Evidence map›Paper›PMID 34919546›Full record

ArticlePLoS computational biology2021

A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells.

Måns Unosson, Marco Brancaccio, Michael Hastings, Adam M Johansen, Bärbel Finkenstädt

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2021. 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
0.2field-weighted citation impact, top 49% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  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

5 authors at 3 institutions in 1 country.

Måns UnossonDepartment of Statistics, University of Warwick, Coventry, United Kingdom.ORCID 0000-0001-6649-2288
Marco BrancaccioUK Dementia Research Institute at Imperial College London, Department of Brain Sciences, Faculty of Medicine, London, United Kingdom.ORCID 0000-0001-8053-5221
Michael HastingsMRC Laboratory of Molecular Biology, Division of Neurobiology, Cambridge, United Kingdom.ORCID 0000-0001-8576-6651
Adam M JohansenDepartment of Statistics, University of Warwick, Coventry, United Kingdom.ORCID 0000-0002-3531-7628
Bärbel FinkenstädtDepartment of Statistics, University of Warwick, Coventry, United Kingdom.
University of Warwick · GBMRC Laboratory of Molecular Biology · GBUK Dementia Research Institute · GB

Funding

Medical Research Council MC_U105170643
6 · The paper itself

Abstract

We propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatio-temporal gene expression in intact networks of cells. The model describes the oscillating time evolution of molecular mRNA counts through a negative transcriptional-translational feedback loop encoded in a chemical Langevin equation with a probabilistic delay distribution. The model is extended spatially by means of a multiplicative random effects model with a first order Markov random field prior distribution. Our methodology effectively separates intrinsic molecular noise, measurement noise, and extrinsic noise and phenotypic variation driving cell heterogeneity, while being amenable to parameter identification and inference. Based on the single-cell model we propose a novel computational stability analysis that allows us to infer two key characteristics, namely the robustness of the oscillations, i.e. whether the reaction network exhibits sustained or damped oscillations, and the profile of the regulation, i.e. whether the inhibition occurs over time in a more distributed versus a more direct manner, which affects the cells' ability to phase-shift to new schedules. We show how insight into the spatio-temporal characteristics of the circadian feedback loop in the suprachiasmatic nucleus (SCN) can be gained by applying the methodology to bioluminescence-reported expression of the circadian core clock gene Cry1 across mouse SCN tissue. We find that while (almost) all SCN neurons exhibit robust cell-autonomous oscillations, the parameters that are associated with the regulatory transcription profile give rise to a spatial division of the tissue between the central region whose oscillations are resilient to perturbation in the sense that they maintain a high degree of synchronicity, and the dorsal region which appears to phase shift in a more diversified way as a response to large perturbations and thus could be more amenable to entrainment.

Indexed as

Circadian Rhythm Signaling Peptides and ProteinsModels, BiologicalAnimalsCircadian ClocksCryptochromesGene Expression RegulationMicePhenotypeSingle-Cell AnalysisStochastic ProcessesSuprachiasmatic NucleusTranscription, GeneticCircadian Rhythm Signaling Peptides and ProteinsCry1 protein, mouseCryptochromes

Identifiers

PMID34919546
PMCPMC8719734
OpenAlexW4225964763

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.