Jun 01, 2026
Visium HD Spatial Gene Expression Protocol: Library Preparation, Sequencing, and Analysis
- Gurveer Gill1,
- Joanna Pyczek1,
- Bo Young Ahn1,
- Jennifer Chan1,
- Sorana Morrissy1
- 1University of Calgary
Protocol Citation: Gurveer Gill, Joanna Pyczek, Bo Young Ahn, Jennifer Chan, Sorana Morrissy 2026. Visium HD Spatial Gene Expression Protocol: Library Preparation, Sequencing, and Analysis. protocols.io https://dx.doi.org/10.17504/protocols.io.bp2l6okd1lqe/v1
Manuscript citation:
Zemp et al. 2026. GPNMB-directed CAR T-cell therapy against MiT/TFE family fusion-driven solid tumors. Nature Cancer. Accepted.
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: May 28, 2026
Last Modified: June 01, 2026
Protocol Integer ID: 318148
Keywords: visium hd spatial gene expression protocol, 10x genomics visium hd platform, resolution spatial transcriptomic profiling of formalin, spatial transcriptomic profiling, biopsy sample, detection of the gcar1 construct, asps xenograft, custom gcar1 probe panel addon
Abstract
High-resolution spatial transcriptomic profiling of formalin-fixed, paraffin-embedded (FFPE) tissue sections using the 10x Genomics Visium HD platform. This includes a custom GCAR1 probe panel addon (12 spike-in probes) for detection of the GCAR1 construct in ASPS xenograft and biopsy samples.
Guidelines
- All 10x Genomics library preparation, CytAssist transfer, and sequencing steps must follow the official Visium HD User Guides (CG000684, CG000685) and Technical Note CG000621 exactly.
- The custom GCAR1 probe panel must be added to the standard human probe panel; verify probe performance during assay optimization.
- Space Ranger version differs by sample type (v3.0.1 vs v4.0.1) — use the version appropriate for each dataset.
- 24 μm binning is standard for Visium HD; deconvolution and neighbourhood analyses account for multi-cellular bin content.
- All R-based analyses assume a high-performance computing environment; custom functions (program_neighbours(), etc.) must be implemented or obtained from the associated repository.
- Include appropriate controls (e.g., no-probe, positive-control tissues) and perform pilot runs to optimize tissue transfer and probe performance.
Materials
**Reagents and Materials**
- Visium HD Spatial Gene Expression Kit (10x Genomics; human probe panel)
- Custom GCAR1 probe panel addon (12 spike-in probes designed against the GCAR1 construct sequence; panel design code available in GitHub repository; designed per 10x Genomics Technical Note CG000621)
| A | B | C | |
| Probe ID | LHS Sequence | RHS Sequence | |
| 1 | GGGGCGTCTGCGCTCCTGCTGAACT | TCACTCTCAGTTCACATCCTCCTTC | |
| 2 | TGCCCCGTTTGCAGTAAAGGGTGAT | AACCAGTGACAGGAGAAGGACCCCA | |
| 3 | TCGCGGCGCTGGGGTTGTGGTGGCT | GCGGCCAGGTCCTCCTCCGAGATGA | |
| 4 | CGCGGGCACAGTAGTACACGGCGGT | GTCCGCAGCAGTCACGGAACTCAGG | |
| 5 | TCAGGGAGAACTGGTTCTTGGAGGT | ATCGACAGAAATAGTCACGCGGGAT | |
| 6 | GATGTAGCCAATCCACTCCAGGCCT | TTGCCGGGGTGGTGGCGGATCCAAC | |
| 7 | AGGCCAGGGCCGCTCTCCTGCAGCT | GCACCTGGCTACCACCGCCTCCGCT | |
| 8 | CGCTTGATCTCCACCTTGGTGCCCT | GACCGAAGGTCCACGGCGGCCAATT | |
| 9 | CTTCGCTCTGCAAAGAAGAGATCGT | CAGGGTGAACTCGGTCCCGGATCCG | |
| 10 | CCGGTAGCACGGGTAGAAGCGCCGT | AAATGAGCAGGCGCGGGGCCTGTCC | |
| 11 | GTTGTTGTCGACGCTCTGTGACGCT | CGGCATGACAGTGTAGCCCGCTCCC | |
| 12 | CGGAAAGGGTGGCGGGACTCTGGGT | CATCACGATCTCAGGCCTAGCCGCA |
- Manufacturer’s recommended buffers for Visium HD slide thawing, washing, and equilibration
- H6E staining reagents (standard histological grade)
**Equipment**
- Olympus VS110 Slide Scanner microscope
- CytAssist instrument (10x Genomics)
- Illumina NextSeq 2000 sequencer
- Dragen BCL Convert v4.2.7 (onboard FASTQ generation on NextSeq 2000)
**Software and Tools**
- Space Ranger (RRID:SCR_025848, v3.0.1 for ASPS-01 xenograft; v4.0.1 for ASPS-02 biopsies)
- Seurat (v5.3.1)
- STdeconvolve (v1.3.1)
- mosaicMPI package (for consensus NMF)
- ComplexHeatmap (v2.15.3)
- R (v4.3.1)
- g:Profiler (via gprofiler2 or web server)
- gggpubr (v0.6.2)
- rstatix (v0.7.3)
- ggplot2 (v3.5.2)
- ALDEx2 (v1.32.0)
Library Preparation and Sequencing
Section tissue samples to 5 µm thickness and mount onto plain glass slides. Perform deparaffinization, H&E staining, and capture tissue morphology using the Olympus VS110 Slide Scanner microscope at 40× magnification. Thaw, wash, and equilibrate Visium HD slides using the manufacturer’s recommended buffers prior to tissue placement. After imaging, transfer tissue analytes from the glass slide to the Visium HD capture areas using the CytAssist instrument. Prepare all tissues using the Visium HD Spatial Gene Expression Kit with the human probe panel plus the custom GCAR1 probe panel addon. Perform probe extension and library construction steps exactly as described in the Visium HD User Guides (CG000684 and CG000685). Sequence libraries on an Illumina NextSeq 2000 with the following cycle settings:
Read 1: 43 cycles
i7 index: 10 cycles
i5 index: 10 cycles
Read 2: 50 cycles Generate FASTQ files onboard using Dragen BCL Convert v4.2.7.
