An integrated cell atlas of the lung in health and disease

Benchmarking methods integrating GWAS and single-cell transcriptomic data for mapping trait-cell type associations

Genome-wide association studies (GWAS) have discovered numerous trait-associated variants, but their biological context remains unclear. Integrating GWAS summary statistics with single-cell RNA-sequencing expression profiles can help identify the cell types in which these variants influence traits. Two main strategies have been developed to integrate these data types. The "single cell to GWAS" strategy (representing most methods) identifies gene sets with cell-type-specific expression and then follows with enrichment analyses applied to GWAS summary statistics. Conversely, the "GWAS to single cell" strategy begins with a list of trait-associated genes and calculates a cumulative disease score per cell based on gene expression count data. We systematically evaluated 19 approaches verses "ground truth" trait-cell type pairs to assess their statistical power and false positive rates. Based on these analyses, we draw seven key conclusions to guide future studies. We also propose a Cauchy approach to combine the two main strategies to maximize power for detecting trait-cell type associations.

Stromal heterogeneity in the adult lung delineated by single-cell genomics

Stromal cells in the lung provide structural support to other cells and play critical roles in inflammation, repair, and fibrosis after injury. Recent technological advancements in single-cell genomics have tremendously improved our knowledge of stromal heterogeneity in the lung. Stromal heterogeneity in single-cell RNA sequencing data is often conserved across different studies despite the different annotation strategies. Spatial analyses suggest that each stromal subset is characterized by unique anatomic locations in the adult lung. This review overviews the stromal heterogeneity delineated by single-cell RNA sequencing studies and highlights the functional characteristics and locations of each population.

Improved Annotation of Asthma Gene Variants with Cell Type Deconvolution of Nasal and Lung Expression Quantitative Trait Loci

Asthma is a genetically complex inflammatory airway disease associated with more than 200 SNPs. However, the functional effects of many asthma-associated SNPs in lung and airway epithelial samples are unknown. Here, we aimed to conduct expression quantitative trait loci (eQTL) analysis using a meta-analysis of nasal and lung samples. We hypothesize that incorporating cell type proportions of airway and lung samples enhances eQTL analysis outcomes. Nasal brush (n = 792) and lung tissue (n = 1,087) samples were investigated separately. Initially, a general eQTL analysis identified genetic variants associated with gene expression levels. Estimated cell type proportions were adjusted based on the Human Lung Cell Atlas. In addition, the presence of significant interaction effects between asthma-associated SNPs and each cell type proportion was explored and considered evidence for cell type-associated eQTL. In nasal brush and lung parenchyma samples, 44 and 116 asthma-associated SNPs were identified as eQTL. Adjusting for cell type proportions revealed eQTL for an additional 17 genes (e.g., FCER1G, CD200R1, and GABBR2) and 16 genes (e.g., CYP2C8, SLC9A2, and SGCD) in nose and lung, respectively. Moreover, we identified eQTL for nine SNPs annotated to genes such as VASP, FOXA3, and PCDHB12 displayed significant interactions with cell type proportions of club, goblet, and alveolar macrophages. Our findings demonstrate increased power for identifying eQTL among asthma-associated SNPs by considering cell type proportion of the bulk RNA-sequencing data from nasal and lung tissues. Integration of cell type deconvolution and eQTL analysis enhances our understanding of asthma genetics and cellular mechanisms, uncovering potential therapeutic targets for personalized interventions.

Bronchopulmonary dysplasia with pulmonary hypertension associates with semaphorin signaling loss and functionally decreased FOXF1 expression

Lung injury in preterm infants leads to structural and functional respiratory deficits, with a risk for bronchopulmonary dysplasia (BPD) that in its most severe form is accompanied by pulmonary hypertension (PH). To identify potential cellular and molecular drivers of BPD in humans, we performed single-cell RNA sequencing of preterm infant lungs with evolving BPD and BPD + PH compared to term infants. Examination of endothelial cells reveals a unique, aberrant capillary cell-state in BPD + PH defined by ANKRD1 expression. Within the alveolar parenchyma in infants with BPD/BPD + PH, predictive signaling analysis identifies surprising deficits in the semaphorin guidance-cue pathway, with decreased expression of pro-angiogenic transcription factor FOXF1. Loss of semaphorin signaling is replicated in a murine BPD model and in humans with causal FOXF1 mutations for alveolar capillary dysplasia (ACDMPV), suggesting a mechanistic link between developmental programs underlying BPD and ACDMPV and uncovering a critical role for semaphorin signaling in normal lung development.

