Identification of Dopaminergic Neurons That Can Both Establish Associative Memory and Acutely Terminate Its Behavioral Expression
2020-07-29, Schleyer, Michael, Weiglein, Aliće, Thoener, Juliane, Strauch, Martin, Hartenstein, Volker, Kantar Weigelt, Melisa, Schuller, Sarah, Saumweber, Timo, Eichler, Katharina, Rohwedder, Astrid, Merhof, Dorit, Zlatic, Marta, Thum, Andreas, Gerber, Bertram
An adaptive transition from exploring the environment in search of vital resources to exploiting these resources once the search was successful is important to all animals. Here we study the neuronal circuitry that allows larval Drosophila melanogaster of either sex to negotiate this exploration-exploitation transition. We do so by combining Pavlovian conditioning with high-resolution behavioral tracking, optogenetic manipulation of individually identified neurons, and EM data-based analyses of synaptic organization. We find that optogenetic activation of the dopaminergic neuron DAN-i1 can both establish memory during training and acutely terminate learned search behavior in a subsequent recall test. Its activation leaves innate behavior unaffected, however. Specifically, DAN-i1 activation can establish associative memories of opposite valence after paired and unpaired training with odor, and its activation during the recall test can terminate the search behavior resulting from either of these memories. Our results further suggest that in its behavioral significance DAN-i1 activation resembles, but does not equal, sugar reward. Dendrogram analyses of all the synaptic connections between DAN-i1 and its two main targets, the Kenyon cells and the mushroom body output neuron MBON-i1, further suggest that the DAN-i1 signals during training and during the recall test could be delivered to the Kenyon cells and to MBON-i1, respectively, within previously unrecognized, locally confined branching structures. This would provide an elegant circuit motif to terminate search on its successful completion.SIGNIFICANCE STATEMENT In the struggle for survival, animals have to explore their environment in search of food. Once food is found, however, it is adaptive to prioritize exploiting it over continuing a search that would now be as pointless as searching for the glasses you are wearing. This exploration-exploitation trade-off is important for animals and humans, as well as for technical search devices. We investigate which of the only 10,000 neurons of a fruit fly larva can tip the balance in this trade-off, and identify a single dopamine neuron called DAN-i1 that can do so. Given the similarities in dopamine neuron function across the animal kingdom, this may reflect a general principle of how search is terminated once it is successful.
larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster
2018-01, Muenzing, Sascha E. A., Strauch, Martin, Truman, James W., Bühler, Katja, Thum, Andreas, Merhof, Dorit
The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0.
Discrimination of cell cycle phases in PCNA-immunolabeled cells
2015, Schönenberger, Felix, Deutzmann, Anja, Ferrando-May, Elisa, Merhof, Dorit
Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images.
We show that the proposed processing chain is capable of automatically classifying cell cycle phases in PCNA-immunolabeled cells from single time point images, independently of the technique of image acquisition. Comparison of confocal and widefield images showed that for the proposed approach, the overall classification accuracy is slightly higher for confocal microscopy images.
Overall, automated identification of cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution, is feasible for both confocal and widefield images.
Automatic framework for tracking honeybee's antennae and mouthparts from low framerate video
2013-09, Shen, Minmin, Szyszka, Paul, Galizia, C. Giovanni, Merhof, Dorit
Automatic tracking of the movement of bee's antennae and mouthparts is necessary for studying associative learning of individuals. However, the problem of tracking them is challenging: First, the different classes of objects possess similar appearance and are close to each other. Second, tracking gaps are often present, due to the low frame-rate of the acquired video and the fast motion of the objects. Most existing insect tracking approaches have been developed for slow moving objects, and are not suitable for this application. In this paper, a novel Bayesian framework is proposed to automatically track bees' antennae and their mouthparts. This framework incorporates information about their kinematics, shape, order and temporal correlation between neighboring frames. Experimental evaluation demonstrates the effectiveness and efficiency of the proposed framework.
Interpolating Maps between Neural Response Spaces for Chemosensing with Fruit Fly Antenna Sensors
2019-10, Strauch, Martin, Krüger, Karl, Mukunda, Latha, Lüdke, Alja, Galizia, C. Giovanni, Merhof, Dorit
The odorant receptor neurons on the fruit fly antenna are highly sensitive to a broad range of chemicals. A compound signal of receptor activity on the antenna can be read out in real time with functional neuroimaging, and individual receptor responses to hundreds of odorants are available in a database. Utilizing the fruit fly antenna as chemosensor enables applications ranging from biomarker detection to identification of unknown chemicals in samples. Here, we propose to connect neural response spaces, mapping odorant responses from one fly to another and to database space. A map is defined exactly for reference odorants common to both subject and target space, while the map for the remaining odorants is estimated based on radial basis function interpolation. On a data set with chemically diverse odorants, mapping to another antenna allows identifying unlabelled subject space odorants by the proximity of their mapped position to labelled odorants in target space. Furthermore, mapping from antenna to database space predicts the individual receptor responses significantly better than a random baseline model, suggesting that receptor responses can be inferred from the compound antenna signal given a sufficiently dense net of reference odorants to support the map.
