Kooperativer Bibliotheksverbund

UID:

Umfang: 1 online resource (568 pages)

Ausgabe: First edition.

ISBN: 0-443-13221-6

Anmerkung: Front Cover -- Computational Biology for Stem Cell Research -- Computational Biology for Stem Cell Research -- Copyright -- Dedication -- Contents -- Abbreviations -- List of figures -- List of tables -- Contributors -- Biographies -- Preface -- Acknowledgments -- Introduction -- I - In silico tools and approaches in stem cell biology -- 1 - Advancement of in silico tools for stem cell research -- 1. Introduction -- 2. In silico tools and approaches -- 2.1 SCENT: Single-Cell Entropy -- 2.2 FateID -- 2.3 hscScore -- 2.4 StemID -- 2.5 Spectre -- 2.6 TransSynW -- 2.7 NETISCE: NETwork-drIven analysiS of CEllular reprogramming -- 2.8 SCENIC: single-cell regulatory network inference and clustering -- 2.9 ESC-T: ESC-Track -- 2.10 DeepNEU -- 2.11 IRENE: Integrative gene REgulatory NEtwork -- 2.12 StemChecker -- 3. Conclusion and future prospects -- References -- 2 - Paradigm shift in stem cell research with computational tools, techniques, and databases -- 1. Introduction -- 1.1 Early embryonic development and potency -- 1.2 Characteristics of stem cells -- 1.3 Need to study stem cells -- 2. Big data in stem cell research -- 2.1 Genomics -- 2.2 Transcriptomics -- 2.3 Proteomics -- 2.4 Epigenomics -- 3. Computational methods for effective analyses of high-throughput stem cells data -- 3.1 Clustering methods -- 3.2 Machine learning approach -- 3.3 Network-based approach -- 4. Computational resources for stem cell data analysis -- 5. Cancer stem cells -- 6. Conclusion -- References -- 3 - Stem cell informatics: Web resources aiding in stem cell research -- 1. Introduction -- 2. Discussion -- 3. Conclusion -- References -- 4 - Stem cell based informatics development and approaches -- 1. Introduction -- 2. Applications of stem cells: a promise to behold -- 3. Expanding horizons of omics and advent of informatics -- 4. Network biology and stem cell informatics. , 5. Recent developments in stem cell informatics -- 6. Future of the stem cell informatics -- References -- 5 - Exploring imaging technologies and computational resources in stem cell research for regenerative medicine: A c ... -- 1. Introduction -- 2. Ideal requisite for molecular imaging -- 3. Imaging strategies employed in regenerative medicine -- 3.1 Direct iron particle labeling -- 3.1.1 Magnetoporation -- 3.1.2 Magnetoelectroporation -- 3.2 Clinical implication of direct iron particle labeling -- 4. Imaging methods using nuclear medicine -- 4.1 Direct imaging -- 4.2 Indirect imaging -- 4.2.1 Receptor-based gene imaging -- 4.2.2 Transporter-based reporter gene imaging system -- 4.2.3 Optical imaging -- 4.2.4 Applications for optical imaging methods -- 4.3 Other imaging methods -- 4.3.1 Ultrasound -- 4.3.2 Intravital microscopy for live cell imaging -- 4.3.3 Microcomputed tomography -- 4.3.4 Computed tomography -- 4.3.5 Optical coherence tomography -- 4.3.6 Microcirculation imaging -- 5. Software programs used in stem cell research -- 6. Current challenges in regenerative medicine imaging -- 7. Artificial intelligence in stem cell imaging -- 8. Future of stem cell imaging with artificial intelligence -- 8.1 Computational requirements and artificial intelligence software programs in stem cell biology -- 9. Conclusion -- References -- Further reading -- 6 - Application of machine learning-based approaches in stem cell research -- 1. Introduction -- 2. Types of stem cells and their therapeutic applications -- 2.1 Embryonic stem cell therapy -- 2.2 Induced pluripotent stem cell therapy -- 2.3 Induced tissue-specific stem cell therapy -- 2.4 Fetal stem cell and adipose tissue-derived stem cell therapy -- 2.5 Hematopoietic stem cell therapy -- 2.6 Mesenchymal stem cell therapy -- 3. Overview of machine learning-based model building. , 3.1 Machine learning algorithms -- 3.2 Deep learning algorithms -- 3.3 Model building and model evaluation metrics -- 4. Applications of machine learning in stem cell-based therapies -- 5. Applications of deep learning in stem cell-based therapies -- 6. Evolution of nature-inspired computing and its applications in stem cell-based therapies -- 7. Conclusion and future aspects -- References -- 7 - Stem cell therapy in the era of machine learning -- 1. Introduction -- 2. Current state of stem cell therapies -- 3. Machine learning applications in different biological and medicine fields -- 4. Current trends in machine learning-based stem cell therapy -- 5. Conclusion and future prospects -- References -- 8 - Computational and stem cell biology: Challenges and future perspectives -- 1. Introduction -- 2. Current challenges and contests: Crossing hurdles in stem cell and