Cell-death pathways and tau-associated neuronal vulnerability in Alzheimer's disease

Neuronal loss is the ultimate driver of neural system dysfunction in Alzheimer's disease (AD). We used single-nucleus RNA sequencing and neuropathological phenotyping to elucidate mechanisms of neurodegeneration in AD by identifying vulnerable neuronal populations and probing for their differentially expressed genes. Evidenced by transcriptomic analyses and quantitative tau immunoassays of human AD and non-AD brain tissue, we identified a neuronal population especially vulnerable to tau pathology. Multiplexed immunohistochemistry and in situ hybridization (CBLN2 and LINC00507) validated the presence of the tau-vulnerable neuronal population and revealed a propensity of this population to bear tau pathology. Differentially expressed genes associated with phospho-tau pathology in these neurons revealed genes involved in apoptosis, cell-component dissociation (e.g., autophagosome maturation and actin filament depolymerization), and regulation of vesicle-mediated transport.

Epithelial damage and ageing: the perfect storm

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive disease of lung parenchymal scarring that is triggered by repeated microinjury to a vulnerable alveolar epithelium. It is increasingly recognised that cellular ageing, whether physiological or accelerated due to telomere dysfunction, renders the epithelium less able to cope with injury and triggers changes in epithelial behaviour that ultimately lead to the development of disease.

AIMS

This review aims to highlight how, with increasing age, the alveolar epithelium becomes vulnerable to exogenous insults. We discuss the downstream consequences of alveolar epithelial dysfunction on epithelial phenotype, alveolar repair and pro-pathogenic interactions with other alveolar niche-resident cell types which drive IPF pathogenesis.

NARRATIVE

We highlight how a wide array of cellular mechanisms that maintain cellular homeostasis become dysfunctional with ageing. Waning replicative capacity, genomic stability, mitochondrial function, proteostasis and metabolic function all contribute to a phenotype of vulnerability to 'second hits'. We discuss how in IPF the alveolar epithelium becomes dysfunctional, highlighting changes in repair capacity and fundamental cellular phenotype and how interactions between abnormal epithelium and other alveolar niche-resident cell types perpetuate disease.

CONCLUSIONS

The ageing epithelium is a vulnerable epithelium which, with the cumulative effects of environmental exposures, fundamentally changes its behaviour towards stalled differentiation, failed repair and profibrotic signalling. Further dissection of aberrant epithelial behaviour, and its impact on other alveolar cell types, will allow identification of novel therapeutic targets aimed at earlier pathogenic events.

FOXA2 promotes metastatic competence in small cell lung cancer

Small cell lung cancer (SCLC) is known for its high metastatic potential, with most patients demonstrating clinically evident metastases in multiple organs at diagnosis. The factors contributing to this exceptional metastatic capacity have not been defined. To bridge this gap, we compare gene expression in SCLC patient samples who never experienced metastasis or relapse throughout their clinical course, versus primary SCLC patient samples from more typical patients who had metastatic disease at diagnosis. This analysis identifies FOXA2 as a transcription factor strongly associated with SCLC metastasis. Subsequent analyses in experimental models demonstrates that FOXA2 induces a fetal neuroendocrine gene expression program and promotes multi-site metastasis. Moreover, we identify ASCL1, a transcription factor known for its initiating role in SCLC tumorigenesis, as a direct binder of the FOXA2 promoter and regulator of FOXA2 expression. Taken together, these data define the ASCL1-FOXA2 axis as a critical driver of multiorgan SCLC metastasis.

