Evidence map›Paper›PMID 28887696›Full record

ReviewCell and tissue research2018

Lessons from single-cell transcriptome analysis of oxygen-sensing cells.

Ting Zhou, Hiroaki Matsunami

Abstract readReview
In one paragraph

Review in Cell and tissue research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Neurotransmitter Modulation of Carotid Body Germinal Niche.International journal of molecular sciences · 2020
    Review
  2. Article
  3. Proteomics and transcriptomics in atrial fibrillation.Herzschrittmachertherapie & Elektrophysiologie · 2018
    Review
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

2 authors.

Ting ZhouDepartment of Molecular Genetics and Microbiology, Duke University Medical Center, 261 CARL Building, Box 3509, Durham, NC, USA. ting.zhou@duke.edu.
Hiroaki MatsunamiDepartment of Molecular Genetics and Microbiology, Duke University Medical Center, 261 CARL Building, Box 3509, Durham, NC, USA.

Funding

Peripheral Odor Coding in MammalsR01DC014423 · NIDCD · DUKE UNIVERSITY · PI MATSUNAMI, HIROAKI · 2015 to 2019
$2.3M
Modulation of Odorant Receptor FunctionR01DC012095 · NIDCD · DUKE UNIVERSITY · PI MATSUNAMI, HIROAKI · 2012 to 2016
$1.8M
Data commons for 'omic, microscopy and analysis core facilitiesS10OD018164 · OD · DUKE UNIVERSITY · PI WRAY, GREGORY A. · 2014 to 2014
$598k
NIDCD NIH HHS R01 DC012095NIDCD NIH HHS R01 DC014423NIH HHS S10 OD018164
6 · The paper itself

Abstract

The advent of single-cell RNA-sequencing (RNA-Seq) technology has enabled transcriptome profiling of individual cells. Comprehensive gene expression analysis at the single-cell level has proven to be effective in characterizing the most fundamental aspects of cellular function and identity. This unbiased approach is revolutionary for small and/or heterogeneous tissues like oxygen-sensing cells in identifying key molecules. Here, we review the major methods of current single-cell RNA-Seq technology. We discuss how this technology has advanced the understanding of oxygen-sensing glomus cells in the carotid body and helped uncover novel oxygen-sensing cells and mechanisms in the mice olfactory system. We conclude by providing our perspective on future single-cell RNA-Seq research directed at oxygen-sensing cells.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisAnimalsCarotid BodyHumansOlfactory Receptor NeuronsOxygenOxygenArterial chemoreceptorCarotid body glomus cellHypoxiaNext-generation sequencingOlfactory sensory neuron type B cell

Identifiers

PMID28887696
PMCPMC5843501

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.