Spatial Omics in Asthma Research (2024–2025): Key Papers, Mechanisms, and Multi-Modal Readouts

Inquiry

Asthma is not one disease in one place. Even within the same airway, inflammation, epithelial remodeling, mucus plugging, and vascular changes can occur in sharply different micro-regions, often driven by distinct cell neighborhoods and local cytokine circuits. That spatial heterogeneity is exactly what bulk profiling averages away—and what many single-cell datasets cannot fully reconstruct once tissue context is lost. Spatial omics in asthma (spatial transcriptomics, spatial proteomics, and spatial lipidomics) helps connect molecular signals to airway structure, immune niches, and region-specific pathology in situ.

Key Takeaways

• Spatial readouts answer the "where" of asthma biology: where remodeling starts, where immune priming is reinforced, and where plugs and metabolic shifts concentrate.
• Five recent papers (2024–2025) illustrate reproducible, mechanism-driven spatial designs across transcriptome, proteome, and lipidome.
• A defensible spatial study often pairs a primary map with a minimal orthogonal validation (RNAscope/IF, multiplex imaging, or LC–MS/MS for lipids).
• Common pitfalls are ROI selection bias, under-sampling small airways, and cross-sample comparability (especially in MSI).
• This is a research-focused literature digest; it does not cover clinical decision-making or patient-specific services.

This curated 2024–2025 digest focuses on five studies that use spatial readouts to answer practical, mechanism-level questions in asthma biology—from early-life vascular remodeling to lymph node cytokine microniches, small-airway epithelial "proximalization," mucus plug subtypes, and pollutant-triggered lipid remodeling. For a broader cross-disease perspective (asthma, COPD, IPF and more), see Airway Disease Spatial Omics: Key Papers & Study Insights.

Why Spatial Omics Matters for Asthma Biology

Asthma research often faces three recurring roadblocks:

1. Endotype heterogeneity inside one tissue section. A single bronchiole can contain adjacent regions with different epithelial programs (for example, mucus gene expression), different immune proximity, and different remodeling signatures—yet many assays treat it as one homogeneous sample.
2. Structure–function coupling. Mucus plugs, small airway obstruction, and microvascular remodeling are spatial phenomena. Knowing which genes are higher is useful; knowing where they are higher (and what they are adjacent to) is frequently the difference between an interpretable mechanism and a list of markers.
3. Spatially restricted signaling circuits. Cytokine microniches, hypoxic zones, and boundary regions (such as T–B borders in lymph nodes) can drive downstream cell fate decisions that are hard to infer from dissociated cells alone.

A practical long-tail question many teams ask is how spatial transcriptomics helps asthma research beyond cell typing. The most actionable answer is that spatial profiling can pinpoint where epithelial remodeling initiates, where TH2 priming is reinforced, where mucus plug biology diverges into subtypes, and where pollutant exposure reshapes metabolism.

Spatial Omics Modalities Used in These Asthma Papers

Different spatial modalities answer different asthma questions. A "best" technology rarely exists; matching the readout to the biology is what improves interpretability.

ROI-Based Targeted Spatial Transcriptomics (Example: GeoMx DSP)

Useful for pathway comparisons across predefined regions (for example, vascular-rich zones vs nearby tissue) and often compatible with FFPE. These approaches are common in studies that need controlled region selection tied to histology.

Tissue-Wide Spatial Transcriptomics (Examples: Visium, Slide-seq)

Helpful when you want to discover tissue-wide programs or gradients (airway-to-alveolar transitions, boundary regions, or niche discovery) and can tolerate some spot-level mixing.

Spatial Proteomics and Highly Multiplex Imaging (Examples: IMC, PhenoCycler)

Well-suited when phenotype is defined by protein markers (granulocyte biology, epithelial states, immune activation) or when cell segmentation and neighborhood analysis are central to conclusions.

Spatial Lipidomics (Example: MALDI-MSI)

Well-suited for regional metabolism and lipid distribution, especially in lung where airway and alveolar compartments differ in baseline lipid composition.
A practical tip many teams learn quickly: when spatial biology is the point, adjacent-section validation (RNAscope, IF/IHC, targeted qPCR, or LC–MS/MS for lipid IDs) often prevents "pretty map, unclear meaning" outcomes.

Method-to-Question Quick Guide

Paper-at-a-Glance: Five 2024–2025 Asthma Spatial Omics Studies

Below are five studies that collectively illustrate multi-modal spatial omics in asthma (transcriptome + proteome + lipidome) and emphasize copy-ready design choices.

