About this Research Topic
The pluripotent and multipotent stem cells are found in the bone marrow, which can differentiate into any tissue in the body. However, pluripotent embryonic stem cells (ESCs) are more efficient than multipotent adult stem cells (ASCs) in producing any cell type, though many ethical issues have been raised regarding the use of ESCs. ASCs, like induced pluripotent stem cells (iPSCs), can be genetically reprogrammed to perform the same function as ESCs. Multipotent ASCs such as hematopoietic stem cells (HSCs), mesenchymal stem cells (MSCs), and neural stem cells (NSCs) are used in regenerative therapies. The human umbilical cord blood is an excellent source of HSCs for transplantation, and the isolation of MSCs, while NSCs, and iPSCs are the new topics in stem cell research. These stem cells possess characteristic properties such as homing, migration, self-renewal, and differentiation into multiple lineages, guided by growth factors, cytokines, small molecules, cell-cell contacts, and niches.
The stem cells’ delivery, differentiation into immune cells in case of opportunistic infections, and immunological rejections during transplantation are the major concerns for using stem cells at the clinical stage. The main goal of this collection is to bring stem cells' potential to the forefront of health care such as treating immunological disorders, regenerative medicine, and drug discovery. So, an in-depth study of the immunological and immunomodulatory properties of stem cells provides insight into successful stem cell transplantation. However, the networks of these stem cells' signaling pathways exhibited interactions are so complex that it is challenging to investigate and interpret them without any system-level analysis or accompanying computational tools. Therefore, computational biology is one of the potential ways to link the healthy stem cells and cancer stem cells (CSCs) research outcomes to connect the scattered resources of stem cell research; these aspects can be addressed by the identification of pluripotent stem cell populations, factors involved in self-renewal and differentiation, the role of immunology in tissue-committed stem cell niches, cellular crosstalk, genes possessing stemness in healthy and cancer stem cells, molecular pathways/processes regulating stem cell differentiation, and differentiation into immune cells by linking single-cell omics with machine learning and network modeling.
The scope of this research topic is mainly focused on the development and applications of computational tools and data analysis pipelines for stem cell transplant research, and we particularly welcome the submission of original research and review articles focusing on, but not limited to the following themes:
• High-throughput data molecular profiling, cell typing, analysis of high-throughput sequencing (RNAseq, WGS, WGBS, WES, scRNAseq, ChipSeq, ATAC-seq, and immunophenotypic analysis, etc.).
• TCR profiling, regulatory networks, system biology approaches, population genetics/genomics, bioinformatics, biostatistics, metabolomics, proteomics, the machine learning program to identify specific patterns within a dataset, tissue engineering for designing 2D or 3D nanomaterials.
• Mathematical modelling to show the origin and three-dimensional organization of niches in stem cells or organoids cluster formation.
• CAR-T therapy, CRISPR/Cas9 gene-editing of iPSCs, modelling, simulations, single-cell genomics to identify new cell types, and intracellular interactions to recognize the cellular behavior.
• Integration of high-throughput transcriptomics and proteomics analysis to identify metabolic behaviour of cancer and healthy stem cells.
• Computational models and network modelling to study the effects of cancer-associated mutations.
• Machine learning or deep learning analysis to identify the CSCs/tumour cells and healthy stem cells.
The stem cells’ delivery, differentiation into immune cells in case of opportunistic infections, and immunological rejections during transplantation are the major concerns for using stem cells at the clinical stage. The main goal of this collection is to bring stem cells' potential to the forefront of health care such as treating immunological disorders, regenerative medicine, and drug discovery. So, an in-depth study of the immunological and immunomodulatory properties of stem cells provides insight into successful stem cell transplantation. However, the networks of these stem cells' signaling pathways exhibited interactions are so complex that it is challenging to investigate and interpret them without any system-level analysis or accompanying computational tools. Therefore, computational biology is one of the potential ways to link the healthy stem cells and cancer stem cells (CSCs) research outcomes to connect the scattered resources of stem cell research; these aspects can be addressed by the identification of pluripotent stem cell populations, factors involved in self-renewal and differentiation, the role of immunology in tissue-committed stem cell niches, cellular crosstalk, genes possessing stemness in healthy and cancer stem cells, molecular pathways/processes regulating stem cell differentiation, and differentiation into immune cells by linking single-cell omics with machine learning and network modeling.
The scope of this research topic is mainly focused on the development and applications of computational tools and data analysis pipelines for stem cell transplant research, and we particularly welcome the submission of original research and review articles focusing on, but not limited to the following themes:
• High-throughput data molecular profiling, cell typing, analysis of high-throughput sequencing (RNAseq, WGS, WGBS, WES, scRNAseq, ChipSeq, ATAC-seq, and immunophenotypic analysis, etc.).
• TCR profiling, regulatory networks, system biology approaches, population genetics/genomics, bioinformatics, biostatistics, metabolomics, proteomics, the machine learning program to identify specific patterns within a dataset, tissue engineering for designing 2D or 3D nanomaterials.
• Mathematical modelling to show the origin and three-dimensional organization of niches in stem cells or organoids cluster formation.
• CAR-T therapy, CRISPR/Cas9 gene-editing of iPSCs, modelling, simulations, single-cell genomics to identify new cell types, and intracellular interactions to recognize the cellular behavior.
• Integration of high-throughput transcriptomics and proteomics analysis to identify metabolic behaviour of cancer and healthy stem cells.
• Computational models and network modelling to study the effects of cancer-associated mutations.
• Machine learning or deep learning analysis to identify the CSCs/tumour cells and healthy stem cells.
Keywords: Pluripotency, Differentiation, HSC, MSCs, NSC, iPSC, CSC, Disease modeling, Stem cell, Stem cell biology, Fate engineering, Single cell transcriptomics, Mathematical modelling, Gene regulatory networks, Molecular pathways
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
