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| http://dx.doi.org/10.1007/s00401-013-1126-5 | DOI Listing |
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Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity.
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Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, United States.
Single-nucleus RNA sequencing (snRNA-seq), an alternative to single-cell RNA sequencing (scRNA-seq), encounters technical challenges in obtaining high-quality nuclei and RNA, persistently hindering its applications. Here, we present a robust technique for isolating nuclei across various tissue types, remarkably enhancing snRNA-seq data quality. Employing this approach, we comprehensively characterize the depot-dependent cellular dynamics of various cell types underlying mouse adipose tissue remodeling during obesity.
View Article and Find Full Text PDFComparing the malignant properties of parental and a knock-in version of HCT116 cell line expressing the CDK2-mutant of eukaryotic elongation factor 2 (eEF2).
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Department of Molecular Biology Vadi Kampüsü, Istanbul Atlas University, Anadolu Cd., No 40, Kağıthane, Istanbul, 34408, Turkey.
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View Article and Find Full Text PDFIntegrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme.
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Department of Medical Imaging, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
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How Binding Site Flexibility Promotes RNA Scanning by TbRGG2 RRM: A Molecular Dynamics Simulation Study.
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Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, 612 00 Brno, Czech Republic.
RNA recognition motifs (RRMs) are a key class of proteins that primarily bind single-stranded RNAs. In this study, we applied standard atomistic molecular dynamics simulations to obtain insights into the intricate binding dynamics between uridine-rich RNAs and TbRGG2 RRM using the recently developed OL3-Stafix AMBER force field, which improves the description of single-stranded RNA molecules. Complementing structural experiments that unveil a primary binding mode with a single uridine bound, our simulations uncover two supplementary binding modes in which adjacent nucleotides encroach upon the binding pocket.
View Article and Find Full Text PDFEnhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.
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Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets. Experimental identification and validation of cancer driver genes are time-consuming and costly.
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