Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic era

  1. Aurel A Lazar  Is a corresponding author
  2. Tingkai Liu
  3. Mehmet Kerem Turkcan
  4. Yiyin Zhou
  1. Columbia University, United States

Abstract

In recent years, a wealth of Drosophila neuroscience data have become available including cell type, connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison and evaluation of circuit functions of the fruit fly brain.

Data availability

Code Availability and InstallationStable and tested FlyBrainLab installation instructions for user-side components and utility libraries are available at https://github.com/FlyBrainLab/FlyBrainLab for Linux, MacOS and Windows. The installation and use of FlyBrainLab does not require a GPU, but a service-side backend must be running, for example, on a cloud service, that the user-side of FlyBrainLab can connect to. By default, the user-side-only installation will access the backend services hosted on our public servers. Note that users do not have write permission to the NeuroArch Database, nor will they be able to access a Neurokernel Server for execution. The server-side backend codebase is publicly available at https://github.com/fruitflybrain and https://github.com/neurokernel.A full installation of FlyBrainLab, including all backend and frontend components, is available as a Docker image at https://hub.docker.com/r/fruitflybrain/fbl. The image requires a Linux host with at least 1 CUDA-enabled GPU and the nvidia-docker package (https://github.com/NVIDIA/nvidia-docker) installed. For a custom installation of the complete FlyBrainLab platform, a shell script is available at https://github.com/FlyBrainLab/FlyBrainLab.To help users get started, a number of tutorials are available written as Jupyter notebooks at https://github.com/FlyBrainLab/Tutorials, including a reference to English queries at https://github.com/FlyBrainLab/Tutorials/blob/master/tutorials/getting_started/1b_nlp_queries.ipynb. An overview of the FlyBrainLab resources is available at https://github.com/FlyBrainLab/FlyBrainLab/wiki/FlyBrainLab-Resources.Data AvailabilityThe NeuroArch Database created from publicly available FlyCircuit, Hemibrain and Larva L1EM datasets can be downloaded from https://github.com/FlyBrainLab/dataset. The same repository provides Jupyter notebooks for loading publicly available datasets, such as the FlyCircuit dataset with inferred connectivity, the Hemibrain dataset and the Larva L1 EM dataset, into the NeuroArch Database.

The following previously published data sets were used

Article and author information

Author details

  1. Aurel A Lazar

    Department of Electrical Engineering, Columbia University, New York, United States
    For correspondence
    aurel@ee.columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4261-8709
  2. Tingkai Liu

    Department of Electrical Engineering, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3075-7648
  3. Mehmet Kerem Turkcan

    Department of Electrical Engineering, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9273-7293
  4. Yiyin Zhou

    Department of Electrical Engineering, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4618-4039

Funding

Air Force Office of Scientific Research (FA9550-16-1-0410)

  • Mehmet Kerem Turkcan

Defense Advanced Research Projects Agency (HR0011-19-9-0035)

  • Aurel A Lazar
  • Tingkai Liu
  • Mehmet Kerem Turkcan
  • Yiyin Zhou

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Lazar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,600
    views
  • 373
    downloads
  • 20
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Aurel A Lazar
  2. Tingkai Liu
  3. Mehmet Kerem Turkcan
  4. Yiyin Zhou
(2021)
Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic era
eLife 10:e62362.
https://doi.org/10.7554/eLife.62362

Share this article

https://doi.org/10.7554/eLife.62362

Further reading

    1. Neuroscience
    Jan H Kirchner, Lucas Euler ... Julijana Gjorgjieva
    Research Article

    Dendritic branching and synaptic organization shape single-neuron and network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a mechanistic model in which dendrite growth and the organization of synapses arise from the interaction of activity-independent cues from potential synaptic partners and local activity-dependent synaptic plasticity. Consistent with experiments, three phases of dendritic growth – overshoot, pruning, and stabilization – emerge naturally in the model. The model generates stellate-like dendritic morphologies that capture several morphological features of biological neurons under normal and perturbed learning rules, reflecting biological variability. Model-generated dendrites have approximately optimal wiring length consistent with experimental measurements. In addition to establishing dendritic morphologies, activity-dependent plasticity rules organize synapses into spatial clusters according to the correlated activity they experience. We demonstrate that a trade-off between activity-dependent and -independent factors influences dendritic growth and synaptic location throughout development, suggesting that early developmental variability can affect mature morphology and synaptic function. Therefore, a single mechanistic model can capture dendritic growth and account for the synaptic organization of correlated inputs during development. Our work suggests concrete mechanistic components underlying the emergence of dendritic morphologies and synaptic formation and removal in function and dysfunction, and provides experimentally testable predictions for the role of individual components.

    1. Developmental Biology
    2. Neuroscience
    Taro Ichimura, Taishi Kakizuka ... Takeharu Nagai
    Tools and Resources

    We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.