Visium HD: What Changes When Resolution Goes Subcellular

Spatial transcriptomics · Visium HD · Xenium · CosMx · platform comparison

Why Resolution Matters (and Why It Is Not Everything)

Visium HD delivers 2 µm bins — roughly single-cell resolution — while the original Visium captures 55 µm spots that each contain 5–20 cells. That 27x improvement in resolution sounds decisive. In practice, the right platform depends on your biological question, not just the spec sheet.

This post covers what Visium HD actually changes analytically, where it outperforms its predecessor, and when Xenium or CosMx is the better choice despite having a fixed gene panel.

Figure 1. Left: spatial resolution comparison — Visium HD achieves 2 µm bins vs. 55 µm spots for Visium v1. Right: capability trade-offs across platforms normalized to maximum observed values.

What Visium HD Changes: The Bin Model

Visium HD does not capture individual cells — it captures 2 µm square bins that tile the tissue continuously. The key workflow difference is binning strategy: you choose a bin size at analysis time, not at capture time.

import squidpy as sq
import scanpy as sc

# Load Visium HD at 8 µm bins (good starting point — balances resolution and sparsity)
adata = sq.read.visium(
    path="visium_hd_output/",
    counts_file="filtered_feature_bc_matrix.h5",
    library_id="your_sample_id",
    bin_size=8,  # options: 2, 8, 16 micrometers
)

print(adata)
# AnnData: n_obs (bins) depends heavily on bin_size
# bin_size=2:  ~500,000 bins (very sparse, ~2-5 UMI/bin)
# bin_size=8:  ~30,000 bins (workable, ~50-100 UMI/bin)
# bin_size=16: ~8,000 bins (Visium-like density)

Start with 8 µm bins for exploratory analysis. At 2 µm, most bins will have fewer than 5 UMI — too sparse for reliable clustering without heavy smoothing. Only use 2 µm bins if you need subcellular resolution for a specific question (e.g., resolving nuclear vs. cytoplasmic transcript localization).

The Critical Difference: Deconvolution Is Optional

With Visium v1, cell2location or RCTD is mandatory to get cell type proportions per spot. With Visium HD at 8 µm bins, many bins will contain a single dominant cell type and can be annotated directly by marker expression — though mixed bins still exist at tissue interfaces.

# Standard clustering works directly on HD bins
sc.pp.filter_cells(adata, min_counts=10)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=3000)
sc.tl.pca(adata)
sc.pp.neighbors(adata, n_neighbors=15)
sc.tl.leiden(adata, resolution=0.4)

# Annotate by marker expression (same workflow as scRNA-seq)
sc.pl.dotplot(adata, markers_dict, groupby="leiden")

# Optional: if you want single-cell precision at 2µm bins,
# still use cell2location with matched scRNA-seq reference

Visium HD vs. Xenium: Choosing the Right Tool

Choose Visium HD when:

• You need whole-transcriptome coverage (not a fixed panel)
• Your question involves discovery — finding unexpected cell states or pathways
• You have FFPE tissue (Visium HD supports FFPE; Xenium also does)
• You want spatial multi-omics (Visium HD + CUT&TAG or protein co-detection)

Choose Xenium/CosMx when:

• You need subcellular resolution (mRNA localization within a cell)
• Your target genes are already known and a panel of 100–500 genes is sufficient
• You need higher sensitivity per transcript (in-situ amplification vs. sequencing)
• Your tissue is thick or 3D (Xenium handles z-stacking)

Squidpy Compatibility

Visium HD AnnData loads exactly like Visium v1 from Squidpy's perspective. All tools covered in this series work without modification:

# Everything from earlier posts works identically on Visium HD
sq.gr.spatial_neighbors(adata, coord_type="generic", n_neighs=6)
sq.gr.nhood_enrichment(adata, cluster_key="leiden")
sq.gr.ligrec(adata, n_perms=500, cluster_key="leiden")
sq.gr.spatial_autocorr(adata, mode="moran")

# The only practical difference: more bins = longer runtime
# nhood_enrichment on 30,000 bins takes ~10 min vs ~2 min for Visium v1

One Practical Warning: Tissue Coverage

Visium HD captures a 6.5 mm × 6.5 mm area — identical to Visium v1. This is enough for most tissue sections but smaller than a typical whole-slide image. If your question requires comparing multiple tumor regions across a large section, you will need multiple capture areas or switch to a platform with larger footprint (some CosMx runs cover full slides).

Interested in what the immune cell types you are mapping at single-cell resolution are actually doing in the tumor? NeoantigenLab covers T cell exhaustion, checkpoint inhibitor mechanisms, and CAR-T biology at the molecular level.

