Complete General Workflow for Single-Cell Analysis - Quality Control
Single-cell analysis refers to the technique of studying gene expression, protein activity, or other biomolecules at the single-cell level. This analytical method can reveal cell heterogeneity and subtle differences between individual cells. The general process of single-cell analysis is as follows.
1. Sample Preparation:
First, obtain a single-cell suspension through methods such as tissue dissociation or flow cytometry sorting.
2. Single-Cell Capture and Reverse Transcription:
Capture individual cells using methods like microfluidic chips, fluorescence-activated cell sorting (FACS), or droplet technology, and transcribe the mRNA within the cells into cDNA.
3. Library Construction and Sequencing:
Construct a library using cDNA, followed by high-throughput sequencing.
4. Quality Control (QC):
- Cell quality control: Exclude dead or damaged cells based on indicators such as cell size, morphology, and capture integrity.
- RNA quality control: Assess RNA integrity and concentration to ensure the quality of mRNA for subsequent steps.
- Sequence quality control: Use software tools to check the quality of sequencing data, removing low-quality reads, adaptor sequences, and contaminants.
- Data quality control: Exclude doublet cells, empty droplets (droplets without captured cells), or genes with low expression due to technical reasons.
5. Data Analysis:
Use bioinformatics tools for data normalization, cell type identification, and gene expression analysis.
6. Validation and Interpretation:
Experimentally validate the cell subpopulations or gene expression patterns of interest using methods such as flow cytometry or immunofluorescence labeling.
Each step requires strict quality control to ensure the accuracy and reproducibility of the final results. Especially in the QC step, controlling cell quality, RNA quality, and sequence quality is crucial as it directly affects the accuracy of data analysis.
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