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Selection of accurate reference genes in mouse trophoblast stem cells for reverse transcription-quantitative polymerase chain reaction

Mouse trophoblast stem cells (TSCs) form colonies of different sizes and morphologies, which might reflect their degrees of differentiation. Therefore, each colony type can have a characteristic gene expression profile; however, the expression levels of internal reference genes may also change, caus...

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Detalles Bibliográficos
Autores principales: MOTOMURA, Kaori, INOUE, Kimiko, OGURA, Atsuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Society for Reproduction and Development 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4919296/
https://www.ncbi.nlm.nih.gov/pubmed/26853688
http://dx.doi.org/10.1262/jrd.2015-170
Descripción
Sumario:Mouse trophoblast stem cells (TSCs) form colonies of different sizes and morphologies, which might reflect their degrees of differentiation. Therefore, each colony type can have a characteristic gene expression profile; however, the expression levels of internal reference genes may also change, causing fluctuations in their estimated gene expression levels. In this study, we validated seven housekeeping genes by using a geometric averaging method and identified Gapdh as the most stable gene across different colony types. Indeed, when Gapdh was used as the reference, expression levels of Elf5, a TSC marker gene, stringently classified TSC colonies into two groups: a high expression groups consisting of type 1 and 2 colonies, and a lower expression group consisting of type 3 and 4 colonies. This clustering was consistent with our putative classification of undifferentiated/differentiated colonies based on their time-dependent colony transitions. By contrast, use of an unstable reference gene (Rn18s) allowed no such clear classification. Cdx2, another TSC marker, did not show any significant colony type-specific expression pattern irrespective of the reference gene. Selection of stable reference genes for quantitative gene expression analysis might be critical, especially when cell lines consisting of heterogeneous cell populations are used.