Airway diseases such as asthma, COPD, IPF, acute lung injury, and diffuse alveolar damage (DAD) are driven by biology that is explicitly "in place": epithelial layers, subepithelial stroma, smooth muscle bundles, glands, vessels, and peribronchial niches that shape which immune cells arrive, where they stay, and how remodeling persists despite therapy. Traditional approaches often collapse this organization—especially when tissues must be dissociated—making it hard to distinguish cell-intrinsic signals from niche-specific programs.

This literature playbook shows how spatial transcriptomics and spatial multi-omics are being used to map airway wall ecosystems, region-specific lung biology, gland-associated immune niches, lineage programs in the distal lung, and anatomically anchored inflammatory microenvironments. You'll find six landmark papers summarized as "paper cards" with actionable spatial findings and copy-ready study blueprints you can adapt for your own research project (research use only).

This content is for research and educational purposes only and does not provide medical advice.

Why spatial omics matters for airway disease research

Spatial omics helps answer three questions that dominate respiratory tissue biology but are difficult to resolve with dissociation-first methods:

• Where: Which molecular programs occur in specific compartments (epithelium, subepithelium, smooth muscle, glands, perivascular zones, peribronchial regions, distal airspaces)?
• Who-near-who: Which cell types are physically adjacent, separated, or re-patterned in disease (neighborhoods and distances), including "contact" versus "barrier" architectures?
• Who-drives-who: Which interactions are plausible in situ when sender and receiver are co-localized (spatially constrained cell–cell signaling and niche-limited axes)?

A practical benefit: spatial readouts (domain/ecotype calling, neighborhood enrichment, distance metrics) often remain interpretable across cohorts even when single-gene effects vary. This is especially useful in asthma heterogeneity, COPD regional differences, and injury models where microenvironments are patchy rather than uniform.

If you want a hands-on guide for analysis concepts (domains, deconvolution, neighborhoods, quality checks), use this resource: Spatial Transcriptomics Data Analysis: A Practical Introduction

Platform and study design fast guide (airway wall vs lung)

Choose spatial omics platforms based on anatomical scale. Airway wall questions typically require cell-resolved neighborhood mapping across tissue layers, while lung parenchyma and atlas-style questions benefit from wide-area discovery plus region-aware sampling. The goal is to use the smallest assay stack that can support your primary contrast and your top spatial claim.

A. Airway wall questions (asthma remodeling, mucosal ecosystems, smooth muscle neighborhoods)

Best fit: cell-resolved, targeted imaging-based spatial transcriptomics with compartment-aware ROI design.

Why: airway wall biology is organized at short spatial scales—immune hubs in mucosa, stromal programs in the subepithelium, and proximity shifts around smooth muscle—so "who-near-who" endpoints often matter as much as cell abundance.

Recommended approach: prioritize cell-resolved neighborhood mapping across epithelium, subepithelium, and smooth muscle, then validate compartment programs using ROI-based profiling in an independent set of samples (see the minimal stack below).

B. Lung parenchyma and regional atlas questions (proximal-to-distal axis, lobe/region variability, COPD/IPF distribution)

Best fit: wide-area discovery first, then targeted refinement in priority regions, with explicit region labels retained throughout.

Why: adult lungs are large and heterogeneous; results can change with sampling location. A region-aware design strengthens comparability across donors and prevents pooling artifacts.

Recommended approach: start with wide-area spatial mapping to define domains/ecotypes and region-specific programs, then use a targeted cell-resolved method to confirm key neighborhoods or niche-restricted signals in selected regions (see the minimal stack below).

C. Minimal reviewer-friendly stack (Discovery → Resolution → Validation)

Discovery: generate a broad spatial map to call domains/ecotypes and prioritize regions or ROIs.

Resolution: add cell-resolved profiling in the highest-priority niches to validate neighborhoods and proximity endpoints.

Validation: confirm one or two top spatial claims using ROI-based profiling in an independent cohort or an orthogonal assay (IF/IHC/RNA-ISH).

Tip: predefine a small number of spatial endpoints (domains, neighborhood enrichment, distance metrics) and report them at the sample level rather than relying on a single representative map.

FFPE and archived respiratory cohorts

If you work with FFPE tissues or mixed sample types common in respiratory research, select FFPE-compatible workflows and keep fixation/processing consistent across groups. See: FFPE Spatial Transcriptomics Service (research use only).

