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Measuring DNA content in live cells by fluorescence microscopy

Overview of attention for article published in Cell Division, September 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 150)
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

blogs
1 blog
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5 X users
wikipedia
1 Wikipedia page

Citations

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30 Dimensions

Readers on

mendeley
97 Mendeley
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1 CiteULike
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Title
Measuring DNA content in live cells by fluorescence microscopy
Published in
Cell Division, September 2018
DOI 10.1186/s13008-018-0039-z
Pubmed ID
Authors

Cecil J. Gomes, Michael W. Harman, Sara M. Centuori, Charles W. Wolgemuth, Jesse D. Martinez

Abstract

Live-cell fluorescence microscopy (LCFM) is a powerful tool used to investigate cellular dynamics in real time. However, the capacity to simultaneously measure DNA content in cells being tracked over time remains challenged by dye-associated toxicities. The ability to measure DNA content in single cells by means of LCFM would allow cellular stage and ploidy to be coupled with a variety of imaging directed analyses. Here we describe a widely applicable nontoxic approach for measuring DNA content in live cells by fluorescence microscopy. This method relies on introducing a live-cell membrane-permeant DNA fluorophore, such as Hoechst 33342, into the culture medium of cells at the end of any live-cell imaging experiment and measuring each cell's integrated nuclear fluorescence to quantify DNA content. Importantly, our method overcomes the toxicity and induction of DNA damage typically caused by live-cell dyes through strategic timing of adding the dye to the cultures; allowing unperturbed cells to be imaged for any interval of time before quantifying their DNA content. We assess the performance of our method empirically and discuss adaptations that can be implemented using this technique. Presented in conjunction with cells expressing a histone 2B-GFP fusion protein (H2B-GFP), we demonstrated how this method enabled chromosomal segregation errors to be tracked in cells as they progressed through cellular division that were later identified as either diploid or polyploid. We also describe and provide an automated Matlab-derived algorithm that measures the integrated nuclear fluorescence in each cell and subsequently plots these measurements into a cell cycle histogram for each frame imaged. The algorithm's accurate assessment of DNA content was validated by parallel flow cytometric studies. This method allows the examination of single-cell dynamics to be correlated with cellular stage and ploidy in a high-throughput fashion. The approach is suitable for any standard epifluorescence microscope equipped with a stable illumination source and either a stage-top incubator or an enclosed live-cell incubation chamber. Collectively, we anticipate that this method will allow high-resolution microscopic analysis of cellular processes involving cell cycle progression, such as checkpoint activation, DNA replication, and cellular division.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 97 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 18 19%
Student > Ph. D. Student 17 18%
Researcher 8 8%
Student > Master 8 8%
Student > Doctoral Student 7 7%
Other 8 8%
Unknown 31 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 27 28%
Agricultural and Biological Sciences 11 11%
Medicine and Dentistry 7 7%
Engineering 3 3%
Physics and Astronomy 3 3%
Other 14 14%
Unknown 32 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 05 December 2022.
All research outputs
#2,645,524
of 24,945,754 outputs
Outputs from Cell Division
#4
of 150 outputs
Outputs of similar age
#52,340
of 340,779 outputs
Outputs of similar age from Cell Division
#1
of 2 outputs
Altmetric has tracked 24,945,754 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 150 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done particularly well, scoring higher than 98% 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 340,779 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 84% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them