Self-renewing embryonic stem (ES) cells have been established from early mouse embryos as permanent cell lines. By cultivation in vitro as three-dimensional aggregates called embryoid bodies (EBs), ES cells can differentiate into derivatives of all three primary germ layers, including cardiomyocytes. ES cells thus represent a useful model system for studying cardiomyocyte developmental paradigms. This chapter describes techniques and protocols for the cultivation and maintenance of ES cell lines, and the differentiation of ES cell lines into all specialized cell types of the heart, including atrial-, ventricular-, sinus nodal- and Purkinje-like cardiomyocytes. We also include protocols for the isolation and evaluation (morphological, molecular, and functional) of in vitro-generated cardiomyocytes. We consider these latter techniques to be prerequisites for the successful use of this model system to study cardiomyocyte differentiation. Finally, our objective in writing this chapter is to provide sufficient detail and explanation so that any competent scientist who is new to the field will be able to successfully establish and employ this model system for the analysis of ES cell-derived cardiomyocytes.
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http://dx.doi.org/10.1385/1-59259-850-1:417 | DOI Listing |
Quant Plant Biol
December 2024
Department of Biology, Howard University, Washington, DC, USA.
Plant growth and development are tightly regulated by cell division, elongation, and differentiation. A visible plant phenotype at the tissue or organ level is coordinated at the cellular level. Among these cellular regulations (cell division, elongation and differentiation), cell division in plants follows the same universal mechanisms across kingdoms of life, and involves conserved cell cycle regulatory proteins (cyclins, cyclin-dependent kinase and cell cycle inhibitors).
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Department of Urology Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored.
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February 2025
Department of Biological Sciences, Tennessee State University, Nashville, TN 37209, USA.
Although peptide vaccines offer a novel venue for cancer immunotherapy, clinical success has been rather limited. Cell-penetrating peptides, due to their ability to translocate through the cell membrane, could be conjugated to the peptide vaccine to2 enhance therapeutic efficiency. The S4 transduction domain of the shaker-potassium channel was conjugated to mammaglobin-A (MamA) immunodominant epitope (MamA2.
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Department of Thoracic Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430079, P.R. China.
The aim of the present study was to investigate the efficacy and safety of anlotinib combined with vinorelbine (NVB) as a second-line treatment for elderly patients with advanced squamous cell lung carcinoma (SqCLC). The present retrospective analysis included 48 elderly patients (aged ≥65 years) diagnosed with advanced SqCLC who received anlotinib in combination with NVB as a second-line therapy between January 2021 and December 2023. The primary endpoints assessed were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR) and safety profile.
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December 2024
Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Guidelines in statistical modeling for genomics hold that simpler models have advantages over more complex ones. Potential advantages include cost, interpretability, and improved generalization across datasets or biological contexts. We directly tested the assumption that small gene signatures generalize better by examining the generalization of mutation status prediction models across datasets (from cell lines to human tumors and vice versa) and biological contexts (holding out entire cancer types from pan-cancer data).
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