relative to their uncertainty rather than by fixed distance thresholds. Our results provide practical guidance for analyzing time-lapse data when only coordinates are available. More broadly, they show that accounting for uncertainty and prioritizing robustness over model complexity leads to more reliable trajectory reconstruction in noisy and heterogeneous biological systems.

Computational research increasingly depends on custom software, yet much of this software begins as project-specific analysis code written to answer one immediate scientific question. Such code can be appropriate and productive in its original context, but it often becomes fragile when it is reused, shared, cited, maintained, or extended by others. In this perspective, we frame the transition from project-specific analysis code to sustainable scientific software as a deliberate development process rather than an automatic consequence of increasing code size, algorithmic sophistication, or wider use. This transition depends on development decisions that make computational work reproducible, inspectable, reusable, and creditable. We propose seven principles for sustainable research software: modular design, version control, automated testing, documentation, public availability and open licensing, persistent citation, and transparent development. These principles are most effective when considered from the beginning of a project, but they can also be introduced retrospectively as part of software maturation. We discuss how the meaning of code changes when it becomes shared scientific infrastructure, why AI-assisted and agentic coding require accountable development practices, and why research software should be understood as a core topic in research data management. Finally, we illustrate how the principles can be implemented in practice through a pseudo-code example. Sustainable scientific software is not merely cleaner code. It is research infrastructure that can be inspected, reused, tested, cited, and maintained.

Competitive spiking networks can form sensory representations through local synaptic plasticity and lateral inhibition. A canonical example is the Diehl–Cook architecture, in which Poisson-encoded sensory inputs drive excitatory neurons through spike-timing-dependent plasticity (STDP), while a separate inhibitory population imposes competition between excitatory units. In most implementations this competitive circuit is fixed, although inhibitory synapses are plastic in cortical circuits and contribute to excitation–inhibition balance. It therefore remains unclear under which conditions biologically motivated inhibitory plasticity can replace fixed lateral competition without disrupting representation learning. Here we study this question in a competitive STDP network trained on MNIST. We compare fixed inhibition with a Vogels-style inhibitory STDP rule, slow homeostatic inhibition, and a budget-constrained slow homeostatic rule that conserves the total inhibitory input received by each excitatory neuron. A fixed-inhibition operating-point map showed that stability depends on the dynamical regime: 56% of tested fixed-inhibition runs completed training, whereas stronger inhibition at higher input gain produced runaway activity. The reference regime g = 2.0, w ^IE = 10.0, selected for subsequent comparisons, combined complete stability across replicate initializations with high classification performance (84.3±0.6% after 30,000 training examples). In this regime, the budget-constrained rule was stable in all replicate runs when its inhibitory budget matched the fixed-inhibition reference ( B _I = 3990), reaching 83.5 ± 0.6% accuracy with firing-rate and inhibitory-conductance statistics close to fixed inhibition, whereas a mismatched budget ( B _I = 7980) produced runaway activity in every replicate. Parameter maps showed that Vogels-derived iSTDP was broadly but not uniformly stable (90% runs completed), slow homeostatic inhibition was even more broadly stable (96%). Budget-constrained stability depended strongly on the conserved budget, i.e., low and matched budgets were stable or nearly stable, intermediate budgets were mixed, and B _I = 7980 failed in all tested runs. These results show that inhibitory plasticity in competitive STDP networks is not governed by a local homeostatic update alone. Stable plastic competition depends jointly on the operating regime and on an inhibitory budget that fixes the total strength of competition while allowing local inhibitory weights to adapt. Mechanistically, budget matching separates local redistribution of inhibitory weights from changes in total inhibitory drive, preventing plasticity from shifting the network away from a stable competitive state. This identifies inhibitory budget matching as a practical design principle for replacing fixed inhibition with plastic, homeostatically regulated competition in STDP-based spiking networks.

