303 research outputs found
Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation
During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology.
Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system.
To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis.
Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance.
Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models.
Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems
Modeling visual-based pitch, lift and speed control strategies in hoverflies
<div><p>To avoid crashing onto the floor, a free falling fly needs to trigger its wingbeats quickly and control the orientation of its thrust accurately and swiftly to stabilize its pitch and hence its speed. Behavioural data have suggested that the vertical optic flow produced by the fall and crossing the visual field plays a key role in this anti-crash response. Free fall behavior analyses have also suggested that flying insect may not rely on graviception to stabilize their flight. Based on these two assumptions, we have developed a model which accounts for hoverflies´ position and pitch orientation recorded in 3D with a fast stereo camera during experimental free falls. Our dynamic model shows that optic flow-based control combined with closed-loop control of the pitch suffice to stabilize the flight properly. In addition, our model sheds a new light on the visual-based feedback control of fly´s pitch, lift and thrust. Since graviceptive cues are possibly not used by flying insects, the use of a vertical reference to control the pitch is discussed, based on the results obtained on a complete dynamic model of a virtual fly falling in a textured corridor. This model would provide a useful tool for understanding more clearly how insects may or not estimate their absolute attitude.</p></div
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The natverse, a versatile toolbox for combining and analysing neuroanatomical data.
To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community
The role of direction-selective visual interneurons T4 and T5 in Drosophila orientation behavior
In order to safely move through the environment, visually-guided animals
use several types of visual cues for orientation. Optic flow provides faithful
information about ego-motion and can thus be used to maintain a straight
course. Additionally, local motion cues or landmarks indicate potentially
interesting targets or signal danger, triggering approach or avoidance, respectively.
The visual system must reliably and quickly evaluate these cues
and integrate this information in order to orchestrate behavior. The underlying
neuronal computations for this remain largely inaccessible in higher
organisms, such as in humans, but can be studied experimentally in more
simple model species. The fly Drosophila, for example, heavily relies on
such visual cues during its impressive flight maneuvers. Additionally, it is
genetically and physiologically accessible. Hence, it can be regarded as an
ideal model organism for exploring neuronal computations during visual
processing.
In my PhD studies, I have designed and built several autonomous virtual
reality setups to precisely measure visual behavior of walking flies. The
setups run in open-loop and in closed-loop configuration. In an open-loop
experiment, the visual stimulus is clearly defined and does not depend on
the behavioral response. Hence, it allows mapping of how specific features
of simple visual stimuli are translated into behavioral output, which can
guide the creation of computational models of visual processing. In closedloop
experiments, the behavioral response is fed back onto the visual stimulus,
which permits characterization of the behavior under more realistic
conditions and, thus, allows for testing of the predictive power of the computational
models.
In addition, Drosophila’s genetic toolbox provides various strategies for
targeting and silencing specific neuron types, which helps identify which
cells are needed for a specific behavior. We have focused on visual interneuron
types T4 and T5 and assessed their role in visual orientation behavior.
These neurons build up a retinotopic array and cover the whole visual field
of the fly. They constitute major output elements from the medulla and have
long been speculated to be involved in motion processing.
This cumulative thesis consists of three published studies: In the first
study, we silenced both T4 and T5 neurons together and found that such flies
were completely blind to any kind of motion. In particular, these flies could
not perform an optomotor response anymore, which means that they lost
their normally innate following responses to motion of large-field moving
patterns. This was an important finding as it ruled out the contribution
of another system for motion vision-based behaviors. However, these flies
were still able to fixate a black bar. We could show that this behavior is
mediated by a T4/T5-independent flicker detection circuitry which exists in
parallel to the motion system.
In the second study, T4 and T5 neurons were characterized via twophoton
imaging, revealing that these cells are directionally selective and
have very similar temporal and orientation tuning properties to directionselective
neurons in the lobula plate. T4 and T5 cells responded in a
contrast polarity-specific manner: T4 neurons responded selectively to ON
edge motion while T5 neurons responded only to OFF edge motion. When
we blocked T4 neurons, behavioral responses to moving ON edges were
more impaired than those to moving OFF edges and the opposite was true
for the T5 block. Hence, these findings confirmed that the contrast polarityspecific
visual motion pathways, which start at the level of L1 (ON) and L2
(OFF), are maintained within the medulla and that motion information is
computed twice independently within each of these pathways.
Finally, in the third study, we used the virtual reality setups to probe the
performance of an artificial microcircuit. The system was equipped with a
camera and spherical fisheye lens. Images were processed by an array of
Reichardt detectors whose outputs were integrated in a similar way to what
is found in the lobula plate of flies. We provided the system with several rotating
natural environments and found that the fly-inspired artificial system
could accurately predict the axes of rotation
Recommended from our members
The natverse, a versatile toolbox for combining and analysing neuroanatomical data.
