10x Genomics Single-Cell Transcriptome Sequencing Analysis
Description
Single-cell RNA sequencing (scRNA-seq) is an innovative technology that sequences the transcriptome at the single-cell level. This allows researchers to study gene expression within individual cells and address the issue of cellular heterogeneity that traditional tissue sample sequencing cannot resolve. It makes it possible to analyze the behavior and mechanisms of individual cells and their relationship with the organism.
The 10X Genomics single-cell RNA sequencing platform employs techniques such as microfluidics, droplet encapsulation, and barcode tagging to achieve high-throughput cell capture. This platform can simultaneously isolate and label 500–10,000 single cells, obtaining 3' end transcriptome information from each cell. It offers advantages like high cell throughput, low library construction cost, and short capture cycles.
This technology is primarily used for cell typing and marker identification, enabling the classification of cell populations and the detection of gene expression differences between these populations. Additionally, it can predict cell differentiation and study developmental trajectories. scRNA-seq is increasingly important in the fields of disease, immunology, oncology, and research on tissues, organs, and development.
10X Genomics Data Analysis Workflow
- Cell Thawing and Viability Counting
- Droplet Generation using 10X Chromium Controller
- Evaluate Droplet Generation Status and Proceed if Successful
- Emulsion PCR: Reverse Transcription and Barcoding
- Break Oil-in-Water Emulsion
- Library Preparation with Sample Indexing
- Library Quality Control
- Sequencing
- Data Analysis
Q&A
Question: How to Identify the Specific Cell Types in a Cluster？
Identify Based on Known Marker Genes:
- Using Prior Experience and Literature: Certain cell types are known to specifically express certain marker genes. For example, T cells express CD3/CD4, and B cells express CD19/CD20.
- Determine Based on Marker Gene Expression in Cluster Cells: Use Loupe Cell Browser or other software to label cells expressing marker genes. This helps to infer which cluster represents a particular cell type.
- Identify Based on Differentially Expressed Genes or Marker Genes Identified by Software: Tools like Cell Ranger and Monocle2 (or other single-cell analysis software) provide results for differentially expressed genes in clusters. If marker genes for a specific cell type are known, check the differential gene list to see if these marker genes are present, which can help infer the cell type of the cluster.
Identify Based on Functional Pathways of Genes:
- When Specific Marker Genes Are Unknown or Not Found in a Cluster: Use the KEGG and GO pathways of differentially expressed or co-expressed genes within a cluster to infer the cell's function and, consequently, its type.
Question: What Is the Use of Pseudotime Analysis?
In many biological processes, cells are not all at the same developmental stage simultaneously. When studying cell differentiation using single-cell gene expression data, the captured cells are often at various stages: some may be undifferentiated, some may be in intermediate states, and others may have completed differentiation. As a result, gene expression across the sample can be highly variable and complex to analyze.
Pseudotime analysis uses gene expression data to compute the temporal distance between cells and estimate the shortest path that aligns all cells along a pseudotime trajectory. This approach helps to understand the types of cells present in the sample and the process of cell differentiation.
Question: What Is the Relationship Between the Analysis Results of CELL-RANGER, MONOCLE2, or Other Software in the Report?
The analyses performed by different software are independent of each other. Although there is some overlap in the specific analysis components, such as cell clustering, differential gene identification, and functional annotation, the algorithms used by each software are different. It is important to keep this in mind when reviewing results and conducting subsequent interpretations.
Cell-Ranger: This is the official analysis software provided by 10x Genomics. Its results are primarily available in the original web report, with limited analysis content and less flexibility for modification or customization.
Monocle2: Developed by Cole Trapnell from the University of Washington (also known for TopHat and Cufflinks), Monocle2 is a popular single-cell analysis software package. It covers most aspects of single-cell analysis and offers greater flexibility with scripts, allowing modifications to parameters and methods to achieve optimal results.
SC3: Additional analysis can be performed using SC3 software, allowing for adjustments or re-analysis based on the specific requirements of subsequent projects.
Different software have variations in their analysis content and result presentation. The choice of which analysis results to use mainly depends on the client's specific requirements and how well the results align with their expectations.
