Introduction to Single-Cell Omics

What is flow cytometry?

• Method used to measure properties of particles in a fluid as they pass through one or several laser beams.
• Rapid: Up to 20000 cells per second.
• Can measure multiple properties of cells, such as size, granularity, and presence of specific proteins.
• Could be used for phenotyping, metabolism, and cell cycle analysis.

Light

• Frequency and Length of light waves.
• Forward Scatter (FSC): Measures light intensity scattered in the forward direction, which is proportional to cell size.
• Side Scatter (SSC): Measures light scattered at a 90-degree angle, which represents the type of cell and its internal complexity.

Flow Cytometer

• Fluidics: Transports cells in a stream of fluid to the interrogation point.
• Lasers & Optics:
  • Stack or Co-linear lasers.
  • Lasers and filters can define how many colours can be used.
• Detectors & Signal Pulse:
  • Photomultiplier tubes (PMTs) or Avalanche photodiodes (APDs).
  • Signal intensity can be used to measure cell size, granularity, and fluorescence.

Cell Sorting

• Electrostatic cell sorting: Cells are charged and deflected into different containers.
• Purpose: To isolate specific cell types or subpopulations for further analysis.

Fluorescence

• Fluorochromes: Emit light at a longer wavelength than the excitation light.
• Purpose: To label specific cell components for detection and quantification.

Single-Cell Omics

Why Single-cell genomics

• Healthy tissues
• Disease states
• Genetic variation
• Drug response

Method for isolating single cells

• Manual picking: Using a micropipette to isolate cells.
• Multiplexing: Isolating multiple cells at once.
• Integrated fluidic circuits: Microfluidics for single-cell isolation.
• Liquid handling robots: Automated single-cell isolation.
• Nanodroplets: Encapsulating single cells in droplets.
• Picowells: Microfabricated wells for single-cell isolation.
• In situ barcoding: Barcoding cells in their native environment.
• Spatial transcriptomics: Capturing spatial information of single cells.

Common platforms for single-cell genomics

• 10x Genomics: Single-cell RNA-seq and spatial transcriptomics (droplet-based).

Important aspects in single-cell RNA-seq

• How many cells to sequence?

  • Only 10~20% of the transcripts are captured.
  • More cells -> More represnetative data.
  • More cells -> More confidence in defining clusters.

• Multiplets in single-cell data:

  • Need accurate cell counting.
  • No aggregation of cells.
  • Enough cells for washer.
  • Quality matters

• Cell viability:

  • Dying cells meaning fewer transcripts.
  • Incorrect data from background ambient RNA.
  • Ideal viability is >90%, >70% is good.
  • Fresh cells if possible.

Advanced Topics in Single-Cell Omics

• T and B Cell Receptor Sequencing:

  • Implications in Immunology: T and B cell receptor sequencing provides detailed insights into the adaptive immune response by profiling the diversity and clonality of T and B cell populations. This is crucial for understanding immune system dynamics, tracking immune responses, and identifying specific receptors in health and disease.
  • Disease Research Applications: This technique is essential in vaccine development, autoimmunity research, cancer immunotherapy, and understanding infectious diseases. It allows for the identification of antigen-specific lymphocytes and monitoring of how these populations change in response to disease or treatment.

• Multiomic Techniques:

  • CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing): A technology that combines single-cell RNA sequencing with antibody-mediated detection of protein expression. This dual approach allows for simultaneous profiling of the transcriptome and proteome of individual cells, providing a comprehensive view of cell states and functions.
  • Combined ATAC-seq and RNA-seq: This technique integrates the analysis of open chromatin regions (ATAC-seq) with gene expression profiling (RNA-seq) at a single-cell level. It reveals how chromatin accessibility impacts gene expression, providing insights into regulatory mechanisms and gene regulatory networks.

• Single-Cell RNA Sequencing:

  • Methodologies: Single-cell RNA sequencing (scRNA-seq) involves isolating individual cells, reverse transcribing their RNA into cDNA, and then sequencing the cDNA to analyze the transcriptome. Advances in technology have led to the development of various platforms and methods for scRNA-seq, each with its advantages and specific applications.
  • Data Analysis: The analysis of scRNA-seq data involves preprocessing steps such as quality control and normalization, followed by clustering analysis, differential gene expression analysis, and the identification of cell types and states. Computational tools and algorithms are continually evolving to handle the complexity and variability of scRNA-seq data.

• Spatial Transcriptomics:

  • Understanding Tissue Context: Spatial transcriptomics techniques allow researchers to visualize the spatial distribution of transcripts within tissues, adding a crucial layer of information by preserving the tissue architecture and context.
  • Applications: This field is rapidly advancing and has significant implications for understanding tissue organization, disease pathology, and the interaction of cells within their microenvironment.

• Chromatin Accessibility and Epigenomics:

  • Single-Cell ATAC-seq (Assay for Transposase-Accessible Chromatin): Provides insights into the regulatory landscape of individual cells by mapping open chromatin regions. This can reveal how accessibility changes during development, in disease, or in response to environmental factors.
  • Integrative Approaches: Combining chromatin accessibility data with gene expression and other epigenetic marks at a single-cell level for a more comprehensive understanding of cellular regulation and identity.

The field of single-cell omics is rapidly evolving, with new technologies and methodologies constantly emerging. Advanced topics like T and B cell receptor sequencing, multiomic techniques, and spatial transcriptomics are pushing the boundaries of what is possible, leading to groundbreaking discoveries in biology, medicine, and therapeutics.

