Course will cover single-cell data, spanning genes, proteins, and phenotypes. Students will engage in hands-on data analysis experiments, device fabrication, protein electrophoresis, and protein signal processing, emphasizing practical application. Students will undertake a final project addressing contemporary disease problems through single-cell analysis, applying acquired bioinformatic skills to process data.
Athena Title
Single Cell Analysis
Prerequisite
Permission of department
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Student learning Outcomes
Students will be able to appraise single-cell analysis techniques and their significance in modern biology, critiquing different techniques and their ability to unravel cellular diversity and extract meaningful insights into health and disease.
Students will be able to compare and contrast of key single-cell analysis methods, including scRNA-seq, scDNA-seq, epigenomics, and protein analysis, and differentiate the principles and practical applications of each.
Students will be able to execute protocols focused on microfluidic device fabrication, protein electrophoresis techniques, and protein signal processing using MATLAB, and interpret the performance of microfluidic experiments and data analysis.
Students will be able to demonstrate proficiency in using state-of-the-art bioinformatics tools for single-cell data analysis, including data preprocessing, quality control, normalization, clustering, and interpretation, ensuring the ability to analyze and interpret single-cell datasets.
Students will be able to examine the application of single-cell analysis in diverse research fields, including developmental biology, cancer research, immunology, and neurobiology, through the examination of case studies and real-world examples.
Students will be able to construct a class presentation that assesses the challenges and limitations in single-cell analysis and proposes innovative solutions, investigating the evolving landscape of single-cell biology research.
Topical Outline
Week 1: Introduction to Single-Cell Analysis
Overview of single-cell analysis and its significance in biology and medicine
Historical development and key milestones in single-cell analysis
Week 2: Single-Cell Isolation Techniques
Microfluidic techniques for single-cell isolation
Fluorescence-activated cell sorting (FACS) and its applications
Week 3-4: Single-Cell RNA Sequencing (scRNA-seq)
Principles of scRNA-seq
Sample preparation, library preparation, and sequencing platforms
Data analysis pipelines for scRNA-seq (e.g., quality control, normalization, clustering)
Practical scRNA-seq data analysis exercises
Week 5-6: Single-Cell Protein Analysis
Mass spectrometry-based methods for single-cell protein analysis
Antibody-based techniques (single cell western blot)
Hands-on lab sessions on single-cell protein analysis
Week 7-8: Single-Cell DNA Sequencing
Single-cell DNA sequencing methods (scDNA-seq)
Applications in genomics, cancer research, and evolutionary biology
Data analysis challenges in scDNA-seq
Week 9-10: Single-Cell Epigenomics
Single-cell DNA methylation analysis
Single-cell ATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing)
Role of epigenetic diversity in development and disease
Week 11-12: Spatial Transcriptomics
Introduction to spatial transcriptomics techniques (e.g., in situ hybridization, spatial transcriptomics platforms)
Analyzing spatially resolved single-cell data
Case studies and applications
Week 13-14: Integration of Single-Cell Data
Combining scRNA-seq, scDNA-seq, and other single-cell omics data
Multi-omics data integration methods
Systems biology and network analysis
Week 15-16: Emerging Trends and Student Presentations
Discuss current trends in single-cell analysis research
Allow students to present on a single-cell analysis topic of their choice
Discuss potential research proposals and projects
