stereo-seq

Stereo-seq: High-Resolution Spatial Transcriptomics

Overview

Stereo-seq (Spatially Resolved Transcriptomics) is a high-resolution spatial transcriptomics technology developed by BGI/MGI. It uses DNA nanoball (DNB) arrays to achieve subcellular resolution spatial gene expression profiling.

When to Use This Skill

This skill should be used when:

• Analyzing Stereo-seq data from BGI/MGI
• Working with high-resolution spatial transcriptomics
• Need subcellular spatial resolution
• Studying tissue architecture at high detail

Data Structure

Stereo-seq Output

• Expression matrix (raw counts)
• Coordinates (x, y)
• Bin sizes: can range from 500nm to several microns
• Optional: morphological imaging

Quick Start

With Python

# Load Stereo-seq data (depends on format)
import scanpy as sc
import pandas as pd

# Common format: CSV or h5ad
# Load from CSV
counts = pd.read_csv("expression.csv", index_col=0)
coords = pd.read_csv("coordinates.csv", index_col=0)

# Create AnnData
adata = sc.AnnData(X=counts)
adata.obsm['spatial'] = coords.values

# Standard analysis
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)

With R

# Using Seurat
library(Seurat)

# Load data
expr <- read.csv("expression.csv", row.names = 1)
coords <- read.csv("coordinates.csv")

# Create object
stereo <- CreateSeuratObject(counts = expr)

# Add spatial coordinates
meta <- coords
rownames(meta) <- colnames(stereo)
stereo <- AddMetaData(stereo, metadata = meta)

Key Considerations

High Resolution

• Subcellular resolution available
• Multiple bin sizes can be used
• More spots than Visium

Analysis Differences

• May need binning for analysis
• Different normalization approaches
• Custom visualization required

Common Analyses

Binning Analysis

# Bin the data for different resolutions
# Common bin sizes: 50, 100, 200 bins