Processing Spatial Transcriptomics Data Using Space Ranger

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Tags: pipeline, RNA-seq

Using spaceranger to process spatial transcriptomics data involves several steps, from preparing the necessary input files to running the analysis and interpreting the results. Below, I'll provide a comprehensive example of how to use spaceranger for processing a hypothetical dataset.

Prerequisites:

Install Space Ranger: Make sure spaceranger is installed on your system. You can download it from the 10x Genomics website.
Required Data: You need three key pieces of data:
   - Spatial Gene Expression FASTQ files: These are generated by the sequencing instrument.
   - Spatial Gene Expression Image: An image of the tissue section.
   - Spatial Gene Expression Slide and Capture Area: This information is usually provided by the manufacturer.

Example Workflow:

  1. Prepare Input Files

    Ensure you have the following files:

    • FASTQ files: Usually named something like SampleName_S1_L001_R1_001.fastq.gz, SampleName_S1_L001_R2_001.fastq.gz.
    • A tissue image file, like tissue_hires_image.png.
    • A slide and capture area file, often provided by the manufacturer.
  2. Create a Reference Dataset

    If you don’t already have a reference dataset for your species of interest, you can create one using the spaceranger mkref command. For example:

    spaceranger mkref --genome=GRCh38 --fasta=GRCh38.fasta --genes=genes.gtf --nthreads=8 --memgb=64
    

    Replace GRCh38.fasta and genes.gtf with the paths to your genome FASTA and gene annotation GTF files, respectively.

  3. Running Space Ranger

    The core of the spaceranger workflow is the count command, which aligns reads, generates feature-barcode matrices, and performs spatial analysis. The command looks something like this:

    spaceranger count --id=sample_output \
                      --transcriptome=/path/to/refdata-cellranger-GRCh38-3.0.0 \
                      --fastqs=/path/to/fastqs \
                      --sample=SampleName \
                      --image=/path/to/tissue_hires_image.png \
                      --slide=V19J01-123 \
                      --area=A1 \
                      --nthreads=16 \
                      --memgb=64
    
        --id: The name of the output folder.
        --transcriptome: Path to the reference dataset.
        --fastqs: Path to the folder containing FASTQ files.
        --sample: Name of the sample.
        --image: Path to the high-resolution tissue image.
        --slide and --area: Slide and capture area information.
        --nthreads and --memgb: Specify computational resources.
    
  4. Analyze Output

    Once the spaceranger count command is complete, it will generate an output directory (sample_output in this case) containing several files, including:

    • Feature-barcode matrices
    • Analysis files (clustering, dimensionality reduction, etc.)
    • Images showing gene expression overlaid on the tissue image
  5. Further Analysis

    The resulting data can be further analyzed using tools like Seurat (R), Scanpy (Python), or Loupe Browser (from 10x Genomics).

Additional Notes:

Always refer to the specific version of the spaceranger documentation you are using, as commands and options might vary slightly between versions.
Ensure your computational environment has enough resources (CPU, memory) to handle the dataset size.
This workflow is a basic example. Depending on your specific experiment and data, additional steps or modifications might be necessary.

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