Section 8: Designing Single-Cell Studies

• Considerations:

  • Experimental Objectives: Clearly define the research question and objectives. Determine whether single-cell resolution is necessary and what it can specifically contribute to understanding the biological system or disease in question.
  • Sample Preparation: Choose appropriate methods for cell isolation and preservation. Consider the type of cells or tissue, the presence of sensitive or rare cell types, and how the preparation method might affect cell viability and integrity.
  • Technology and Method Selection: Decide on the most suitable single-cell technology based on the study’s goals, available resources, and the type of data needed (e.g., RNA, DNA, protein, chromatin). Consider factors like throughput, sensitivity, specificity, and cost.
  • Controls and Replicates: Design the experiment to include appropriate controls and sufficient replicates to ensure that results are robust, reproducible, and interpretable.

• Challenges and Solutions:

  • Technical Noise and Variability: Single-cell experiments can be subject to high levels of technical noise. Solutions include using UMIs (Unique Molecular Identifiers) for more accurate quantification, optimizing cell capture and library preparation methods, and employing robust statistical methods for data analysis.
  • Data Complexity and Analysis: Single-cell data is often high-dimensional and complex. Solutions involve using advanced bioinformatics tools and algorithms for data normalization, dimensionality reduction, clustering, and visualization. Collaboration with experienced bioinformaticians or investing in training can be crucial.
  • Cost and Resource Intensity: Single-cell studies can be resource-intensive and expensive. Solutions include careful planning to optimize the use of resources, seeking collaborations, and considering alternative or complementary methods that might provide similar insights at a lower cost.
  • Ethical and Regulatory Considerations: Particularly in clinical studies, consider ethical implications and comply with regulatory requirements for human subjects research, data privacy, and genomic data sharing.

Designing single-cell studies requires careful consideration of multiple factors, from the formulation of research questions to the selection of appropriate technologies and the analysis of complex data. Anticipating challenges and planning accordingly can significantly improve the quality and impact of single-cell research.

Section 9: Future Directions

• Emerging Trends:

  • Technological Advancements: Ongoing innovations in microfluidics, optics, and computational methods are expected to further increase the throughput, resolution, and affordability of single-cell analysis. Techniques like single-cell multi-omics, which simultaneously measure multiple cellular parameters, are becoming more accessible and will provide a more holistic view of cell function and identity.
  • Personalized Medicine: As our understanding of the cellular heterogeneity in tissues and tumors improves, single-cell analysis will likely play a pivotal role in personalized and precision medicine. It could enable more accurate disease classification, identification of therapeutic targets, and monitoring of treatment responses at an individual cell level.
  • Understanding Complex Systems: Single-cell techniques will continue to unravel the complexity of biological systems, including the brain, immune system, and developing embryos. This could lead to significant breakthroughs in neuroscience, immunology, developmental biology, and more.

• Integration with Other Fields:

  • Systems Biology: Combining single-cell analysis with systems biology approaches will enhance our understanding of the emergent properties of biological systems. It will allow researchers to build more accurate models of cellular behavior, interactions, and system dynamics.
  • Synthetic Biology: Single-cell techniques can be used to monitor and understand the behaviors of engineered cells in synthetic biology applications. This integration can lead to the development of more sophisticated and controllable synthetic organisms and systems.
  • Ecology and Evolution: Applying single-cell genomics in ecology and evolutionary biology can provide insights into the microevolutionary processes and ecological interactions at the level of individual cells, offering a new perspective on the dynamics of microbial communities and ecosystems.
  • Clinical Diagnostics and Therapeutics: The integration of single-cell analysis into clinical diagnostics will continue to grow, particularly in cancer research, infectious diseases, and immune disorders. It offers the potential for more detailed disease diagnostics, monitoring, and tailored therapeutic strategies.

The future of single-cell analysis is incredibly promising, with ongoing innovations driving the field forward. As new technologies emerge and integrate with various biological disciplines, the potential for discovery and application seems boundl

Section 10: Summary and Key Takeaways

• Recap:

  • Fundamentals of Flow Cytometry: Flow cytometry is a robust technique for analyzing physical and chemical characteristics of cells or particles, utilizing principles of light scattering and fluorescence.
  • Single-Cell Analysis: The field has revolutionized our understanding of cellular heterogeneity, allowing for detailed analysis of individual cells’ genomic, transcriptomic, and proteomic profiles.
  • Advanced Applications: Techniques like T and B cell receptor sequencing, multiomic analysis, and spatial transcriptomics are expanding the capabilities of single-cell analysis, providing comprehensive insights into cellular function and disease.
  • Experimental Considerations: Effective design and execution of single-cell studies require understanding of the technology, careful planning, and awareness of potential challenges.
  • Future Directions: The field is rapidly evolving with technological innovations enhancing throughput, resolution, and integration with other disciplines, promising to unlock further mysteries of biology and medicine.

• Further Reading:

  • Books and Reviews:
    • “Flow Cytometry: First Principles” by Alice Longobardi Givan.
    • “Single-Cell Analysis: Methods and Protocols” edited by Anja M. Junker and Michael Kiehntopf.
    • “Single Cell Sequencing and Systems Immunology” edited by Xiangdong Wang.
  • Research Articles: Look for recent articles in journals like Nature, Science, Cell, and specialized journals in genomics and cytometry for the latest research findings and technological advancements.
  • Online Resources:
    • Nature’s Focus on Single-Cell Biology: A collection of research, reviews, and news articles about single-cell biology.
    • 10x Genomics’ Resource Center: Offers webinars, tutorials, and application notes related to single-cell genomics.
    • Flow Cytometry Community Forums and Websites: For practical advice, troubleshooting, and staying updated with the latest trends and techniques.

This chapter provides a comprehensive overview of flow cytometry and single-cell omics, from basic principles and techniques to advanced applications and future directions. The key takeaways emphasize the importance of these methods in understanding the complexity of biological systems and their potential to drive forward research and clinical