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In vivo large-scale analysis of Drosophila neuronal calcium traces by automated tracking of single somata
- Source :
- Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020), Scientific Reports, Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.7153. ⟨10.1038/s41598-020-64060-x⟩, Scientific Reports, 2020, 10 (1), pp.7153. ⟨10.1038/s41598-020-64060-x⟩
- Publication Year :
- 2020
- Publisher :
- Nature Publishing Group, 2020.
-
Abstract
- How does the concerted activity of neuronal populations shape behavior? Impediments to address this question are primarily due to critical experimental barriers. An integrated perspective on large scale neural information processing requires an in vivo approach that can combine the advantages of exhaustively observing all neurons dedicated to a given type of stimulus, and simultaneously achieve a resolution that is precise enough to capture individual neuron activity. Current experimental data from in vivo observations are either restricted to a small fraction of the total number of neurons, or are based on larger brain volumes but at a low spatial and temporal resolution. Consequently, fundamental questions as to how sensory information is represented on a population scale remain unanswered. In Drosophila melanogaster, the mushroom body (MB) represents an excellent model to analyze sensory coding and memory plasticity. In this work, we present an experimental setup coupled with a dedicated computational method that provides in vivo measurements of the activity of hundreds of densely packed somata uniformly spread in the MB. We exploit spinning-disk confocal 3D imaging over time of the whole MB cell body layer in vivo while it is exposed to olfactory stimulation. Importantly, to derive individual signal from densely packed somata, we have developed a fully automated image analysis procedure that takes advantage of the specificities of our data. After anisotropy correction, our approach operates a dedicated spot detection and registration over the entire time sequence to transform trajectories to identifiable clusters. This enabled us to discard spurious detections and reconstruct missing ones in a robust way. We demonstrate that this approach outperformed existing methods in this specific context and made possible high-throughput analysis of approximately 500 single somata uniformly spread over the MB in various conditions. Applying this approach, we find that learned experiences change the population code of odor representations in the MB. After long-term memory (LTM) formation, we quantified an increase in responsive somata count and a stable single neuron signal. We predict that this method, which should further enable studying the population pattern of neuronal activity, has the potential to uncover fine details of sensory processing and memory plasticity.
- Subjects :
- 0301 basic medicine
Memory, Long-Term
Sensory processing
Computer science
medicine.medical_treatment
[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology
Population
[SCCO.COMP]Cognitive science/Computer science
lcsh:Medicine
Sensory system
Stimulus (physiology)
Article
Long-term memory
03 medical and health sciences
Automation
0302 clinical medicine
Image processing
In vivo
[SCCO.COMP] Cognitive science/Computer science
Sensory coding
medicine
Premovement neuronal activity
Animals
education
lcsh:Science
Neurons
education.field_of_study
Multidisciplinary
business.industry
lcsh:R
[SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology
Pattern recognition
030104 developmental biology
medicine.anatomical_structure
Drosophila melanogaster
Mushroom bodies
Calcium
lcsh:Q
Neuron
Artificial intelligence
business
030217 neurology & neurosurgery
Software
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 10
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....f1586db53398039c7b512bc87f4b1df7