SpatiaBio

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.

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.

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

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")

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)"
)

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.

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.

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

By Lociven · SpatiaBio · August 2026

Visium spots are not single cells. Each spot captures RNA from multiple cells, and without knowing which cell types contributed what, your spatial analysis is blurry. Cell2location solves this by deconvolving Visium spots using a scRNA-seq reference. This post explains how it works and how to run it.

The problem: spots contain multiple cells

A standard Visium v2 spot is 55 µm in diameter. In most human tissues, that area contains 2–15 cells depending on cell size and packing density. When you cluster spots and call marker genes, you're clustering mixtures — and the "spatial domains" you find may reflect the composition of those mixtures rather than true biological boundaries.

Cell type deconvolution maps each spot to a probability distribution over cell types: "this spot is 60% cancer epithelial, 25% macrophage, 15% fibroblast." That's what cell2location produces.

Why cell2location over other methods? RCTD and NNLS are faster but assume cell type proportions sum to 1 (full tissue coverage). Cell2location uses a hierarchical Bayesian model that accounts for differences in RNA capture efficiency between scRNA-seq and Visium — which matters a lot in practice.

What you need

Visium AnnData — raw counts (not normalized), spatial coordinates
scRNA-seq reference — same tissue type, annotated cell types. Does not need to be the same sample.
GPU strongly recommended (CPU works but is slow for large datasets)

The scRNA-seq reference is the critical input. If your reference has poorly annotated cell types, or doesn't cover all cell types present in the tissue, the deconvolution will be wrong. More cell types in the reference is generally better, as long as they're well-annotated.

Step 1: Prepare the scRNA-seq reference

import cell2location
import scanpy as sc
import numpy as np

# Load scRNA-seq reference
adata_ref = sc.read_h5ad("scrna_reference.h5ad")

# Keep raw counts
adata_ref.X = adata_ref.layers["counts"].copy()

# Filter to cell types with enough cells
cell_type_counts = adata_ref.obs["cell_type"].value_counts()
keep = cell_type_counts[cell_type_counts >= 5].index
adata_ref = adata_ref[adata_ref.obs["cell_type"].isin(keep)].copy()

# Filter genes
sc.pp.filter_genes(adata_ref, min_cells=1)

print(f"Reference: {adata_ref.n_obs} cells, {adata_ref.n_vars} genes")
print(f"Cell types: {adata_ref.obs['cell_type'].nunique()}")

Step 2: Train the reference model

from cell2location.models import RegressionModel

# Set up the regression model on the reference
RegressionModel.setup_anndata(
    adata=adata_ref,
    batch_key="sample",        # batch correction if multiple samples
    labels_key="cell_type",
    layer="counts"
)

mod = RegressionModel(adata_ref)
mod.train(max_epochs=250, use_gpu=True)

# Export the estimated expression signatures
adata_ref = mod.export_posterior(
    adata_ref,
    sample_kwargs={"num_samples": 1000, "batch_size": 2500, "use_gpu": True}
)

# Save the cell type signatures
inf_aver = adata_ref.varm["means_per_cluster_mu_fg"][
    [f"means_per_cluster_mu_fg_{i}" for i in adata_ref.uns["mod"]["factor_names"]]
].copy()
inf_aver.columns = adata_ref.uns["mod"]["factor_names"]
inf_aver.iloc[0:5, 0:5]

Step 3: Run deconvolution on Visium

import squidpy as sq

# Load Visium data
adata_vis = sq.read.visium("path/to/visium/")
adata_vis.var_names_make_unique()

# Keep only genes present in both datasets
intersect = np.intersect1d(adata_vis.var_names, inf_aver.index)
adata_vis = adata_vis[:, intersect].copy()
inf_aver = inf_aver.loc[intersect, :]

# Set up cell2location model on Visium
cell2location.models.Cell2location.setup_anndata(
    adata=adata_vis, layer="counts"
)

mod_vis = cell2location.models.Cell2location(
    adata_vis,
    cell_state_df=inf_aver,
    N_cells_per_location=10,    # expected cells per spot
    detection_alpha=20
)

mod_vis.train(
    max_epochs=30000,
    batch_size=None,
    train_size=1,
    use_gpu=True
)

Step 4: Extract and visualize cell type abundances

# Export posterior
adata_vis = mod_vis.export_posterior(
    adata_vis,
    sample_kwargs={"num_samples": 1000, "batch_size": 10, "use_gpu": True}
)

# Add 5% quantile cell type abundances to obsm
adata_vis.obs[adata_vis.uns["mod"]["factor_names"]] = adata_vis.obsm["q05_cell_abundance_w_sf"]

# Plot spatial distribution of each cell type
import matplotlib.pyplot as plt

cell_types_to_plot = ["Cancer_epithelial", "CD8_T_cell", "Macrophage", "Fibroblast"]

fig, axes = plt.subplots(1, len(cell_types_to_plot), figsize=(16, 4))
for ax, ct in zip(axes, cell_types_to_plot):
    sq.pl.spatial_scatter(
        adata_vis, color=ct, ax=ax,
        cmap="magma", size=1.5, title=ct
    )
plt.tight_layout()
plt.savefig("cell2location_output.png", dpi=150, bbox_inches="tight")

How deconvolution changes what a spot means. Left: a 55 µm Visium spot physically contains several cells of different types, and sequencing returns one averaged profile for all of them. Right: cell2location returns a composition instead — the worked example from this post. Read the q05 column, the conservative 5th-percentile estimate, rather than the mean.

Interpreting the output

The key output column is q05_cell_abundance_w_sf — the 5th percentile of the posterior distribution of cell type abundance per spot. Using the 5th percentile (rather than the mean) gives conservative, high-confidence estimates.

Common patterns to look for:

Cancer cells concentrated in the tumor core, stromal cells at the margin
CD8+ T cells enriched at the invasive margin vs excluded from the tumor core (a key immunotherapy biomarker)
Macrophage subtypes (M1/M2) in different spatial zones
Spots with very low total abundance — these may be necrotic areas or technical artifacts

Common mistake: Setting N_cells_per_location too high or too low drastically affects results. Use histology or DAPI staining to estimate the actual cell density in your tissue before running the model.

Using cell2location output with Squidpy

Once you have cell type abundances per spot, you can feed them directly into Squidpy's neighborhood enrichment and co-occurrence functions — treating cell types as categorical annotations rather than gene expression clusters.

import squidpy as sq

# Assign dominant cell type per spot
adata_vis.obs["dominant_cell_type"] = (
    adata_vis.obs[adata_vis.uns["mod"]["factor_names"]]
    .idxmax(axis=1)
)

# Now run neighborhood enrichment on deconvolved cell types
sq.gr.spatial_neighbors(adata_vis, coord_type="grid", n_rings=2)
sq.gr.nhood_enrichment(adata_vis, cluster_key="dominant_cell_type")
sq.pl.nhood_enrichment(adata_vis, cluster_key="dominant_cell_type", method="ward")

Get the complete pack

SpatiaBio Pack 2 — Deconvolution & Spatial Pattern Detection

The full cell2location deconvolution notebook — real scRNA-seq reference mapping, tissue zone clustering, and spatial neighborhood enrichment between zones. Plus a SpatialDE2 SVG detection notebook.

Get it for $19 →

From the NeoantigenLab sister blog

Applying spatial transcriptomics to tumor immunology?

NeoantigenLab covers the biology behind what you're profiling — neoantigen pipelines, TIL therapy, checkpoint inhibitors, and cancer immunotherapy for experimental researchers.