Pattern Formation of Glioma Cells: Effects of Adhesion

We investigate clustering of malignant glioma cells. \emph{In vitro} experiments in collagen gels identified a cell line that formed clusters in a region of low cell density, whereas a very similar cell line (which lacks an important mutation) did not cluster significantly. We hypothesize that the mutation affects the strength of cell-cell adhesion. We investigate this effect in a new experiment, which follows the clustering dynamics of glioma cells on a surface. We interpret our results in terms of a stochastic model and identify two mechanisms of clustering. First, there is a critical value of the strength of adhesion; above the threshold, large clusters grow from a homogeneous suspension of cells; below it, the system remains homogeneous, similarly to the ordinary phase separation. Second, when cells form a cluster, we have evidence that they increase their proliferation rate. We have successfully reproduced the experimental findings and found that both mechanisms are crucial for cluster formation and growth.Comment: 6 pages, 6 figure

The role of cell-cell adhesion in wound healing

We present a stochastic model which describes fronts of cells invading a wound. In the model cells can move, proliferate, and experience cell-cell adhesion. We find several qualitatively different regimes of front motion and analyze the transitions between them. Above a critical value of adhesion and for small proliferation large isolated clusters are formed ahead of the front. This is mapped onto the well-known ferromagnetic phase transition in the Ising model. For large adhesion, and larger proliferation the clusters become connected (at some fixed time). For adhesion below the critical value the results are similar to our previous work which neglected adhesion. The results are compared with experiments, and possible directions of future work are proposed.Comment: to appear in Journal of Statistical Physic

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Heterogenous mean-field analysis of a generalized voter-like model on networks

We propose a generalized framework for the study of voter models in complex networks at the the heterogeneous mean-field (HMF) level that (i) yields a unified picture for existing copy/invasion processes and (ii) allows for the introduction of further heterogeneity through degree-selectivity rules. In the context of the HMF approximation, our model is capable of providing straightforward estimates for central quantities such as the exit probability and the consensus/fixation time, based on the statistical properties of the complex network alone. The HMF approach has the advantage of being readily applicable also in those cases in which exact solutions are difficult to work out. Finally, the unified formalism allows one to understand previously proposed voter-like processes as simple limits of the generalized model

The complete connectome of a learning and memory center in an insect brain

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the $\textit{Drosophila}$ larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.AL-K was supported by NIH grant #F32DC014387. AL-K and LFA were supported by the Simons Collaboration on the Global Brain. LFA was also supported by the Gatsby, Mathers and Kavli Foundations. CEP and YP were supported by the DARPA XDATA program (AFRL contract FA8750-12-2-0303) and the NSF BRAIN EAGER award DBI-1451081. KE and AST thank the Deutsche Forschungsgemeinschaft, TH1584/1-1, TH1584/3- 1; the Swiss National Science Foundation, 31003A 132812/1; the Baden Wurttemberg Stiftung; Zukunftskolleg of the University of ¨ Konstanz and DAAD. BG and TS thank the Deutsche Forschungsgemeinschaft, CRC 779, GE 1091/4-1; the European Commission, FP7-ICT MINIMAL. We thank the Fly EM Project Team at HHMI Janelia for the gift of the EM volume, the Janelia Visiting Scientist program, the HHMI visa office, and HHMI Janelia for funding

The complete connectome of a learning and memory center in an insect brain

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the $\textit{Drosophila}$ larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.AL-K was supported by NIH grant #F32DC014387. AL-K and LFA were supported by the Simons Collaboration on the Global Brain. LFA was also supported by the Gatsby, Mathers and Kavli Foundations. CEP and YP were supported by the DARPA XDATA program (AFRL contract FA8750-12-2-0303) and the NSF BRAIN EAGER award DBI-1451081. KE and AST thank the Deutsche Forschungsgemeinschaft, TH1584/1-1, TH1584/3- 1; the Swiss National Science Foundation, 31003A 132812/1; the Baden Wurttemberg Stiftung; Zukunftskolleg of the University of ¨ Konstanz and DAAD. BG and TS thank the Deutsche Forschungsgemeinschaft, CRC 779, GE 1091/4-1; the European Commission, FP7-ICT MINIMAL. We thank the Fly EM Project Team at HHMI Janelia for the gift of the EM volume, the Janelia Visiting Scientist program, the HHMI visa office, and HHMI Janelia for funding

Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain

Trabajo presentado en la 21st International Conference Medical Image Computing and Computer Assisted Intervention, celebrada en Granada, del 16 al 20 de septiembre de 2018Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.Peer reviewe