Evidence map›Paper›PMID 23361227›Full record

ArticleAnalytical and bioanalytical chemistry2013

Quantitative, solution-phase profiling of multiple transcription factors in parallel.

Betul Bilgin, Li Liu, Christina Chan, S Patrick Walton

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Analytical and bioanalytical chemistry, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Betul BilginDepartment of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.
Li Liu
Christina Chan
S Patrick Walton
Michigan State University · US

Funding

Develop a Dynamic Model that Incoporates Text-mining to Reconstruct NetworksR01GM079688 · NIGMS · MICHIGAN STATE UNIVERSITY · PI CHAN, CHRISTINA · 2007 to 2012
$1.2M
Maximizing siRNA Function through Mechanism-based Sequence and Vehicle DesignR01GM089866 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WALTON, STEPHEN PATRICK · 2010 to 2013
$922k
Development of a parallel, array-based transcription factor expression assayR21RR024439 · NCRR · MICHIGAN STATE UNIVERSITY · PI WALTON, STEPHEN PATRICK · 2008 to 2010
$540k
Phenotype-Targeted Inference of Flux-Enzyme Correlations in Adipocyte MetabolismR56DK081768 · NIDDK · TUFTS UNIVERSITY MEDFORD · PI LEE, KYONGBUM · 2010 to 2011
$489k
Engineering an in vitro model of adipose tissue formation and metabolismR56DK088251 · NIDDK · TUFTS UNIVERSITY MEDFORD · PI LEE, KYONGBUM · 2010 to 2010
$205k
NCRR NIH HHS R21 RR024439NCRR NIH HHS RR024439NIDDK NIH HHS DK081768NIDDK NIH HHS DK088251NIDDK NIH HHS R56 DK081768NIDDK NIH HHS R56 DK088251NIGMS NIH HHS GM079688NIGMS NIH HHS GM089866NIGMS NIH HHS R01 GM079688NIGMS NIH HHS R01 GM089866
6 · The paper itself

Abstract

Transcription factors are regulatory proteins that bind to specific sites of chromosomal DNA to enact responses to intracellular and extracellular stimuli. Transcription factor signalling networks are branched and interconnected so that any single transcription factor can activate many different genes and one gene can be activated by a combination of different transcription factors. Thus, trying to characterize a cellular response to a stimulus by measuring the level of only one transcription factor potentially ignores important simultaneous events that contribute to the response. Hence, parallel measurements of transcription factors are necessary to capture the breadth of valuable information about cellular responses that would not be obtained by measuring only a single transcription factor. We have sought to develop a new, scalable, flexible, and sensitive approach to analysis of transcription factor levels that complements existing parallel approaches. Here, we describe proof-of-principle analyses of purified human transcription factors and breast cancer nuclear extracts. Our assay can successfully quantify transcription factors in parallel with ~10-fold better sensitivity than current techniques. Sensitivity of the assay can be further increased by 200-fold through the use of PCR for signal amplification.

Indexed as

Cell Line, TumorCell NucleusChemistry Techniques, AnalyticalHumansPolymerase Chain ReactionTranscription FactorsTranscriptomeTranscription Factors

Identifiers

PMID23361227
PMCPMC3582790
OpenAlexW2052541844

What OpenQuestion holds

Textmetadata
LicenceTDM
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.