A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography

Developing high-resolution reference maps of disease-susceptible spatial niches is a critical step to mitigating the profound effects of lung disease. Here, we present an integrated multimodal single-nucleus human lung atlas (snHLA) profiling 746,047 nuclei from 49 mapped lung blocks spanning clinically relevant distal airways, alveoli, and interstitium across 11 healthy adults. Integrating snRNA-seq and SNARE-seq2, which co-assays chromatin accessibility and gene expression from the same nucleus, we resolved 70 molecularly distinct populations and captured 332,846 accessible chromatin regions, nominating new transcriptional regulators of human lung cell diversity. Spatial transcriptomics using MERFISH mapped 25 cell populations across 7 structural neighborhoods and multiplexed immunofluorescence localized cell subtypes and distal airway-defining protein markers, expanding and validating distinct lung structure-specific cell populations. This open access snHLA and companion Cell Type and Marker Gene Dictionary with anatomically aligned nomenclature delivers a foundational resource at an unprecedented resolution to interrogate the origins of lung pathophysiology.

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

Determining spatial location of cells within tissues gives vital insight into the interactions between resident and inflammatory cells and is a critical factor for uncoupling the mechanisms driving disease. Here, we apply single-cell spatial transcriptomics to reveal the airway wall landscape in health and during asthma. We identified proinflammatory cellular ecosystems that exist within discrete spatial niches in healthy and asthma samples. These cellular hubs are characterized by a high level of chemokine and alarmin expression, along with unique combinations of stromal cells. Mechanistically, we demonstrated that receptors, such as ACKR1, retain immune mediators locally, while amphiregulin-expressing mast cells are prominent within these proinflammatory hubs. Despite anti-inflammatory treatments, the asthma airway mucosa exhibited a distinct remodeling program within these cellular ecosystems, marked by increased proximity between key cell types. This study provides an unprecedented view of the topography of the airway wall, revealing distinct, specific ecosystems within spatial niches to target for therapeutic intervention.

Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment

Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.

Bat organoids reveal antiviral responses at epithelial surfaces

Bats can host viruses of pandemic concern without developing disease. The mechanisms underlying their exceptional resilience to viral infections are largely unresolved, necessitating the development of physiologically relevant and genetically tractable research models. Here, we developed respiratory and intestinal organoids that recapitulated the cellular diversity of the in vivo epithelium present in Rousettus aegyptiacus, the natural reservoir for the highly pathogenic Marburg virus (MARV). In contrast to human counterparts, bat organoids and mucosal tissue exhibited elevated constitutive expression of innate immune effectors, including type I interferon-ε (IFNε) and IFN-stimulated genes (ISGs). Upon infection with diverse zoonotic viruses, including MARV, bat organoids strongly induced type I and III IFN responses, which conferred robust antiviral protection. Type III IFNλ3 additionally displayed virus-independent self-amplification, acting as an ISG to enhance antiviral immunity. Our organoid platform reveals key features of bat epithelial antiviral immunity that may inform therapeutic strategies for viral disease resilience.

Tumour and microenvironment crosstalk in NSCLC progression and response to therapy

The treatment landscape of non-small-cell lung cancer (NSCLC) is evolving rapidly, driven by advances in the development of targeted agents and immunotherapies. Despite this progress, some patients have suboptimal responses to treatment, highlighting the need for new therapeutic strategies. In the past decade, the important role of the tumour microenvironment (TME) in NSCLC progression, metastatic dissemination and response to treatment has become increasingly evident. Understanding the complexity of the TME and its interactions with NSCLC can propel efforts to improve current treatment modalities, overcome resistance and develop new treatments, which will ultimately improve the outcomes of patients. In this Review, we provide a comprehensive view of the NSCLC TME, examining its components and highlighting distinct archetypes characterized by spatial niches within and surrounding tumour nests, which form complex neighbourhoods. Next, we explore the interactions within these components, focusing on how inflammation and immunosuppression shape the dynamics of the NSCLC TME. We also address the emerging influences of patient-related factors, such as ageing, sex and health disparities, on the NSCLC-TME crosstalk. Finally, we discuss how various therapeutic strategies interact with and are influenced by the TME in NSCLC. Overall, we emphasize the interconnectedness of these elements and how they influence therapeutic outcomes and tumour progression.