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedded behavioral phenotyping. Yet the analysis layer often remains less standardized than the experiment itself: raw exports, phase definitions, exclusion rules, time alignment, and derived behavioral metrics are transformed by lab-specific scripts that are difficult to audit, compare, or reuse. We present ic-analysis, an open-source Python toolkit for standardized, scriptable, and shareable IntelliCage workflows. The toolkit separates user-defined experiment metadata and workflow scripts from a reusable analysis core with modular analysis and plotting functions, allowing users to flexibly assemble experiment-specific pipelines without editing package internals. Due to its modular design, the analysis core can be applied to a broad range of experimental paradigms, including general activity, exploratory, motivational, cognitive, and social readouts, rather than being limited to a single fixed protocol. It aligns biological phase windows across staggered cage runs and exports plots together with quantitative result tables, applied settings, and audit files that support reproducible and FAIR reporting. Here, we demonstrate this flexible design in a realistic synthetic place-learning/place-reversal experiment with two mouse groups and deliberately offset cage starts. The workflow recovered the implanted behavioral differences while preserving the required experimental-time alignment. Group A showed stronger endpoint saccharin preference (81.7 +/- 1.7% vs. 27.4 +/- 3.4%, p = 1.9e⁻⁹), higher liquid uptake, faster place-learning onset (56.6 +/- 8.1 vs. 278.3 +/- 33.2 visits), and better reversal performance (64.1 +/- 1.3% vs. 25.6 +/- 1.7% rewarded correct-corner visits) compared to Group B. This demonstration shows how standardized, explicitly defined readouts can turn complex IntelliCage exports into interpretable behavioral profiles while preserving the analysis history needed for inspection and reuse. ic-analysis therefore provides both a working analysis scaffold and an extensible, community-friendly route toward IntelliCage workflows that are easier to reproduce, compare, extend, and share.

Aging-associated loss of chromatin compaction is linked to derepression of retrotransposable elements (RTEs) in mouse and human tissues. Whether such RTE transcription contributes to the microglia activation that is common in aged brains is unknown. Here, we show that DAXX, a histone chaperone and RTE repressor, is downregulated during aging, preserves microglia homeostasis and inhibits cellular senescence. Loss of Daxx in young-adult microglia drives a reactive phenotype marked by chromatin decompaction at RTEs, loss of homeostatic markers, cell cycle re-entry and behavioral changes. This state leads to DNA damage and microglial depletion, followed by replacement with DAXX-deficient/ Apoe ^high microglia displaying features of senescence. Sustained induction of senescence relies on promyelocytic leukemia protein, a DAXX-interacting factor and interferon target. Together, these findings highlight the importance of heterochromatin maintenance in preserving adult microglial identity and plasticity, with broader implications for brain homeostasis, healthy aging and behavior.

Microglia are innate immune cells of the central nervous system (CNS). They extend their processes and migrate toward injuries in vivo. However, how the fractalkine receptor (CX3CR1) influences microglial migration remains unknown. Label‐free proteomic profiling predicted changes in Ras homology family (RHO)‐signaling activity that hint at dysregulated cytoskeleton signaling in Cx3cr1‐ deficient murine cortex tissue. To further investigate microglial migration, we carried out two‐photon in vivo imaging at 4‐h intervals for 72 h after a laser lesion in the cortex. Cx3cr1 ‐deficient microglia showed enhanced migration toward the lesion. Additionally, the length and velocity of microglial fine processes extending toward the lesion were increased in Cx3cr1‐ deficient microglia. Migration remained unchanged in Ccr2 ‐deficient mice, indicating that monocyte‐derived macrophages/microglia did not contribute to microglia accumulation around the lesion. These results demonstrate that CX3CR1 modulates microglia migration toward laser‐induced CNS injury. Manipulating microglia migration via the CX3CR1 signaling axis is therefore a potential target for the treatment of CNS injury.

