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Transcriptome analysis for the identification of cellular markers related to trabecular meshwork differentiation

Overview of attention for article published in BMC Genomics, May 2017
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Title
Transcriptome analysis for the identification of cellular markers related to trabecular meshwork differentiation
Published in
BMC Genomics, May 2017
DOI 10.1186/s12864-017-3758-7
Pubmed ID
Authors

Padmapriya Sathiyanathan, Cheryl Y. Tay, Lawrence W. Stanton

Abstract

Development of primary open-angle glaucoma (POAG) is associated with the malfunctioning trabecular meshwork (TM). Cell therapy offers great potential for the treatment of POAG, but requires the generation of functional TM cells in vitro to replace the lost/dysfunctional cells. TM differentiation in vitro from various stem cell types must be monitored by the expression of specific markers. However, no single definitive marker of the TM has been identified. To identify robust markers of TM differentiation, we performed global transcriptome profiling using high-density oligonucleotide microarray on ex vivo TM tissue and cultured TM progenitors. Corneal and scleral tissues were also used in the analysis. After removal of genes expressed in the cornea and sclera, 18 genes were identified that were differentially expressed in the TM relative to the other samples. CDH23, F5, KCNAB1, FGF9, SPP1, and HEY1 were selected among the genes highly expressed in the TM, together with BDNF which was repressed, compared to progenitors for further investigation. Expression analysis by qPCR verified the differential expression and immunofluorescence of the anterior segment confirmed strong expression in the TM. Three independent cohort of expression studies have identified novel markers, fitting in identifying TM cells and in evaluating directed TM differentiation in vitro.

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Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 11 24%
Researcher 5 11%
Other 3 7%
Student > Ph. D. Student 3 7%
Student > Master 3 7%
Other 8 18%
Unknown 12 27%
Readers by discipline Count As %
Unspecified 11 24%
Agricultural and Biological Sciences 7 16%
Medicine and Dentistry 5 11%
Neuroscience 3 7%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 5 11%
Unknown 12 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 May 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from BMC Genomics
#9,845
of 11,247 outputs
Outputs of similar age
#286,362
of 327,133 outputs
Outputs of similar age from BMC Genomics
#190
of 214 outputs
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