Single-cell RNA Sequencing (scRNA-seq)

Spatial Transcriptomics

Single-cell RNA Sequencing

Overview

• Single-cell RNA sequencing (scRNA-seq) is a technique used to analyze the transcriptome of individual cells.
• It allows for the study of gene expression profiles at the single-cell level, enabling the identification of cellular heterogeneity and rare cell types.

Key Features

• High-throughput: scRNA-seq enables the analysis of thousands of cells in a single experiment.
• High-resolution: scRNA-seq provides a detailed view of gene expression at the single-cell level.
• Sensitive: scRNA-seq can detect lowly expressed genes and subtle changes in gene expression.

Methods

• Cell isolation: Cells are isolated from tissues or cell cultures using techniques such as fluorescence-activated cell sorting (FACS) or microfluidics.
• Library preparation: Isolated cells are converted into sequencing libraries using techniques such as SMART-seq or Chromium.
• Sequencing: Sequencing libraries are then sequenced using next-generation sequencing (NGS) technologies such as Illumina or PacBio.

Analytical Techniques

• Dimensionality reduction: Techniques such as PCA, t-SNE, or UMAP are used to reduce the high-dimensional data to 2D or 3D representations.
• Clustering: Clustering algorithms such as k-means, hierarchical clustering, or DBSCAN are used to identify cell clusters and cell types.
• Differential expression analysis: Statistical tests such as DESeq2 or edgeR are used to identify differentially expressed genes between cell clusters.

Applications in Spatial Transcriptomics

• Cell-type identification: scRNA-seq can be used to identify and characterize cell types in spatially resolved transcriptomics datasets.
• Spatial gene expression analysis: scRNA-seq can be used to analyze gene expression patterns in spatially resolved transcriptomics datasets.
• Cell-cell interaction analysis: scRNA-seq can be used to analyze cell-cell interactions and communication in spatially resolved transcriptomics datasets.

Single-cell RNA Sequencing (scRNA-seq)

• Analyzes the transcriptome of individual cells, enabling the study of gene expression profiles at the single-cell level.
• Identifies cellular heterogeneity and rare cell types.

Key Features

• High-throughput: analyzes thousands of cells in a single experiment.
• High-resolution: provides a detailed view of gene expression at the single-cell level.
• Sensitive: detects lowly expressed genes and subtle changes in gene expression.

Methods

• Cell isolation: uses techniques such as FACS or microfluidics to isolate cells from tissues or cell cultures.
• Library preparation: converts isolated cells into sequencing libraries using techniques such as SMART-seq or Chromium.
• Sequencing: uses next-generation sequencing (NGS) technologies such as Illumina or PacBio.

Analytical Techniques

• Dimensionality reduction: uses techniques such as PCA, t-SNE, or UMAP to reduce high-dimensional data to 2D or 3D representations.
• Clustering: uses algorithms such as k-means, hierarchical clustering, or DBSCAN to identify cell clusters and cell types.
• Differential expression analysis: uses statistical tests such as DESeq2 or edgeR to identify differentially expressed genes between cell clusters.

Applications in Spatial Transcriptomics

• Cell-type identification: identifies and characterizes cell types in spatially resolved transcriptomics datasets.
• Spatial gene expression analysis: analyzes gene expression patterns in spatially resolved transcriptomics datasets.
• Cell-cell interaction analysis: analyzes cell-cell interactions and communication in spatially resolved transcriptomics datasets.