computational biology -- 3. Disease modeling and computational biology: Current challenges -- 4. Differentiating stem cells, desired cell types, and organoids: Challenges and future aspects -- 5. Future prospective and outlook -- 6. Conclusion -- References -- II - Application of genomic and proteomic approaches in stem cell research -- 9 - Single-cell transcriptome profiling in unraveling distinct molecular signatures from cancer stem cells -- 1. Introduction -- 2. Cancer stem cells -- 3. Single-cell transcriptomics -- 3.1 Databases for storing scRNA-seq raw data -- 3.1.1 CancerSEA -- 3.1.2 Cancer single-cell expression map -- 4. Single-cell data analysis workflow -- 5. Single-cell studies on detecting distinct molecular signature in cancer -- 6. Future perspectives -- References -- 10 - The single-cell big data analytics: A game changer in bioscience -- 1. Introduction -- 2. Techniques for producing single-cell big data in revolutionizing regenerative medicine healthcare. , 2.1 Single-cell RNA sequencing -- 2.2 Advancements in single-cell ATAC-seq for profiling chromatin accessibility -- 2.3 Single-cell bisulfite sequencing -- 2.4 Fluorescence-activated cell sorting and flow cytometry in stem cell research and analysis -- 3. Unlocking the potential of stem cells through single-cell data analysis -- 4. The promising future of single-cell data in advancing stem cell biology research -- 4.1 Improved understanding of stem cell behavior -- 4.2 Development of new therapies -- 4.3 Better understanding of disease states -- 4.4 Improved tissue engineering -- 5. Conclusion -- References -- 11 - Unravelling the genomics and proteomics aspects of the stemness phenotype in stem cells -- 1. Introduction -- 2. Genomic and proteomic aspects to understand stem cell phenotype -- 2.1 Genomic platform to characterize SCs phenotype -- 2.2 Proteomic platform to characterize stem cell phenotype -- 3. Mechanistic insights into the stemness phenotype of stem cells through genomic and proteomic platform -- 3.1 Molecular signatures of stemness in embryonic stem cells through genomics -- 3.2 Molecular signatures of stemness in embryonic stem cells through proteomics -- 4. Conclusion -- References -- 12 - Cutting-edge proteogenomics approaches to analyze stem cells at the therapeutic level -- 1. Stem cell characterization -- 1.1 Genetic approaches -- 1.1.1 Karyotyping -- 1.1.2 Fluorescence in situ hybridization -- 1.1.3 Comparative genomic hybridization -- 1.1.4 Single-nucleotide polymorphism analysis -- 1.1.5 Epigenetic profiling -- 1.1.6 Digital, RT, and qRT-PCR -- 1.1.7 Next-generation sequencing -- 1.2 Proteomic approaches -- 1.2.1 Flow cytometry -- 1.2.1.1 Immunocyto- and histochemistry -- 1.2.2 ELISA and DELFIA assays -- 1.2.3 Western blotting -- 1.2.4 Mass spectrometry-based proteomic biomarker analysis. , 1.2.5 CyTOF or mass cytometry analysis -- 1.3 Artificial intelligence -- 2. Conclusion -- References -- 13 - Cheminformatics, metabolomics, and stem cell tissue engineering: A transformative insight -- 1. Introduction -- 2. Computational tools and stem cell biology -- 3. Computational approaches, genomics and proteomics -- 4. Cheminformatics, tissue engineering, and stem cell research -- 5. Metabolomics data acquisition and preprocessing -- 6. Manipulation of stem cells and tissue regeneration -- 7. Stem cell microenvironment and cardiac tissue regeneration: engineered approaches -- 8. Future prospective and present outlook -- 9. Conclusion -- References -- 14 - Advances in regenerative medicines based on mesenchymal stem cell secretome -- 1. Introduction -- 2. MSC-Secretome: conditioned media from mesenchymal stem cells and exosomes -- 3. Effector biological functions of MSC-Secretome and their applications -- 3.1 Immunomodulation -- 3.2 Antiapoptotic function -- 3.3 Neuroprotective and neurotrophic effects -- 3.4 Wound healing and tissue repair -- 3.5 Regulation of angiogenesis -- 3.6 Antitumor and antimicrobial effects -- 4. Clinical studies with secretome from mesenchymal stem cells -- 5. Applications of computational tools in stem cell-based therapies -- 6. Conclusion -- References -- 15 - Paradigms of omics in bioinformatics for accelerating current trends and prospects of stem cell research -- 1. Introduction -- 2. A brief overview of current therapeutic uses of stem cell treatment -- 3. Characterization of stem cells using OMICS -- 4. Transcriptomic and proteomic methods in stem cell characterization -- 5. Multiomics collaboration -- 6. Potency, omics approaches, and knowledge of the mechanism of action -- 7. Importance of computational modeling in stem cell research -- 8. Research on stem cells: applications of several modeling types. , 9. Bioinformatics in stem cell research.