Literature series: 6 paper cards with spatial findings and study blueprints

Paper 1 — Airway wall ecosystems in health and asthma (single-cell spatial map)

Title: A single-cell spatial chart of the airway wall reveals proinflammatory cellular ecosystems and their interactions in health and asthma

Journal: Nature Immunology (2025)

DOI: https://doi.org/10.1038/s41590-025-02161-3

Study question (in one line): What are the spatially organized cellular ecosystems in the bronchial wall that sustain inflammation and remodeling in asthma—even under anti-inflammatory treatment?

Cohort and groups:

• Healthy controls: n = 8
• Asthma: n = 20 (mild-to-severe; inhaled steroids ± biologics)

Spatial stack:

• Cell-resolved targeted spatial transcriptomics (Xenium-style) with a 339-gene panel
• ROI-based whole-transcriptome spatial profiling (GeoMx DSP-style) to validate compartment programs in an independent cohort
• Morphology markers used to define ROIs (epithelium, subepithelium, smooth muscle)

Key spatial findings:

• Proinflammatory "ecosystems" are niche-anchored, not diffuse: specific hubs show high chemokine/alarmin programs with distinctive stromal compositions.
• A receptor-based retention concept emerges in situ: local mediator retention (e.g., ACKR1) is positioned as a spatial mechanism that helps maintain inflammatory hubs.
• Mast cells with amphiregulin-like programs occupy dominant positions within these hubs, aligning with a niche-maintenance model.
• Despite anti-inflammatory treatment, airway mucosa shows remodeling within the ecosystems, including increased distances between key cell types—suggesting re-patterning rather than simple cell depletion.

Figure 2. Joulia et al., Nature Immunology (2025), Fig. 1: single-cell spatial transcriptomics to analyze airway wall heterogeneity and cellular interactions in health and asthma.

Study blueprint (what to replicate):

• Design: predefine airway wall compartments (epithelium, subepithelium, smooth muscle) and compare health vs asthma across severity or therapy exposure.
• Minimum assays: cell-resolved spatial transcriptomics for neighborhoods + ROI-based validation in a second set of samples.
• Core readouts: niche calling (hub detection), distance metrics (cell–cell spacing shifts), and compartment-specific gene programs.
• Common pitfalls: mixing airway levels without annotation (large vs small bronchi), and relying on bulk-like averaging instead of spatial proximity endpoints.

Related reading: Bulk vs Single-Cell vs Spatial (decision guide)

Paper 2 — Regional lung variation in healthy vs diseased lung (spatial single-cell atlas)

This atlas-style study is most useful when your question is about regional variation (proximal–distal, lobe, airway vs parenchyma), not just airway wall remodeling.

Title: Spatial single-cell atlas reveals regional variations in healthy and diseased human lung

Journal: Nature Communications (2025)

DOI: https://doi.org/10.1038/s41467-025-65704-0

Study question: How do cell states and neighborhoods differ across lung regions in health, and how are those patterns altered in diseased lung?

Cohort and groups (as described):

• Healthy lobe samples from organ donors: n = 4
• Diseased lobe samples: n = 4 (including COPD and non-COPD contexts)

Spatial stack:

Multiple complementary spatially resolved transcriptomics (SRT) methods:

• Two cell-level targeted imaging approaches (HybISS for broad cell-type coverage; SCRINSHOT for sensitive detection of selected cell states)
• A lower-resolution, unbiased mRNA approach used to validate regional patterns

Key spatial findings:

• Lung cell states and neighborhoods show strong region dependence, so "where you sample" can change both cell-type composition and inferred interactions.
• Spatial mapping enables region-anchored cell annotation, reducing ambiguity that occurs when dissociated references are used without anatomical context.
• Proximity-defined neighborhoods provide a consistent unit for comparing microenvironments across regions and across donors.
• Disease-associated shifts are often localized (region- and niche-specific) rather than uniform across the lung, supporting stratified region-by-condition comparisons.
• Using multiple spatial methods yields complementary sensitivity/coverage and strengthens confidence in region-specific patterns.

Study blueprint:

• Design: predefine a regional sampling grid (lobe + anatomical landmarks) and keep region labels throughout analysis and reporting.
• Minimum assays: one cell-resolved targeted SRT method plus a second modality to cross-check regional expression and cell-state calls.
• Core readouts: region-aware cell-type mapping, neighborhood enrichment per region, and region-by-condition contrasts (not pooled).
• Pitfalls: pooling regions, underpowered stratification, and claiming "lung-wide" effects without region-matched comparisons.

