Before running the pipeline, check that you have the following inputs prepared:

• The corresponding CytAssist image in TIFF format (--cytaimage).
• Microscope image (optional) in either TIFF, QPTIFF, BTF, or JPEG format:
  • --image for a brightfield microscope image.
  • --darkimage for a dark background fluorescence microscope image.
  • --colorizedimage for a composite colored fluorescence microscope image.
• Slide parameters specified by:
  • --slide & --area if spaceranger has access to internet (optional with CytAssist metadata).
  • --slidefile, --slide & --area if spaceranger has no access to the internet (the slide layout file must be directly downloaded.
  • --unknown-slide if Visium slide details are unknown.
• The reference transcriptome (--transcriptome). Human and mouse references are available for download, or build a custom reference.
• The probe set CSV (--probe-set). These files can be found in the probe_sets directory in the Space Ranger software package or downloaded here. When analyzing Visium HD 3' data, do not specify this parameter.
• In most cases, Space Ranger will perform automatic image to fiducial alignment and tissue detection. When automatic alignment fails, you must also run manual alignment in Loupe Browser and specify the alignment JSON file with the --loupe-alignment option.

The following command assumes all inputs are in the current working directory; change paths as needed. Run a separate instance of the spaceranger count pipeline on each capture area.

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 spaceranger count --id=hd_count \
 --transcriptome=/path/to/refdata-gex-GRCh38-2020-A \
 --fastqs=/path/to/fastq \
 --probe-set=/path/to/Visium_Human_Transcriptome_Probe_Set_v2.0_GRCh38-2020-A.csv \
 --slide=H1-YD7CDZK \
 --area=A1 \
 --cytaimage=/path/to/CAVG10539_2023-11-16_14-56-24_APPS115_H1-YD7CDZK_A1_S11088.tif \
 --image=/path/to/APPS115_11088_rescan_01.btf \
 --create-bam=false

For a complete list of arguments and options, see the Command Line Argument Reference, or run spaceranger count --help.

Space Ranger v4.0 and later includes support for nucleus and cell segmentation, and downstream secondary analysis based on the segmented results, in the spaceranger count pipeline. By default, the pipeline will perform cell segmentation automatically as long as an H&E tissue image is provided using the --image option (fluorescence images are not supported). Users now have the following options:

• Users can override Space Ranger's default nucleus segmentation algorithm by providing their own nucleus segmentation mask with the --custom-segmentation-file option. If this is provided to spaceranger count, the --nucleus-expansion-distance-micron option must also be set. Custom segmentation masks can be generated using third party tools or the spaceranger segment pipeline (also new in v4.0).
• For exceptionally large cells (expected diameter >40 µm), users can specify the maximum nucleus diameter with the --max-nucleus-diameter-px option.
• Space Ranger's segmentation algorithm can be disabled altogether by setting --nucleus-segmentation=FALSE.

For more information, see the spaceranger segment, Space Ranger Commands and the Image Processing Algorithms pages.

Starting with v4.1, you can enable cell annotations during a count run. The library type must be Visium HD or Visium HD 3', the species must be human or mouse, and cell segmentation must be enabled.

• --matrix specifies the path to the filtered feature-barcode matrix in H5 format.
• --cell-annotation-model determines the 10x Genomics cloud-based model used for cell type annotation. When set to auto, the pipeline automatically selects the appropriate model(s). Currently available models are human_pca_v1_beta (10x human model), mouse_pca_v1_beta (10x mouse model).
• --tenx-cloud-token-path is the path to the 10x Genomics Cloud Access Token, which is necessary for communication with the cloud-based models. If not supplied, will default to the location stored through spaceranger cloud auth setup. If the token file does not exist, there is an error.
• If you do not provide --cell-annotation-model and your sample is derived from a human, the Pan-Human Azimuth model will still be run locally.
• If you wish to disable all annotations, use the --disable-cell-annotation option.

Example command:

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 spaceranger count --id=hd_count \
     --transcriptome=/path/to/refdata-gex-GRCh38-2020-A \
     --fastqs=/path/to/fastq \
     --probe-set=/path/to/Visium_Human_Transcriptome_Probe_Set_v2.0_GRCh38-2020-A.csv \
     --slide=H1-YD7CDZK \
     --area=A1 \
     --cytaimage=/path/to/CAVG10539_2023-11-16_14-56-24_APPS115_H1-YD7CDZK_A1_S11088.tif \
     --image=/path/to/APPS115_11088_rescan_01.btf \
     --create-bam=false
     --tenx-cloud-token-path=/path/to/10xcloud_token.json \
     --cell-annotation-model=auto

To learn how to generate and access your 10x Cloud Analysis token, visit the spaceranger annotate page.

Please note that the 10x Genomics cell type annotation models are currently a beta feature.

A successful spaceranger count run concludes with a message similar to this:

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Outputs:
- Run summary HTML:                                  /outs/web_summary.html
- Outputs of spatial pipeline:
    Slide image annotated with fiducial alignment:                             /outs/spatial/aligned_fiducials.jpg
    Slide image annotated with detected tissue:                                /outs/spatial/detected_tissue_image.jpg

...

2024-02-26 18:00:10 [perform] Serializing pipestance performance data.
Waiting 6 seconds for UI to do final refresh.
Pipestance completed successfully!

To continue, see Understanding Space Ranger Outputs. It is recommended to check the web_summary.html file first to assess quality control metrics.