Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Rebecca A. Siwicki"'
Autor:
Zachary B Madaj, Michael S. Dahabieh, Vijayvardhan Kamalumpundi, Brejnev Muhire, J. Pettinga, Rebecca A. Siwicki, Abigail E. Ellis, Christine Isaguirre, Martha L. Escobar Galvis, Lisa DeCamp, Russell G. Jones, Scott A. Givan, Marie Adams, Ryan D. Sheldon
Publikováno v:
RNA Biology, Vol 20, Iss 1, Pp 186-197 (2023)
Here, we provide an in-depth analysis of the usefulness of single-sample metabolite/RNA extraction for multi-‘omics readout. Using pulverized frozen livers of mice injected with lymphocytic choriomeningitis virus (LCMV) or vehicle (Veh), we isolate
Externí odkaz:
https://doaj.org/article/04874c3865a64c46a4a4eabd9d1a05ee
Autor:
Mike R. Wilson, Shannon Harkins, Jake J. Reske, Rebecca A. Siwicki, Marie Adams, Victoria L. Bae-Jump, Jose M. Teixeira, Ronald L. Chandler
Publikováno v:
Reproductive Biology and Endocrinology, Vol 21, Iss 1, Pp 1-11 (2023)
Abstract Endometrial epithelia are known to harbor cancer driver mutations in the absence of any pathologies, including mutations in PIK3CA. Insulin plays an important role in regulating uterine metabolism during pregnancy, and hyperinsulinemia is as
Externí odkaz:
https://doaj.org/article/12fe81f9ee214cd59a993db686c57cab
Autor:
Jake J Reske, Mike R Wilson, Jeanne Holladay, Rebecca A Siwicki, Hilary Skalski, Shannon Harkins, Marie Adams, John I Risinger, Galen Hostetter, Ken Lin, Ronald L Chandler
Publikováno v:
PLoS Genetics, Vol 17, Iss 12, p e1009986 (2021)
TP53 and ARID1A are frequently mutated across cancer but rarely in the same primary tumor. Endometrial cancer has the highest TP53-ARID1A mutual exclusivity rate. However, the functional relationship between TP53 and ARID1A mutations in the endometri
Externí odkaz:
https://doaj.org/article/50d23fb13fd7476982447aba9e632021
Autor:
Zachary B Madaj, Michael S. Dahabieh, Vijayvardhan Kamalumpundi, Brejnev Muhire, Dean J. Pettinga, Rebecca A. Siwicki, Abigail E. Ellis, Christine Isaguirre, Martha L. Escobar Galvis, Lisa DeCamp, Russell G. Jones, Scott A. Givan, Marie Adams, Ryan D. Sheldon
ObjectiveMetabolomics and RNA sequencing (RNAseq) each provide powerful readouts of phenotype, and integration of these data can provide information greater than the sum of their parts. The ability to conduct such analysis on a single sample has many
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17ce8c58fbc514cf35dc01051d415dca
https://doi.org/10.1101/2022.08.26.505340
https://doi.org/10.1101/2022.08.26.505340
Autor:
Benjamin K. Johnson, Mary Rhodes, Marc Wegener, Pamela Himadewi, Kelly Foy, Joshua L. Schipper, Rebecca A. Siwicki, Larissa L. Rossell, Emily J. Siegwald, Dave W. Chesla, Jose M. Teixeira, Rachael T. C. Sheridan, Marie Adams, Timothy J. Triche, Hui Shen
We present Single-cell TOtal RNA Miniaturized sequencing (STORM-seq), a full-length single-cell ribo-reduced RNA sequencing protocol, optimized to profile thousands of cells per run. Using off-the-shelf reagents and random hexamer priming, STORM-seq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbfeedabe75f94243bbb7d80995fdaf0
https://doi.org/10.1101/2022.03.14.484332
https://doi.org/10.1101/2022.03.14.484332