Long description:

Handbook of Translational Transcriptomics: Research, Protocols and Applications provides a comprehensive overview of the field of transcriptomics. With an emphasis on the various protocols and techniques available for investigation, it acts as a practical guide to researchers for implementing their own investigations in the field.

This book begins with an overview of the past, present, and potential approaches in the field of transcriptomics, with discussions of choosing the correct approach based on the research needed. It also highlights the pros and cons of each approach. Following this, it explores techniques and protocols for investigating specific approaches focusing on RNA sequencing, expression arrays, and gene expression. It then delves into data analysis and offers recommendations, guidelines, and approaches related to data interpretation. This book also considers the translation of transcriptomics to clinical use and applications in molecular diagnostics, biomarkers in medicine, and personalized medicine specific to oncology, as well as biotechnology for pharmaceutical research.

Handbook of Translational Transcriptomics: Research, Protocols and Applications is a detailed reference that provides a complete view of transcriptomics, ranging from methods to handling data and medical applications. This book is an invaluable guide for researchers working across molecular biology, genetics, bioinformatics and related fields, as well as graduate and PhD students in these areas.

• Features practical guidance and protocols for researchers to replicate transcriptomic methods and techniques, including wet lab techniques.
• Includes useful information on analyzing and interpreting transcriptomic data.
• Offers a detailed introduction into translational transcriptomics, exploring both coding and noncoding RNAs including microRNA.
• Investigates clinical implications of transcriptomics and applications to treating disease.
• Considers some experimental and bioinformatic techniques in the field of transcriptomics, as well as more established approaches.

Table of Contents:

Contributors
Acknowledgments

1. Past, current, and future of transcriptomics
Xinmin Li, Ilya Belalov, and Anton Buzdin

1.1 Introduction
1.2 Historical development
1.3 Methodological advancements
1.4 Microarrays
1.5 RNA sequencing (RNA-seq)
1.6 Single-cell RNA sequencing (scRNA-seq)
1.7 Spatial transcriptomics
1.8 Noncoding RNAs in gene regulation
1.9 MicroRNAs (miRNAs)
1.10 Long noncoding RNAs (lncRNAs)
1.11 Regulatory networks
1.12 Single-cell and spatial transcriptomics in understanding cellular heterogeneity
1.13 Single-cell transcriptomics
1.14 Spatial transcriptomics
1.15 Insights and applications
1.16 Challenges and future directions
1.17 Emerging trends and future directions in transcriptomics
1.18 RNA sequencing (RNA-seq) and beyond
1.19 Dual RNA-seq for host-pathogen interactions
1.20 Transcriptomics in food microbiology and agriculture
1.21 Multi-omics integration
1.22 Single-cell transcriptomics
1.23 Challenges and future prospects
1.24 Current challenges and controversies in transcriptomics
1.25 Technical limitations and data complexity
1.26 Interpretation of dual RNA-seq data
1.27 Environmental influences on transcriptomic profiles
1.28 Transcriptomics in food microbiology and safety
1.29 Toxicogenomics and risk assessment
1.30 Functional annotation and novel transcripts
1.31 Conclusion
References

2. Pitfalls of transcriptomics and selection of the most appropriate transcriptomic technique
Xinmin Li, Ilya Belalov, and Anton Buzdin

2.1 Introduction
2.2 Pitfalls in specific transcriptomic techniques
2.3 Choosing the right tool for the job
2.4 Case studies and examples
2.5 Addressing pitfalls and challenges: Insights from recent research
2.6 Future directions in transcriptomics
2.7 Practical considerations and conclusion
References
Further reading