Weitere Ausg.: ISBN 9780443132223

Sprache: Englisch

UID:

Umfang: 1 online resource (568 pages)

Ausgabe: First edition.

ISBN: 0-443-13221-6

Anmerkung: Front Cover -- Computational Biology for Stem Cell Research -- Computational Biology for Stem Cell Research -- Copyright -- Dedication -- Contents -- Abbreviations -- List of figures -- List of tables -- Contributors -- Biographies -- Preface -- Acknowledgments -- Introduction -- I - In silico tools and approaches in stem cell biology -- 1 - Advancement of in silico tools for stem cell research -- 1. Introduction -- 2. In silico tools and approaches -- 2.1 SCENT: Single-Cell Entropy -- 2.2 FateID -- 2.3 hscScore -- 2.4 StemID -- 2.5 Spectre -- 2.6 TransSynW -- 2.7 NETISCE: NETwork-drIven analysiS of CEllular reprogramming -- 2.8 SCENIC: single-cell regulatory network inference and clustering -- 2.9 ESC-T: ESC-Track -- 2.10 DeepNEU -- 2.11 IRENE: Integrative gene REgulatory NEtwork -- 2.12 StemChecker -- 3. Conclusion and future prospects -- References -- 2 - Paradigm shift in stem cell research with computational tools, techniques, and databases -- 1. Introduction -- 1.1 Early embryonic development and potency -- 1.2 Characteristics of stem cells -- 1.3 Need to study stem cells -- 2. Big data in stem cell research -- 2.1 Genomics -- 2.2 Transcriptomics -- 2.3 Proteomics -- 2.4 Epigenomics -- 3. Computational methods for effective analyses of high-throughput stem cells data -- 3.1 Clustering methods -- 3.2 Machine learning approach -- 3.3 Network-based approach -- 4. Computational resources for stem cell data analysis -- 5. Cancer stem cells -- 6. Conclusion -- References -- 3 - Stem cell informatics: Web resources aiding in stem cell research -- 1. Introduction -- 2. Discussion -- 3. Conclusion -- References -- 4 - Stem cell based informatics development and approaches -- 1. Introduction -- 2. Applications of stem cells: a promise to behold -- 3. Expanding horizons of omics and advent of informatics -- 4. Network biology and stem cell informatics. , 5. Recent developments in stem cell informatics -- 6. Future of the stem cell informatics -- References -- 5 - Exploring imaging technologies and computational resources in stem cell research for regenerative medicine: A c ... -- 1. Introduction -- 2. Ideal requisite for molecular imaging -- 3. Imaging strategies employed in regenerative medicine -- 3.1 Direct iron particle labeling -- 3.1.1 Magnetoporation -- 3.1.2 Magnetoelectroporation -- 3.2 Clinical implication of direct iron particle labeling -- 4. Imaging methods using nuclear medicine -- 4.1 Direct imaging -- 4.2 Indirect imaging -- 4.2.1 Receptor-based gene imaging -- 4.2.2 Transporter-based reporter gene imaging system -- 4.2.3 Optical imaging -- 4.2.4 Applications for optical imaging methods -- 4.3 Other imaging methods -- 4.3.1 Ultrasound -- 4.3.2 Intravital microscopy for live cell imaging -- 4.3.3 Microcomputed tomography -- 4.3.4 Computed tomography -- 4.3.5 Optical coherence tomography -- 4.3.6 Microcirculation imaging -- 5. Software programs used in stem cell research -- 6. Current challenges in regenerative medicine imaging -- 7. Artificial intelligence in stem cell imaging -- 8. Future of stem cell imaging with artificial intelligence -- 8.1 Computational requirements and artificial intelligence software programs in stem cell biology -- 9. Conclusion -- References -- Further reading -- 6 - Application of machine learning-based approaches in stem cell research -- 1. Introduction -- 2. Types of stem cells and their therapeutic applications -- 2.1 Embryonic stem cell therapy -- 2.2 Induced pluripotent stem cell therapy -- 2.3 Induced tissue-specific stem cell therapy -- 2.4 Fetal stem cell and adipose tissue-derived stem cell therapy -- 2.5 Hematopoietic stem cell therapy -- 2.6 Mesenchymal stem cell therapy -- 3. Overview of machine learning-based model building. , 3.1 Machine learning algorithms -- 3.2 Deep learning algorithms -- 3.3 Model building and model evaluation metrics -- 4. Applications of machine learning in stem cell-based therapies -- 5. Applications of deep learning in stem cell-based therapies -- 6. Evolution of nature-inspired computing and its applications in stem cell-based therapies -- 7. Conclusion and future aspects -- References -- 7 - Stem cell therapy in the era of machine learning -- 1. Introduction -- 2. Current state of stem cell therapies -- 3. Machine learning applications in different biological and medicine fields -- 4. Current trends in machine learning-based stem cell therapy -- 5. Conclusion and future prospects -- References -- 8 - Computational and stem cell biology: Challenges and future perspectives -- 1. Introduction -- 2. Current challenges and contests: Crossing hurdles in