Biology subjects

Musacchio, F.

Publications and source records attributed to Musacchio, F..

12 recordsLinked to original sources

bioRxiv2026

Robust geometric drift correction and identity reconstruction for biological 3D time-lapse point clouds

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.

bioinformatics↗

bioRxiv2026

Inhibitory budget matching constrains homeostatic plasticity in competitive spiking networks

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, wIE = 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 (BI = 3990), reaching 83.5 {+/-} 0.6% accuracy with firing-rate and inhibitory-conductance statistics close to fixed inhibition, whereas a mismatched budget (BI = 7980) produced runaway activity in every replicate. Parameter maps showed that Vogels-derived iSTDP was broadly but not uniformly stable (90% runs completed), while 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 BI = 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.

neuroscience↗

bioRxiv2026

Toward standardized behavioral analysis in IntelliCage experiments

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-), 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.

animal behavior and cognition↗

bioRxiv2026

ZenReg: A modular Python platform for fast and memory-efficient N-dimensional microscopy image registration

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.

neuroscience↗

bioRxiv2026

CellColoc: A modular, open-source workflow for cell colocalization, segmentation, and feature extraction in microscopy images

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.

neuroscience↗

bioRxiv2026

Spectral Unmixing: A modular and reproducible Python package for directed and blind spectral unmixing in multidimensional microscopy stacks

Background: Spectral bleed-through is a persistent source of bias in multichannel fluorescence microscopy, where signal from one fluorophore is recorded in another detection channel. Routine correction workflows often remain fragmented across manual graphical procedures, laboratory-specific scripts, or method-specific blind-unmixing implementations with limited provenance. Results: We present spectral-unmixing, an open-source Python package for reproducible directed and blind bleed-through correction in multidimensional microscopy stacks. The package combines directed two-channel correction with multiple coefficient-estimation strategies, optional bidirectional two-channel correction through explicit inversion of a two-by-two mixing model, and PICASSO-family blind unmixing for multichannel data. Input stacks from multiple microscopy file formats are normalized to a canonical axis order before processing, reducing the need for manual pre-formatting. Each processing run also writes a machine-readable sidecar file that records the effective configuration and estimated coefficients. Synthetic and real-data-derived benchmarks demonstrated robust correction across directed, bidirectional, and blind-unmixing settings. In fixed-alpha two-channel simulations, directed correction reduced target-channel normalized root mean squared error from approximately 0.029 to about 0.003. In time-varying data, per-time-point estimation reduced mean absolute alpha error from approximately 0.099 to 0.003 compared with reference-time-point estimation, while bidirectional inverse-model correction reduced reciprocal-mixture channel errors from approximately 0.022 to 0.037 to about 0.004. Multichannel benchmarks further showed that blind-unmixing workflows can reduce residual inter-channel dependence while preserving fluorophore identity, and that source-sink priors provide a controllable alternative when plausible contamination pathways are known. Conclusions: Spectral-unmixing provides a modular, scriptable, and extensible platform that standardizes directed, bidirectional, and blind spectral-unmixing workflows, thereby lowering barriers to reproducible bleed-through correction in quantitative bioimage analysis.

neuroscience↗

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

Deficiency of actin depolymerizing factors ADF/Cfl1 in microglia decreases motility and impairs memory

Microglia are highly motile cells that play a crucial role in the central nervous system in health and disease. Here we show that actin depolymerizing factors ADF and Cofilin1 (Cfl1) are key factors of microglia integrity and function. We found a profound morphological phenotype in absence of ADF and Cfl1 in microglia. In vivo two-photon imaging of microglia with ADF/Cfl1-KO revealed reduced microglial fine processes motility and impaired microglia migration towards a laser-induced lesion. We found increased accumulation of stabilized F-actin and altered microtubule dynamics in ADF/Cfl1-KO microglia, indicating that ADF/Cfl1 are necessary for microglial cytoskeleton dynamics. Interestingly, microglial ADF/Cfl1-deficiency decreased learning and memory, suggesting that impaired microglial cytoskeleton dynamics affect neuronal functions relevant for cognition. Our results reveal a fundamental role of ADF/Cfl1 in microglia function and underscore the importance of these innate immune cells for higher cognitive functions.

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

The diagonal band of broca continually regulates olfactory-mediated behaviors by modulating odor-evoked responses within the olfactory bulb

Sensory perception is modulated in a top-down fashion by higher brain regions to regulate the strength of its own input resulting in the adaptation of behavioral responses. In olfactory perception, the horizontal diagonal band of broca (HDB), embedded in the basal forebrain modulates olfactory information processing by recruiting olfactory bulb (OB) interneurons to shape excitatory OB output. Currently, little is known about how specific HDB to OB top-down signaling affects complex olfactory-mediated behaviors. Here we show that the olfactory bulb is strongly and differentially innervated by HDB projections. HDB-silencing via tetanus toxin light chain led to reduced odor-evoked Ca2+-responses in glomeruli of the OB, underscoring HDBs role in odor response modulation. Furthermore, selective, light-mediated silencing of HDB to OB afferents completely prevented olfactory-mediated habituation and discrimination behaviors. Notably, also social habituation and discrimination behaviors were affected. Mono-transsynaptic tracing studies indicated a robust HDB innervation from the paraventricular nucleus (PVN). Here we provide evidence for a novel tri-synaptic PVN-HDB-OB axis responsible for modulating these types of behavior. Thus, HDB to OB projections constitute a central top-down pathway for olfactory-mediated habituation and discrimination behaviors.

neuroscience↗