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