Send to

Choose Destination
PLoS One. 2018 Oct 23;13(10):e0206172. doi: 10.1371/journal.pone.0206172. eCollection 2018.

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

Author information

Georgia Institute of Technology, G. W. Woodruff School of Mechanical Engineering, Atlanta, GA, United States of America.
Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, United States of America.
Allen Institute for Brain Science, Seattle, WA, United States of America.
Georgia Institute of Technology, H. Milton Stewart School of Industrial & Systems Engineering, Atlanta, GA, United States of America.
Georgia Institute of Technology, Coulter Department of Biomedical Engineering, Atlanta, GA, United States of America.


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 (~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 μm (1 s.d.), y-direction: 60 ± 150 μm (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.

Conflict of interest statement

The authors have declared that no competing interests exist.

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center