iPS cells are derived from somatic cells via transduction and expression of selective transcription factors. Both viral-integrating (like retroviral) and non-integrating (like, mRNA or protein-based) techniques are available for the production of iPS cells. In the field of dentistry, iPS cells have been derived from stem cells of apical papilla, dental pulp stem cells, and stem cells from exfoliated deciduous teeth, gingival and periodontal ligament fibroblasts, and buccal mucosa fibroblasts. iPS cells have the potential to differentiate into all derivatives of the 3 primary germ layers i.e. ectoderm, endoderm, and mesoderm. They are autogeneically accessible, and can produce patient-specific or disease-specific cell lines without the issue of ethical controversy. They have been successfully tested to produce mesenchymal stem cells-like cells, neural crest-like cells, ameloblasts-like cells, odontoblasts-like cells, and osteoprogenitor cells. These cells can aid in regeneration of periodontal ligament, alveolar bone, cementum, dentin-pulp complex, as well as possible Biotooth formation. However certain key issues like, epigenetic memory of iPS cells, viral-transduction, tumorgenesis and teratoma formation need to be overcome, before they can be successfully used in clinical practice. The article discusses the sources, pros and cons, and current applications of iPS cells in dentistry with an emphasis on encountered challenges and their solutions.
Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.