stem cell and computational biology -- 3. Disease modeling and computational biology: Current challenges -- 4. Differentiating stem cells, desired cell types, and organoids: Challenges and future aspects -- 5. Future prospective and outlook -- 6. Conclusion -- References -- II - Application of genomic and proteomic approaches in stem cell research -- 9 - Single-cell transcriptome profiling in unraveling distinct molecular signatures from cancer stem cells -- 1. Introduction -- 2. Cancer stem cells -- 3. Single-cell transcriptomics -- 3.1 Databases for storing scRNA-seq raw data -- 3.1.1 CancerSEA -- 3.1.2 Cancer single-cell expression map -- 4. Single-cell data analysis workflow -- 5. Single-cell studies on detecting distinct molecular signature in cancer -- 6. Future perspectives -- References -- 10 - The single-cell big data analytics: A game changer in bioscience -- 1. Introduction -- 2. Techniques for producing single-cell big data in revolutionizing regenerative medicine healthcare. , 2.1 Single-cell RNA sequencing -- 2.2 Advancements in single-cell ATAC-seq for profiling chromatin accessibility -- 2.3 Single-cell bisulfite sequencing -- 2.4 Fluorescence-activated cell sorting and flow cytometry in stem cell research and analysis -- 3. Unlocking the potential of stem cells through single-cell data analysis -- 4. The promising future of single-cell data in advancing stem cell biology research -- 4.1 Improved understanding of stem cell behavior -- 4.2 Development of new therapies -- 4.3 Better understanding of disease states -- 4.4 Improved tissue engineering -- 5. Conclusion -- References -- 11 - Unravelling the genomics and proteomics aspects of the stemness phenotype in stem cells -- 1. Introduction -- 2. Genomic and proteomic aspects to understand stem cell phenotype -- 2.1 Genomic platform to characterize SCs phenotype -- 2.2 Proteomic platform to characterize stem cell phenotype -- 3. Mechanistic insights into the stemness phenotype of stem cells through genomic and proteomic platform -- 3.1 Molecular signatures of stemness in embryonic stem cells through genomics -- 3.2 Molecular signatures of stemness in embryonic stem cells through proteomics -- 4. Conclusion -- References -- 12 - Cutting-edge proteogenomics approaches to analyze stem cells at the therapeutic level -- 1. Stem cell characterization -- 1.1 Genetic approaches -- 1.1.1 Karyotyping -- 1.1.2 Fluorescence in situ hybridization -- 1.1.3 Comparative genomic hybridization -- 1.1.4 Single-nucleotide polymorphism analysis -- 1.1.5 Epigenetic profiling -- 1.1.6 Digital, RT, and qRT-PCR -- 1.1.7 Next-generation sequencing -- 1.2 Proteomic approaches -- 1.2.1 Flow cytometry -- 1.2.1.1 Immunocyto- and histochemistry -- 1.2.2 ELISA and DELFIA assays -- 1.2.3 Western blotting -- 1.2.4 Mass spectrometry-based proteomic biomarker analysis. , 1.2.5 CyTOF or mass cytometry analysis -- 1.3 Artificial intelligence -- 2. Conclusion -- References -- 13 - Cheminformatics, metabolomics, and stem cell tissue engineering: A transformative insight -- 1. Introduction -- 2. Computational tools and stem cell biology -- 3. Computational approaches, genomics and proteomics -- 4. Cheminformatics, tissue engineering, and stem cell research -- 5. Metabolomics data acquisition and preprocessing -- 6. Manipulation of stem cells and tissue regeneration -- 7. Stem cell microenvironment and cardiac tissue regeneration: engineered approaches -- 8. Future prospective and present outlook -- 9. Conclusion -- References -- 14 - Advances in regenerative medicines based on mesenchymal stem cell secretome -- 1. Introduction -- 2. MSC-Secretome: conditioned media from mesenchymal stem cells and exosomes -- 3. Effector biological functions of MSC-Secretome and their applications -- 3.1 Immunomodulation -- 3.2 Antiapoptotic function -- 3.3 Neuroprotective and neurotrophic effects -- 3.4 Wound healing and tissue repair -- 3.5 Regulation of angiogenesis -- 3.6 Antitumor and antimicrobial effects -- 4. Clinical studies with secretome from mesenchymal stem cells -- 5. Applications of computational tools in stem cell-based therapies -- 6. Conclusion -- References -- 15 - Paradigms of omics in bioinformatics for accelerating current trends and prospects of stem cell research -- 1. Introduction -- 2. A brief overview of current therapeutic uses of stem cell treatment -- 3. Characterization of stem cells using OMICS -- 4. Transcriptomic and proteomic methods in stem cell characterization -- 5. Multiomics collaboration -- 6. Potency, omics approaches, and knowledge of the mechanism of action -- 7. Importance of computational modeling in stem cell research -- 8. Research on stem cells: applications of several modeling types. , 9. Bioinformatics in stem cell research.

