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Dynamic mapping of long-term memory formation – MemoMap

A computational biology approach to decipher long-term memory formation

Development of a single cell 3D detection and tracking device to study long-term memory traces in a whole memory centre, the Drosophila mushroom body

Development of a single cell 3D detection and tracking device to study long-term memory traces in a whole memory centre

Memory encoding takes place in a subset of sparsely distributed neurons among a larger assembly. The main bottleneck to get an integrated view of a memorization event comes from the lack of methodology to study memory-specific biochemical processes in heterogeneous neuron assemblies. We use olfactory conditioning in the Drosophila model to develop, for the first time, a spot detection and multi-target tracking system able to simultaneously and accurately monitor the dynamic activity the 2.000 densely packed neurons of the drosophila’s olfactory learning and memory center, the mushroom body. The main challenges to overcome are resolution constrains as well as moving tissue from the behaving animal. By optimization of brain dissection techniques as well as applying rigid and non-rigid algorithms, we have successfully established a 3D tracking system that is able to detect in mean 500 cells and follows their activity over time. It enables to monitor accurately the signal of single neurons from the mushroom body and compare comprehensive odor response patterns after different training conditions.

We use specific genetic control tools and reporters of neuronal activity available in drosophila and spinning disk ultrafast in vivo imaging to record all mushroom body neurons at once. With an odorant supply system synchronized with the microscope, we are able to apply odor stimulants during the acquisition of neuronal activity of living flies. For image processing, we developed algorithms to completely track the position of at least 90% of imaged nuclei all along the sequence. 3D sequences are converted to matrices made of nuclei (in row) versus time (in columns) filled with fluorescent values. On this basis, we developed algorithms to track the stimulation signal through time for each neuron.

Our fully automated procedure made it possible to robustly recover the signal of approximately 500 single neurons from the whole MB cell body layer for 216 flies in vivo. This level of throughput, which has never been attained before, offers new perspectives as it is large-scale and can operate with single-neuron precision. Using this approach, we identified for the first time an increase in responsive neurons count after LTM formation, suggesting neuronal recruitment. We predict that this method, which should further enable studying the population pattern of neuronal activity, has the potential to uncover fine details of memory formation and plasticity.


A manuscript is currently in revision that describes the LTM trace analysis by the 3D spot detection and tracking algorithm. Therein, we describe how this technique allows to automatically detect and follow 500 mushroom body neurons individually and simultaneously. The code of this method, that enables reproducing these results and the tracking evaluation on annotated and synthetic data is available from the following Github repository: github.com/biocompibens/memotrack. We encountered fruitful interactions on scientific meetings with great potential to establish new collaborations and adapt this technique also for other model organisms.

The uniqueness of each human being is linked to what we learn and remember – or not – during a lifetime of experience. Yet, how the brain encodes, stores, and retrieves memories remains very partially known. Since memory encoding generally takes place in a subset of sparsely distributed neurons among a larger assembly, the main bottleneck to get an integrated view of a memorization event comes from the lack of methodology to study memory-specific biochemical processes in heterogeneous neuron assemblies. Thus many of the biochemical studies have been carried out globally at memory centers level, with the study of cell populations containing only a small portion of memory-encoding neurons. In consequence, the low signal to noise ratio affects the interpretation of most in vivo studies. Worse, a memory event is not a series of chemical processes taking place independently in different cells. Coordinated electrical and biochemical activities in the relevant cells is at the heart of memory processes, and their comprehensive analysis is still out of reach of current approaches. To overcome these severe limitations, there is a need to monitor electrical and/or biochemical activity of a whole neural network at cellular resolution and over time.
Our project proposes to address this challenge. It will take advantage of powerful genetic tools available in drosophila, and in particular of new genetically encoded fluorescent reporters, and of the extensive knowledge of Preat and Genovesio groups in drosophila memory neurobiology and image processing, respectively. Critical locks should be removed such as the development of fast accurate imaging, movement corrections in living animals and classification of responses. Thus, comprehensive monitoring and analysis in 3D over time of a whole memory center at single cell level should become available. With this methodology, we propose to address three key issues, currently unanswered, concerning the formation of associative long-term memories in the drosophila olfactory learning and memory center, the mushroom body.
Our first objective will be to precisely identify the set of neurons specifically recruited to encode the association between the olfactory stimulus and valence-encoding stimulus, either punishing or rewarding. Global analyses have determined that the neurons recruited for long-term memory belong to a functionally heterogeneous subpopulation of mushroom body neurons, so-called the a/ß neurons, but current studies have not further characterized these neurons. We propose two complementary approaches, the analysis of electrical activity of all a/ß neurons and the monitoring of protein kinases activity at the nuclear level to identify individually the neurons that are relevant for long-term memory formation. We will determine in particular whether memory-encoding neurons are recruited among the small population of odorant responsive neurons in naïve flies, or whether additional mushroom body neurons are recruited. Our analyzes will also reveal with unmatched details the role of different nuclear effectors in the formation of long-term memory.
Our second objective concerns the selective processes at work during long-term memories formation. Indeed, all learned associations are not transformed into long-term memories and in pioneering works, we showed that dopaminergic signaling plays a critical role in that gating. Single-cell biochemical monitoring will open the analysis of dopaminergic signaling in relevant mushroom body neurons.
Finally, it is generally admitted that individual mushroom body a/ß neurons are able to encode aversive and appetitive long-term memory. We will challenge this model and test whether single-cell analysis reveals specific subpopulations specialized in the long-lasting storage of aversive and appetitive memories.
In conclusion, the scientific and technical contribution of the project will enable major breakthrough in our understanding of long-term memory formation.

Project coordinator

Monsieur Thomas Preat (Laboratoire Plasticité du cerveau - UMR8249)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


CNRS Laboratoire Plasticité du cerveau - UMR8249
ENS Institut de Biologie de l’Ecole Normale Supérieure

Help of the ANR 275,000 euros
Beginning and duration of the scientific project: December 2015 - 24 Months

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