Interactive tracking of insect posture
2016, Shen, Minmin, Li, Chen, Huang, Wei, Szyszka, Paul, Shirahama, Kimiaki, Grzegorzek, Marcin, Merhof, Dorit, Deussen, Oliver
In this paper, we present an association based tracking approach to track multiple insect body parts in a set of low frame-rate videos. The association is formulated as a MAP problem and solved by the Hungarian algorithm. Different from a traditional track-and-then-rectification scheme, this framework refines the tracking hypotheses in an interactive fashion: it integrates a key frame selection approach to minimize the number of frames for user correction while optimizing the final hypotheses. Given user correction, it takes user inputs to rectify the incorrect hypotheses on the other frames. Thus, the framework improves the tracking accuracy by introducing active key frame selection and interactive components, enabling a flexible strategy to achieve a trade-off between human effort and tracking precision. Given the refined tracks at a bounding box (BB) level, the tip of each body part is estimated, and multiple body parts in a BB are further differentiated. The efficiency and the effectiveness of the framework are verified on challenging video datasets for insect behavioral experiments.
Interactive Framework for Insect Tracking with Active Learning
2014, Shen, Minmin, Huang, Wei, Szyszka, Paul, Galizia, C. Giovanni, Merhof, Dorit
Extracting motion trajectories of insects is an important prerequisite in many behavioral studies. Despite great efforts to design efficient automatic tracking algorithms, tracking errors are unavoidable. In this paper, we propose general principles that help to minimize the human effort required for accurate multi-target tracking in the form of applications that can track the antennae and mouthparts of a honey bee based on a set of low frame rate videos. This interactive framework estimates which key frames will require user correction, i.e. those that are used for user correction, which are used for 1) incrementally learning an object classifier and 2) data association based tracking. To this framework we apply a standard classification algorithm (i.e. naive Bayesian classification) and an association optimization algorithm (i.e. Hungarian algorithm). The precision of tracking results by our framework on real-world video data is above 98%.
Recovering a Chemotopic Feature Space from a Group of Fruit Fly Antenna Chemosensors
2018-10, Strauch, Martin, Mukunda, Latha, Lüdke, Alja, Galizia, C. Giovanni, Merhof, Dorit
The ensemble of odorant receptors on the antenna of the fruit fly Drosophila melanogaster acts as an encoder for chemical molecules. Chemically similar odorants elicit activity in similar subsets of the receptors, spanning a so-called chemotopic feature space that enables chemical similarity search. A compound signal of receptor activity can be read out by calcium imaging of the antenna, yet without revealing corresponding receptors on different antennae. Employing Canonical Correlation Analysis (CCA) for multiple sets, we show that a consensus feature space can nevertheless be recovered from a group of variable antenna sensors that all respond to a common sequence of odorants. In the chemotopic consensus feature space, properties of novel odorants can be inferred, demonstrating how fruit fly antenna chemosensors may be employed as an alternative to electronic noses.
Automated tracking and analysis of behavior in restrained insects
2015, Shen, Minmin, Szyszka, Paul, Deussen, Oliver, Galizia, C. Giovanni, Merhof, Dorit
Insect behavior is often monitored by human observers and measured in the form of binary responses. This procedure is time costly and does not allow a fine graded measurement of behavioral performance in individual animals. To overcome this limitation, we have developed a computer vision system which allows the automated tracking of body parts of restrained insects.
Our system crops a continuous video into separate shots with a static background. It then segments out the insect's head and preprocesses the detected moving objects to exclude detection errors. A Bayesian-based algorithm is proposed to identify the trajectory of each body part.
We demonstrate the application of this novel tracking algorithm by monitoring movements of the mouthparts and antennae of honey bees and ants, and demonstrate its suitability for analyzing the behavioral performance of individual bees using a common associative learning paradigm.
Comparison with existing methods
Our tracking system differs from existing systems in that it does not require each video to be labeled manually and is capable of tracking insects’ body parts even when working with low frame-rate videos. Our system can be generalized for other insect tracking applications.
Our system paves the ground for fully automated monitoring of the behavior of restrained insects and accounts for individual variations in graded behavior.
Evaluation of a human neurite growth assay as specific screen for developmental neurotoxicants
2013-12, Krug, Anne K., Stiegler, Nina, Matt, Florian, Schönenberger, Felix, Merhof, Dorit, Leist, Marcel
Organ-specific in vitro toxicity assays are often highly sensitive, but they lack specificity. We evaluated here examples of assay features that can affect test specificity, and some general procedures are suggested on how positive hits in complex biological assays may be defined. Differentiating human LUHMES cells were used as potential model for developmental neurotoxicity testing. Forty candidate toxicants were screened, and several hits were obtained and confirmed. Although the cells had a definitive neuronal phenotype, the use of a general cell death endpoint in these cultures did not allow specific identification of neurotoxicants. As alternative approach, neurite growth was measured as an organ-specific functional endpoint. We found that neurite extension of developing LUHMES was specifically inhibited by diverse compounds such as colchicine, vincristine, narciclasine, rotenone, cycloheximide, or diquat. These compounds reduced neurite growth at concentrations that did not compromise cell viability, and neurite growth was affected more potently than the integrity of developed neurites of mature neurons. A ratio of the EC50 values of neurite growth inhibition and cell death of >4 provided a robust classifier for compounds associated with a developmental neurotoxic hazard. Screening of unspecific toxicants in the test system always yielded ratios <4. The assay identified also compounds that accelerated neurite growth, such as the rho kinase pathway modifiers blebbistatin or thiazovivin. The negative effects of colchicine or rotenone were completely inhibited by a rho kinase inhibitor. In summary, we suggest that assays using functional endpoints (neurite growth) can specifically identify and characterize (developmental) neurotoxicants.