Evidence map›Paper›PMID 18342743›Full record

ArticleJournal of structural biology2008

Reprint of "On the feasibility of visualizing ultrasmall gold labels in biological specimens by STEM tomography" [J. Struct. Biol. 159 (2007) 507-522].

A A Sousa, M A Aronova, Y C Kim, L M Dorward, G Zhang, R D Leapman

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of structural biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 11% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Electron tomography in life science.Seminars in cell & developmental biology · 2009
    Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

A A SousaNational Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Building 13, Room 3N17, 13 South Drive, Bethesda, MD 20892-5766, USA.
M A Aronova
Y C Kim
L M Dorward
G Zhang
R D Leapman
National Institutes of Health · USNational Institute of Biomedical Imaging and Bioengineering · US

Funding

Intramural NIH HHS
6 · The paper itself

Abstract

Labeling with heavy atom clusters attached to antibody fragments is an attractive technique for determining the 3D distribution of specific proteins in cells using electron tomography. However, the small size of the labels makes them very difficult to detect by conventional bright-field electron tomography. Here, we evaluate quantitative scanning transmission electron microscopy (STEM) at a beam voltage of 300kV for detecting 11-gold atom clusters (Undecagold) and 1.4nm-diameter nanoparticles (Nanogold) for a variety of specimens and imaging conditions. STEM images as well as tomographic tilt series are simulated by means of the NIST Elastic-Scattering Cross-Section Database for gold clusters embedded in carbon. The simulations indicate that the visibility in 2D of Undecagold clusters in a homogeneous matrix is maximized for low inner collection semi-angles of the STEM annular dark-field detector (15-20mrad). Furthermore, our calculations show that the visibility of Undecagold in 3D reconstructions is significantly higher than in 2D images for an inhomogeneous matrix corresponding to fluctuations in local density. The measurements demonstrate that it is possible to detect Nanogold particles in plastic sections of tissue freeze-substituted in the presence of osmium. STEM tomography has the potential to localize specific proteins in permeabilized cells using antibody fragments tagged with small heavy atom clusters. Our quantitative analysis provides a framework for determining the detection limits and optimal experimental conditions for localizing these small clusters.

Identifiers

PMID18342743
OpenAlexW1963488656

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.