Nature GeneticsGenomicsPeer-reviewed journal article
Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis
Annika VannaniD, Ruqian LyuiD, Arianna L. Williams-KatekiD, Nicholas M. NegrettiiD, Evan D. MeeiD, Joseph HirshiD, Samuel HirshiD, Niran HadadiD, David S. NicholsiD, Carla L. Calvi, Chase J. TayloriD, VV Polosukhin, Ana Paula Moreira SerezaniiD, A. Scott McCalliD, Jason J. GokeyiD, Heejung ShimiD, Lorraine B. WareiD, Matthew D. BacchettaiD, Ciara M. ShaveriD, Timothy S. BlackwelliD, Rajat WaliaiD, Jennifer M. S. SucreiD, Jonathan A. KropskiiD, Davis James McCarthyiD, Nicholas E. BanovichiD
Abstract
The human lung is structurally complex, with a diversity of specialized epithelial, stromal and immune cells playing specific functional roles in anatomically distinct locations, and large-scale changes in the structure and cellular makeup of this distal lung is a hallmark of pulmonary fibrosis (PF) and other progressive chronic lung diseases. Single-cell transcriptomic studies have revealed numerous disease-emergent/enriched cell types/states in PF lungs, but the spatial contexts wherein these cells contribute to disease pathogenesis has remained uncertain. Using sub-cellular resolution image-based spatial transcriptomics, we analyzed the gene expression of more than 1 million cells from 19 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell-types, established the cellular and molecular basis of classical PF histopathologic disease features, and identified a diversity of distinct molecularly-defined spatial niches in control and PF lungs. Using machine-learning and trajectory analysis methods to segment and rank airspaces on a gradient from normal to most severely remodeled, we identified a sequence of compositional and molecular changes that associate with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially-resolved characterization of the cellular and molecular programs of PF and control lungs, provide new insights into the heterogeneous pathobiology of PF, and establish analytical approaches which should be broadly applicable to other imaging-based spatial transcriptomic studies.
- organism: human
- assay: spatial transcriptomics
- assay: imaging
- research focus: spatial domain detection
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- approach: graph neural network
- approach: machine learning (classical)
- approach: statistical model
- approach: experimental assay
In-depth summary
Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 2 (17 Dec 2023) · CC BY 4.0
Xenium profiling of 343 genes in 1,126,820 nuclei from 19 human lungs maps fibrosis niches and orders alveolar remodeling from alveolar-capillary disruption to SPP1+ macrophage accumulation.
Single-cell RNA-seq has catalogued disease-emergent cell states in pulmonary fibrosis (PF), but where these states act within spatially heterogeneous, remodeled lung tissue was unresolved. This work places them in histopathologic lesions and molecular niches, and it introduces segmentation-free niche detection and airspace-level pseudotime methods for imaging-based spatial transcriptomics.
Key results
ShownImaging-based profiling recovered 39 cell types, including disease-emergent states plus endothelial and mesenchymal cells that scRNA-seq tends to underrepresent. PF samples had lower proportions of alveolar cell types and higher proportions of activated fibroblasts, lymphoid cells and airway-type epithelium.
1,126,820 nuclei holding 69,406,577 transcripts were kept, out of 210,370,118 high-quality transcripts across 28 samples. The 39 types split into 6 endothelial, 11 epithelial, 13 immune and 9 mesenchymal. propeller found that 24 of 39 types (61.5%) differed across the unaffected, LF and MF groups at FDR < 0.05. · Diverse cellular landscape of the lung; Dimensionality reduction, clustering, and cell…; Differential cell type composition by disease…
ShownSample-level pathology scores tracked both composition and expression: capillary and alveolar cells fell, and activated fibroblasts and lymphoid cells rose, with severity. Annotated lesions showed which cells make up fibroblastic foci, honeycombing and hyperplastic alveolar epithelium.