Integration of biobank-scale genetics and plasma proteomics reveals evidence for causal processes in asthma risk and heterogeneity

Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.

Integrating single-cell data with biological variables

Constructing single-cell atlases requires preserving differences attributable to biological variables, such as cell types, tissue origins, and disease states, while eliminating batch effects. However, existing methods are inadequate in explicitly modeling these biological variables. Here, we introduce SIGNAL, a general framework that leverages biological variables to disentangle biological and technical effects, thereby linking these metadata to data integration. SIGNAL employs a variant of principal component analysis to align multiple batches, enabling the integration of 1 million cells in approximately 2 min. SIGNAL, despite its computational simplicity, surpasses state-of-the-art methods across multiple integration scenarios: 1) heterogeneous datasets, 2) cross-species datasets, 3) simulated datasets, 4) integration on low-quality cell annotations, and 5) reference-based integration. Furthermore, we demonstrate that SIGNAL accurately transfers knowledge from reference to query datasets. Notably, we propose a self-adjustment strategy to restore annotated cell labels potentially distorted during integration. Finally, we apply SIGNAL to multiple large-scale atlases, including a human heart cell atlas containing 2.7 million cells, identifying tissue- and developmental stage-specific subtypes, as well as condition-specific cell states. This underscores SIGNAL's exceptional capability in multiscale analysis.

scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis

MOTIVATION

Combining single-cell sequencing with ligand-receptor (LR) analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs naturally represent cell-cell communication events. However, current computational methods cannot yet analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the nonlinear nature of cell-cell communication and the high variability given by the patient-specific single-cell RNAseq datasets.

RESULTS

Here, we present scACCorDiON (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Benchmarking indicates that scACCorDiON performs a better clustering of samples according to their disease status than competing methods that use undirected graphs. We provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. Our study case corroborates that clusters provide a robust and explainable representation of cell-cell communication events and that the expression of detected LR pairs is predictive of pancreatic cancer survival.

AVAILABILITY AND IMPLEMENTATION

The code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest/. and https://doi.org/10.5281/zenodo.15267648. The survival analysis package can be found at https://github.com/CostaLab/scACCorDiON.su.

A unified analysis of atlas single-cell data

Recent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues and modalities. Existing methods use cells as the reference unit, hindering downstream gene-based analysis and removing genuine biological variation. Here we present GIANT, an integration method designed for atlas-scale gene analysis across cell types and tissues. GIANT converts data sets into gene graphs and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells from different tissues.

Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics

Automatic cell-type annotation methods assign cell-type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit feature distributions that diverge from it. These scenarios can significantly affect the reliability of cell type predictions, a factor often overlooked in current automatic annotation methods. The field of out-of-distribution detection (OOD), primarily focused on computer vision, addresses the identification of instances that differ from the training distribution. Therefore, the implementation of OOD methods in the context of novel cell type annotation and data shift detection for single-cell transcriptomics may enhance annotation accuracy and trustworthiness. We evaluate six OOD detection methods: LogitNorm, MC dropout, Deep Ensembles, Energy-based OOD, Deep NN, and Posterior networks, for their annotation and OOD detection performance in both synthetical and real-life application settings. We show that OOD detection methods can accurately identify novel cell types and demonstrate potential to detect significant data shifts in non-integrated datasets. Moreover, we find that integration of the OOD datasets does not interfere with OOD detection of novel cell types.

The Central Role of Cytochrome P450 Reductase (CPR) in Hyperoxic Lung Injury

INTRODUCTION

Hyperoxic lung injury results from excessive supplemental oxygen therapy in conditions such as bronchopulmonary dysplasia (BPD) in preterm infants and acute respiratory distress syndrome (ARDS) in adults. This review explores the role of cytochrome P450 reductase (CPR) in hyperoxic lung injury.

