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Related Concept Videos

Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Overview of Electron Microscopy01:25

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The wavelengths of visible light ultimately limit the maximum theoretical resolution of images created by light microscopes. Most light microscopes can only magnify 1000X, and a few can magnify up to 1500X. Electrons, like electromagnetic radiation, can behave like waves, but with wavelengths of 0.005 nm, they produce significantly greater resolution up to 0.05 nm as compared to 500 nm for visible light. An electron microscope (EM) can create a sharp image that is magnified up to 2,000,000X.
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Updated: Sep 2, 2025

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neuPrint: An open access tool for EM connectomics.

Stephen M Plaza1, Jody Clements1, Tom Dolafi1

  • 1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States.

Frontiers in Neuroinformatics
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

NeuPrint provides web and API tools for analyzing large-scale neural connectome data. This open-science approach facilitates broader access and faster scientific discovery by making complex brain data accessible to all researchers.

Keywords:
APIsconnectomicsformal publicationopen sciencepreprintweb access

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Area of Science:

  • Neuroscience
  • Bioinformatics
  • Computational Biology

Background:

  • Advances in electron microscopy and deep learning enable reconstruction of large-scale neural connectomes.
  • Previous connectome reconstructions were smaller, manageable by experts, and posed no significant performance issues.
  • Current and future reconstructions involve tens of thousands of neurons and millions of connections, requiring new data handling tools.

Purpose of the Study:

  • To introduce neuPrint, a system designed to address the data analysis challenges posed by large-scale connectome reconstructions.
  • To provide accessible tools for both non-specialists and computer-savvy scientists to query and analyze connectome data.
  • To facilitate open science practices by enabling easy access to and utilization of complex neural datasets.

Main Methods:

  • NeuPrint utilizes a web interface for intuitive biological queries accessible via a browser.
  • Programmer APIs are provided for more complex, high-volume data queries by advanced users.
  • Connectome data is internally organized as a graph in a neo4j database, queried using Cypher for high performance.

Main Results:

  • NeuPrint successfully enables scientists worldwide to query and analyze large connectome datasets.
  • The system supports assessment of reconstruction quality.
  • Preprint publication led to immediate data inquiries, demonstrating the value of accessible data for scientific dissemination.

Conclusions:

  • NeuPrint addresses the need for efficient, user-friendly tools for large-scale connectome data analysis.
  • Open data access and analysis tools accelerate scientific discovery, as evidenced by rapid engagement with preprints.
  • Sustaining online data access incurs significant costs, highlighting the need for dedicated funding in connectomics research.