TY  - CHART
AU  - Gockel, Nala
AU  - Nieves-Rivera, Nayadoleni
AU  - Druart, Mélanie
AU  - Jaako, Külli
AU  - Fuhrmann, Falko
AU  - Rožkalne, Rebeka
AU  - Musacchio, Fabrizio
AU  - Poll, Stefanie
AU  - Baiba, Jansone
AU  - Fuhrmann, Martin
AU  - Le Magueresse, Corentin
TI  - Dataset: Example Datasets for Microglial Motility Analysis Using the MotilA Pipeline
PB  - Zenodo
M1  - DZNE-2025-00959
PY  - 2025
AB  - This dataset contains two 5D time-lapse imaging stacks of the mouse frontal cortex acquired using in vivo two-photon microscopy. The data were acquired to study microglial process motility in the context of complement C4 overexpression, a genetic risk factor for schizophrenia. These stacks are provided as example input data for the MotilA (Microglial Motility Analysis) pipeline. This dataset accompanies the manuscript by Gockel </td><td width="150">
AB  -  Nieves-Rivera et al. (2025), currently under revision. This record will be updated with the final reference upon publication. Dataset details Each file is a 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)
KW  - two-photon imaging (Other)
KW  - microglia (Other)
KW  - in vivo imaging (Other)
KW  - Python analysis pipeline (Other)
KW  - mouse model (Other)
KW  - neuroscience (Other)
LB  - PUB:(DE-HGF)32
DO  - DOI:10.5281/zenodo.15061565
UR  - https://pub.dzne.de/record/280738
ER  -