Evidence map›Paper›PMID 36893111›Full record

ArticlePloS one2023

Guided-deconvolution for correlative light and electron microscopy.

Fengjiao Ma, Rainer Kaufmann, Jaroslaw Sedzicki, Zoltán Cseresnyés, Christoph Dehio, Stephanie Hoeppener, Marc Thilo Figge, Rainer Heintzmann

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Fengjiao MaInstitute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Jena, Thuringia, Germany.
Rainer KaufmannCentre for Structural Systems Biology, Hamburg, Germany.
Jaroslaw SedzickiBiozentrum, University of Basel, Basel, Switzerland.
Zoltán CseresnyésApplied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Thuringia, Germany.ORCID 0000-0002-6574-2588
Christoph DehioBiozentrum, University of Basel, Basel, Switzerland.
Stephanie HoeppenerJena Center for Soft Matter, University of Jena, Jena, Thuringia, Germany.
Marc Thilo FiggeJena Center for Soft Matter, University of Jena, Jena, Thuringia, Germany.
Rainer HeintzmannInstitute of Physical Chemistry and Abbe Center of Photonics, University of Jena, Jena, Thuringia, Germany.ORCID 0000-0002-4950-1936

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. The EM images only contain contrast information. Therefore, some of the detailed structures cannot be specified from these images alone, especially when different cell organelle are contacted. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. This applies to living cells structures before fixation as well as previously fixed sample. It attempts to automatically assign fluorescence-labeled structures to structural details visible in the EM image to bridge the gaps in both resolution and specificity between the two imaging modes. We tested our approach on simulations, correlative data of multi-color beads and previously published data of biological samples.

Indexed as

OrganellesHeLa CellsHumansMicroscopy, Electron

Identifiers

PMID36893111
PMCPMC9997956

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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.