Murine Tissue‐Resident PDGFRα+ Fibro‐Adipogenic Progenitors Spontaneously Acquire Osteogenic Phenotype in an Altered Inflammatory Environment

2020 ◽  
Vol 35 (8) ◽  
pp. 1525-1534 ◽  
Author(s):  
Christine Eisner ◽  
Michael Cummings ◽  
Gabrielle Johnston ◽  
Lin Wei Tung ◽  
Elena Groppa ◽  
...  
Keyword(s):  
1997 ◽  
Vol 52 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Rodney E. Shackelford ◽  
Paul B. Alford ◽  
Yan Xue ◽  
Sheau-Fung Thai ◽  
Dolph O. Adams ◽  
...  

2007 ◽  
Vol 13 (9) ◽  
pp. 2291-2300 ◽  
Author(s):  
Filip Stillaert ◽  
Michael Findlay ◽  
Jason Palmer ◽  
Rejhan Idrizi ◽  
Shirley Cheang ◽  
...  

1998 ◽  
Vol 273 (37) ◽  
pp. 23959-23968 ◽  
Author(s):  
Milagros Balbı́n ◽  
Antonio Fueyo ◽  
Vera Knäuper ◽  
Alberto M. Pendás ◽  
José M. López ◽  
...  

1991 ◽  
Vol 10 (7) ◽  
pp. 1661-1669 ◽  
Author(s):  
P.R. Crocker ◽  
S. Kelm ◽  
C. Dubois ◽  
B. Martin ◽  
A.S. McWilliam ◽  
...  

2020 ◽  
Author(s):  
Shah R. Ali ◽  
Dan Nguyen ◽  
Brandon Wang ◽  
Steven Jiang ◽  
Hesham A. Sadek

ABSTRACTProper identification and annotation of cells in mammalian tissues is of paramount importance to biological research. Various approaches are currently used to identify and label cell types of interest in complex tissues. In this report, we generated an artificial intelligence (AI) deep learning model that uses image segmentation to predict cardiomyocyte nuclei in mouse heart sections without a specific cardiomyocyte nuclear label. This tool can annotate cardiomyocytes highly sensitively and specifically (AUC 0.94) using only cardiomyocyte structural protein immunostaining and a global nuclear stain. We speculate that our method is generalizable to other tissues to annotate specific cell types and organelles in a label-free way.


1999 ◽  
Vol 1 (2) ◽  
pp. 130-136 ◽  
Author(s):  
J Shirai ◽  
A Ida ◽  
Y Jiang ◽  
R Sanokawa-Akakura ◽  
Y Miura ◽  
...  

2007 ◽  
Vol 91 (17) ◽  
pp. 173901 ◽  
Author(s):  
Christopher M. Laperle ◽  
Philip Wintermeyer ◽  
Jack R. Wands ◽  
Daxin Shi ◽  
Mark A. Anastasio ◽  
...  

2010 ◽  
Vol 24 (9) ◽  
pp. 1715-1727 ◽  
Author(s):  
Terry D. Hinds ◽  
Sadeesh Ramakrishnan ◽  
Harrison A. Cash ◽  
Lance A. Stechschulte ◽  
Garrett Heinrich ◽  
...  

Abstract Glucocorticoid hormones control diverse physiological processes, including metabolism and immunity, by activating the major glucocorticoid receptor (GR) isoform, GRα. However, humans express an alternative isoform, human (h)GRβ, that acts as an inhibitor of hGRα to produce a state of glucocorticoid resistance. Indeed, evidence exists that hGRβ contributes to many diseases and resistance to glucocorticoid hormone therapy. However, rigorous testing of the GRβ contribution has not been possible, because rodents, especially mice, are not thought to express the β-isoform. Here, we report expression of GRβ mRNA and protein in the mouse. The mGRβ isoform arises from a distinct alternative splicing mechanism utilizing intron 8, rather than exon 9 as in humans. The splicing event produces a form of β that is similar in structure and functionality to hGRβ. Mouse (m)GRβ has a degenerate C-terminal region that is the same size as hGRβ. Using a variety of newly developed tools, such as a mGRβ-specific antibody and constructs for overexpression and short hairpin RNA knockdown, we demonstrate that mGRβ cannot bind dexamethasone agonist, is inhibitory of mGRα, and is up-regulated by inflammatory signals. These properties are the same as reported for hGRβ. Additionally, novel data is presented that mGRβ is involved in metabolism. When murine tissue culture cells are treated with insulin, no effect on mGRα expression was observed, but GRβ was elevated. In mice subjected to fasting-refeeding, a large increase of GRβ was seen in the liver, whereas mGRα was unchanged. This work uncovers the much-needed rodent model of GRβ for investigations of physiology and disease.


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