AREAS COVERED

Hyperoxia induces the production of reactive oxygen species in excessive amounts, overwhelming the body's antioxidant defenses and exacerbating lung injury in ARDS/BPD. This review examines the differential roles of CPR-dependent enzymes in the context of hyperoxic lung injury. Additionally, we highlight the potential of targeting CPR to study mechanisms of lung injury and leverage gene-editing technologies to deepen our understanding of CPR-mediated pathways. This review consolidates existing knowledge on CPR-dependent processes and their roles in hyperoxic lung injury, based on a literature search conducted in the PubMed database for studies published between 1988 and 2024.

EXPERT OPINION

This review emphasizes the need for a deeper understanding of disease mechanisms, particularly CPR-mediated pathways. As a regulatory hub for ROS modulation and enzyme activity, CPR represents a promising target, offering a unified strategy to mitigate hyperoxic lung injury and improve outcomes in BPD/ARDS.

Decoding functional hematopoietic progenitor cells in the adult human lung

ABSTRACT

Although the bone marrow is the main site of blood cell production in adults, rare pools of hematopoietic stem and progenitor cells have been found in extramedullary organs. In mice, we have previously shown that the lung contains hematopoietic progenitor cells and is a site of platelet production. Here, in the adult human lung, we show that functional hematopoietic precursors reside in the extravascular spaces with a frequency similar to the bone marrow and are capable of proliferation and engraftment in mice. The gene signature of pulmonary and medullary CD34+ hematopoietic progenitors indicates greater baseline activation of immune-, megakaryocyte/platelet-, and erythroid-related pathways in lung progenitors. Spatial transcriptomics mapped blood progenitors in the lung to an alveolar interstitium niche with only a few cells identified in an intravascular location. In human blood samples collected for stem cell transplantation, CD34+ cells with a lung signature enriched the mobilized pool of hematopoietic stem cells. These results identify the lung as a pool for uniquely programmed blood stem and progenitor cells with the potential to support hematopoiesis in humans.

Aberrant basal cell clonal dynamics shape early lung carcinogenesis

Preinvasive squamous lung lesions are precursors of lung squamous cell carcinoma (LUSC). The cellular events underlying lesion formation are unknown. Using a carcinogen-induced model of LUSC with no added genetic hits or cell type bias, we find that carcinogen exposure leads to non-neutral competition among basal cells, aberrant clonal expansions, and basal cell mobilization along the airways. Ultimately, preinvasive lesions develop from a few highly mutated clones that dominate most of the bronchial tree. Multi-site sequencing in human patients confirms the presence of clonally related preinvasive lesions across distinct airway regions. Our work identifies a transition in basal cell clonal dynamics, and an associated shift in basal cell fate, as drivers of field cancerization in the lung.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Cell Population-resolved Multiomics Atlas of the Developing Lung

The lung is a vital organ that undergoes extensive morphological and functional changes during postnatal development. To disambiguate how different cell populations contribute to organ development, we performed proteomic and transcriptomic analyses of four sorted cell populations from the lung of human subjects 0-8 years of age with a focus on early life. The cell populations analyzed included epithelial, endothelial, mesenchymal, and immune cells. Our results revealed distinct molecular signatures for each of the sorted cell populations that enable the description of molecular shifts occurring in these populations during postnatal development. We confirmed that the proteome of the different cell populations was distinct regardless of age and identified functions specific to each population. We identified a series of cell population protein markers, including those located at the cell surface, that show differential expression and distribution on RNA in situ hybridization and immunofluorescence imaging. We validated the spatial distribution of alveolar type 1 and endothelial cell surface markers. Temporal analyses of the proteomes of the four populations revealed processes modulated during postnatal development and clarified the findings obtained from whole-tissue proteome studies. Finally, the proteome was compared with a transcriptomics survey performed on the same lung samples to evaluate processes under post-transcriptional control.

