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contact: tyck@vcu.edu

File creation: June 04, 2025

Update:

Approximate time: 60 - 120 minutes

I. Introduction

1.1. Overview of Spatial Transcriptomics Data

1.2. Objectives

1.3. Requiremnts

If only molecule_info.h5 file exist, SpaceRanger Count will have to be run on this sample in order to extract infromation needed for loading to R or python.

##== linux command ==##
...add info here...

10x Visium HD data files were collected from 10x Genomics Resource page. The files we collect are:

Visium_HD_Mouse_Brain_Fresh_Frozen_molecule_info.h5
Visium_HD_Mouse_Brain_Fresh_Frozen_probe_set.csv
Visium_HD_Mouse_Brain_Fresh_Frozen_spatial.tar.gz

##== linux command ==##
tar -xvzf Visium_HD_Mouse_Brain_Fresh_Frozen_spatial.tar.gz

This should contain a CytAssist file. We will need it to esxtract information for loading this data into R/Seurat pipeline.

II. Data Pre-processing

2.1 Spaceranger count (Optional)

spaceranger count needs --slide=<slide_id> and --area=<capture_area>. Both variables should use the exact values provided by 10x Genomics for that specific Visium HD dataset. These are required to map spatial barcodes correctly and are usually provided in the associated metadata or sample sheet. In our case, the information we need was:

Slide serial number: H1-7JN9RJG
Area: A-1
Instrument: Visium CytAssist
Probe set: Visium Mouse Transcriptome Probe Set v2.0
Sequencing

##== linux command ==##
spaceranger count \
  --id=Visium_HD_Mouse_Brain_Fresh_Frozen \
  --transcriptome=../refdata-gex-mm10-2020-A \
  --probe-set=Visium_HD_Mouse_Brain_Fresh_Frozen_probe_set.csv \
  --molecule-h5=Visium_HD_Mouse_Brain_Fresh_Frozen_molecule_info.h5 \
  --image=./spatial/cytassist_image.tiff \
  --slide=H1-7JN9RJG \
  --area=A-1

Loading and Visualizing 10x Visium HD Binned Data (Without SpaceRanger Count)

10x Genomics now provides binned spatial transcriptomics outputs that allow you to skip the spaceranger count step entirely. You can work directly with the binned data available for download from their website.

✅ This tutorial demonstrates how to load the binned data in R using Seurat v5, and visualize gene expression using SpatialFeaturePlot().

2.1 Download and Unpack Binned Data

10x Genomics (https://www.10xgenomics.com/datasets/) provides preprocessed binned spatial transcriptomics outputs for Visium HD datasets. These files allow you to skip the spaceranger count step entirely.

Once you’ve downloaded and extracted the dataset (e.g., for 8 µm bin resolution), the directory should look like this:

binned_outputs/square_008um/
├── filtered_feature_bc_matrix.h5
├── spatial/
│   ├── tissue_positions.parquet
│   ├── scalefactors_json.json
│   ├── tissue_lowres_image.png
│   ├── aligned_fiducials.jpg
│   ├── aligned_tissue_image.jpg
│   ├── cytassist_image.tiff
│   ├── detected_tissue_image.jpg
│   └── tissue_hires_image.png

This directory contains everything needed to load the dataset into Seurat v5 for downstream analysis and visualization.

2.2 Load and Normalize the Data in R

library(Seurat)
library(ggplot2)
library(dplyr)

# Load the Visium HD binned data
seurat_obj <- Load10X_Spatial(data.dir = "./binned_outputs/square_008um/")

# Normalize the data to create the 'data' slot used for plotting
seurat_obj <- NormalizeData(seurat_obj)

top_genes <- rowSums(seurat_obj@assays$Spatial@counts) %>%
  sort(decreasing = TRUE) %>%
  head(20)

top_gene <- names(top_genes)[3]  # Change index to explore other genes

SpatialFeaturePlot(
  seurat_obj,
  features = top_gene,
  images = NULL,            # remove the H&E image
  pt.size.factor = 2        # larger spots
) +
  ggtitle(paste("Top expressed gene:", top_gene)) +
  scale_fill_gradient(low = "white", high = "purple4")

Figure 1. Visualization of Top Expressed Gene. Example visualization of a highly expressed gene across spatial bins, displayed using a single-color gradient ranging from white to a saturated hue. This approach highlights spatial expression patterns while maintaining sensitivity to low-abundance signals. .

2.2. Quality Control

SpatialFeaturePlot(seurat_obj, features = "nCount_Spatial") +
  ggtitle("Total UMI counts per bin") +
  scale_fill_gradient(low = "white", high = "purple4")

2.3. Visualizing the sequencing depth and alignment results.

III. Dimensionality Reduction and Clustering

seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = "vst", nfeatures = 2000)
head(VariableFeatures(seurat_obj), 20)

IV. Cell Typing

V. Advanced: Overlaying with Bacterial Load

VI. Gene Expression Analysis in Spatial Context

VII. Additional Tutorials

VIII. References

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS 15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       httr_1.4.7        cli_3.6.5         knitr_1.50       
 [5] rlang_1.1.6       xfun_0.52         stringi_1.8.7     processx_3.8.4   
 [9] promises_1.3.3    jsonlite_2.0.0    glue_1.8.0        rprojroot_2.0.4  
[13] git2r_0.36.2      htmltools_0.5.8.1 httpuv_1.6.16     ps_1.7.6         
[17] sass_0.4.10       rmarkdown_2.29    jquerylib_0.1.4   tibble_3.2.1     
[21] evaluate_1.0.3    fastmap_1.2.0     yaml_2.3.10       lifecycle_1.0.4  
[25] whisker_0.4.1     stringr_1.5.1     compiler_4.4.0    fs_1.6.6         
[29] pkgconfig_2.0.3   Rcpp_1.0.14       rstudioapi_0.16.0 later_1.4.2      
[33] digest_0.6.37     R6_2.6.1          pillar_1.10.2     callr_3.7.6      
[37] magrittr_2.0.3    bslib_0.9.0       tools_4.4.0       cachem_1.1.0     
[41] getPass_0.2-4

Visium (HD) Analysis Tutorial

Miranda, Andrew, Francisco, Max, Katarzyna