Databases and Resources: How to Find Spatial Omics Datasets and Protocols

Paper 3 — Gland-associated immune niche in the lung

Title: A spatially resolved atlas of the human lung characterizes a gland-associated immune niche

Journal: Nature Genetics (2023)

DOI: https://doi.org/10.1038/s41588-022-01243-4

Study question:

Does the human lung contain specialized mucosal immune niches, and if so, where are they and what sustains them?

Cohort and sampling:

Deep sampling across five locations along the respiratory tree (trachea, multiple bronchial generations, and parenchyma) from deceased donors, with multi-omic profiling.

Spatial stack:

• Spatial transcriptomics across defined lung locations
• Single-nucleus RNA-seq for cell-state resolution
• VDJ sequencing to characterize immune repertoires in context

Key spatial findings:

• The atlas prioritizes the airway axis (proximal-to-distal) rather than focusing only on thin-walled regions, expanding spatial interpretation beyond parenchyma-centric views.
• A gland-associated immune niche is described in submucosal glands (SMG), supporting IgA plasma cell survival and local antibody biology.
• A niche-support model is proposed in which gland epithelial programs (e.g., CCL28, APRIL, IL-6) recruit B cells and support IgA plasma cell persistence in place.

Figure 3. Madissoon et al., Nature Genetics (2022), Fig. 1: spatial multi-omics atlas of the human lung enabling identification of cell types and their anatomical locations across proximal-to-distal regions.

Study blueprint:

• Design: include SMG-containing airway regions in your sampling plan if mucosal immunity or IgA biology matters for your question.
• Minimum assays: spatial transcriptomics across airway levels + single-nucleus reference for epithelial/immune resolution.
• Core readouts: axis-aware mapping (proximal vs distal), SMG-specific niches, and co-localization metrics for gland epithelium and IgA plasma cells.
• Common pitfalls: omitting gland-rich regions, and interpreting mucosal immunity without distinguishing airway compartments.

Suggested reading: Spatial Transcriptomics Cancer Research: 4 Core Strategies (useful for immune niche thinking)

Paper 4 — Distal lung maps and lineage hierarchies (bipotent progenitor)

Title: Human distal lung maps and lineage hierarchies reveal a bipotent progenitor

Journal: Nature (2022)

DOI: https://doi.org/10.1038/s41586-022-04541-3

Study question:

What cell types and transitional states govern distal lung repair and disease-associated remodeling, and how do they map in space?

Cohort:

Healthy adult lung; fetal/postnatal lung; disease lungs (IPF/COPD/ALI); non-human primates including injury models; plus functional perturbations and co-culture experiments.

Spatial stack:

• Spatial transcriptomics (Visium-class) for distal lung architecture
• scRNA-seq plus in situ validation (FISH/ISH, immunostaining)
• Trajectory tools used to infer lineage hierarchies in context

Key spatial findings:

• Previously under-characterized distal airway cell states and transitional programs are positioned as spatially patterned rather than randomly distributed.
• A bipotent progenitor concept is introduced through lineage hierarchies; transitional states (e.g., AT0-like intermediates) are framed as repair-relevant states that appear during development and injury.
• A stromal signaling hub model is highlighted (e.g., LGR5+ fibroblast niche signals), emphasizing microenvironment control over lineage outcomes.

Study blueprint:

Design: if your endpoint is repair/remodeling, include time points (or injury gradients) and annotate distal microanatomy carefully; align spatial maps to lineage questions.

Minimum assays: wide-area spatial discovery plus a cell-state reference; validate one key transitional state with in situ methods.

Core readouts: spatial localization of transitional states, niche association with stromal/vascular features, and trajectory-informed markers mapped back to tissue.

Common pitfalls: transferring mouse lineage assumptions directly to human without spatial confirmation, and ignoring microanatomical context (terminal vs respiratory bronchioles).

Suggested reading: Computational Strategies and Machine Learning for Spatial Genomics Data

Paper 5 — Spatiotemporal inflammatory states in diffuse alveolar damage (mouse model)

Title: Spatiotemporal single-cell transcriptomic profiling reveals inflammatory cell states in a mouse model of diffuse alveolar damage

Journal: Exploration (2023)

DOI: https://doi.org/10.1002/exp.20220171

Study question: During acute injury with severe neutrophil infiltration, which structural-cell states shape neutrophil recruitment, and where does the inflammatory microenvironment form?

