Evidence map›Paper›PMID 28288157›Full record

ArticlePLoS computational biology2017

Modelling T cell proliferation: Dynamics heterogeneity depending on cell differentiation, age, and genetic background.

Julien Vibert, Véronique Thomas-Vaslin

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

14 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
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  8. Article
  9. Dynamic changes in tRNA modifications and abundance during T cell activation.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  10. Article
  11. Review
  12. Quantifying the limits of CAR T-cell delivery in mice and men.Journal of the Royal Society, Interface · 2021
    Article
  13. The Rules of Human T Cell FateFrontiers in immunology · 2020
    Article
  14. 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

2 authors at 1 institution in 1 country.

Julien VibertSorbonne Universités, UPMC Univ Paris 06, INSERM, Immunology-Immunopathology-Immunotherapy (I3) UMRS959; Paris, France.ORCID 0000-0002-2370-3302
Véronique Thomas-VaslinSorbonne Universités, UPMC Univ Paris 06, INSERM, Immunology-Immunopathology-Immunotherapy (I3) UMRS959; Paris, France.ORCID 0000-0001-7521-8635
Inserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell proliferation is the common characteristic of all biological systems. The immune system insures the maintenance of body integrity on the basis of a continuous production of diversified T lymphocytes in the thymus. This involves processes of proliferation, differentiation, selection, death and migration of lymphocytes to peripheral tissues, where proliferation also occurs upon antigen recognition. Quantification of cell proliferation dynamics requires specific experimental methods and mathematical modelling. Here, we assess the impact of genetics and aging on the immune system by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Our investigation is based on single-cell multicolour flow cytometry analysis revealing the active incorporation of a thymidine analogue during S phase after pulse-chase-pulse experiments in vivo, versus cell DNA content. A generic mathematical model of state transition simulates through Ordinary Differential Equations (ODEs) the evolution of single cell behaviour during various durations of labelling. It allows us to fit our data, to deduce proliferation rates and estimate cell cycle durations in sub-populations. Our model is simple and flexible and is validated with other durations of pulse/chase experiments. Our results reveal that T cell proliferation is highly heterogeneous but with a specific "signature" that depends upon genetic origins, is specific to cell differentiation stages in thymus and spleen and is altered with age. In conclusion, our model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxyuridine (EdU) data, revealing T cell proliferation heterogeneity and specific signatures.

Indexed as

Models, GeneticAnimalsCell DifferentiationCell ProliferationCells, CulturedCellular SenescenceComputer SimulationGenetic BackgroundImmunogenetic PhenomenaMiceModels, ImmunologicalT-Lymphocytes

Identifiers

PMID28288157
PMCPMC5367836
OpenAlexW2596947528

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.