Evidence map›Paper›PMID 27547924›Full record

ArticleNanoscale2016

A nanobiosensor for dynamic single cell analysis during microvascular self-organization.

S Wang, J Sun, D D Zhang, P K Wong

Abstract read
In one paragraph

Article in Nanoscale, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

8 citing papers in PubMed, 21 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 2 institutions in 1 country.

S WangDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
J Sun
D D Zhang
P K Wong
University of Arizona · USPennsylvania State University · US

Funding

Mechanoregulation of Tissue MorphogenesisDP2OD007161 · OD · UNIVERSITY OF ARIZONA · PI WONG, PAK KIN · 2010 to 2010
$2.3M
NIH HHS DP2 OD007161
6 · The paper itself

Abstract

The formation of microvascular networks plays essential roles in regenerative medicine and tissue engineering. Nevertheless, the self-organization mechanisms underlying the dynamic morphogenic process are poorly understood due to a paucity of effective tools for mapping the spatiotemporal dynamics of single cell behaviors. By establishing a single cell nanobiosensor along with live cell imaging, we perform dynamic single cell analysis of the morphology, displacement, and gene expression during microvascular self-organization. Dynamic single cell analysis reveals that endothelial cells self-organize into subpopulations with specialized phenotypes to form microvascular networks and identifies the involvement of Notch1-Dll4 signaling in regulating the cell subpopulations. The cell phenotype correlates with the initial Dll4 mRNA expression level and each subpopulation displays a unique dynamic Dll4 mRNA expression profile. Pharmacological perturbations and RNA interference of Notch1-Dll4 signaling modulate the cell subpopulations and modify the morphology of the microvascular network. Taken together, a nanobiosensor enables a dynamic single cell analysis approach underscoring the importance of Notch1-Dll4 signaling in microvascular self-organization.

Indexed as

Biosensing TechniquesNanotubesSignal TransductionSingle-Cell AnalysisAdaptor Proteins, Signal TransducingCalcium-Binding ProteinsGoldHumansHuman Umbilical Vein Endothelial CellsIntercellular Signaling Peptides and ProteinsNucleic AcidsReceptor, Notch1Adaptor Proteins, Signal TransducingCalcium-Binding ProteinsDLL4 protein, humanGoldIntercellular Signaling Peptides and ProteinsNOTCH1 protein, humanNucleic AcidsReceptor, Notch1

Identifiers

PMID27547924
PMCPMC5042875
OpenAlexW2509424873

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.