1. Mast cell activation disrupts endothelial–pericyte interactions during early life allergic asthma (J Clin Invest, 2024; DOI: 10.1172/jci173676)
2. Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen (Nat Immunol, 2024; DOI: 10.1038/s41590-024-01986-8)
3. Cellular and molecular features of asthma mucus plugs provide clues about their formation and persistence (J Clin Invest, 2025; DOI: 10.1172/jci186889)
4. Airway Epithelial Heterogeneity and Mucus Plugging in Asthmatic Bronchioles (Am J Respir Crit Care Med, 2025; DOI: 10.1164/rccm.202409-1849oc)
5. Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning (Nat Commun, 2025; DOI: 10.1038/s41467-025-58135-4)

Case Study 1: Early-Life Vascular Remodeling Mapped by ROI-Based Spatial Transcriptomics

Study: Mast cell activation disrupts interactions between endothelial cells and pericytes during early life allergic asthma (J Clin Invest, 2024; DOI: 10.1172/jci173676)
What they asked
Early-life allergen exposure can leave long-lasting airway consequences. This study focused on a spatially defined question: how does early allergic inflammation reshape the lung microvasculature—especially the endothelial–pericyte interface that stabilizes vessels?
What they did (design choices worth copying)

• Time-course model: neonatal mice exposed to house dust mite (HDM) across development and remission/relapse windows.
• Spatial strategy: region selection in vascular-rich adventitial zones rather than treating lung as one unit.
• Mechanistic bridge: combine spatial transcript profiling with 3D imaging of precision-cut lung slices and in vitro pericyte contraction assays.

What the spatial readout added
Region-focused spatial transcript profiling supported pathway differences aligned to microvascular regions, consistent with histologic changes such as reduced pericyte coverage and increased hypoxia-associated signatures. Mechanistic validation linked mast cell degranulation products (including proteases) to pericyte contraction and reduced N-cadherin, connecting the spatial signal to a plausible causal axis.

Figure 2. Early-life allergen exposure is associated with reduced pericyte coverage and hypoxia-related signals in lung microvascular regions.

Practical takeaways for asthma spatial study design

• If your hypothesis involves vascular remodeling, define ROIs by microanatomy first (perivascular adventitia, airway wall, alveolar septa) rather than broad "lung section" labels.
• Pair spatial readouts with one "ground-truth" spatial measure (pericyte coverage quantification or hypoxia staining). It reduces over-interpretation of expression maps.

Case Study 2: IL-2 and IL-10 Microniches Define Where TH2 Lineages Start

Study: Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen (Nat Immunol, 2024; DOI: 10.1038/s41590-024-01986-8)
What they asked
Many asthma studies focus on effector responses in the lung. This study moved upstream and asked: where does TH2 fate begin during inhaled allergen priming, and what local cytokine circuits support that decision?
What they did (multi-layer spatial logic)

• Spatial focus tissue: lymph node architecture during early priming.
• Multi-omic integration: tissue-wide spatial transcriptomics (reported with Visium and Slide-seq), RNAscope, and single-cell approaches to identify precursor states.
• Perturbations: genetic and antibody-based perturbations of IL-10 signaling and IL-2/STAT5 pathways, plus timing-specific Blimp-1 deletion.

What the spatial readout added
A key finding is that IL-2 forms spatial microniches—notably at the T–B border—where TH2 precursor signatures concentrate. The spatial constraint helps explain why bulk cytokine measurements can miss decisive microenvironments. The mechanistic chain connects IL-10 (STAT3 support of Blimp-1) with IL-2/STAT5 regulation via repression of inhibitory factors.

Practical takeaways for "asthma spatial immune niches" projects

• If the hypothesis involves lineage commitment, consider dual-compartment spatial sampling: priming sites (lymph node) and effector sites (lung).
• When you map cytokine-driven programs spatially, plan at least one orthogonal localization assay (RNAscope or multiplex IF) for the cytokine or its receptor.

