Evidence map›Paper›PMID 26616110›Full record

ReviewAging cell2016

Using measures of single-cell physiology and physiological state to understand organismic aging.

Alexander Mendenhall, Monica Driscoll, Roger Brent

Open access · goldAbstract readReview
In one paragraph

Review in Aging cell, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

10 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Stochasticity in Dietary Restriction-Mediated Lifespan Outcomes inbioRxiv : the preprint server for biology · 2024
    Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. The potential of erythrocytes as cellular aging models.Cell death and differentiation · 2017
    Article
  10. 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

3 authors at 2 institutions in 1 country.

Alexander MendenhallDivision of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Monica DriscollDepartment of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Roger BrentDivision of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Fred Hutch Cancer Center · USRutgers, The State University of New Jersey · US

Funding

Dynamics of gradient sensing in single cellsR01GM097479 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRENT, ROGER · 2011 to 2014
$2.1M
MicroRNA Modulation of Aging & HealthspanR01AG033858 · NIA · RUTGERS, THE STATE UNIV OF N.J. · PI DRISCOLL, MONICA A. · 2010 to 2014
$1.5M
Bio-computational Image Analysis to Define Indicators of C. elegans HealthspanR21AG027513 · NIA · RUTGERS, THE STATE UNIV OF N.J. · PI DRISCOLL, MONICA A. · 2006 to 2007
$290k
Causes and consequences of lifespan biomarker variation in Caenorhabditis elegansK99AG045341 · NIA · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MENDENHALL, ALEXANDER RICHARD · 2014 to 2015
$220k
NIA NIH HHS AG027513NIA NIH HHS AG033858NIA NIH HHS K99 AG045341NIA NIH HHS K99AG045341NIA NIH HHS R01 AG033858NIA NIH HHS R21 AG027513NIGMS NIH HHS R01 GM097479NIGMS NIH HHS R01GM97479
6 · The paper itself

Abstract

Genetically identical organisms in homogeneous environments have different lifespans and healthspans. These differences are often attributed to stochastic events, such as mutations and 'epimutations', changes in DNA methylation and chromatin that change gene function and expression. But work in the last 10 years has revealed differences in lifespan- and health-related phenotypes that are not caused by lasting changes in DNA or identified by modifications to DNA or chromatin. This work has demonstrated persistent differences in single-cell and whole-organism physiological states operationally defined by values of reporter gene signals in living cells. While some single-cell states, for example, responses to oxygen deprivation, were defined previously, others, such as a generally heightened ability to make proteins, were, revealed by direct experiment only recently, and are not well understood. Here, we review technical progress that promises to greatly increase the number of these measurable single-cell physiological variables and measureable states. We discuss concepts that facilitate use of single-cell measurements to provide insight into physiological states and state transitions. We assert that researchers will use this information to relate cell level physiological readouts to whole-organism outcomes, to stratify aging populations into groups based on different physiologies, to define biomarkers predictive of outcomes, and to shed light on the molecular processes that bring about different individual physiologies. For these reasons, quantitative study of single-cell physiological variables and state transitions should provide a valuable complement to genetic and molecular explanations of how organisms age.

Indexed as

EnvironmentPhenotypeAgingAnimalsCell Physiological PhenomenaChromatinDNA MethylationHumansChromatinagingchancephysiologyquantitative microscopyreporter genessingle-cellstochastic

Identifiers

PMID26616110
PMCPMC4717262
OpenAlexW2174030083

What OpenQuestion holds

Textmetadata
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.