3. Bulk RNA sequencing in wet lab
Anton Buzdin, Ilya Belalov, Maria Suntsova, and Xinmin Li

3.1 Introduction to bulk RNA sequencing
3.2 Fundamental principles of bulk RNA sequencing
3.3 Key technologies and methodologies in bulk RNA sequencing
3.4 Sample collection and storage
3.5 RNA extraction and purification
3.6 Quality control measures
3.7 Library preparation for bulk RNA sequencing
3.8 Early barcoding and cost efficiency
3.9 Direct RNA isolation strategies
3.10 Sequencing platforms and technologies
3.11 RNA sequencing protocols
3.12 Overview of advanced RNA sequencing protocols
3.13 Reagents and equipment for bulk RNA sequencing
3.14 Technical and analytical quality control in RNA sequencing
3.15 Bioinformatics and data analysis
3.16 Data preprocessing and normalization methods for bulk RNA sequencing
3.17 Sequencing depth and coverage in bulk RNA sequencing
3.18 Differential expression analysis in RNA sequencing
3.19 Functional genomics and pathway analysis in RNA sequencing
3.20 Emerging applications of bulk RNA sequencing in disease research, immunology, and oncology
3.21 Challenges and limitations of current RNA sequencing methodologies
3.22 Future directions and technologies in RNA sequencing
3.23 Best practices for bulk RNA sequencing in the wet lab
3.24 Ethical considerations in transcriptomics research
3.25 Conclusion
References

4. Single-cell RNA sequencing in wet lab
Anton Buzdin, Xinmin Li, Maria Suntsova, and Ilya Belalov

4.1 Overview of what scRNA-seq is and why it is indispensable in research
4.2 Types of scRNA-seq protocols
4.3 Reagents and materials
4.4 scRNA-seq platforms: Comparison of chromium 10X, Drop-seq, and Smart-seq
4.5 Sample preparation and handling
4.6 Single-cell isolation techniques
4.7 Library preparation
4.8 Sequencing technologies
4.9 Techniques to ensure the integrity and quality of scRNA-seq data
4.10 Data analysis methods to validate the quality of sequencing results
4.11 Recommended sequencing depths for different types of analyses
4.12 Overview of computational tools and methods for analyzing scRNA-seq data
4.13 Integrating scRNA-seq data with other data types
4.14 Research and clinical applications of scRNA-seq
4.15 Innovations and future trends in scRNA-seq technology
References
Further reading

5. Spatial transcriptomics
Ilya Belalov, Ye Wang, Anton Buzdin, and Xinmin Li

5.1 Introduction
5.2 Spatial transcriptomics technologies
5.3 Imaging-based technologies
5.4 Sequencing-based technologies
5.5 Applications for basic, translational, and clinical research
5.6 Considerations for a first ST experiment
5.7 Concluding remarks
References

6. High-throughput and multiplex PCR in wet laboratory to measure gene expression
Anton Buzdin, Ilya Belalov, and Galina Zakharova

6.1 Introduction
6.2 Importance of high-throughput and multiplex PCR
6.3 Protocols for high-throughput and multiplex PCR
6.4 Reagents and requirements for material
6.5 Material requirements
6.6 Technical and analytical quality control (QC)
6.7 Analytical QC
6.8 Dynamic intervals and sensitivity
6.9 Sensitivity and specificity
6.10 Applications and sequencing depth recommendations
6.11 Recommended sequencing depths
6.12 Key studies and contributions
6.13 Further reading
6.14 Future directions and innovations
6.15 Challenges and limitations
6.16 Conclusion
References

7. Processing primary gene expression data: Normalization, harmonization and data quality control
Nicolas Borisov, Maksim Sorokin, and Anton Buzdin

7.1 Background
7.2 Principles of harmonization algorithms
7.3 Evaluation of the quality of harmonization
7.4 Validation of Shambhala-1/2 protocol on bulk mRNA-seq profiles
7.5 Differential clustering of human normal and cancer expression profiles
7.6 Correlation, regression, and sign-change analysis of cancer drug balanced efficiency scores after application of different methods of harmonization
7.7 Retention of biological properties after uniformly shaped harmonization
7.8 Validation of Shambhala-1/2 protocol on sc-mRNA-seq profiles
7.9 Discussion
Abbreviations
References

8. Data check and differential gene analysis
Maksim Sorokin and Anton Buzdin

8.1 Primary RNA expression data analysis
8.2 Finding differentially expressed genes and gene sets
8.3 Concluding remarks
References

