To unravel single-cell RNA sequencing data, scientists can use Cell Ranger, a tool developed by 10x Genomics that combines with most 10x Genomics services. The CellRanger software empowers researchers to identify distinct cell types and unveil gene expression insights. How does it work? And how can you use it?
- CellRanger by 10x Genomics is software for analyzing single-cell RNA sequencing (scRNA-seq) data. It allows researchers to perform clustering, cell type identification, basic gene expression analysis, and more advanced analyses.
- To run CellRanger, install the software, organize your raw sequencing data, and reference genome files. Afterward, use the command line interface to start the analysis pipeline.
- After processing, the software generates an output directory containing a count matrix and other analysis outputs. This one can explore further using visualization and analysis tools like Seurat or Scanpy.
- What is it?
- What is it used for?
- How does it work?
- How can I run Cell Ranger?
- Where can I learn more?
What is CellRanger?
Cell Ranger is a software suite developed by 10x Genomics to process and analyze single-cell RNA sequencing data. It helps researchers extract valuable insights from single-cell datasets. Hence, it enables them to understand, e.g., heterogeneity and gene expression dynamics across tissues at the single-cell level.
What is CellRanger for?
Scientists primarily use Cell Ranger for analyzing scRNA-seq data. Single-cell RNA sequencing allows researchers to profile the gene expression of individual cells within a heterogeneous sample, such as a tissue or tumor. Its outputs include:
- Count tables – consisting of transcripts or gene counts per cell;
- Clusters – cell clustering results based on each cell’s gene expression profiles;
- Differential gene expression – basic analysis output of gene expression differences between clusters.
In turn, single-cell data analysts can pursue these outputs with more advanced types of single-cell data analyses. These have numerous applications, including but not limited to:
- Cell Type Identification: Researchers can classify and identify different cell types based on their unique gene expression profiles.
- Cell state analysis: By comparing gene expression patterns, researchers can identify different cellular states, such as quiescent, activated, or differentiated cells.
- Biomarker discovery: Cell Ranger can help identify genes specifically expressed in specific cell types or conditions, which can serve as potential biomarkers.
- Differential gene expression analysis: Researchers can identify differentially expressed genes between different conditions or cell types, providing insights into molecular differences.
- Cell Trajectory Analysis: By analyzing gene expression changes, researchers can infer cell developmental trajectories and transitions.
How does it work?
CellRanger processes raw single-cell sequencing data from the 10x Genomics Chromium platform through several key steps:
- Read Alignment: The raw sequencing data, which consists of short reads, is aligned to a reference genome using a suitable alignment algorithm. This step assigns reads to their respective genomic locations.
- Barcode and UMI Processing: Each cell captured by the Chromium platform has a unique barcode and unique molecular identifier (UMI). It uses these barcodes and UMIs to find low-quality cells and identify reads originating from the same cell.
- Transcript Counting: The aligned reads are then used to count the number of times each unique molecular identifier (UMI) appears for each gene. That helps in quantifying the gene expression levels in each cell.
- Data Aggregation and Quality Control: The CellRanger pipeline aggregates the counts from all cells into a count matrix, representing each cell’s gene expression profile. Quality control steps are applied to filter out low-quality cells and genes.
- Normalization and Analysis: The CellRanger pipeline normalizes the count matrix to account for cell sequencing depth differences. Then, it applies differential gene expression analysis, dimensionality reduction (such as principal component analysis or t-SNE), and clustering methods to reveal patterns and relationships among cells.
- Visualization: Scientists typically visualize the results using various tools that help them interpret and present their findings effectively. These tools include the BioTuring BBrowserX and the 10x Genomics Loupe Browser.
How can I run CellRanger?
Before running CellRanger, you will need the following:
- Raw Sequencing Data: This is the scRNA-seq data that you or we have generated using the 10x Genomics Chromium platform.
- Reference Genome: The appropriate reference genome for the organism you are studying.
- CellRanger Software: Download and install the software from the 10x Genomics website. You can also run it in the 10x Genomics cloud if you live in Canada or the US. Learn more about that on the 10x Genomics Cloud Analysis Support page.
Here is a general outline of the steps you need to take to run CellRanger:
Firstly, install CellRanger on your PC according to the instructions provided in the documentation.
Secondly, organize your raw sequencing data (FASTQ files) and ensure you have the reference genome files necessary for alignment.
Command Line Interface
CellRanger is typically run from the command line. Open a terminal or command prompt.
Run CellRanger Pipeline
- Navigate to the directory where you installed CellRanger.
- Use the appropriate command to start the CellRanger pipeline. The command will look something like this:
cellranger count –id=sample1 –transcriptome=path_to_transcriptome_folder –fastqs=path_to_fastq_folder
Here, sample1 is a unique identifier for your analysis, path_to_transcriptome_folder is the path to the reference genome files, and path_to_fastq_folder is the path to the folder containing your raw sequencing data.
It will begin processing your data. It will perform read alignment, barcode processing, transcript counting, and generate a count matrix.
Results and Output
- Once the pipeline is complete, CellRanger will generate an output directory for your analysis.
- This directory contains various files and folders containing the count matrix, quality control metrics, and other analysis outputs.
Visualization and Analysis
- You can use tools like Seurat, Scanpy, or other specialized software to further analyze and visualize the results from the count matrix generated by CellRanger.
- These tools can also help you perform clustering, dimensionality reduction, and differential expression analysis.
- Finally, to deploy professional expertise on your data set, contact our data consultancy team for help. We designed our data consultancy service for scientists who want easy access to extensive bioinformatics expertise.
Consult the official Cell Ranger documentation for detailed information on command options, troubleshooting, and best practices.
Remember that this is a simplified overview, and the actual steps and commands may vary based on your specific dataset, reference genome, and analysis goals. It’s crucial to refer to the latest 10x Genomics documentation for accurate and up-to-date instructions.
Where can I learn more?
10x Genomics provides excellent resources for learning how to use CellRanger. Their tutorials will guide you to excellence step by step. Also, they provide many tutorials and algorithm explanations for specific applications, such as capturing neutrophils or fixed RNA profiling.
Find 10x Genomics’ tutorial database here.