Weitere Ausg.: ISBN 9780443132223

Sprache: Englisch

UID:

Umfang: 1 online resource (568 pages)

Ausgabe: First edition.

ISBN: 0-443-13221-6

Anmerkung: Front Cover -- Computational Biology for Stem Cell Research -- Computational Biology for Stem Cell Research -- Copyright -- Dedication -- Contents -- Abbreviations -- List of figures -- List of tables -- Contributors -- Biographies -- Preface -- Acknowledgments -- Introduction -- I - In silico tools and approaches in stem cell biology -- 1 - Advancement of in silico tools for stem cell research -- 1. Introduction -- 2. In silico tools and approaches -- 2.1 SCENT: Single-Cell Entropy -- 2.2 FateID -- 2.3 hscScore -- 2.4 StemID -- 2.5 Spectre -- 2.6 TransSynW -- 2.7 NETISCE: NETwork-drIven analysiS of CEllular reprogramming -- 2.8 SCENIC: single-cell regulatory network inference and clustering -- 2.9 ESC-T: ESC-Track -- 2.10 DeepNEU -- 2.11 IRENE: Integrative gene REgulatory NEtwork -- 2.12 StemChecker -- 3. Conclusion and future prospects -- References -- 2 - Paradigm shift in stem cell research with computational tools, techniques, and databases -- 1. Introduction -- 1.1 Early embryonic development and potency -- 1.2 Characteristics of stem cells -- 1.3 Need to study stem cells -- 2. Big data in stem cell research -- 2.1 Genomics -- 2.2 Transcriptomics -- 2.3 Proteomics -- 2.4 Epigenomics -- 3. Computational methods for effective analyses of high-throughput stem cells data -- 3.1 Clustering methods -- 3.2 Machine learning approach -- 3.3 Network-based approach -- 4. Computational resources for stem cell data analysis -- 5. Cancer stem cells -- 6. Conclusion -- References -- 3 - Stem cell informatics: Web resources aiding in stem cell research -- 1. Introduction -- 2. Discussion -- 3. Conclusion -- References -- 4 - Stem cell based informatics development and approaches -- 1. Introduction -- 2. Applications of stem cells: a promise to behold -- 3. Expanding horizons of omics and advent of informatics -- 4. Network biology and stem cell informatics. , 5. Recent developments in stem cell informatics -- 6. Future of the stem cell informatics -- References -- 5 - Exploring imaging technologies and computational resources in stem cell research for regenerative medicine: A c ... -- 1. Introduction -- 2. Ideal requisite for molecular imaging -- 3. Imaging strategies employed in regenerative medicine -- 3.1 Direct iron particle labeling -- 3.1.1 Magnetoporation -- 3.1.2 Magnetoelectroporation -- 3.2 Clinical implication of direct iron particle labeling -- 4. Imaging methods using nuclear medicine -- 4.1 Direct imaging -- 4.2 Indirect imaging -- 4.2.1 Receptor-based gene imaging -- 4.2.2 Transporter-based reporter gene imaging system -- 4.2.3 Optical imaging -- 4.2.4 Applications for optical imaging methods -- 4.3 Other imaging methods -- 4.3.1 Ultrasound -- 4.3.2 Intravital microscopy for live cell imaging -- 4.3.3 Microcomputed tomography -- 4.3.4 Computed tomography -- 4.3.5 Optical coherence tomography -- 4.3.6 Microcirculation imaging -- 5. Software programs used in stem cell research -- 6. Current challenges in regenerative medicine imaging -- 7. Artificial intelligence in stem cell imaging -- 8. Future of stem cell imaging with artificial intelligence -- 8.1 Computational requirements and artificial intelligence software programs in stem cell biology -- 9. Conclusion -- References -- Further reading -- 6 - Application of machine learning-based approaches in stem cell research -- 1. Introduction -- 2. Types of stem cells and their therapeutic applications -- 2.1 Embryonic stem cell therapy -- 2.2 Induced pluripotent stem cell therapy -- 2.3 Induced tissue-specific stem cell therapy -- 2.4 Fetal stem cell and adipose tissue-derived stem cell therapy -- 2.5 Hematopoietic stem cell therapy -- 2.6 Mesenchymal stem cell therapy -- 3. Overview of machine learning-based model building. , 3.1 Machine learning algorithms -- 3.2 Deep learning algorithms -- 3.3 Model building and model evaluation metrics -- 4. Applications of machine learning in stem cell-based therapies -- 5. Applications of deep learning in stem cell-based therapies -- 6. Evolution of nature-inspired computing and its applications in stem cell-based therapies -- 7. Conclusion and future aspects -- References -- 7 - Stem cell therapy in the era of machine learning -- 1. Introduction -- 2. Current state of stem cell therapies -- 3. Machine learning applications in different biological and medicine fields -- 4. Current trends in machine learning-based stem cell therapy -- 5. Conclusion and future prospects -- References -- 8 - Computational and stem cell biology: Challenges and future perspectives -- 1. Introduction -- 2. Current challenges and contests: Crossing hurdles in stem cell and computational biology -- 3. Disease modeling and computational biology: Current challenges -- 4. Differentiating stem cells, desired cell types, and organoids: Challenges and future aspects -- 5. Future prospective and outlook -- 6. Conclusion -- References -- II - Application of genomic and proteomic approaches in stem cell research -- 9 - Single-cell transcriptome profiling in unraveling distinct molecular signatures from cancer stem cells -- 1. Introduction -- 2. Cancer stem cells -- 3. Single-cell transcriptomics -- 3.1 Databases for storing scRNA-seq raw data -- 3.1.1 CancerSEA -- 3.1.2 Cancer single-cell expression map -- 4. Single-cell data analysis workflow -- 5. Single-cell studies on detecting distinct molecular signature in cancer -- 6. Future perspectives -- References -- 10 - The single-cell big data analytics: A game changer in bioscience -- 1. Introduction -- 2. Techniques for producing single-cell big data in revolutionizing regenerative medicine healthcare. , 2.1 Single-cell RNA sequencing -- 2.2 Advancements in single-cell ATAC-seq for profiling chromatin accessibility -- 2.3 Single-cell bisulfite sequencing -- 2.4 Fluorescence-activated cell sorting and flow cytometry in stem cell research and analysis -- 3. Unlocking the potential of stem cells through single-cell data analysis -- 4. The promising future of single-cell data in advancing stem cell biology research -- 4.1 Improved understanding of stem cell behavior -- 4.2 Development of new therapies -- 4.3 Better understanding of disease states -- 4.4 Improved tissue engineering -- 5. Conclusion -- References -- 11 - Unravelling the genomics and proteomics aspects of the stemness phenotype in stem cells -- 1. Introduction -- 2. Genomic and proteomic aspects to understand stem cell phenotype -- 2.1 Genomic platform to characterize SCs phenotype -- 2.2 Proteomic platform to characterize stem cell phenotype -- 3. Mechanistic insights into the stemness phenotype of stem cells through genomic and proteomic platform -- 3.1 Molecular signatures of stemness in embryonic stem cells through genomics -- 3.2 Molecular signatures of stemness in embryonic stem cells through proteomics -- 4. Conclusion -- References -- 12 - Cutting-edge proteogenomics approaches to analyze stem cells at the therapeutic level -- 1. Stem cell characterization -- 1.1 Genetic approaches -- 1.1.1 Karyotyping -- 1.1.2 Fluorescence in situ hybridization -- 1.1.3 Comparative genomic hybridization -- 1.1.4 Single-nucleotide polymorphism analysis -- 1.1.5 Epigenetic profiling -- 1.1.6 Digital, RT, and qRT-PCR -- 1.1.7 Next-generation sequencing -- 1.2 Proteomic approaches -- 1.2.1 Flow cytometry -- 1.2.1.1 Immunocyto- and histochemistry -- 1.2.2 ELISA and DELFIA assays -- 1.2.3 Western blotting -- 1.2.4 Mass spectrometry-based proteomic biomarker analysis. , 1.2.5 CyTOF or mass cytometry analysis -- 1.3 Artificial intelligence -- 2. Conclusion -- References -- 13 - Cheminformatics, metabolomics, and stem cell tissue engineering: A transformative insight -- 1. Introduction -- 2. Computational tools and stem cell biology -- 3. Computational approaches, genomics and proteomics -- 4. Cheminformatics, tissue engineering, and stem cell research -- 5. Metabolomics data acquisition and preprocessing -- 6. Manipulation of stem cells and tissue regeneration -- 7. Stem cell microenvironment and cardiac tissue regeneration: engineered approaches -- 8. Future prospective and present outlook -- 9. Conclusion -- References -- 14 - Advances in regenerative medicines based on mesenchymal stem cell secretome -- 1. Introduction -- 2. MSC-Secretome: conditioned media from mesenchymal stem cells and exosomes -- 3. Effector biological functions of MSC-Secretome and their applications -- 3.1 Immunomodulation -- 3.2 Antiapoptotic function -- 3.3 Neuroprotective and neurotrophic effects -- 3.4 Wound healing and tissue repair -- 3.5 Regulation of angiogenesis -- 3.6 Antitumor and antimicrobial effects -- 4. Clinical studies with secretome from mesenchymal stem cells -- 5. Applications of computational tools in stem cell-based therapies -- 6. Conclusion -- References -- 15 - Paradigms of omics in bioinformatics for accelerating current trends and prospects of stem cell research -- 1. Introduction -- 2. A brief overview of current therapeutic uses of stem cell treatment -- 3. Characterization of stem cells using OMICS -- 4. Transcriptomic and proteomic methods in stem cell characterization -- 5. Multiomics collaboration -- 6. Potency, omics approaches, and knowledge of the mechanism of action -- 7. Importance of computational modeling in stem cell research -- 8. Research on stem cells: applications of several modeling types. , 9. Bioinformatics in stem cell research.

Weitere Ausg.: ISBN 9780443132223

Sprache: Englisch