How macrophage heterogeneity affects tuberculosis disease and therapy

Macrophages are the primary host cell type for infection by Mycobacterium tuberculosis in vivo. Macrophages are also key immune effector cells that mediate the control of bacterial growth. However, the specific macrophage phenotypes that are required for optimal immune control of M. tuberculosis infection in vivo remain poorly defined. There are two distinct macrophage lineages in the lung, comprising embryonically derived, tissue-resident alveolar macrophages and recruited, blood monocyte-derived interstitial macrophages. Recent studies have shown that these lineages respond divergently to similar immune environments within the tuberculosis granuloma. Here, we discuss how the differing responses of macrophage lineages might affect the control or progression of tuberculosis disease. We suggest that the ability to reprogramme macrophage responses appropriately, through immunological or chemotherapeutic routes, could help to optimize vaccines and drug regimens for tuberculosis.

Single-cell analysis and machine learning-based integration develop an immune-responsive signature of antigen-presenting cancer-associated fibroblasts in lung adenocarcinoma

Background

Cancer-associated fibroblasts (CAFs) are pivotal regulators of the tumor immune microenvironment, shaping immune responses and influencing therapeutic outcomes. While previous studies have predominantly focused on CAF subpopulations that impair responses to immune checkpoint inhibitors (ICIs), CAF subsets associated with favorable ICIs responses in lung adenocarcinoma (LUAD) remain underexplored. In this study, we integrated bulk RNA and single-cell RNA sequencing data from LUAD samples to identify CAF subpopulations relevant to ICIs efficacy.

Methods

Using a machine learning-driven approach, we developed a robust immune response signature based on this antigen-presenting CAFs (apCAFs) subset to predict ICIs responses.

Results

We uncovered a novel subset of apCAFs exhibiting macrophage-like features, characterized by the expression of major histocompatibility complex (MHC) class II, CD74, and costimulatory molecules (CD80, CD86, CD83, and CD40). This subset, distinct from classic apCAFs described in other cancer types, is strongly associated with favorable ICIs responses across multiple datasets. Notably, these macrophage-like apCAFs are present in LUAD samples prior to treatment, although their abundance varies among individuals. Patients classified as high-risk using signature calculated by a machine learning-driven approach exhibited lower overall survival rates and diminished immune cell infiltration following ICIs therapy.

Conclusions

Collectively, our findings establish a critical link between macrophage-like apCAFs and ICIs efficacy, offering a clinically applicable signature for patient stratification and guiding therapeutic strategies targeting the tumor microenvironment.

Collagen VII Is Associated with Airway Remodeling, Honeycombing, and Fibroblast Foci in Usual Interstitial Pneumonia/Idiopathic Pulmonary Fibrosis

Collagen VII is an essential anchoring protein in the basement membrane zone, maintaining the attachment of stratified and pseudostratified epithelia to the underlying interstitial matrix. However, collagen VII is largely unexplored in normal lungs and idiopathic pulmonary fibrosis (IPF), a disease characterized by excessive accumulation of extracellular matrix and aberrant re-epithelialization of fibrotic lung parenchyma. Analysis of collagen VII mRNA and protein in IPF distal lungs demonstrated elevated levels compared with normal lungs. To investigate its cellular source and spatial distribution in lung tissue, immunohistochemistry, RNAscope in situ hybridization, and cell culture experiments in combination with analysis of public transcriptomic data sets were performed. In IPF lungs, collagen VII was abundant in pathologic remodeled airways and honeycomb cysts, associated with increased basal cell populations. In contrast, in the control lungs, collagen VII was mainly localized in larger airways. RNA sequencing data revealed that epithelial basal cells and keratin 5-/keratin 17+ aberrant basaloid cells are the primary sources of COL7A1 expression. Furthermore, COL7A1 expression was found in mesenchymal subsets, and both collagen VII mRNA and protein were observed in fibroblast foci, another histopathologic feature of IPF. In vitro, COL7A1 expression was found to be increased in normal human lung fibroblasts treated with transforming growth factor-β1. These findings suggest that collagen VII could be involved in the process of abnormal re-epithelialization in lung fibrosis.

Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST

We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large spatial transcriptomics (ST) data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conquer framework especially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential over-smoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulations, HERGAST consistently outperforms other methods across all settings with more than a 10% increase in average adjusted rand index (ARI). In real-world datasets, HERGAST's high-precision spatial clustering identifies SPP1+ macrophages intermingled within colorectal tumors, while the enhanced gene expression signals reveal unique spatial expression patterns of key genes in breast cancer.

Hic-5 transduces mechanical force that drives a vicious cycle of bronchoconstriction

Mechanical forces are essential for the function of key organs, including the bladder, bowel, heart, and lung<1/sup>. These organs often encounter excessive or dysregulated mechanical forces, which are associated with pathological conditions. However, the key regulators of mechanotransduction remain poorly understood. As an example of how excessive mechanical force imposed on airway epithelia could lead to mechanotransduction<2/sup> that alters the transcriptome<3/sup> and secretome<4/sup> and induces cell death<5/sup>, all of which contribute to disease progression<6,7/sup>, we used human airway epithelial cells in air-liquid interface culture to mimic bronchoconstriction. We show that Hic-5, a focal adhesion adaptor protein, functions as a key regulator of mechanoresponses in the airway. Hic-5 expression is significantly induced in airway basal cells following mechanical compression or bronchoconstriction. Hic-5 knockdown using antisense oligonucleotides protects against stress fiber formation and abolishes approximately 70% of transcripts differentially regulated by mechanical compression. Moreover, Hic-5 deficiency attenuates secretion of ET-1, a potent bronchoconstrictor. Our data show that during an asthma exacerbation, Hic-5 reinforces a vicious cycle of bronchoconstriction through the secretion of ET-1. We establish Hic-5 as a critical link between mechanical stress and epithelial activation in human disease, implicating dysregulated mechanical forces as active drivers of disease progression with therapeutic relevance.

Dissecting cross-population polygenic heterogeneity across respiratory and cardiometabolic diseases

Biological mechanisms underlying multimorbidity remain elusive. To dissect the polygenic heterogeneity of multimorbidity in twelve complex traits across populations, we leveraged biobank resources of genome-wide association studies (GWAS) for 232,987 East Asian individuals (the 1st and 2nd cohorts of BioBank Japan) and 751,051 European individuals (UK Biobank and FinnGen). Cross-trait analyses of respiratory and cardiometabolic diseases, rheumatoid arthritis, and smoking identified negative genetic correlations between respiratory and cardiometabolic diseases in East Asian individuals, opposite from the positive associations in European individuals. Associating genome-wide polygenic risk scores (PRS) with 325 blood metabolome and 2917 proteome biomarkers supported the negative cross-trait genetic correlations in East Asian individuals. Bayesian pathway PRS analysis revealed a negative association between asthma and dyslipidemia in a gene set of peroxisome proliferator-activated receptors. The pathway suggested heterogeneity of cell type specificity in the enrichment analysis of the lung single-cell RNA-sequencing dataset. Our study highlights the heterogeneous pleiotropy of immunometabolic dysfunction in multimorbidity.

FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis

The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all of which impact annotation accuracy and reliability. To address these limitations, we developed FPCAM, a fully automated pulmonary fibrosis cell-type annotation model. Built on the R Shiny platform, FPCAM utilizes a matrix of up-regulated marker genes and a manually curated gene-cell association dictionary specific to pulmonary fibrosis. It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. The results showed that FPCAM and SCSA both achieved an accuracy of 89.7%, outperforming SingleR and SciBet. Furthermore, FPCAM demonstrated high accuracy in annotating the external validation dataset GSE135893, successfully identifying multiple cell subtypes. In summary, FPCAM provides an efficient, flexible, and accurate solution for cell-type identification and serves as a powerful tool for scRNA-seq research in pulmonary fibrosis and other related diseases.