Case Study 3: Mucus Plug Subtypes Defined by Spatial Proteomics and Quantitative Pathology

Study: Cellular and molecular features of asthma mucus plugs provide clues about their formation and persistence (J Clin Invest, 2025; DOI: 10.1172/jci186889)
What they asked
Mucus plugs are recognized in severe disease and fatal outcomes, but mechanistic understanding lags. This study asked: are mucus plugs biologically uniform, or do they fall into distinct, spatially defined subtypes with different immune features?
What they did (why the design works)

• Cohort framing: asthma vs COPD vs non-diseased controls, with asthma stratified by fatal vs non-fatal.
• Spatial proteomics: imaging mass cytometry (IMC) and confocal readouts to characterize cell types and activation states around plugs.
• Quantitative definitions: mucus composition markers (for example, MUC5AC vs MUC5B) used to define plug categories.

What the spatial readout added
The paper describes two plug patterns in asthma: a MUC5AC-dominant subtype and a more mixed granulocyte-associated subtype with extracellular trap features. Spatial proteomics connects plug composition with adjacent immune cell states and epithelial programs consistent with IL-13-associated mucus biology.

Figure 3. Spatial profiling supports distinct mucus plug phenotypes that differ in mucin composition and inflammatory cell features.

Practical takeaways for "asthma mucus plug spatial proteomics"

• Define plug subtypes using a small, defensible marker set first (MUC5AC/MUC5B, granulocyte markers), then expand panels for mechanism.
• Avoid relying on a single field of view. Plug biology is patchy; plan multiple airways per case (or a microarray strategy) to reduce selection bias.

Case Study 4: Small Airway Epithelial Heterogeneity Drives Plugging Without Requiring Immune Proximity

Study: Airway Epithelial Heterogeneity and Mucus Plugging in Asthmatic Bronchioles (Am J Respir Crit Care Med, 2025; DOI: 10.1164/rccm.202409-1849oc)
What they asked
Small airway disease is strongly tied to airflow limitation, yet bronchiolar mechanisms can differ from central airways. This study asked: what epithelial programs correlate with mucus plugging in bronchioles, and are they spatially uniform within a bronchiole?
What they did (a replicable ROI framework)

• Airway stratification: bronchioles grouped by diameter and mucus marker intensity.
• Spatial readouts: ROI-based transcript profiling + multiplex phenotyping (PhenoCycler) + RNA-ISH for key transcripts.
• Within-airway comparison: "MUC5AC-high" vs "MUC5AC-low" regions from the same bronchiole.

What the spatial readout added
A central spatial insight is within-airway heterogeneity: the same bronchiole can contain adjacent regions with different epithelial states. The paper highlights a "proximalization" pattern in distal airways—loss of distal secretory programs and increased mucus-associated programs. Importantly, while immune differences exist overall, MUC5AC-high regions were not necessarily explained by immune proximity, supporting an epithelial-intrinsic axis.
Practical takeaways for "small airway spatial transcriptomics in severe asthma"

• When you compare cases, also compare regions within the same airway. It controls for donor-level variability and helps isolate local drivers.
• Use an ROI strategy that is easy to defend: define regions by histology plus a single marker intensity map, then test whether transcript signatures match that spatial label.

Case Study 5: Pollutant Exacerbation Read Through Regional Lipid Remodeling

Study: Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning (Nat Commun, 2025; DOI: 10.1038/s41467-025-58135-4)
What they asked
Ozone is a known trigger for exacerbations, but mechanisms are often studied without regional lung context. This work asked: how does combined allergen + ozone exposure reshape lipid distributions across airway and alveolar regions?
What they did (workflow details that translate)

• Spatial lipidomics platform: MALDI-MSI at fine spatial scale.
• Computational strategy: normalization + segmentation + region-aware statistics for multi-sample comparisons.
• Validation: LC–MS/MS used to support lipid annotation confidence for selected features.

What the spatial readout added
They quantified baseline compartment differences and then detected exposure-associated changes that were not uniform across tissue regions. A practical emphasis was cross-sample comparability, addressing a common barrier in MSI studies: interpreting biological replicates at the region level rather than as single pooled images.

Figure 4. Region-aware segmentation helps interpret spatial lipidomics by aligning histology with lipid distributions across lung compartments.
Practical takeaways for "spatial lipidomics in asthma exacerbation models"

• Treat MSI as both an assay and a pipeline. Pre-analytics (inflation/fixation choices, freezing, matrix application) and normalization can dominate outcomes.
• Plan to validate a subset of lipids using LC–MS/MS where feasible. It improves traceability and reduces the risk of over-interpreting tentative annotations.

