Single Cell Analysis II

Expression Matrix: Combines genes with cells based on barcodes and unique molecular identifiers.
Filtering Cells (Quality Control):
- Removing dead cells, often indicated by high levels of mitochondrial genes.
- Removing multiplets (cells clumped together).
Normalization: Adjusting for relative gene expression, especially when comparing different conditions or cells.
Variable Gene Identification: Determining which genes are expressed differently by individual cells.
Dimensionality Reduction: Using methods like TSNE plots or UMAP plots to visualize cell similarity.
- Cells close to each other are more similar; distant cells are more different.
Clustering: Grouping cells based on their similarity in gene expression.
Exploring Known Gene Markers: Annotating clusters based on known marker genes (e.g., CD3E for T cells).
Differential Gene Expression Analysis: Identifying genes that are differentially expressed between clusters.
Cell Type Assignment: Assigning cell types to clusters based on gene expression profiles.
Functional Annotation: Identifying the functions associated with differentially expressed genes (e.g., pro-inflammatory state).
Pseudo Time Analysis: Tracking cellular differentiation or maturation by analyzing gene expression trajectories.
- Following gene expression changes as cells differentiate (e.g., from stem cell to different cell types).
- Superimposing disease conditions to observe changes in the transition.

Cellular Resolution: Ability to identify expression profiles of individual cells and subclusters.
Heterogeneity: Revealing great heterogeneity even within one cell type.
Rare Cell Type Detection: Identifying rare cell types or subpopulations.

Used to identify cells based on differentially expressed genes.
Show the top 5 most highly expressed genes for each cell type.
Enable identification of cell types based on unique gene expression patterns.
Example: Epicardial cells have a unique set of top 5 genes not expressed by other cells.

Visualize different clusters of cells.
Demonstrate cellular heterogeneity within populations like fibroblasts.
Example: In hypertensive heart disease, a specific fibroblast population appears that is not present in healthy hearts.
- This population expresses new genes and may represent a new drug target for treating hypertensive heart failure.

Single-cell RNA sequencing can uncover new cell states or types that are missed by bulk RNA sequencing.
Bulk RNA sequencing provides an average of gene expression, masking subtle changes in specific cell types.
*Example: A study in aorta of healthy and hypertensive mice revealed novel fibroblast cluster upregulated in hypertensive vessels

Single-cell data allows quantification of cell type abundance.
Example: Stack graphs showing an increase in a novel fibroblast type (Fibro 1) in hypertensive vessels compared to healthy vessels.

Dot plots can identify unique genes expressed by specific cell clusters.
Focusing on the top 10 genes expressed by a novel population (e.g., Fibro 1) helps identify its characteristics.
These genes may be associated with disease processes like fibrosis (upregulating fibrotic genes).

Using top 10 expressed genes to identify markers for specific cells.
Example: CTHRC1 is highly expressed in Fibro 1 but not in other fibroblasts, making it a potential marker.

Highlight the expression of a specific gene.
Example: A feature plot showing CTHRC1 expression predominantly in the Fibro 1 population.

Single-cell RNA sequencing can challenge existing beliefs about cell function in disease.
Example: Myofibroblasts are traditionally thought to be the main cells driving collagen deposition in fibrotic diseases, but single-cell data suggests another cell type (Fibro CTHRC1) is also involved.

Gene ontology analysis and gene set enrichment analysis can identify functions or cellular pathways associated with specific cell types.
Pathways are associated with clusters of genes.
Example: Collagen or extracellular matrix molecules form a pathway associated with extracellular matrix deposition or fibrosis.

The X-axis shows the number of genes associated with each pathway.
Graph visualizes genes that are upregulated in disease.
Single-cell RNA sequencing enables multiple comparisons, such as comparing a specific cell to an entire healthy organ.

Orders cells along a developmental pathway.
Reveals insights not obtainable with bulk RNA sequencing.
Identifies directionality of differentiating cells through genes that are switched on as they head toward a different cell type.
Limitations: Requires determination of a starting point (origin), which can greatly influence analysis.
Selecting genes is often done by reading the literature to look at genes turned on by certain cells during differentiation or maturation processes.

Ligands (e.g., cytokines, cellular mediators) bind to receptors on other cells, initiating cell signaling.

Visualizes cell-to-cell communication networks.
Arrowheads show the direction of signals between cells, with colors indicating cell types.

Small Cell Populations: If the population of cells is small, or a unique cell type that's not at a high level, an imbalance in sequencing can occur.
Technical Noise: Noisy data can influence the data in the sense that amplification biases or even dropouts of signals can occur.
Cellular Heterogeneity: Can be problematic with imbalanced populations.
Lowly Expressed Genes: Unlikely to be detected with single-cell RNAC.
Computational Complexity: Requires specialized tools, software packages, computing, coding, and bioinformatic skills.

Artefacts In Silica: High risk of artefacts with analysis using a computer.
Relative Gene Expression: Lowly expressed genes may not be picked up, or really abundant cell information is picked up.
Validation Needed: Relative gene expression should be validated with validation experiments.