Stereo-CITE FF

This tutorial will teach you how to run SAW count pipeline on the Stereo-seq chip derived from a fresh frozen (FF) mouse spleen in Stereo-CITE Proteo-Transcriptomics Solution.

Prerequisites

To run SAW count pipeline smoothly, you should:

• Be acquainted with the Linux system.

• Be familiar with running command line tools.

• Ensure access to a system that meets the minimum system requirements.

Adequate storage and sufficient permissions should be paid more attention to, before running pipelines.

Overview of SAW count pipeline

Stereo-seq sequencing data from FF tissues is analyzed with SAW count.

The pipeline usually begins with:

• a chip mask file (recording CIDs of the Stereo-seq chip),

• gene expression and ADT FASTQ files (from Stereo-seq sequencing),

• a reference library (chosen by the organism information), including the protein panel and transcriptome reference. You can refer to Reference libraries and build the reference library in CSV format.

• one or more microscope images (TIFF or image .tar.gz from StereoMap).

The compressed image .tar.gz file, from StereoMap, saves the original microscope images and the QC information.

Output results mainly include:

• BAMs of alignment and annotation,

• processed images,

• gene and protein expression matrices at different dimensions,

• transcriptomics clustering and differential expression analysis,

• proteomics clustering,

• an integrated visualization.tar.gz for StereoMap.

Demo data

Demo data of the mouse spleen from Stereo-seq Chip T is provided in this tutorial.

Key features of demo - C04776D6:

• Chip size: 1cm * 1cm (S1)

• Bin1: 500nm * 500nm

• Tissue section of 10μm thickness

• DAPI-stained image acquired using Motic

The dataset page allows you to download the chip mask file, the raw sequencing files in FASTQ format, a TIFF image or an image tar.gz, a transcriptome reference, and a protein panel. For better organization, creating new folders for corresponding data is a wise choice.

$ cd /saw

# Create sub-folders of different datasets
$ mkdir -p datasets/STOmics-RNA-fastqs datasets/STOmics-ADT-fastqs datasets/mask datasets/image datasets/reference

Command lines

Please create the reference library ref_libraries.csv before submitting the task. You can refer to Reference libraries and build the reference library.

Set up SAW count analysis command in your working directory.

saw count \    
    --id=<task_id> \
    --sn=<SN> \
    --omics=transcriptomics,proteomics \
    --kit-version="Stereo-CITE T FF V1.1" \
    --sequencing-type="PE75_50+100" \
    --chip-mask=/path/to/chip/mask \
    --organism=<organism> \
    --tissue=<tissue> \
    --fastqs=/saw/datasets/STOmics-RNA-fastqs \
    --adt-fastqs=/saw/datasets/STOmics-ADT-fastqs \
    --ref-libraries=/saw/datasets/reference/ref_libraries.csv \
    --image-tar=/path/to/image/tar

Brief descriptions of the mentioned parameters in command lines:

Parameter	Description
--id	(Optional, default to None) A unique task id ([a-zA-Z0-9_-]+) which will be displayed as the output folder name and the title of HTML report. If the parameter is absent, --sn will play the same role.
--sn <SN>	(Required, default to None) SN (serial number) of the Stereo-seq chip.
--omics <OMICS>	(Required, default to "transcriptomics") Omics information. "transcriptomics,proteomics" for Stereo-CITE analysis.
--kit-version <TEXT>	(Required, default to None) The version of the product kit. More in count pipeline introduction.
--sequencing-type <TEXT>	(Required, default to None) Sequencing type of FASTQs which is recorded in the sequencing report.
--chip-mask <MASK>	(Required, default to None) Stereo-seq chip mask file.
--organism <TEXT>	(Optional, default to None) Organism type of sample, usually referring to species.
--tissue <TEXT>	(Optional, default to None) Physiological tissue of sample.
--ref-libraries <CSV>	(Optional, default to None) Path to a ref_libraries.csv which declares the Transcriptome reference index built by SAW makeRef --mode=STAR , and the protein panel. Not compatible with --reference.
--fastqs <PATH>	(Required, default to None) Path(s) to folder(s), containing all needed gene expression FASTQs. If FASTQs are stored in multiple directories, use it as: --fastqs=/path/to/directory1,/path/to/directory2,.... Notice that all FASTQ files under these directories will be loaded for analysis.
--adt-fastqs <PATH>	(Optional, default to None) Path(s) to folder(s), containing all needed ADT FASTQs. If FASTQs are stored in multiple directories, use it as: --adt-fastqs=/path/to/directory1,/path/to/directory2,.... Notice that all FASTQ files under these directories will be loaded for analysis.
--image <TIFF>	(Optional, default to None) TIFF image for QC (quality control), combined with expression matrix for analysis. Name rule for input TIFF : a. <SN>_<stain_type>.tif b. <SN>_<stain_type>.tiff c. <SN>_<stain_type>.TIF d. <SN>_<stain_type>.TIFF <stainType> includes: a. ssDNA b. DAPI c. HE (referring to H&E) d. <_IF_name1>_IF, <IF_name2>_IF, ...
--image-tar <TAR>	(Optional, default to None) The compressed image .tar.gz file from StereoMap has been through prepositive QC (quality control).

Run SAW count

Set up SAW count analysis command in your working directory.

cd /saw/runs

saw count \
    --id=Demo_Mouse_Spleen \
    --sn=C04776D6 \
    --omics=transcriptomics,proteomics \
    --kit-version="Stereo-CITE T FF V1.1" \
    --sequencing-type="PE75_50+100" \
    --chip-mask=/saw/datasets/mask/C04776D6.barcodeToPos.h5 \
    --organism=mouse \
    --tissue=spleen \
    --fastqs=/saw/datasets/STOmics-RNA-fastqs \
    --adt-fastqs=/saw/datasets/STOmics-ADT-fastqs \
    --ref-libraries=/saw/datasets/reference/ref_libraries.csv \
    --image-tar=/saw/datasets/image/C04776D6_SC_20250306_110538_4.1.1.tar.gz

Content of ref_libraries.csv :

Reference,Type
/saw/datasets/reference/reference-data-mouse_SAW_v8,STAR
/saw/datasets/reference/ProteinPanel_128_mouse_V2.list,ProteinMap

If you input a or certain images in TIFF, the prefixes of the file names should be:

<SN>_<stain_type>_*.tif

e.g.:

• C04144D5_ssDNA.tif

• SS200000135TL_D1_DAPI.tif

Explore the output structure

After pipeline analysis is completed, a new folder named Demo_Mouse_Spleen (which is provided by --id, or by --sn in the absence of --id) will appear in your working directory.

All the metadata and outputs generated from SAW count are listed below:

Demo_Mouse_Thymus
├── pipeline-logs
├── STEREO_ANALYSIS_WORKFLOW_PROCESSING
└── outs
    ├── analysis
    ├── bam
    ├── feature_expression
    ├── image
    ├── <SN>.report.html
    └── visualization.tar.gz

If you want to dig deeper into the results,

• Jump to the report.html inside <SN>.report.tar.gz.

• Explore the visualization.tar.gz in StereoMap.

• Learn more about the individual files on the Outputs page.