9. Molecular pathway analysis using transcriptomic data
Nicolas Borisov, Maksim Sorokin, Igor Kovalchuk, Marianna Zolotovskaia, and Anton Buzdin

9.1 Background
9.2 Topology-based methods for pathway activation assessment
9.3 Methods for database preparation for pathway activation assessment
9.4 Personalized ranking of cancer drugs based on pathway activation levels
9.5 Concluding remarks
Abbreviations
References

10. Omics wise analysis of RNA splicing
Alexander Modestov, Anton Buzdin, and Vladimir Prassolov

10.1 Introduction
10.2 Omics technologies for RNA splicing analysis
10.3 RNA splicing products and regulators as biomarkers and targets of therapy
10.4 Challenges and future prospects
References

11. Transcriptome-wide analysis of protein synthesis: Ribosome profiling and beyond
Sergey E. Dmitriev, Daniil Luppov, Leonid M. Kats, Aleksandra S. Anisimova, and Ilya M. Terenin

11.1 Introduction
11.2 Ribosome profiling quantifies gene expression at the translational level
11.3 Ribosome profiling deciphers cryptic coding potential of the genome
11.4 Analyses of ribosome positional distribution and transcriptome-wide metagene profile reveal new phenomena
11.5 Molecular mechanisms revealed by variants of ribosome profiling and relative methods
11.6 Technical challenges, limitations, and artifacts in ribosome profiling studies
11.7 Ribosome profiling of cultured mammalian cells: An example protocol
Acknowledgments
References
Further readings

12. Transcriptomic biomarkers in biomedicine
Anton Buzdin, Alf Giese, Xinmin Li, and Ye Wang

12.1 Theoretical background
12.2 Molecular pathway biomarkers in oncology
12.3 Other applications of pathway biomarker analysis in biomedicine
12.4 Conclusion
References

13. Translational transcriptomics for personalized oncology
Anton Buzdin, Alexander Seryakov, Marianna Zolotovskaia, Maksim Sorokin, Victor Tkachev, Alf Giese, Marina Sekacheva, Elena Poddubskaya, Julian Rozenberg, and Tharaa Mohammad

13.1 Transcriptomics in clinical oncology
13.2 Detection of oncogenic fusion events
13.3 Detection of pathogenic splicing or exon skipping events
13.4 Gene signatures
13.5 Algorithmic approach in personalized oncology
13.6 Molecular pathway approach for personalized prediction of cancer drug efficacy
13.7 Concluding remarks
References

14. Transcriptomics for modern biotechnology
Anton Buzdin, Denis Kuzmin, Andrew Garazha, and Ilya Belalov

14.1 Introduction
14.2 High-throughput transcriptomic pipelines
14.3 Oncobox pathway activation levels quantization
14.4 Connectivity map assay
14.5 Databases and software for transcriptomic analysis
14.6 Industrial and startup landscape
14.7 Case studies and recent advances
14.8 Conclusion
References

15. Transcriptomics and quantitative proteomics: Competition or symbiosis?
Anton Buzdin, Sergey Moshkovskii, Xinmin Li, and Ilya Belalov

15.1 Introduction
15.2 Rationale for direct protein level measurement
15.3 Current progress in quantitative proteomics
15.4 Quality metrics comparison
15.5 Gene products interrogated
15.6 Integration of multi-omics data
15.7 Future perspectives
15.8 Conclusion
References

16. Quality assessment of differentially expressed gene signatures
Alexey Stupnikov and Anton Buzdin

16.1 Introduction
16.2 Conclusions
Acknowledgments
References

17. Detection of fusion transcripts by RNA-sequencing data
Ivan Musatov, Maksim Sorokin, and Anton Buzdin

17.1 Introduction
17.2 Basic software methods for detecting fusion genes
17.3 Algorithms based on alignment. STAR and STAR-Fusion
17.4 LongGF
17.5 Pros and cons of existing tools for bioinformatic fusion detection
17.6 Prospects for the development of tools and approaches to search for fusion transcripts
17.7 Conclusion
References

Index