A multiple cell tracking method dedicated to the analysis of memory formation in vivo
Felipe Delestro
A multiple cell tracking method dedicated to the analysis of memory formation in vivo
If the brain were so simple we could understand it, we would be so simple we couldn't. Lyall Watson
Drosophila
melanogaster
1 mm
ODOR
Octan-3-ol (OCT)
ODOR
A. Pascual, & T. Préat,
Localization of long-term memory within the Drosophila mushroom body.
Science 294, 1115–1117 (2001).
Octan-3-ol (OCT)
In quantitative terms, what are the changes in the brain after learning, at the single cell level ?
Strategy
-
Record the full extent of the Mushroom body in 3D
By using of a confocal spinning disk we'll be able to perform fast and complete acquisitions
-
Automatically detect the neurons in 3D
An automated procedure guarantees an unbiased analysis
-
Analyse the difference between groups
A quantitative approach will allow us to infer how the memory trace is represented in the brain
Dimensions
X: 256 pixels (41.28µm)
Y: 512 pixels (82.56µm)
Z: 45 slices (67.50µm)
Acquisition time
20ms per slice
0.9 s per stack
135 s per acquisition
mCherry
GCaMP
Nuclei detection
Estimating nuclei size
Using the Full Width at Half maximum, we can estimate the nuclei diameter
time frames
0
250
Nuclei tracking
Non-rigid deformation
Natural movements of the brain cause deformations during the acquisition
top view
side view
Myronenko, A. & Song, X.
Point set registration: coherent point drift.
IEEE Trans. Pattern Anal. Mach. Intell. 32, 2262–75 (2010).
Ester, M., et al.
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226-231. (1996)
DBSCAN
Original detections
Registered detections
time frames
clusters
Clusters in original coordinates
Measuring signal
Nuclei & Signal
Axial max projection
before stim
during stim
Memory traces
What are the quantitative changes in the brain to represent the memory trace ?
Unpaired (no long-term memory)
Paired (long-term memory formed)
Neuron recruitment
Response intensity
Mann-Whitney two-sided test
p-value: 0.0014
t-test
p-value: 0.248
Perspectives
Graph features
More subtle characteristics of the neuronal activation pattern could be revealed by the use of graphs
Training during acquisition
An update on the fly chamber could allow the electric stimulation during the imaging, showing live how the brain is reacting
Genetic modifications
The system here developed would allow us to verify how different levels of expression of specific genes change the neuron recruitment
Song, L. L., Liang, R., Li, D. D. & Al., E. A
Systematic Analysis of Human Disease-Associated Gene Sequences In Drosophila melanogaster.
Genome Res. 59, 1114–1125 (2001)
2015
2016
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct
2014
AUGUSTE GENOVESIO
FELIPE DELESTRO
MINHUI WU
THOMAS PREAT
PAUL TCHENIO
MELANIE PEDRAZZANI
YANN DROMARD
LISA SCHEUNEMANN
2017
2018
Computational bioimaging and bioinformatics
Auguste Genovesio, Elise Laruelle, France Rose, Nikita Menezes, Tiphaine Champetier, Toni Paternina, Solène Weill, Mathieu Bahin, Amira Kramdi, Ouardia Ait Mohamed, Maxime Corbé, Guillaume Delevoye, Fatemeh Habibolahi, , Benoit Noël, Delase Amesefe, Alice Othmani, Ayfer-Marie Montibus, Asm Shivuddin, Perrine Lacour, Sreetama Basu, Charles Bernard, Jennifer Salazar, Alexis Renault, Leila Bastianelli, Minhui Wu, Elton Rexhepaj, Quentin Viautour, Charles , ang, Yingbo Li
Computational bioimaging and bioinformatics
neubias2019
By Felipe Delestro
neubias2019
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