Abstract

The anterior pituitary gland drives highly conserved physiologic processes in mammalian species. These hormonally controlled processes are central to somatic growth, pubertal transformation, fertility, lactation, and metabolism. Current cellular models of mammalian anteiror pituitary, largely built on candidate gene based immuno-histochemical and mRNA analyses, suggest that each of the seven hormones synthesized by the pituitary is produced by a specific and exclusive cell lineage. However, emerging evidence suggests more complex relationship between hormone specificity and cell plasticity. Here we have applied massively parallel single-cell RNA sequencing (scRNA-seq), in conjunction with complementary imaging-based single-cell analyses of mRNAs and proteins, to systematically map both cell-type diversity and functional state heterogeneity in adult male and female mouse pituitaries at single-cell resolution and in the context of major physiologic demands. These quantitative single-cell analyses reveal sex-specific cell-type composition under normal pituitary homeostasis, identify an array of cells associated with complex complements of hormone-enrichment, and undercover non-hormone producing interstitial and supporting cell-types. Interestingly, we also identified a Pou1f1-expressing cell population that is characterized by a unique multi-hormone gene expression profile. In response to two well-defined physiologic stresses, dynamic shifts in cellular diversity and transcriptome profiles were observed for major hormone producing and the putative multi-hormone cells. These studies reveal unanticipated cellular complexity and plasticity in adult pituitary, and provide a rich resource for further validating and expanding our molecular understanding of pituitary gene expression programs and hormone production.

Details

Title
Single-cell transcriptomic analysis of adult mouse pituitary reveals sexual dimorphism and physiologic demand-induced cellular plasticity
Author
Ho Yugong 1   VIAFID ORCID Logo  ; Hu, Peng 2   VIAFID ORCID Logo  ; Peel, Michael T 1 ; Chen Sixing 1 ; Camara, Pablo G 3 ; Epstein, Douglas J 1 ; Wu, Hao 2   VIAFID ORCID Logo  ; Liebhaber, Stephen A 4 

 University of Pennsylvania, Departments of Genetics, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
 University of Pennsylvania, Departments of Genetics, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Penn Epigenetics Institute, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
 University of Pennsylvania, Departments of Genetics, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Penn Institute for Biomedical Informatics, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
 University of Pennsylvania, Departments of Genetics, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Departments of Medicine, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
Pages
565-583
Publication year
2020
Publication date
Aug 2020
Publisher
Springer Nature B.V.
ISSN
1674800X
e-ISSN
16748018
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2426710688
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.