Visium HD Analysis
Generate a custom reference genome using spaceranger mkref (Space Ranger v3.0.1), incorporating human GRCh38 (GENCODE v32/Ensembl98) and the full GCAR1 construct sequences. Align FASTQ files and perform automated image detection/alignment using spaceranger count (Space Ranger v3.0.1 for ASPS-01 xenograft; v4.0.1 for ASPS-02 biopsies). Aggregate data to 24 µm bins. Process binned data in Seurat (v5.3.1)1:
Normalize with NormalizeData()
Select variable features with FindVariableFeatures()
Scale data with ScaleData()
Run Principal Component Analysis with RunPCA()
Cluster bins using FindNeighbors() and FindClusters(). Exclude low-quality clusters (e.g., regions of hemorrhage or bins outside tissue) based on low gene counts.
Since 24 µm bins may contain multiple cells,2 perform unsupervised deconvolution to identify cell types and cell states.3 Identify over-dispersed genes per sample using STdeconvolve (v1.3.1) with the function restrictCorpus(removeAbove = 1.0, removeBelow = 0.01, alpha = 0.05).4 Input results to consensus Non-negative Matrix Factorization (cNMF) via the mosaicMPI package5 to derive expression programs and quantify their relative usage across bins. Annotate programs using established reference marker genes6-10 and perform pathway enrichment analysis with g:Profiler.4,11-12
Load 24 µm bin data into Seurat (v5.3.1), merge samples with merge(), join layers with JoinLayers(), and filter for bins with non-zero counts (subset(SeuratObject, nCount_Spatial.024um > 0)). Extract spatial bin coordinates with seurat_object_coords() and identify neighbors for each bin using custom program_neighbours() functions. Restrict analysis to bins with non-infinite normalized program usage. Identify T-cell niches as bins positive for a T-cell program (usage ≥ 0.05), termed “bins of interest” (BOI). Binarize program usage values (≥ 0.05 = 1, else 0). For each BOI and its neighbouring bins (radius up to 3), calculate mean binarized program usage and visualize with ComplexHeatmap (v2.15.3).13 Split the column dendrogram to define distinct niches; assign niche labels and visualize with Seurat’s DotPlot() function. Calculate the percent of bins expressing specific genes of interest per niche. Perform statistical analyses on contingency tables using Fisher’s Exact Test (fisher.test() from base R stats v4.3.1) and post-hoc pairwise comparisons with pairwise_fisher_test() from rstatix (v0.7.3).14 For T cell-specific gene expression (independent of deconvolution): subset bins expressing any of CD3D, CD3E, CD3G, CD8A, or GCAR; classify neighbouring bins (radii 0–3) as “inside” or “outside” the T-cell radius; compare average expression of genes of interest using Student’s t-test via compare_means() from ggpubr (v0.6.2); visualize with ggplot2 (v3.5.2).15 Perform differential gene expression on pseudobulked bins using ALDEx2 (v1.32.0)16 with the call: aldex(mc.samples=128, test="t", effect=TRUE, denom="iVha", verbose=FALSE, paired.test=FALSE).
Protocol references
1. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).
2. Abdelkareem, A. O., Gill, G. S., Manoharan, V. T., Verhey, T. B. 6 Morrissy, A. S. spOT-NMF: Optimal Transport-Based Matrix Factorization for Accurate Deconvolution of Spatial Transcriptomics. Preprint at https://doi.org/10.1101/2025.08.02.668292 (2025).
3. Miller, B. F., Huang, F., Atta, L., Sahoo, A. 6 Fan, J. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat. Commun. 13, 2339 (2022).
4. Manoharan, V. T. et al. Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma. Genome Biol. 25, 264 (2024).
5. Verhey, T. B. et al. mosaicMPI: a framework for modular data integration across cohorts and -omics modalities. Nucleic Acids Res. 52, e53–e53 (2024).
6. Ma, R.-Y., Black, A. 6 Qian, B.-Z. Macrophage diversity in cancer revisited in the era of single-cell omics. Trends Immunol. 43, 546–563 (2022).
7. Lai, Y. et al. Multimodal cell atlas of the ageing human skeletal muscle. Nature 629, 154–164 (2024).
8. Abdulla, S. et al. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Nucleic Acids Res. 53, D886–D900 (2025).
9. Galbo, P. M., Zang, X. 6 Zheng, D. Molecular Features of Cancer-associated Fibroblast Subtypes and their Implication on Cancer Pathogenesis, Prognosis, and Immunotherapy Resistance. Clinical Cancer Research 27, 2636–2647 (2021).
10. Kotliar, D. et al. Reproducible single-cell annotation of programs underlying T cell subsets, activation states and functions. Nat. Methods 22, 1964–1980 (2025).
11. Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. 6 Peterson, H. gprofiler2 — an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Res. 9, 709 (2020).
12. Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).
13. Gu, Z., Eils, R. 6 Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
14. Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. CRAN: Contributed Packages Preprint at https://doi.org/10.32614/CRAN.package.rstatix (2019).
15. Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4. https://ggplot2.tidyverse.org. (2016)
16. Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. 6 Gloor, G. B. ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq. PLoS One 8, e67019 (2013).