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A prototypical non-malignant epithelial model to study genome dynamics and concurrently monitor micro-RNAs and proteins in situ during oncogene-induced senescence

Overview of attention for article published in BMC Genomics, January 2018
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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Title
A prototypical non-malignant epithelial model to study genome dynamics and concurrently monitor micro-RNAs and proteins in situ during oncogene-induced senescence
Published in
BMC Genomics, January 2018
DOI 10.1186/s12864-017-4375-1
Pubmed ID
Authors

Eirini-Stavroula Komseli, Ioannis S. Pateras, Thorbjørn Krejsgaard, Konrad Stawiski, Sophia V. Rizou, Alexander Polyzos, Fani-Marlen Roumelioti, Maria Chiourea, Ioanna Mourkioti, Eleni Paparouna, Christos P. Zampetidis, Sentiljana Gumeni, Ioannis P. Trougakos, Dafni-Eleftheria Pefani, Eric O’Neill, Sarantis Gagos, Aristides G. Eliopoulos, Wojciech Fendler, Dipanjan Chowdhury, Jiri Bartek, Vassilis G. Gorgoulis

Abstract

Senescence is a fundamental biological process implicated in various pathologies, including cancer. Regarding carcinogenesis, senescence signifies, at least in its initial phases, an anti-tumor response that needs to be circumvented for cancer to progress. Micro-RNAs, a subclass of regulatory, non-coding RNAs, participate in senescence regulation. At the subcellular level micro-RNAs, similar to proteins, have been shown to traffic between organelles influencing cellular behavior. The differential function of micro-RNAs relative to their subcellular localization and their role in senescence biology raises concurrent in situ analysis of coding and non-coding gene products in senescent cells as a necessity. However, technical challenges have rendered in situ co-detection unfeasible until now. In the present report we describe a methodology that bypasses these technical limitations achieving for the first time simultaneous detection of both a micro-RNA and a protein in the biological context of cellular senescence, utilizing the new commercially available SenTraGorTM compound. The method was applied in a prototypical human non-malignant epithelial model of oncogene-induced senescence that we generated for the purposes of the study. For the characterization of this novel system, we applied a wide range of cellular and molecular techniques, as well as high-throughput analysis of the transcriptome and micro-RNAs. This experimental setting has three advantages that are presented and discussed: i) it covers a "gap" in the molecular carcinogenesis field, as almost all corresponding in vitro models are fibroblast-based, even though the majority of neoplasms have epithelial origin, ii) it recapitulates the precancerous and cancerous phases of epithelial tumorigenesis within a short time frame under the light of natural selection and iii) it uses as an oncogenic signal, the replication licensing factor CDC6, implicated in both DNA replication and transcription when over-expressed, a characteristic that can be exploited to monitor RNA dynamics. Consequently, we demonstrate that our model is optimal for studying the molecular basis of epithelial carcinogenesis shedding light on the tumor-initiating events. The latter may reveal novel molecular targets with clinical benefit. Besides, since this method can be incorporated in a wide range of low, medium or high-throughput image-based approaches, we expect it to be broadly applicable.

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X Demographics

The data shown below were collected from the profiles of 4 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 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 22%
Researcher 11 14%
Student > Bachelor 11 14%
Student > Master 7 9%
Student > Doctoral Student 4 5%
Other 11 14%
Unknown 15 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 34%
Agricultural and Biological Sciences 10 13%
Medicine and Dentistry 7 9%
Neuroscience 4 5%
Nursing and Health Professions 2 3%
Other 8 11%
Unknown 19 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 January 2018.
All research outputs
#12,867,456
of 23,015,156 outputs
Outputs from BMC Genomics
#4,432
of 10,697 outputs
Outputs of similar age
#202,704
of 443,289 outputs
Outputs of similar age from BMC Genomics
#89
of 214 outputs
Altmetric has tracked 23,015,156 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,697 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 57% 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 443,289 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 214 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.