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From the NeoantigenLab sister blog

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SPATIAL TRANSCRIPTOMICS · TUTORIAL

Tangram: Mapping Single-Cell Annotations onto Spatial Coordinates

Transfer cell type labels, gene programs, and custom scores from scRNA-seq directly onto your Visium or Xenium data

July 2026  ·  10 min read  ·  SpatiaBio

Cell2location (covered in Post 12) infers cell type abundances per spot using a Bayesian model. Tangram takes a complementary approach: it learns a mapping from single-cell space to spatial space by aligning gene expression profiles, then transfers any annotation — cell types, transcription factor activity, pseudotime, custom gene scores — onto spatial coordinates. If you have a well-annotated scRNA-seq atlas and want to ask "where do these cells sit in tissue?", Tangram is your tool.

Figure 1. Tangram-mapped dominant cell type per spatial location (left) and deconvolution method comparison by accuracy and compute speed (right).

How Tangram Works

Tangram frames cell-to-space mapping as an optimal transport problem. Given a scRNA-seq dataset with C cells and a spatial dataset with S spots, it learns a mapping matrix M (C × S) that minimizes the difference between:

• The spatially reconstructed gene expression (M^T × scRNA matrix)
• The observed spatial gene expression

The key insight: you only need a shared set of marker genes to align the two modalities. Tangram then uses the learned mapping to project everything else — including genes not measured in the spatial assay.

import tangram as tg
import scanpy as sc
import squidpy as sq

# Load data
sc_adata  = sc.read_h5ad('sc_annotated.h5ad')   # scRNA-seq with cell_type labels
sp_adata  = sc.read_h5ad('visium_filtered.h5ad') # Visium spatial data

# Identify shared marker genes (top 100 per cell type works well)
sc.tl.rank_genes_groups(sc_adata, groupby='cell_type', method='wilcoxon')
markers = tg.pp.select_genes(sc_adata, n_genes=100)

tg.pp.filter_gene_list(sc_adata, sp_adata, gene_list=markers)

# Train mapping (GPU recommended for large datasets)
tg.mapping.map_cells_to_space(
    sc_adata,
    sp_adata,
    mode='cells',        # or 'clusters' for memory efficiency
    target_count=sp_adata.obs.cell_count.sum(),
    density_prior='rna_count_based',
    num_epochs=500,
    device='cpu',
)

Three Mapping Modes

cells mode

Maps each individual cell to a spot. Best for datasets <50k cells. Gives highest resolution but memory-intensive.

clusters mode

Maps cluster centroids instead of individual cells. Scalable to large atlases. Recommended starting point.

constrained mode

Uses prior cell density estimates (e.g. from DAPI) to constrain the mapping. More accurate when cell counts per spot are known.

Projecting Cell Types and Custom Scores

After mapping, Tangram stores results in sp_adata.obsm['tangram_ct_pred']. You can then project back to the spatial grid:

# Project cell type probabilities onto spatial coords
tg.pl.plot_cell_annotation_sc(sp_adata, annotation='cell_type', perc=0.02)

# Project any custom score from scRNA-seq (e.g. exhaustion score)
sc_adata.obs['exhaustion_score'] = (
    sc_adata[:, ['PDCD1','HAVCR2','LAG3','TIGIT','TOX']].X.mean(axis=1)
)
tg.pp.project_genes(adata_map=sp_adata, adata_sc=sc_adata)

# Now sp_adata has exhaustion_score — plot spatially
import squidpy as sq
sq.pl.spatial_scatter(sp_adata, color='exhaustion_score', cmap='Reds')

Pro tip: Project T cell exhaustion scores, EMT signatures, or pVACseq neoantigen burden per cell — then map them spatially. This links neoantigen-rich tumor zones to immune infiltration patterns.

Tangram vs cell2location: When to Use Which

	Tangram	cell2location
Approach	Optimal transport mapping	Bayesian deconvolution
Output	Cell-to-spot probability matrix	Cell type abundance per spot
Custom score transfer	✓ Any obs/obsm column	✗ Cell types only
Gene imputation	✓ Projects unmeasured genes	✗ No
Scalability	Moderate (<200k cells)	Large atlases OK
Best for	Transferring rich annotations	Quantifying cell composition

Common Pitfalls

• Too few marker genes — use at least 50-100 per cell type. With <20, mapping is unreliable.
• Batch effects between sc and spatial — if both datasets are from different labs or platforms, normalize carefully before running Tangram. Harmony or scVI integration first helps.
• Overinterpretation of imputed genes — genes projected via Tangram are predicted, not measured. Don't use imputed values for differential expression.
• Using cells mode on >100k cells — switch to clusters mode. The mapping matrix becomes O(C × S) and will OOM on most machines.

Key Takeaways

• Tangram maps any scRNA-seq annotation onto spatial coordinates via optimal transport
• Use clusters mode for large atlases, cells mode for maximum resolution
• Combine with Squidpy for spatial statistics on projected scores
• Complements cell2location — use both for a complete picture

Working on neoantigen biology or cancer immunotherapy?

The spatial exhaustion score workflow above connects directly to neoantigen research — see NeoantigenLab for the immunology side.