To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community
Modelling Neuron Morphology: Automated Reconstruction from Microscopy Images
Understanding how the brain works is, beyond a shadow of doubt, one of the greatest challenges for modern science. Achieving a deep knowledge about the structure, function and development of the nervous system at the molecular, cellular and network levels is crucial in this attempt, as processes at all these scales are intrinsically linked with higher-order cognitive functions. The research in the various areas of neuroscience deals with advanced imaging techniques, collecting an increasing amounts of heterogeneous and complex data at different scales. Then, computational tools and neuroinformatics solutions are required in order to integrate and analyze the massive quantity of acquired information.
Within this context, the development of automaticmethods and tools for the study of neuronal anatomy has a central role. The morphological properties of the soma and of the axonal and dendritic arborizations constitute a key discriminant for the neuronal phenotype and play a determinant role in network connectivity. A quantitative analysis allows the study of possible factors influencing neuronal development, the neuropathological abnormalities related to specific syndromes, the relationships
between neuronal shape and function, the signal transmission and the network connectivity. Therefore, three-dimensional digital reconstructions of soma, axons and dendrites are indispensable for exploring neural networks. This thesis proposes a novel and completely automatic pipeline for neuron reconstruction with operations ranging from the detection and segmentation of the soma to the dendritic arborization tracing. The pipeline can deal with different datasets and acquisitions both at the network and at the single scale level without any user interventions or manual adjustment. We developed an ad hoc approach for the localization and segmentation of neuron bodies. Then, various methods and research lines have been investigated for the reconstruction of the whole dendritic arborization of each neuron, which is solved both in 2D and in 3D images
Biologically Inspired Visual Control of Flying Robots
Insects posses an incredible ability to navigate their environment at high speed, despite
having small brains and limited visual acuity. Through selective pressure they have
evolved computationally efficient means for simultaneously performing navigation tasks
and instantaneous control responses. The insect’s main source of information is visual,
and through a hierarchy of processes this information is used for perception; at the
lowest level are local neurons for detecting image motion and edges, at the higher level
are interneurons to spatially integrate the output of previous stages. These higher
level processes could be considered as models of the insect's environment, reducing the
amount of information to only that which evolution has determined relevant. The scope
of this thesis is experimenting with biologically inspired visual control of flying robots
through information processing, models of the environment, and flight behaviour.
In order to test these ideas I developed a custom quadrotor robot and experimental
platform; the 'wasp' system. All algorithms ran on the robot, in real-time or better,
and hypotheses were always verified with flight experiments.
I developed a new optical flow algorithm that is computationally efficient, and able
to be applied in a regular pattern to the image. This technique is used later in my
work when considering patterns in the image motion field.
Using optical flow in the log-polar coordinate system I developed attitude estimation
and time-to-contact algorithms. I find that the log-polar domain is useful for
analysing global image motion; and in many ways equivalent to the retinotopic arrange-
ment of neurons in the optic lobe of insects, used for the same task.
I investigated the role of depth in insect flight using two experiments. In the first
experiment, to study how concurrent visual control processes might be combined, I
developed a control system using the combined output of two algorithms. The first
algorithm was a wide-field optical flow balance strategy and the second an obstacle
avoidance strategy which used inertial information to estimate the depth to objects in
the environment - objects whose depth was significantly different to their surround-
ings. In the second experiment I created an altitude control system which used a model
of the environment in the Hough space, and a biologically inspired sampling strategy,
to efficiently detect the ground. Both control systems were used to control the flight
of a quadrotor in an indoor environment.
The methods that insects use to perceive edges and control their flight in response
had not been applied to artificial systems before. I developed a quadrotor control
system that used the distribution of edges in the environment to regulate the robot
height and avoid obstacles. I also developed a model that predicted the distribution of
edges in a static scene, and using this prediction was able to estimate the quadrotor
altitude
Object manipulation without hands
Funding: This work was supported by BBSRC Discovery Fellowship (BB/S01019X/1) to S.S.Our current understanding of manipulation is based on primate hands, resulting in a detailed but narrow perspective of ways to handle objects. Although most other animals lack hands, they are still capable of flexible manipulation of diverse objects, including food and nest materials, and depend on dexterity in object handling to survive and reproduce. Birds, for instance, use their bills and feet to forage and build nests, while insects carry food and construct nests with their mandibles and legs. Bird bills and insect mandibles are much simpler than a primate hand, resembling simple robotic grippers. A better understanding of manipulation in these and other species would provide a broader comparative perspective on the origins of dexterity. Here we contrast data from primates, birds and insects, describing how they sense and grasp objects, and the neural architectures that control manipulation. Finally, we outline techniques for collecting comparable manipulation data from animals with diverse morphologies and describe the practical applications of studying manipulation in a wide range of species, including providing inspiration for novel designs of robotic manipulators.PostprintPeer reviewe
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