Evidence map›Paper›PMID 32841238›Full record

ArticlePLoS computational biology2020

Partitioning stable and unstable expression level variation in cell populations: A theoretical framework and its application to the T cell receptor.

Thiago S Guzella, Vasco M Barreto, Jorge Carneiro

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. 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 47% 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. 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

3 authors at 2 institutions in 1 country.

Thiago S GuzellaInstituto Gulbenkian de Ciência, Oeiras, Portugal.ORCID 0000-0002-1374-7379
Vasco M BarretoCEDOC - Chronic Diseases Research Center, NOVA Medical School, Universidade Nova de Lisboa, Lisboa, Portugal.ORCID 0000-0002-0010-8607
Jorge CarneiroInstituto Gulbenkian de Ciência, Oeiras, Portugal.ORCID 0000-0002-7520-3406
Instituto Gulbenkian de Ciência · PTUniversidade Nova de Lisboa · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phenotypic variation in the copy number of gene products expressed by cells or tissues has been the focus of intense investigation. To what extent the observed differences in cellular expression levels are persistent or transient is an intriguing question. Here, we develop a quantitative framework that resolves the expression variation into stable and unstable components. The difference between the expression means in two cohorts isolated from any cell population is shown to converge to an asymptotic value, with a characteristic time, τT, that measures the timescale of the unstable dynamics. The asymptotic difference in the means, relative to the initial value, measures the stable proportion of the original population variance [Formula: see text]. Empowered by this insight, we analysed the T-cell receptor (TCR) expression variation in CD4 T cells. About 70% of TCR expression variance is stable in a diverse polyclonal population, while over 80% of the variance in an isogenic TCR transgenic population is volatile. In both populations the TCR levels fluctuate with a characteristic time of 32 hours. This systematic characterisation of the expression variation dynamics, relying on time series of cohorts' means, can be combined with technologies that measure gene or protein expression in single cells or in bulk.

Indexed as

AnimalsCD4-Positive T-LymphocytesCell SeparationFlow CytometryMiceMice, Inbred C57BLMice, TransgenicReceptors, Antigen, T-CellReceptors, Antigen, T-Cell

Identifiers

PMID32841238
PMCPMC7498022
OpenAlexW3081231282

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.