Explore NeoantigenLab →

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From the NeoantigenLab sister blog

Working with tumor transcriptomics for neoantigen research?

NeoantigenLab covers neoantigen biology, WES pipelines, pVACseq, and HLA typing for experimental researchers — the biology behind what you're sequencing.

Visit NeoantigenLab →

Spatial Multi-omics: Combining RNA, ATAC, and Protein in the Same Tissue Section

By Lociven · SpatiaBio · August 2026

Spatial transcriptomics tells you where genes are expressed. Spatial multi-omics adds a second layer — chromatin accessibility, protein, or DNA — measured in the same tissue section. The result is a much richer picture of cell state that neither modality alone can give you.

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Why spatial multi-omics

Gene expression tells you what a cell is doing right now. Chromatin accessibility tells you what it could do — which regulatory regions are open and which transcription factors are active. Combining both in the same spatial context reveals regulatory programs that are spatially organized: not just "macrophage" but "macrophage in an open chromatin state consistent with inflammatory activation, in the tumor margin."

Protein (CITE-seq spatial / CosMx protein) adds surface markers that are still the clinical standard for cell identity — CD3, CD8, PD-L1 — with precise spatial coordinates.

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Current platforms

Platform	Modalities	Resolution	Status
10x Visium HD + CytAssist	RNA + H&E image	2 µm	Commercial
10x Multiome Spatial (ATAC+RNA)	RNA + ATAC	Spot-level	Beta / early access
DBiT-seq	RNA + protein (CITE)	10–50 µm	Academic protocol
CosMx (NanoString)	RNA + protein (same slide)	Single cell	Commercial
MIBI-TOF / CODEX	Protein only (40–60 markers)	Single cell	Commercial

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What multi-omics buys you: three different measurements indexed by the same spatial coordinates, so a question that spans modalities — does open chromatin here predict expression here — becomes answerable. The caveat in the platform table still applies: most fully-integrated options are early access or academic protocols today.

Spatial ATAC + RNA: the workflow

The most analytically powerful combination is simultaneous spatial ATAC-seq and RNA-seq on the same section. 10x Genomics has a spatial Multiome protocol in early access. The analysis workflow in Squidpy/Muon follows the same pattern as single-cell Multiome — with spatial coordinates added.

import muon as mu
import squidpy as sq
import scanpy as sc

# Load the paired RNA + ATAC AnnData objects
rna = sc.read_h5ad("spatial_rna.h5ad")
atac = sc.read_h5ad("spatial_atac.h5ad")

# Combine into MuData — spots are shared
mdata = mu.MuData({"rna": rna, "atac": atac})

# Standard RNA preprocessing
sc.pp.normalize_total(mdata["rna"])
sc.pp.log1p(mdata["rna"])
sc.pp.highly_variable_genes(mdata["rna"], n_top_genes=3000)

# ATAC: TF-IDF + LSI
mu.atac.pp.tfidf(mdata["atac"])
mu.atac.tl.lsi(mdata["atac"])

# Multi-modal embedding (WNN)
mu.pp.neighbors(mdata, key_added="wnn")
mu.tl.umap(mdata, neighbors_key="wnn")
mu.tl.leiden(mdata, neighbors_key="wnn", key_added="wnn_leiden")

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Linking open chromatin to spatial gene expression

The key analysis in spatial ATAC+RNA is linking regulatory elements to their target genes — and mapping those links spatially. Peak-to-gene correlations tell you which ATAC peaks are correlated with nearby gene expression across spatial locations.

# Peak-to-gene correlation across spatial spots
mu.atac.tl.rank_genes_groups_df(mdata, groupby="wnn_leiden", mod="atac")

# Motif enrichment in spatially variable peaks
mu.atac.tl.motifs(mdata["atac"])

# Visualize chromatin accessibility overlaid on tissue
sq.pl.spatial_scatter(
    mdata["rna"],
    color="wnn_leiden",
    title="WNN clusters (RNA + ATAC)"
)

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Practical advice

Start with RNA only. Spatial multi-omics protocols are more technically demanding and more expensive. Do a Visium run first to understand the biology, then add ATAC or protein if the question requires it.

H&E is already a modality. Every Visium slide comes with an H&E image. Integrating the histology image with gene expression using tools like Tangram or BayesSpace is spatial multi-omics in practice — and it's free with your existing data.

Data integration is the bottleneck. The biology is interesting; the analysis is hard. WNN, MOFA+, and totalVI are the current standards for integrating spatial multi-modal data, but none of them are one-click.

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From the NeoantigenLab sister blog

Profiling the tumor immune microenvironment?

NeoantigenLab covers neoantigen biology, TIL therapy, checkpoint inhibitors, and cancer immunotherapy — the biology behind your spatial data.

Visit NeoantigenLab →

Tags: spatial multi-omics, spatial ATAC-seq, Visium, Multiome, CosMx, CITE-seq, Muon, WNN, chromatin accessibility, tumor microenvironment, Squidpy