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.

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.

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.

In vertebrates, the entire central nervous system is derived from the neural tube, which is formed through a conserved early developmental morphogenetic process called neurulation. Although the perturbations in neurulation caused by genetic or environmental factors lead to neural tube defects (NTDs), the most common congenital malformation and the precise molecular pathological cascades mediating NTDs are not well understood. Recently, we have developed human spinal cord organoids (hSCOs) that recapitulate some aspects of human neurulation and observed that valproic acid (VPA) could cause neurulation defects in an organoid model. In this study, we identified and verified the significant changes in cell–cell junctional genes/proteins in VPA-treated organoids using transcriptomic and immunostaining analysis. Furthermore, VPA-treated mouse embryos exhibited impaired gene expression and NTD phenotypes, similar to those observed in the hSCO model. Collectively, our data demonstrate that hSCOs provide a valuable biological resource for dissecting the molecular pathways underlying the currently unknown human neurulation process using destructive biological analysis tools.

Neuromesodermal progenitors (NMPs) constitute a bipotent cell population that generates a wide variety of trunk cell and tissue types during embryonic development. Derivatives of NMPs include both mesodermal lineage cells such as muscles and vertebral bones, and neural lineage cells such as neural crests and central nervous system neurons. Such diverse lineage potential combined with a limited capacity for self-renewal, which persists during axial elongation, demonstrates that NMPs are a major source of trunk tissues. This review describes the identification and characterization of NMPs across multiple species. We also discuss key cellular and molecular steps for generating neural and mesodermal cells for building up the elongating trunk tissue.

Neuromesodermal progenitors (NMPs) constitute a bipotent cell population that generates a wide variety of trunk cell and tissue types during embryonic development. Derivatives of NMPs include both mesodermal lineage cells such as muscles and vertebral bones, and neural lineage cells such as neural crests and central nervous system neurons. Such diverse lineage potential combined with a limited capacity for self-renewal, which persists during axial elongation, demonstrates that NMPs are a major source of trunk tissues. This review describes the identification and characterization of NMPs across multiple species. We also discuss key cellular and molecular steps for generating neural and mesodermal cells for building up the elongating trunk tissue.