Model and groups (as described):

Mouse DAD induced by aerosolized insult; time-point sampling with matched controls; scRNA-seq and spatial transcriptomics integrated.

Spatial stack:

• Single-cell (or single-nucleus) transcriptomics for state discovery
• Spatial transcriptomics to anchor states to anatomic locations
• Flow cytometry used as supporting quantification

Key spatial findings:

• A fibroblast activation trajectory is linked to acute inflammation, emphasizing structural cells as active signal senders rather than passive scaffolds.
• A chemokine axis (CXCL1–CXCR2) is framed as a recruitment mechanism where activated fibroblast-like states promote neutrophil localization.
• An anatomically anchored inflammatory microenvironment is described in peribronchial regions, characterized by tight spatial contact between recruited neutrophils and activated fibroblast states.

Figure 4. Su et al., Exploration (2023), Fig. 1: scRNA-seq reveals diverse lung cell populations and inflammatory changes in a mouse diffuse alveolar damage model.

Study blueprint:

Design: use a time series and define "injury-adjacent" versus "non-injured" ROIs to avoid averaging away localized inflammation.

Minimum assays: sc/snRNA-seq for cell-state discovery plus spatial maps for localization of the key axis.

Core readouts: sender–receiver co-localization (chemokine axis constrained to ROIs), peribronchial niche calling, and contact/proximity metrics.

Common pitfalls: treating inflammation as uniform across the lung, and over-interpreting ligand–receptor pairs without demonstrating spatial co-occurrence.

Paper 6 — Multimodal spatial-omics in lung precursor lesions (co-evolution of progenitors and inflammatory niches)

Title: Multimodal spatial-omics reveal co-evolution of alveolar progenitors and proinflammatory niches in progression of lung precursor lesions

Journal: Cancer Cell (2025)

DOI: https://doi.org/10.1016/j.ccell.2025.10.004

Study question: How do epithelial progenitors and inflammatory niches co-evolve across precursor lesions, and which stage-specific microenvironments define intervention opportunities?

Cohort and scope (as described):

Human tissue spanning normal lung, precursor lesions (AAH/AIS/MIA), and LUAD; plus independent spatial validation and mechanistic models.

Spatial stack:

• Multimodal integration: spatial transcriptomics across many samples, snRNA-seq, WES, plus cell-resolved spatial profiling in validation settings
• Animal models and organoid assays used to test functional relevance of niches

Key spatial findings:

• Stage-specific, region-specific programs distinguish precursor lesions from invasive disease, showing that progression is not a uniform trajectory.
• A proinflammatory niche enriched for specific macrophage-like states (e.g., IL1B-high programs) is positioned as a supportive microenvironment for alveolar progenitors in early stages.
• The epithelial–proinflammatory niche appears broadly in precancer but less frequently in later LUAD, suggesting time-limited vulnerabilities.
• Targeting inflammation alone or combined with checkpoint blockade is presented as a strategy that can reduce progenitor-like programs in early-stage settings (research interpretation, not clinical guidance).

Study blueprint:

Design: sample across lesion stages and include spatially matched "adjacent normal" where possible; report stage-specific niches rather than pooled averages.

Minimum assays: spatial transcriptomics for stage maps + a cell-state reference (snRNA-seq) + targeted validation in the niche of interest.

Core readouts: stage-specific niche frequency, progenitor–immune co-localization, and spatially constrained clone/region analysis (when genomics is included).

Common pitfalls: collapsing lesion stages into one group, and claiming mechanisms without showing niche localization and a minimal functional test.

Cross-paper insights you can reuse

Insight 1: Structural compartments create "rules of engagement"

Across asthma airway wall maps, gland-associated niches, and injury models, the recurring theme is that epithelial, stromal, smooth muscle, and gland compartments define which interactions are plausible. When your hypothesis depends on immune–structural crosstalk, the compartment should be a first-class variable in design and reporting.

Insight 2: Distances and neighborhoods are durable endpoints

Several studies emphasize re-patterning (who is closer or farther) rather than only abundance changes. This is especially helpful when therapies shift localization without fully eliminating cell types, or when disease effects appear in discrete hotspots.

Insight 3: Regional sampling is not a footnote in lung studies

For large organs like adult lungs, region-aware design is essential. "Atlas" claims become stronger when regions are explicitly defined, matched across samples, and analyzed as stratified layers rather than pooled.

