Evidence map›Paper›PMID 20372881›Full record

ArticleAnalytical and bioanalytical chemistry2010

Novel antibody derivatives for proteome and high-content analysis.

Katrin Schmidthals, Jonas Helma, Kourosh Zolghadr, Ulrich Rothbauer, Heinrich Leonhardt

Abstract readEvaluation Study
In one paragraph

Article in Analytical and bioanalytical chemistry, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
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  5. Single-molecule labeling for studying trafficking of renal transporters.American journal of physiology. Renal physiology · 2018
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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

5 authors.

Katrin SchmidthalsDepartment of Biology II, Ludwig Maximilians University, Grosshaderner Str. 2, 82152 Martinsried, Germany.
Jonas Helma
Kourosh Zolghadr
Ulrich Rothbauer
Heinrich Leonhardt

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The understanding of cellular processes and their pathophysiological alterations requires comprehensive data on the abundance, distribution, modification, and interaction of all cellular components. On the one hand, artificially introduced fluorescent fusion proteins provide information about their distribution and dynamics in living cells but not about endogenous factors. On the other hand, antibodies can detect endogenous proteins, posttranslational modifications, and other cellular components but mostly in fixed and permeabilized cells. Here we highlight a new technology based on the antigen-binding domain of heavy-chain antibodies (V(H)H) from Camelidae. These extremely stable V(H)H domains can be produced in bacteria, coupled to matrices, and used for affinity purification and proteome studies. Alternatively, these V(H)H domains can be fused with fluorescent proteins and expressed in living cells. These fluorescent antigen-binding proteins called "chromobodies" can be used to detect and trace proteins and other cellular components in vivo. Chromobodies can, in principle, detect any antigenic structure, including posttranslational modifications, and thereby dramatically expand the quality and quantity of information that can be gathered in high-content analysis. Depending on the epitope chosen, chromobodies can also be used to modulate protein function in living cells.

Indexed as

AnimalsAntibodiesCell LineCellsFluorescent Antibody TechniqueHumansImmunoglobulin Heavy ChainsProtein ConformationProteinsProteomeRecombinant ProteinsAntibodiesImmunoglobulin Heavy ChainsProteinsProteomeRecombinant Proteins

Identifiers

PMID20372881
PMCPMC2911542

What OpenQuestion holds

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LicenceCC BY-NC
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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.