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Maintaining RNA Integrity for Transcriptomic Profiling of Ex Vivo Cultured Limbal Epithelial Stem Cells after Fluorescence-Activated Cell Sorting (FACS)

Overview of attention for article published in Biological Procedures Online, December 2017
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Title
Maintaining RNA Integrity for Transcriptomic Profiling of Ex Vivo Cultured Limbal Epithelial Stem Cells after Fluorescence-Activated Cell Sorting (FACS)
Published in
Biological Procedures Online, December 2017
DOI 10.1186/s12575-017-0065-2
Pubmed ID
Authors

Lei Liu, Frederik Mølgaard Nielsen, Simone Elkjær Riis, Jeppe Emmersen, Trine Fink, Jesper Østergaard Hjortdal, Chris Bath, Vladimir Zachar

Abstract

Transcriptomic profiling of ex vivo cultured human limbal epithelial stem cells (hLESCs) will foster better understanding of corneal physiology and novel treatment paradigms to limbal stem cell deficiency (LSCD). However, currently such profiling studies are hampered due to difficulties with producing sufficient amounts of intact mRNA for deep RNA sequencing (RNA-seq) from subpopulations sorted on the basis of co-expression of membrane and intracellular antigens by fluorescence-activated cell sorting (FACS). To address this problem, we systematically analyzed the critical steps, and found that ethanol fixation together with optimized downstream procedures provided a pipeline that yielded high quality total RNA in amounts to readily support the RNA-seq procedure, while still preserving good discrimination between the individual hLESC immunophenotypes. The average RNA integrity number (RIN) was 7.7 ± 0.4, and the average yield was 4.6 ± 1.7 pg of RNA per cell. The sequencing analysis of the isolated RNA produced high quality data with more than 70% of read pairs mapping uniformly to the reference genome and 80% of bases with a Phred score of at least 30. In this study, we developed a reliable FACS-based procedure using ethanol as a fixative that would support accurate isolation of limbal epithelial progenitor subpopulations along with RNA yield and quality sufficient to enable deep transcriptomic profiling.

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The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 21%
Researcher 9 19%
Student > Bachelor 5 11%
Student > Postgraduate 4 9%
Student > Master 4 9%
Other 6 13%
Unknown 9 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 28%
Agricultural and Biological Sciences 9 19%
Medicine and Dentistry 8 17%
Immunology and Microbiology 4 9%
Neuroscience 2 4%
Other 1 2%
Unknown 10 21%