Insight 4: Mechanisms become more credible when constrained to niches

Rather than listing many ligand–receptor pairs, prioritize a small number of axes that are (a) enriched in a defined niche and (b) supported by spatial co-occurrence of sender and receiver. This improves both interpretability and reviewer confidence.

Study blueprint you can reuse

Step 1 — Define your primary contrast

Pick one dominant contrast and keep it consistent:

• Health vs disease (asthma/COPD/IPF/ALI)
• Severity strata or endotypes
• Treatment exposure or response classes (in research cohorts)
• Proximal vs distal axis, airway vs parenchyma, or region-by-region comparisons
• Time-course (especially for injury models)

Step 2 — Build an ROI plan that matches airway anatomy

For airway wall projects, predefine:

• Epithelium / subepithelium / smooth muscle ROIs
• Gland-rich regions (SMG) when mucosal immunity matters
• Peribronchial/perivascular zones for inflammatory recruitment questions
• Injury-adjacent versus non-injured controls (for acute models)

Step 3 — Choose a minimal assay stack

• Discovery: wide-area spatial map for domains/ecotypes
• Resolution: cell-resolved spatial profiling in priority ROIs
• Validation: ROI-based profiling in a second cohort or orthogonal staining for one key spatial relationship

Step 4 — Commit to core readouts upfront

• Spatial domains/ecotypes (where programs cluster)
• Neighborhood enrichment (who co-occurs locally)
• Distance metrics (contact vs barrier patterns)
• Spatially constrained signaling axes (only within defined ROIs)
• Patient- or sample-level statistics (avoid "one-map" conclusions)

Step 5 — Reporting checklist (what reviewers look for)

• A clear tissue annotation layer (compartments/regions)
• At least one spatial relationship supporting each major claim (co-localization, distance shift, or niche restriction)
• One orthogonal validation for the top mechanism (IF/IHC/ISH, or a focused functional test in models)

FAQs

Q1: Which platform is best for airway wall remodeling in asthma?

A: Use cell-resolved spatial profiling to capture airway wall neighborhoods, then validate compartment programs with ROI-based profiling in an independent cohort.

Q2: How should I sample lungs for a regional atlas study?

A: Predefine anatomical regions (and lobes if relevant), match regions across donors/conditions, and analyze region-stratified results instead of pooled averages.

Q3: What are the most reproducible spatial endpoints?

A: Domains/ecotypes, neighborhood enrichment, and distance metrics (who is closer/farther). These often generalize better than single-gene markers.

Q4: How do I integrate sc/snRNA-seq with spatial data without overfitting?

A: Use sc/snRNA-seq as a reference for cell states, but anchor conclusions on spatial co-occurrence and ROI-limited analyses, not only deconvolution.

Q5: Can I add regulation layers (chromatin/epigenomics) to strengthen mechanism claims?

A: Yes—spatial epigenomics can support regulatory hypotheses, but prioritize one or two niche-specific mechanisms rather than broad multi-assay coverage.

Conclusion

Spatial multi-omics is reshaping airway disease research by turning cell states into cell states in place—revealing niche-specific ecosystems in the airway wall, region-dependent lung biology, gland-associated immune support zones, distal lung lineage programs, and anatomically anchored inflammatory microenvironments. Across the six papers summarized here, the most transferable strategy is a focused contrast plus a minimal discovery-to-validation stack that reports spatial relationships (domains, neighborhoods, distances) at the sample level. Use the study playbook to design a compartment-aware, region-aware spatial project that prioritizes one or two niche-specific mechanisms you can validate credibly.

References

1. Joulia, R., et al. "A single-cell spatial chart of the airway wall reveals proinflammatory cellular ecosystems and their interactions in health and asthma." Nature Immunology, 2025.
2. Firsova, A. B., et al. "Spatial single-cell atlas reveals regional variations in healthy and diseased human lung." Nature Communications, 2025.
3. Madissoon, E., et al. "A spatially resolved atlas of the human lung characterizes a gland-associated immune niche." Nature Genetics, 2023.
4. Murthy, P. K. L., et al. "Human distal lung maps and lineage hierarchies reveal a bipotent progenitor." Nature, 2022.
5. Su, D., et al. "Spatiotemporal single-cell transcriptomic profiling reveals inflammatory cell states in a mouse model of diffuse alveolar damage." Exploration, 2023.
6. Peng, F., et al. "Multimodal spatial-omics reveal co-evolution of alveolar progenitors and proinflammatory niches in progression of lung precursor lesions." Cancer Cell, 2025.