4.6 Article

Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation

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PLOS ONE
卷 13, 期 10, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0206172

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资金

  1. National Science Foundation [ECCS-1542174]
  2. Intelligence Advanced Research Project Activity [D16PC00004]
  3. Intelligence Advanced Research Project Activity (IARPA) via Department of Interior/Interior Business Center (Dol/IBC) [D16PC00004]

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Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in batches. Batches are quantized groups of individual sections that, in LASSO, are cut with a diamond knife, picked up from an attached waterboat, and placed onto microfabricated TEM substrates using rapid, accurate, and repeatable robotic tools. Additionally, we introduce mathematical models for ssTEM with respect to yield, throughput, and cost to access ssTEM scalability. To validate the method experimentally, we processed 729 serial sections of human brain tissue (similar to 40 nm x 1 mm x 1 mm). Section yield was 727/729 (99.7%). Sections were placed accurately and repeatably (x-direction: -20 +/- 110 mu m (1 s.d.), y-direction: 60 +/- 150 mu m (1 s.d.)) with a mean cycle time of 43 s +/- 12 s (1 s.d.). High-magnification (2.5 nm/px) TEM imaging was conducted to measure the image quality. We report no significant distortion, information loss, or substrate-derived artifacts in the TEM images. Quantitatively, the edge spread function across vesicle edges and image contrast were comparable, suggesting that LASSO does not negatively affect image quality. In total, LASSO compares favorably with traditional serial sectioning methods with respect to throughput, yield, and cost for large-scale experiments, and represents a flexible, scalable, and accessible technology platform to enable the next generation of neuroanatomical studies.

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