Evidence map›Paper›PMID 26107175›Full record

ArticlePloS one2015

Characterizing the DNA Damage Response by Cell Tracking Algorithms and Cell Features Classification Using High-Content Time-Lapse Analysis.

Walter Georgescu, Alma Osseiran, Maria Rojec, Yueyong Liu, Maxime Bombrun, Jonathan Tang, Sylvain V Costes

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. 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
1.8field-weighted citation impact, top 15% 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, 36 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. 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

7 authors at 3 institutions in 2 countries.

Walter GeorgescuLawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America.
Alma OsseiranLawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America.
Maria RojecLawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America.
Yueyong LiuDepartment of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, United States of America.
Maxime BombrunCNRS, UMR 6158, LIMOS, F-63173, Aubiere, France.
Jonathan TangLawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America.
Sylvain V CostesLawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America.
Lawrence Berkeley National Laboratory · USBeth Israel Deaconess Medical Center · USCentre National de la Recherche Scientifique · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditionally, the kinetics of DNA repair have been estimated using immunocytochemistry by labeling proteins involved in the DNA damage response (DDR) with fluorescent markers in a fixed cell assay. However, detailed knowledge of DDR dynamics across multiple cell generations cannot be obtained using a limited number of fixed cell time-points. Here we report on the dynamics of 53BP1 radiation induced foci (RIF) across multiple cell generations using live cell imaging of non-malignant human mammary epithelial cells (MCF10A) expressing histone H2B-GFP and the DNA repair protein 53BP1-mCherry. Using automatic extraction of RIF imaging features and linear programming techniques, we were able to characterize detailed RIF kinetics for 24 hours before and 24 hours after exposure to low and high doses of ionizing radiation. High-content-analysis at the single cell level over hundreds of cells allows us to quantify precisely the dose dependence of 53BP1 protein production, RIF nuclear localization and RIF movement after exposure to X-ray. Using elastic registration techniques based on the nuclear pattern of individual cells, we could describe the motion of individual RIF precisely within the nucleus. We show that DNA repair occurs in a limited number of large domains, within which multiple small RIFs form, merge and/or resolve with random motion following normal diffusion law. Large foci formation is shown to be mainly happening through the merging of smaller RIF rather than through growth of an individual focus. We estimate repair domain sizes of 7.5 to 11 µm2 with a maximum number of ~15 domains per MCF10A cell. This work also highlights DDR which are specific to doses larger than 1 Gy such as rapid 53BP1 protein increase in the nucleus and foci diffusion rates that are significantly faster than for spontaneous foci movement. We hypothesize that RIF merging reflects a "stressed" DNA repair process that has been taken outside physiological conditions when too many DSB occur at once. High doses of ionizing radiation lead to RIF merging into repair domains which in turn increases DSB proximity and misrepair. Such finding may therefore be critical to explain the supralinear dose dependence for chromosomal rearrangement and cell death measured after exposure to ionizing radiation.

Indexed as

DNA Breaks, Double-StrandedAlgorithmsBreastCell LineCell NucleusCell TrackingDNA RepairEpithelial CellsFemaleHistonesHumansImage Processing, Computer-AssistedIntracellular Signaling Peptides and ProteinsKineticsLuminescent ProteinsMicroscopy, FluorescenceHistonesIntracellular Signaling Peptides and ProteinsLuminescent ProteinsRed Fluorescent ProteinTP53BP1 protein, humanTumor Suppressor p53-Binding Protein 1

Identifiers

PMID26107175
PMCPMC4479605
OpenAlexW1879759474

What OpenQuestion holds

Textmetadata
LicenceCC0
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.