@article {69975, title = {Statistical signature of subtle behavioral changes in large-scale assays.}, journal = {PLoS Comput Biol}, volume = {21}, year = {2025}, month = {2025 Apr 21}, pages = {e1012990}, abstract = {

The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the Drosophila melanogaster larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280 716 larvae across 569 genetic lines.

Preprint:\ https://www.biorxiv.org/content/10.1101/2024.05.03.591825v1

}, keywords = {Animals, Behavior, Animal, Computational Biology, Drosophila melanogaster, Larva, Machine Learning, Models, Neurological, Models, Statistical, Neurons}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1012990}, url = {https://doi.org/10.1371/journal.pcbi.1012990}, author = {Blanc, Alexandre and Laurent, Francois and Barbier-Chebbah, Alex and Van Assel, Hugues and Cocanougher, Benjamin T and Jones, Benjamin M W and Hague, Peter and Zlatic, Marta and Chikhi, Rayan and Vestergaard, Christian L and Jovanic, Tihana and Masson, Jean-Baptiste and Barre, Chloe} } @article {69526, title = {LarvaTagger: Manual and automatic tagging of drosophila larval behaviour.}, journal = {Bioinformatics}, year = {2024}, month = {2024 Jul 05}, abstract = {

MOTIVATION: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data.We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.

RESULTS: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data.

AVAILABILITY: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btae441}, author = {Laurent, Francois and Blanc, Alexandre and May, Lilly and G{\'a}ndara, Lautaro and Cocanougher, Benjamin T and Jones, Benjamin M W and Hague, Peter and Barre, Chloe and Vestergaard, Christian L and Crocker, Justin and Zlatic, Marta and Jovanic, Tihana and Masson, Jean-Baptiste} } @article {69169, title = {Motion of VAPB molecules reveals ER-mitochondria contact site subdomains.}, journal = {Nature}, volume = {626}, year = {2024}, month = {2024 Jan 24}, pages = {169-176}, abstract = {

To coordinate cellular physiology, eukaryotic cells rely on the rapid exchange of molecules at specialized organelle-organelle contact sites. Endoplasmic reticulum-mitochondrial contact sites (ERMCSs) are particularly vital communication hubs, playing key roles in the exchange of signalling molecules, lipids and metabolites. ERMCSs are maintained by interactions between complementary tethering molecules on the surface of each organelle. However, due to the extreme sensitivity of these membrane interfaces to experimental perturbation, a clear understanding of their nanoscale organization and regulation is still lacking. Here we combine three-dimensional electron microscopy with high-speed molecular tracking of a model organelle tether, Vesicle-associated membrane protein (VAMP)-associated protein B (VAPB), to map the structure and diffusion landscape of ERMCSs. We uncovered dynamic subdomains within VAPB contact sites that correlate with ER membrane curvature and undergo rapid remodelling. We show that VAPB molecules enter and leave ERMCSs within seconds, despite the contact site itself remaining stable over much longer time scales. This metastability allows ERMCSs to remodel with changes in the physiological environment to accommodate metabolic needs of the cell. An amyotrophic lateral sclerosis-associated mutation in VAPB perturbs these subdomains, likely impairing their remodelling capacity and resulting in impaired interorganelle communication. These results establish high-speed single-molecule imaging as a new tool for mapping the structure of contact site interfaces and reveal that the diffusion landscape of VAPB at contact sites is a crucial component of ERMCS homeostasis.

}, keywords = {Amyotrophic Lateral Sclerosis, Endoplasmic Reticulum, Humans, Mitochondria, Mitochondrial Membranes, Signal Transduction, Vesicular Transport Proteins}, issn = {1476-4687}, doi = {10.1038/s41586-023-06956-y}, author = {Obara, Christopher J and Nixon-Abell, Jonathon and Moore, Andrew S and Riccio, Federica and Hoffman, David P and Shtengel, Gleb and Xu, C Shan and Schaefer, Kathy and Pasolli, H Amalia and Masson, Jean-Baptiste and Hess, Harald F and Calderon, Christopher P and Blackstone, Craig and Lippincott-Schwartz, Jennifer} } @article {68414, title = {High-throughput automated methods for classical and operant conditioning of larvae.}, journal = {eLife}, volume = {11}, year = {2022}, month = {2022 Oct 28}, abstract = {

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i. e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i. e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

}, issn = {2050-084X}, doi = {10.7554/eLife.70015}, author = {Croteau-Chonka, Elise C and Clayton, Michael S and Venkatasubramanian, Lalanti and Harris, Samuel N and Jones, Benjamin M W and Narayan, Lakshmi and Winding, Michael and Masson, Jean-Baptiste and Zlatic, Marta and Klein, Kristina T} } @article {67225, title = {Comparative single-cell transcriptomics of complete insect nervous systems}, journal = {bioRxiv}, year = {2020}, month = {01/2020}, abstract = {

Molecular profiles of neurons influence information processing, but bridging the gap between genes, circuits, and behavior has been very difficult. Furthermore, the behavioral state of an animal continuously changes across development and as a result of sensory experience. How behavioral state influences molecular cell state is poorly understood. Here we present a complete atlas of the Drosophila larval central nervous system composed of over 200,000 single cells across four developmental stages. We develop polyseq, a python package, to perform cell-type analyses. We use single-molecule RNA-FISH to validate our scRNAseq findings. To investigate how internal state affects cell state, we optogentically altered internal state with high-throughput behavior protocols designed to mimic wasp sting and over activation of the memory system. We found nervous system-wide and neuron-specific gene expression changes. This resource is valuable for developmental biology and neuroscience, and it advances our understanding of how genes, neurons, and circuits generate behavior.