Motion artifacts are almost unavoidable in functional time-lapse and structural volumetric multiphoton microscopy. They arise from respiration, heartbeat, locomotion, awake behavior, instrument heating, mechanical vibration, and slow drift, while the recorded signal is often photon-limited, blurred by scattering, and biologically time varying. Consequently, motion correction is frequently an essential prerequisite for quantitative bioimage analysis rather than a merely cosmetic preprocessing operation. Edge- and landmark-centric registration strategies are often poorly matched to these data because useful structures may be sparse, diffuse, out-of-focus, or changing in fluorescence intensity. We present ZenReg, an open-source Python platform that formulates common 2D+t, 3D, and 3D+t microscopy registration tasks as modular, geometry-preserving alignment problems. ZenReg combines FFT- and intensity-based translational registration, projection-based rotation estimation, piecewise translational motion correction, and dense or sparse six-degree-of-freedom volume registration within one canonical microscopy stack model. Disk-backed arrays support chunked processing of large or remote image stacks, and every run can produce registered images together with shift tables, correlation metrics, summary plots, and machine-readable settings. In synthetic benchmarks with known ground truth, ZenReg recovered global 2D and 3D translations with subpixel accuracy across moderate noise and drift regimes. High-noise and large-drift tests separated the registration models: FFT-based methods failed abruptly once image information or shared support became insufficient, intensity-based translational alignment degraded more gradually under severe noise, and piecewise translational correction improved spatially varying local motion where a single global transform was inadequate. In real biological data, ZenReg increased mean template correlation in a 3000-frame calcium-imaging movie from 0.334 to 0.487 and recovered imposed continuous three-photon volume motion with a mean translational error of 0.055 px or, for six-degree-of-freedom rigid motion, a mean shift error of 0.068 px and a mean rotation error of 0.015 degrees. By coupling modular registration backends to transparent sidecar outputs, ZenReg turns motion correction into an inspectable, memory-aware, and FAIR-oriented component of reproducible bioimage analysis.

Quantitative cell colocalization in fluorescence microscopy often depends on ad hoc combinations of image loading, segmentation, region selection, manual inspection, and spreadsheet post-processing. Such workflows are difficult to transfer across projects and often obscure how intermediate results were produced. We present CellColoc, an open-source Python workflow pipeline for segmentation-based cell colocalization, single-channel segmentation, and cell feature extraction in 2D and 3D microscopy images. CellColoc provides a modular workflow layer that integrates existing segmentation backends, including Cellpose and threshold-based methods, into reusable, script-driven analyses. The package supports channel-wise backend selection, interactive or reusable regions of interest, optional third-channel occupancy and cell-positivity analysis, z-cropping and z-projection, cached post hoc refinement of Cellpose thresholds, and reanalysis after manual mask editing. Analyses are executed from concise user scripts while reusable functionality is kept in a core package. Intermediate artifacts such as ROI masks, per-channel label masks, positive-cell masks, and structured result tables are written to a standardized results directory, promoting transparent inspection, reproducibility, and FAIR-aligned reuse. Public example datasets, a synthetic benchmark, and archived software releases accompany the package. By separating reusable analysis logic from project-specific configuration, CellColoc offers an extensible foundation for community-driven microscopy workflows that need transparent per-cell overlap classification, morphology readouts, and reusable batch analysis.

The development of cancer immunotherapies requires preclinical models that capture intravenous delivery, immune cell recruitment and intratumoral T cell activation. Conventional 3D in vitro systems lack perfused vascular networks, whereas mouse models are limited by throughput. The chick chorioallantoic membrane (CAM) assay enables rapid growth of vascularized tumors derived from human cancer cells in ovo, but its application to human T cell-based immunotherapy testing is constrained by CAM-derived vascularization and species-specific barriers between human immune cells and avian endothelium. Here, we establish an endothelial graft-enhanced CAM tumor model that incorporates an immortalized murine endothelial cell line capable of anastomosing with the chick vasculature. This generates a perfused and branched mammalian vascular interface within human tumor xenografts growing on the CAM. Human ICAM-1 expression on the grafted endothelial cells further enhances recruitment of intravenously delivered human T cells into CAM tumors. Using this platform, we demonstrate target-dependent intratumoral T cell activation by bispecific T cell engagers (TCEs) across tumor models, including evaluation of the clinically approved DLL3-targeting TCE tarlatamab in small cell lung cancer models.