Evidence map›Paper›PMID 23435261›Full record

ArticleNature methods2013

Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells.

Charlotte Stadler, Elton Rexhepaj, Vasanth R Singan, Robert F Murphy, Rainer Pepperkok, Mathias Uhlén, Jeremy C Simpson, Emma Lundberg

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers.

0numbers the graph read from it
0cells of the map it votes in
144citing papers in PubMed
11.1field-weighted citation impact, top 1% 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

144 citing papers in PubMed, 266 citations in OpenAlex.

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  8. Imaging Approach to DNA Damage Induction and Quantification.Methods in molecular biology (Clifton, N.J.) · 2026
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84 more citing papers are in PubMed but not listed here.

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 at 5 institutions in 4 countries.

Charlotte StadlerKTH Royal Institute of Technology, Science for Life Laboratory, School of Biotechnology, Stockholm, Sweden.
Elton Rexhepaj
Vasanth R Singan
Robert F Murphy
Rainer Pepperkok
Mathias Uhlén
Jeremy C Simpson
Emma Lundberg
KTH Royal Institute of Technology · SEScience for Life Laboratory · SEUniversity College Dublin · IECarnegie Mellon University · USEuropean Molecular Biology Laboratory · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Imaging techniques such as immunofluorescence (IF) and the expression of fluorescent protein (FP) fusions are widely used to investigate the subcellular distribution of proteins. Here we report a systematic analysis of >500 human proteins comparing the localizations obtained in live versus fixed cells using FPs and IF, respectively. We identify systematic discrepancies between IF and FPs as well as between FP tagging at the N and C termini. The analysis shows that for 80% of the proteins, IF and FPs yield the same subcellular distribution, and the locations of 250 previously unlocalized proteins were determined by the overlap between the two methods. Approximately 60% of proteins localize to multiple organelles for both methods, indicating a complex subcellular protein organization. These results show that both IF and FP tagging are reliable techniques and demonstrate the usefulness of an integrative approach for a complete investigation of the subcellular human proteome.

Indexed as

Fluorescent Antibody TechniqueAnimalsChlorocebus aethiopsGene Expression RegulationHeLa CellsHumansLuminescent ProteinsProtein Interaction MappingProtein TransportStaining and LabelingVero CellsLuminescent Proteins

Identifiers

PMID23435261
OpenAlexW2144337984

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.