In linear models, 19 cell types (48.7%) and 115 genes (33.5%) were associated with pathology score at FDR < 0.01. Authors annotated 21 feature types in 22 samples. Fibroblastic foci held CTHRC1+ and other fibroblast subtypes, smooth muscle cells and macrophages. Hyperplastic regions were mostly transitional AT2 cells. · Cellular and molecular characterization of…; Pathology score assignment; Annotation of histological features; Differential expression, cell type composition…
SuggestedTwo complementary niche methods each gave 12 niches: k-means on cell neighborhoods, and GraphSAGE transcript embeddings that need no cell segmentation. Healthy alveolar niches T4/C12 were depleted in PF, and less fibrotic regions resembled more fibrotic ones rather than controls.
GraphSAGE was trained for ten epochs on a joint graph of 18,594,542 nodes and 421,316,086 edges (training accuracy 0.82). A GMM (k = 12) then clustered 206,449,064 transcripts. Cell niches used each cell's 25 nearest neighbors in Seurat v5. The LF versus MF similarity is only shown descriptively; no statistical test is reported. · Disease-specific cellular interactions are…; Transcript-based niches; Cell-based niches
SuggestedNiches T3 and C7 held most KRT5-/KRT17+ aberrant basaloid cells at fibrotic fronts next to CTHRC1+/FAP+ fibroblasts. They marked epithelium detaching from its basement membrane, and they led to detachment sites a clinician had not annotated plus structurally intact regions that may be sub-pathologic.
In both analyses, annotated epithelial detachment was made up mainly of T3 (transcript-based) and C7 (cell-based). Cells in these niches appeared in LF, MF and intermixed PF samples but were virtually absent from controls. Detachment was also seen in adjacent sections that were not run on Xenium, although an ex vivo artifact cannot be fully excluded. · Disease-specific cellular interactions are…; Discussion
SuggestedRanking alveolar airspaces by composition suggested an order of remodeling. First came loss of capillary endothelium, AT1 cells, alveolar fibroblasts and interstitial macrophages, with a transient rise in AT2 cells. Activated fibroblasts appeared at the airway-like transition, then FABP4+ and later SPP1+ macrophages.
slingshot ordered 1,233 alveolar airspaces on PCA of cell type proportions, and tradeSeq GAMs tested features along pseudotime. In cell type level GAMs across 24 cell types, early-transition hits were 37% epithelial and 15.8% AT2. Late-remodeling hits were 20% epithelial and 37% myeloid. 87 genes changed during the early transition. · A timeline of alveolar dysregulation; Ranking alveoli along a pseudotime by severity…; Assessing changes in cell types, niches, and…; Figure legends
Methods, data and reproducibility
FFPE lung cores on tissue microarrays were profiled with a 343-gene Xenium panel, and transcripts within nuclei were clustered and annotated. Niches came from Seurat k-means neighborhoods and from GraphSAGE transcript embeddings with GMM clustering. Lumen segmentation followed by slingshot/tradeSeq ranked airspaces by remodeling.
- Xenium in situ spatial transcriptomics of FFPE lung cores (3-5 mm) on tissue microarrays; 343-gene panel (246 from base panel PD_336, 97 from custom panel CVEVZD) · human · n = 28 samples from 19 lungs (6 unaffected, 13 PF) · GEO GSE250346
- PF explant lungs from transplant surgery (VUMC, Norton Thoracic Institute); paired less/more fibrotic regions from 9 lungs, intermixed samples from 4 · human · n = 13 PF donors (IPF n = 7)
- Control lungs declined for organ donation · human · n = 6 unaffected donors
- Post-run H&E images registered to DAPI; used for pathology scoring and annotation of 21 histological feature types · human · n = 22 samples annotated
- Natri et al. (2023) sc-eQTL lung scRNA-seq, used to compare lineage recovery · human · n = 47 samples (19 control, 28 ILD)
- Human Lung Cell Atlas (HLCA) scRNA-seq studies, used to compare lineage recovery · human · n = 14 datasets
- Segmented alveolar airspaces used for pseudotime analysis · human · n = 1,233 airspaces
- Methods & tools
- 10X Genomics Xenium Analyzer (onboard nuclear/cell segmentation), Seurat v4 / v5 (BuildNicheAssay k-means niches), GraphSAGE graph neural network, Gaussian Mixture Model (PyCave), propeller and limma (voom), slingshot, tradeSeq (fitGAM, associationTest), mgcv, BigWarp plugin in ImageJ, QuPath, scikit-image, RAPIDS cuCIM, CuPy, alphashape
- Software & environment
- Xenium instrument software 1.1.2.4, analysis xenium-1.1.0.2; Seurat v4/v5; ImageJ 2.14.0 (BigWarp); QuPath 0.4.3; Python 3.9.7 with scikit-image 0.19.3, RAPIDS cuCIM 22.12.00, CuPy 11.2.0, alphashape 1.3.1.
