Evidence map›Paper›PMID 30352088›Full record

ArticlePloS one2018

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

Timothy J Lee, Aditi Kumar, Aishwarya H Balwani, Derrick Brittain, Sam Kinn, Craig A Tovey, Eva L Dyer, Nuno M da Costa, R Clay Reid, Craig R Forest and 1 more

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Timothy J LeeGeorgia Institute of Technology, G. W. Woodruff School of Mechanical Engineering, Atlanta, GA, United States of America.ORCID 0000-0002-3395-026X
Aditi KumarGeorgia Institute of Technology, G. W. Woodruff School of Mechanical Engineering, Atlanta, GA, United States of America.
Aishwarya H BalwaniGeorgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, United States of America.ORCID 0000-0002-9234-1632
Derrick BrittainAllen Institute for Brain Science, Seattle, WA, United States of America.
Sam KinnAllen Institute for Brain Science, Seattle, WA, United States of America.ORCID 0000-0003-1951-8224
Craig A ToveyGeorgia Institute of Technology, H. Milton Stewart School of Industrial & Systems Engineering, Atlanta, GA, United States of America.
Eva L DyerGeorgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, United States of America.
Nuno M da CostaAllen Institute for Brain Science, Seattle, WA, United States of America.
R Clay ReidAllen Institute for Brain Science, Seattle, WA, United States of America.
Craig R ForestGeorgia Institute of Technology, G. W. Woodruff School of Mechanical Engineering, Atlanta, GA, United States of America.
Daniel J BumbargerAllen Institute for Brain Science, Seattle, WA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BrainHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMicroscopy, Electron, TransmissionNeuroanatomyReproducibility of Results

Identifiers

PMID30352088
PMCPMC6198950

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.