scholarly journals Regional and strain-specific gene expression mapping in the adult mouse brain

2000 ◽  
Vol 97 (20) ◽  
pp. 11038-11043 ◽  
Author(s):  
R. Sandberg ◽  
R. Yasuda ◽  
D. G. Pankratz ◽  
T. A. Carter ◽  
J. A. Del Rio ◽  
...  
genesis ◽  
2005 ◽  
Vol 43 (4) ◽  
pp. 205-212 ◽  
Author(s):  
Aubin Michalon ◽  
Kyoko Koshibu ◽  
Karsten Baumgärtel ◽  
Dominique Haingotiana Spirig ◽  
Isabelle M. Mansuy

Cell Reports ◽  
2019 ◽  
Vol 26 (9) ◽  
pp. 2477-2493.e9 ◽  
Author(s):  
Nicolas Merienne ◽  
Cécile Meunier ◽  
Anne Schneider ◽  
Jonathan Seguin ◽  
Satish S. Nair ◽  
...  

2020 ◽  
Author(s):  
Shaina Lu ◽  
Cantin Ortiz ◽  
Daniel Fürth ◽  
Stephan Fischer ◽  
Konstantinos Meletis ◽  
...  

AbstractBackgroundSpatial gene expression is particularly interesting in the mammalian brain, with the potential to serve as a link between many data types. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain sub-divisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (ABA) and a similar dataset collected using Spatial Transcriptomics (ST). With the advent of tractable spatial techniques, for the first time we are able to benchmark the Allen Institute’s whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome.ResultsWe use LASSO, linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen reference atlas labels are classifiable using transcription, but that performance is higher in the ABA than ST. Further, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bi-directionally. Finally, while an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset.ConclusionsIn general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability.


2010 ◽  
Vol 299 (2) ◽  
pp. F387-F395 ◽  
Author(s):  
Haiping Zhou ◽  
Yan Liu ◽  
Feng He ◽  
Lan Mo ◽  
Tung-Tien Sun ◽  
...  

Urothelium that lines almost the entire urinary tract performs important functions and is prone to assaults by urinary microbials, metabolites, and carcinogens. To improve our understanding of urothelial physiology and disease pathogenesis, we sought to develop two novel transgenic systems, one that would allow inducible and urothelium-specific gene expression, and another that would allow inducible and urothelium-specific knockout. Toward this end, we combined the ability of the mouse uroplakin II promoter (mUPII) to drive urothelium-specific gene expression with a versatile tetracycline-mediated inducible system. We found that, when constructed under the control of mUPII, only a modified, reverse tetracycline trans-activator (rtTA-M2), but not its original version (rtTA), could efficiently trans-activate reporter gene expression in mouse urothelium on doxycycline (Dox) induction. The mUPII/rtTA-M2-inducible system retained its strict urothelial specificity, had no background activity in the absence of Dox, and responded rapidly to Dox administration. Using a reporter gene whose expression was secondarily controlled by histone remodeling, we were able to identify, colocalize with 5-bromo-2-deoxyuridine incorporation, and semiquantify newly divided urothelial cells. Finally, we established that, when combined with a Cre recombinase under the control of the tetracycline operon, the mUPII-driven rtTA-M2 could inducibly inactivate any gene of interest in mouse urothelium. The establishment of these two new transgenic mouse systems enables the manipulation of gene expression and/or inactivation in adult mouse urothelium at any given time, thus minimizing potential compensatory effects due to gene overexpression or loss and allowing more accurate modeling of urothelial diseases than previously reported constitutive systems.


2020 ◽  
Vol 225 (7) ◽  
pp. 2045-2056
Author(s):  
Ilias Kalafatakis ◽  
Konstantinos Kalafatakis ◽  
Alexandros Tsimpolis ◽  
Nikos Giannakeas ◽  
Markos Tsipouras ◽  
...  

2005 ◽  
Vol 102 (29) ◽  
pp. 10357-10362 ◽  
Author(s):  
M. A. Zapala ◽  
I. Hovatta ◽  
J. A. Ellison ◽  
L. Wodicka ◽  
J. A. Del Rio ◽  
...  

2008 ◽  
Vol 9 (1) ◽  
pp. 153 ◽  
Author(s):  
Christopher Lau ◽  
Lydia Ng ◽  
Carol Thompson ◽  
Sayan Pathak ◽  
Leonard Kuan ◽  
...  

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