- Assay conditions & compute
- Lumen segmentation ran as a GPU-accelerated pipeline (cuCIM, CuPy). To reduce memory, GraphSAGE training used per-sample subgraphs of 5,000 root nodes. The GPU model, runtime and memory footprint are not reported.
Limitations (6)
- The cohort is small (n=19), drawn from declined donor lungs and end-stage explants, and mostly of European ancestry. · stated by the authors
- The 343-gene panel was chosen from prior scRNA-seq to identify cells and probe known PF pathways. The platform is therefore semi-targeted and not suited to unbiased discovery. · stated by the authors
- Irregular lung cell shapes near the optical resolution limit make segmentation hard. Even with counts restricted to nuclei, transcripts from neighboring cells still leak in, so cross-condition differential expression is less robust than with sc/snRNA-seq. · stated by the authors
- Sampling at a single time point cannot establish where epithelium-free remnant alveoli come from, and an ex vivo origin for epithelial detachment cannot be fully excluded. · stated by the authors
- The airspace pseudotime is inferred from cross-sectional composition and pools diagnoses (IPF, sarcoidosis, CTD-ILD, cHP). The proposed order of events may therefore mix disease subtypes rather than reflect true longitudinal progression. · noted in this summary
- Pathology scores are semiquantitative 0 to 3 ratings, and 5 mm cores were rescaled by a fixed factor of 0.36. No inter-rater agreement is reported, which bears on the 115-gene association. · noted in this summary
Read this if you study pulmonary fibrosis cell states, or if you need segmentation-free niche detection and airspace pseudotime methods for Xenium or other imaging-based spatial data.
- Citations
- 169
- FWCI
- 106.39
- Field percentile
- top 1%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Early Pulmonary Fibrosis is Defined by Niche- and Cell-Specific Molecular Programs
Alan Waich, Scott A. Ochsner, … Iván O. Rosas·bioRxiv·Jun 4, 2026
- Resolving the molecular niche of pulmonary fibrosis using cryopreserved human precision cut lung slices
Indu Sivankutty, Maniteja Arava, … Sangita Choudhury·bioRxiv·Apr 22, 2025
- Defining transcriptomic niches in human fibrotic lung with multi-sample spatial transcriptomics analysis using the MAPLE algorithm
Hyeongseon Jeon, Carter Allen, … Dongjun Chung·bioRxiv·Feb 28, 2022
- Spatial Transcriptomic Characterization of Novel Pathologic Niches in IPF
Christoph H. Mayr, Diana Santacruz, … Matthew James Thomas·bioRxiv·Dec 13, 2023
- Spatial multiomic profiling reveals distinct fibrotic epithelial niches in idiopathic pulmonary fibrosis
Bin Liu, Konain Bhatti, … Alison E. John·bioRxiv·Dec 10, 2024
- Translational mapping of spatially resolved transcriptomes in human and mouse pulmonary fibrosis
Lovisa Franzén, Martina Olsson Lindvall, … Jorrit J. Hornberg·Nature GeneticsPeer-reviewed·Jul 1, 2024