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@MISC{Gockel:280738,
author = {Gockel, Nala and Nieves-Rivera, Nayadoleni and Druart,
Mélanie and Jaako, Külli and Fuhrmann, Falko and
Rožkalne, Rebeka and Musacchio, Fabrizio and Poll, Stefanie
and Baiba, Jansone and Fuhrmann, Martin and Le Magueresse,
Corentin},
title = {{D}ataset: {E}xample {D}atasets for {M}icroglial {M}otility
{A}nalysis {U}sing the {M}otil{A} {P}ipeline},
publisher = {Zenodo},
reportid = {DZNE-2025-00959},
year = {2025},
abstract = {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 $\&$ 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)},
keywords = {two-photon imaging (Other) / microglia (Other) / in vivo
imaging (Other) / Python analysis pipeline (Other) / mouse
model (Other) / neuroscience (Other)},
cin = {AG Fuhrmann},
cid = {I:(DE-2719)1011004},
pnm = {352 - Disease Mechanisms (POF4-352)},
pid = {G:(DE-HGF)POF4-352},
typ = {PUB:(DE-HGF)32},
doi = {10.5281/zenodo.15061565},
url = {https://pub.dzne.de/record/280738},
}