Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference

In the preimplantation mammalian embryo, stochastic cell-to-cell expression heterogeneity is followed by signal reinforcement to initiate the specification of Inner Cell Mass (ICM) cells into Epiblast (Epi). The expression of NANOG, the key transcription factor for the Epi fate, is necessary but not sufficient: coincident expression of other factors is required. To identify possible Nanog-helper genes, we analyzed gene expression variability in five time-stamped single-cell transcriptomic datasets using differential entropy, a quantitative measure of cell-to-cell heterogeneity. The entropy of Nanog displays a peak-shaped temporal pattern from the 16-cell to the 64-cell stage, consistent with its key role in Epi specification. By estimating the entropy profiles of the 21 genes common to all five datasets, we identified three genes - Pecam1, Sox2, and Hnf4a - whose variability in expression patterns mirrors that of Nanog. We further performed gene regulatory network inference using CARDAMOM, an algorithm that exploits temporal dynamics and transcriptional bursting. The results revealed that these three genes exhibit reciprocal activation with Nanog at the 32-cell stage. This regulatory motif reinforces fate-switching decisions and co-expression states. Our innovative analysis of single-cell transcriptomic data thus uncovers a likely role for Pecam1, Sox2, and Hnf4a as key genes that, when coincidentally expressed with Nanog, initiate ICM differentiation.

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Journal
Scientific Reports
Published
2025-06-06
DOI
https://doi.org/10.1038/s41598-025-03956-y
Citations
1
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
0.81

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article

Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference

Yannick De Decker, Ulysse Herbach, Corentin Robert, Didier Gonze et al.
1 citations
Scientific Reports
Gene Regulatory Network Analysis
0.81
article

Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference

Yannick De Decker, Ulysse Herbach, Corentin Robert, Didier Gonze, Francisco Prista von Bonhorst, Geneviève Dupont, Claire Chazaud, Olivier Gandrillon
article en
1 citations

Abstract

In the preimplantation mammalian embryo, stochastic cell-to-cell expression heterogeneity is followed by signal reinforcement to initiate the specification of Inner Cell Mass (ICM) cells into Epiblast (Epi). The expression of NANOG, the key transcription factor for the Epi fate, is necessary but not sufficient: coincident expression of other factors is required. To identify possible Nanog-helper genes, we analyzed gene expression variability in five time-stamped single-cell transcriptomic datasets using differential entropy, a quantitative measure of cell-to-cell heterogeneity. The entropy of Nanog displays a peak-shaped temporal pattern from the 16-cell to the 64-cell stage, consistent with its key role in Epi specification. By estimating the entropy profiles of the 21 genes common to all five datasets, we identified three genes - Pecam1, Sox2, and Hnf4a - whose variability in expression patterns mirrors that of Nanog. We further performed gene regulatory network inference using CARDAMOM, an algorithm that exploits temporal dynamics and transcriptional bursting. The results revealed that these three genes exhibit reciprocal activation with Nanog at the 32-cell stage. This regulatory motif reinforces fate-switching decisions and co-expression states. Our innovative analysis of single-cell transcriptomic data thus uncovers a likely role for Pecam1, Sox2, and Hnf4a as key genes that, when coincidentally expressed with Nanog, initiate ICM differentiation.

Scientific ReportsVol. 15(1)
Université Claude Bernard Lyon 1 (FR), École Normale Supérieure de Lyon (FR), Centre National de la Recherche Scientifique (FR), Inserm (FR), Université Clermont Auvergne (FR), Université de Lorraine (FR)
Institut National de la Santé et de la Recherche Médicale, Fonds De La Recherche Scientifique - FNRS
Openalex Percentile: Top 27%
Gene Regulatory Network Analysis
0.81
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