↓ Skip to main content

Asymmetry of Cell Division in CFSE-Based Lymphocyte Proliferation Analysis

Overview of attention for article published in Frontiers in immunology, January 2013
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
1 news outlet
twitter
1 X user

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
104 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Asymmetry of Cell Division in CFSE-Based Lymphocyte Proliferation Analysis
Published in
Frontiers in immunology, January 2013
DOI 10.3389/fimmu.2013.00264
Pubmed ID
Authors

Gennady Bocharov, Tatyana Luzyanina, Jovana Cupovic, Burkhard Ludewig

Abstract

Flow cytometry-based analysis of lymphocyte division using carboxyfluorescein succinimidyl ester (CFSE) dye dilution permits acquisition of data describing cellular proliferation and differentiation. For example, CFSE histogram data enable quantitative insight into cellular turnover rates by applying mathematical models and parameter estimation techniques. Several mathematical models have been developed using different types of deterministic or stochastic approaches. However, analysis of CFSE proliferation assays is based on the premise that the label is halved in the two daughter cells. Importantly, asymmetry of protein distribution in lymphocyte division is a basic biological feature of cell division with the degree of the asymmetry depending on various factors. Here, we review the recent literature on asymmetric lymphocyte division and CFSE-based lymphocyte proliferation analysis. We suggest that division- and label-structured mathematical models describing CFSE-based cell proliferation should take into account asymmetry and time-lag in cell proliferation. Utilization of improved modeling algorithms will permit straightforward quantification of essential parameters describing the performance of activated lymphocytes.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 <1%
Germany 1 <1%
Indonesia 1 <1%
Belgium 1 <1%
Korea, Republic of 1 <1%
Unknown 99 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 22%
Researcher 19 18%
Student > Master 12 12%
Student > Bachelor 9 9%
Student > Doctoral Student 8 8%
Other 17 16%
Unknown 16 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 26%
Immunology and Microbiology 16 15%
Biochemistry, Genetics and Molecular Biology 15 14%
Medicine and Dentistry 14 13%
Engineering 3 3%
Other 9 9%
Unknown 20 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 September 2023.
All research outputs
#3,709,974
of 25,373,627 outputs
Outputs from Frontiers in immunology
#4,151
of 31,513 outputs
Outputs of similar age
#36,339
of 288,991 outputs
Outputs of similar age from Frontiers in immunology
#47
of 503 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 31,513 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 288,991 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 503 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.