Evidence map›Paper›PMID 28300162›Full record

ArticleScientific reports2017

Platform for combined analysis of functional and biomolecular phenotypes of the same cell.

L Kelbauskas, S Ashili, J Zeng, A Rezaie, K Lee, D Derkach, B Ueberroth, W Gao, T Paulson, H Wang and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

14 authors.

L KelbauskasCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
S AshiliCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
J ZengCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
A RezaieCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
K LeeCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
D DerkachCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
B UeberrothCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
W GaoCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
T PaulsonFred Hutchinson Cancer Research Center, Seattle, WA, USA.
H WangCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
Y TianCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
D SmithCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
B ReidFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Deirdre R MeldrumCenter for Biosignatures Discovery Automation, Biodesign Institute, Arizona State University, Tempe, AZ, USA.

Funding

CEGSTech: Integrated Biologically-Active MicrosystemsP50HG002360 · NHGRI · UNIVERSITY OF WASHINGTON · PI MELDRUM, DEIRDRE R. · 2001 to 2012
$37.8M
NHGRI NIH HHS P50 HG002360
6 · The paper itself

Abstract

Functional and molecular cell-to-cell variability is pivotal at the cellular, tissue and whole-organism levels. Yet, the ultimate goal of directly correlating the function of the individual cell with its biomolecular profile remains elusive. We present a platform for integrated analysis of functional and transcriptional phenotypes in the same single cells. We investigated changes in the cellular respiration and gene expression diversity resulting from adaptation to repeated episodes of acute hypoxia in a premalignant progression model. We find differential, progression stage-specific alterations in phenotypic heterogeneity and identify cells with aberrant phenotypes. To our knowledge, this study is the first demonstration of an integrated approach to elucidate how heterogeneity at the transcriptional level manifests in the physiologic profile of individual cells in the context of disease progression.

Indexed as

Cell RespirationGene Expression ProfilingGene Expression RegulationHumansMolecular Sequence AnnotationOxygen ConsumptionPhenotypePrincipal Component AnalysisSingle-Cell Analysis

Identifiers

PMID28300162
PMCPMC5353596

What OpenQuestion holds

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LicenceCC BY
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.