Cut-Out Example Datasets for Microglial Motility Analysis Using the MotilA Pipeline

Cutout of dataset from: https://zenodo.org/records/15061566

This record provides a spatial sub-volume (cutout) extracted from the 5D in vivo two-photon imaging stacks described in the parent dataset (see link above). The cutout contains a representative subset of the original data and is intended for testing, demonstration, and methodological development for the analysis pipeline MotilA – A pipeline for microglial fine process motility analysis

All biological, experimental, and imaging details, including mouse model, brain region, acquisition parameters, and data structure, are fully documented in the parent Zenodo record. Users should refer to the parent dataset for complete metadata, scientific context, and citation information.

Dataset details

The file is a registered 5D TIFF stack with axes in the order (T, C, Z, Y, X):
 • T: time points (imaged every 5 minutes for 40 minutes)
 • C: imaging channels (channel 0 = microglia [Cx3cr1-GFP], channel 1 = neurons [tdTomato])
 • Z: z-slices (~60 slices at 1 µm spacing)
 • Y, X: spatial dimensions (~125 × 125 μm^2, ~1200 × 1200 px; pixel size: 0.0950785 μm)

Animal details

 • Model: Cx3cr1-GFP mice (microglia), in utero electroporation with tdTomato (neurons)
 • Age at imaging: P15–P19
 • Brain region: Frontal cortex 
 • Condition 1: Control
 • Condition 2: C4 overexpression (C4HA plasmid, frontal cortex)

Imaging parameters

 • Microscope: In vivo two-photon microscope (Zeiss 7MP multiphoton microscope)
 • Laser: Tunable IR laser at 980 nm (InSight X3 tunable laser from Spectra-Physics)
 • Time-lapse: 5 min intervals over 40 minutes
 • Mode: Mice were headfixed during acquisition 

Applications

These datasets were used to evaluate:
 • Microglial process motility
 • Gained, lost, and stable microglial pixels across time
 • Turnover ratio (TOR) as a proxy for fine process dynamics

MotilA Compatibility

The files are directly compatible with the MotilA pipeline, which performs sub-volume extraction, z-projection, spectral unmixing, filtering, segmentation, and motility quantification based on pixel-wise comparisons.

Acknowledgments

We thank the Cell and Tissue Imaging Facility at the IFM (Theano Eirinopoulou, Mythili Savariradjane), the Light Microscopy Facility at DZNE Bonn (Hans Fried, Severin Filser), and the Animal Research Facilities at DZNE Bonn and IFM.

Funding

This work was supported by:
 • DZNE (MF)
 • University of Latvia (BJ)
 • INSERM (CLM)
 • Sorbonne University (CLM)
 • Fondation de France to CLM (FDF#00112562)
 • ERANET Neuron grants to CLM (ANR-18-NEUR-008-02), MF (BMBF 01EW1905), and BJ (VIAA 1.1.1.5/ERANET/20/01)
 • DIM C-BRAINS (Conseil Régional d’Ile-de-France) – CLM’s team is a member
 • Fédération pour la Recherche sur le Cerveau (CLM)
 • European Union ERC-CoG (MicroSynCom 865618)
 • German Research Foundation (DFG): SFB1089 (C01, B06), SPP2395 (MF, NG, FF, FM)
 • DFG Excellence Cluster ImmunoSensation2 (MF)
 • iBehave network to MF and SP (Ministry of Culture and Science of the State of North Rhine-Westphalia)