}, doi = {10.1101/785931}, url = {https://www.biorxiv.org/content/early/2020/01/16/785931}, author = {Cocanougher, Benjamin T. and Wittenbach, Jason D. and Long, Xi Salina and Kohn, Andrea B. and Norekian, Tigran P. and Yan, Jinyao and Colonell, Jennifer and Masson, Jean-Baptiste and Truman, James W. and Cardona, Albert and Turaga, Srinivas C. and Singer, Robert H. and Moroz, Leonid L. and Zlatic, Marta} } @article {65301, title = {Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila.}, journal = {PLoS Genetics}, volume = {16}, year = {2020}, month = {2020 Feb 14}, pages = {e1008589}, abstract = {

Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, we combined a large-scale neuronal inactivation screen with automated action detection in response to a mechanosensory cue in Drosophila larva. We analyzed behaviors from 2.9x105 larvae and identified 66 candidate lines for mechanosensory responses out of which 25 for competitive interactions between actions. We further characterize in detail the neurons in these lines and analyzed their connectivity using electron microscopy. We found the neurons in the mechanosensory network are located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system.

}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008589}, author = {Masson, Jean-Baptiste and Laurent, Francois and Cardona, Albert and Barre, Chloe and Skatchkovsky, Nicolas and Zlatic, Marta and Jovanic, Tihana} } @article {47935, title = {Competitive disinhibition mediates behavioral choice and sequences in Drosophila.}, journal = {Cell}, volume = {167}, year = {2016}, month = {2016 Oct 5}, pages = {858-70}, abstract = {

Even a simple sensory stimulus can elicit distinct innate behaviors and sequences. During sensorimotor decisions, competitive interactions among neurons that promote distinct behaviors must ensure the selection and maintenance of one behavior, while suppressing others. The circuit implementation of these competitive interactions is still an open question. By combining comprehensive electron microscopy reconstruction of inhibitory interneuron networks, modeling, electrophysiology, and behavioral studies, we determined the circuit mechanisms that\ contribute to the Drosophila larval sensorimotor decision to startle, explore, or perform a sequence of the two in response to a mechanosensory stimulus. Together, these studies reveal that, early in sensory processing, (1) reciprocally connected feedforward inhibitory interneurons implement behavioral choice, (2) local feedback disinhibition provides positive feedback that consolidates and maintains the chosen behavior, and (3) lateral disinhibition promotes sequence transitions. The combination of these interconnected circuit motifs can implement both behavior selection and the serial organization of behaviors into a sequence.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2016.09.009}, author = {Jovanic, Tihana and Schneider-Mizell, Casey M and Shao, Mei and Masson, Jean-Baptiste and Denisov, Gennady and Fetter, Richard D and Mensh, Brett D and Truman, James W and Cardona, Albert and Zlatic, Marta} } @article {47410, title = {A primer on the Bayesian approach to high-density single-molecule trajectories analysis.}, journal = {Biophysical Journal}, volume = {110}, year = {2016}, month = {2016 Mar 29}, pages = {1209-15}, abstract = {

Tracking single molecules in living cells provides invaluable information on their environment and on the interactions that underlie their motion. New experimental techniques now permit the recording of large amounts of individual trajectories, enabling the implementation of advanced statistical tools for data analysis. In this primer, we present a Bayesian approach toward treating these data, and we discuss how it can be fruitfully employed to infer physical and biochemical parameters from single-molecule trajectories.

}, issn = {1542-0086}, doi = {10.1016/j.bpj.2016.01.018}, author = {El Beheiry, Mohamed and T{\"u}rkcan, Silvan and Richly, Maximilian U and Triller, Antoine and Alexandrou, Antigone and Dahan, Maxime and Masson, Jean-Baptiste} } @article {47160, title = {Dynamics of CRISPR-Cas9 genome interrogation in living cells.}, journal = {Science (New York, N.Y.)}, volume = {350}, year = {2015}, month = {2015 Nov 13}, pages = {823-6}, abstract = {

The RNA-guided CRISPR-associated protein Cas9 is used for genome editing, transcriptional modulation, and live-cell imaging. Cas9-guide RNA complexes recognize and cleave double-stranded DNA sequences on the basis of 20-nucleotide RNA-DNA complementarity, but the mechanism of target searching in mammalian cells is unknown. Here, we use single-particle tracking to visualize diffusion and chromatin binding of Cas9 in living cells. We show that three-dimensional diffusion dominates Cas9 searching in vivo, and off-target binding events are, on average, short-lived (\<1 second). Searching is dependent on the local chromatin environment, with less sampling and slower movement within heterochromatin. These results reveal how the bacterial Cas9 protein interrogates mammalian genomes and navigates eukaryotic chromatin structure.

}, issn = {1095-9203}, doi = {10.1126/science.aac6572}, author = {Knight, Spencer C and Xie, Liangqi and Deng, Wulan and Guglielmi, Benjamin and Witkowsky, Lea B and Bosanac, Lana and Zhang, Elisa T and El Beheiry, Mohamed and Masson, Jean-Baptiste and Dahan, Maxime and Liu, Zhe and Doudna, Jennifer A and Tjian, Robert} } @article {46885, title = {InferenceMAP: mapping of single-molecule dynamics with Bayesian inference.}, journal = {Nature Methods}, volume = {12}, year = {2015}, month = {2015 Jun 30}, pages = {594-5}, issn = {1548-7105}, doi = {10.1038/nmeth.3441}, author = {Beheiry, Mohamed El and Dahan, Maxime and Masson, Jean-Baptiste} }