Cross-Study Synthesis: Five Mechanistic Themes Emerging From Spatial Asthma Omics

These papers converge on five reusable themes that can guide hypothesis, ROI definition, and sampling:

1. Microvascular Remodeling Is Spatially Anchored
   The endothelial–pericyte interface and hypoxia-associated signatures are not evenly distributed. Model your sampling around microvascular zones when vascular stability is the endpoint (Case Study 1).
2. TH2 Fate Is Shaped by Cytokine Microniches, Not Only Cytokine Levels
   IL-2 and IL-10 effects can be architecture-dependent. Spatial mapping in priming tissues can explain why dissociated immune profiles sometimes miss early determinants (Case Study 2).
3. Epithelial "Proximalization" Can Be Region-Specific Within the Same Airway
   MUC5AC-high regions can coexist with MUC5AC-low regions, supporting within-airway comparisons as a high-signal design element (Case Study 4).
4. Mucus Plug Biology Includes Distinct Subtypes
   Plug patterns with different composition and immune context suggest that "mucus plugging" is a phenotype class, not a single pathway (Case Study 3).
5. Pollutant Exacerbation Has Metabolic Geography
   Spatial lipid distributions provide a readout of tissue physiology and injury responses that can be missed by homogenized lipidomics (Case Study 5).

Copy-Ready Study Blueprint for Asthma Spatial Omics Projects

If you are designing a research-use-only spatial omics study in asthma, the fastest path to interpretable results is to decide what the map should explain before deciding what to profile.

Step 1: Choose the biological question (and the spatial unit)

• Vascular remodeling: perivascular regions, adventitia, hypoxia-positive zones
• Small airway plugging: bronchioles stratified by diameter and MUC5AC intensity
• Immune priming: lymph node architecture (T–B borders) plus downstream lung readouts
• Exacerbation metabolism: airway vs alveolar compartments in exposure models

Step 2: Define ROIs with one "anchor feature"

ROI selection is more defensible when anchored to one measurable feature:

• marker intensity (MUC5AC-high vs low)
• structure (perivascular ring, airway wall, epithelial layer)
• pathology state (plug present vs absent; hypoxia-positive vs negative)

Step 3: Pair modalities to reduce ambiguity

Common pairing strategies in asthma:

• Spatial transcriptomics + targeted localization validation (RNAscope/IF for key cytokines, receptors, or epithelial markers)
• Spatial proteomics for phenotype + spatial transcriptomics for pathways (especially for plug biology)
• Spatial lipidomics + LC–MS/MS confirmation for select metabolites/lipids

If you are weighing resolution, coverage, and FFPE compatibility across platforms, Guide to Spatial Transcriptomics Platforms: Sequencing vs Imaging and How to Choose provides a practical decision framework.

Step 4: Build analysis realism early

For many projects, the bottleneck is not differential expression—it is segmentation, QC, and region comparability. Spatial Transcriptomics Data Analysis: Workflow & Tips outlines a reviewer-friendly path from raw matrices/images to defensible region-level conclusions.

Step 5: Working with a service partner in RUO settings

For teams that prefer a service partner for research workflows (sample processing, profiling, and analysis reporting), CD Genomics supports end-to-end execution through Spatial Omics Lab; see Spatial Transcriptomics Services. For metabolism-anchored questions (for example, allergen plus oxidant exposure models), spatial MSI-based profiling may be considered; a service overview is available at Spatial Metabolomics Services.

Practical Pitfalls and Lab-Tested Tips From Spatial Asthma Projects

These experience-driven points are not universal rules, but they reflect common failure modes in lung spatial workflows and the design patterns used in peer-reviewed studies.

1. Lung handling can create artificial spatial gradients.
   Inflation, fixation, and freezing steps can unevenly preserve airway vs alveolar compartments. If your project depends on comparing airway wall to parenchyma, standardize handling tightly and record parameters per sample (inflation medium, time-to-freeze, section thickness).
2. ROI bias is real—predefine ROI logic before reviewing expression maps.
   A practical approach is to lock ROI rules using histology plus one marker map (for example, MUC5AC IF), then perform profiling. This reduces post hoc region selection and helps keep conclusions defensible.
3. Small airways are easy to under-sample.
   If bronchioles are central to your hypothesis, plan enough sections/fields to capture them consistently. Within-airway contrasts (MUC5AC-high vs low) can control for donor-level variability while preserving spatial meaning.
4. Spatial MSI needs a reproducibility plan, not just a run plan.
   Matrix application, ion suppression, and batch effects can dominate signals. If comparing groups, include interleaved runs, technical controls, and a defined normalization approach. Validate a subset of key lipids with LC–MS/MS where feasible.
5. Interpretation improves when you close the loop with one causal test.
   Even in profiling-centered studies, a modest perturbation (inhibitor tests, cytokine blocking in a model, or ex vivo co-culture validation) can strengthen the mechanism narrative and reduce over-interpretation of correlations.

Data Availability and How to Reuse These Datasets

The findings summarized above are traceable to the peer-reviewed sources listed in the References section (by DOI). For reanalysis, the most reliable starting point is each paper's Supplementary Information and Methods, which typically describe data deposition (when applicable) and provide processing details. Where public deposition is available, authors commonly use community repositories (for example, GEO/ArrayExpress for transcript data or proteomics/metabolomics repositories for mass spectrometry outputs). For reuse, align your reanalysis plan to the paper's reported ROI definitions, segmentation logic, and QC criteria to avoid mixing incompatible units of comparison.

FAQs

How Do I Choose Between GeoMx DSP and Visium for Asthma Airway Tissue?
If your question depends on comparing predefined regions (for example, MUC5AC-high vs MUC5AC-low bronchiolar epithelium, or perivascular zones), ROI-based targeted spatial transcriptomics can be efficient and interpretable. If you need tissue-wide discovery—gradients, boundary regions, or niche finding—whole-transcriptome spatial methods may be a better fit. In both cases, plan at least one orthogonal validation step (RNAscope or IF) for key drivers.
What ROI Strategy Works for MUC5AC-High vs MUC5AC-Low Regions in Bronchioles?
A reviewer-friendly approach is to define bronchioles by diameter, then label ROIs using a single marker intensity map (MUC5AC staining) before running transcript/protein profiling. Use multiple ROIs per airway and multiple airways per sample to reduce selection bias, and keep ROI rules consistent across cases.
Can Spatial Transcriptomics and IMC Be Integrated on Adjacent Sections Reliably?
Yes—adjacent-section integration is common, especially when transcript data provides pathways and IMC provides cell phenotypes and neighborhood context. The practical keys are careful section alignment, transparent ROI matching rules, and reporting how regions were paired (for example, "same bronchiole, matched by histology and marker intensity").
What Pre-Analytics Most Often Break Lung Spatial Omics Data Quality?
The most frequent issues are inconsistent inflation/fixation, variable time-to-freeze (for frozen workflows), section thickness variability, and uneven staining quality that affects ROI selection or segmentation. Document these parameters prospectively and standardize them as much as possible across cohorts.
How Many ROIs Per Sample Are Typical for Heterogeneous Airway Pathology?
There is no universal number, but heterogeneity claims generally require both biological replication (subjects/animals) and spatial replication (multiple ROIs and multiple airways per sample). A practical planning heuristic is to allocate enough ROIs to represent key compartments (airway wall, perivascular regions, plug-adjacent epithelium, parenchyma as needed) while keeping ROI definitions fixed across samples.

Ready to Explore Spatial Omics for Asthma Research

Spatial omics is most informative in asthma when the map is tied to a concrete biological unit—an airway segment, a mucus plug, a perivascular ring, or an immune priming niche—and when region definitions are standardized across samples. The five studies above show how multi-modal spatial readouts can move asthma biology from "what changes" to "where it changes, with whom, and under what microenvironmental constraints," in ways that are practical to replicate and extend in research programs.

References

1. Joulia R, Puttur F, Stölting H, et al. Mast cell activation disrupts interactions between endothelial cells and pericytes during early life allergic asthma. J Clin Invest. 2024. DOI: 10.1172/jci173676.
2. He K, Xiao H, MacDonald WA, et al. Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TH2 cells in response to inhaled allergen. Nat Immunol. 2024. DOI: 10.1038/s41590-024-01986-8.
3. Liegeois MA, Hsieh A, Al-Fouadi M, et al. Cellular and molecular features of asthma mucus plugs provide clues about their formation and persistence. J Clin Invest. 2025. DOI: 10.1172/jci186889.
4. Schworer SA, Murano H, Dang H, et al. Airway Epithelial Heterogeneity and Mucus Plugging in Asthmatic Bronchioles. Am J Respir Crit Care Med. 2025. DOI: 10.1164/rccm.202409-1849oc.
5. Stevens NC, Shen T, Martinez J, et al. Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning. Nat Commun. 2025. DOI: 10.1038/s41467-025-58135-4.