Prolonged airway explant culture enables study of health, disease, and viral pathogenesis

In vitro models play a major role in studying airway physiology and disease. However, the native lung's complex tissue architecture and nonepithelial cell lineages are not preserved in these models. Ex vivo tissue models could overcome in vitro limitations, but methods for long-term maintenance of ex vivo tissue have not been established. Here, we describe methods to culture human large airway explants, small airway explants, and precision-cut lung slices for at least 14 days. Human airway explants recapitulate genotype-specific electrophysiology; characteristic epithelial, endothelial, stromal, and immune cell populations; and model viral infection after 14 days in culture. These methods also maintain mouse, rabbit, and pig tracheal explants. Notably, intact airway tissue can be cryopreserved, thawed, and used to generate viable explants with recovery of function 14 days postthaw. These studies highlight the broad applications of airway tissue explants and their use as translational intermediates between in vitro and in vivo studies.

Comprehensive single-cell RNA-sequencing study of Tollip deficiency effect in IL-13-stimulated human airway epithelial cells

OBJECTIVE

Toll-interacting protein (Tollip) suppresses excessive pro-inflammatory signaling, but its function in airway epithelial responses to IL-13, a key mediator in allergic diseases, remains unclear. This study investigates Tollip knockdown (TKD) effects in primary human airway epithelial cells using single-cell RNA sequencing, providing the first single-cell analysis of TKD and the first exploring its interaction with IL-13.

RESULTS

IL-13 treatment upregulated key genes, including SPDEF, MUC5AC, POSTN, ALOX15, and CCL26, confirming IL-13's effects and validating our methods. IL-13 reduced TNF-α signaling and epithelial-mesenchymal transition in certain cell types, suggesting a dual role in promoting type 2 inflammation while suppressing Th1-driven inflammation. Tollip deficiency alone significantly amplified TNF-α signaling and inflammatory pathways in goblet, club, and suprabasal cells. Comparisons between TKDIL13 vs IL13 and TKD vs CTR revealed that IL-13 does not substantially alter Tollip deficiency response in most cell types, reinforcing findings in TKD vs CTR. Tollip deficiency alters the response to IL-13 in a cell-type-specific manner, strongly downregulating TNF-α signaling in goblet cells but only weakly in basal and club cells. Tollip deficiency enhances IL-13's suppression of Th1 inflammatory responses in goblet cells. These novel insights in Tollip-IL-13 interactions offer potential therapeutic targets for asthma and related diseases.

scPRINT: pre-training on 50 million cells allows robust gene network predictions

A cell is governed by the interaction of myriads of macromolecules. Inferring such a network of interactions has remained an elusive milestone in cellular biology. Building on recent advances in large foundation models and their ability to learn without supervision, we present scPRINT, a large cell model for the inference of gene networks pre-trained on more than 50 million cells from the cellxgene database. Using innovative pretraining tasks and model architecture, scPRINT pushes large transformer models towards more interpretability and usability when uncovering the complex biology of the cell. Based on our atlas-level benchmarks, scPRINT demonstrates superior performance in gene network inference to the state of the art, as well as competitive zero-shot abilities in denoising, batch effect correction, and cell label prediction. On an atlas of benign prostatic hyperplasia, scPRINT highlights the profound connections between ion exchange, senescence, and chronic inflammation.

Morphogenic, molecular and cellular adaptations for unidirectional airflow in the chicken lung

Unidirectional airflow in the avian lung enables gas exchange during both inhalation and exhalation. The underlying developmental process and how it deviates from that of the bidirectional mammalian lung are poorly understood. Sampling key developmental stages with multiscale 3D imaging and single-cell transcriptomics, we delineate morphogenic, molecular and cellular features that accommodate the unidirectional airflow in the chicken lung. Primary termini of hyper-elongated branches undergo proximal-short and distal-long fusions, forming parabronchi for air conduction. Through the parabronchial smooth muscle, neoform termini extend radially to form gas-exchanging alveoli. Supporting this radial alveologenesis, branch stalks halt their proximalization, defined by SOX9-SOX2 transition, and become SOX9low parabronchi. Primary and secondary vascular plexi interface with primary and neoform termini, respectively. Single-cell and Stereo-seq spatial transcriptomics reveal a third, chicken-specific alveolar cell type expressing KRT14, hereby named luminal cells. Luminal, alveolar type 2 and alveolar type 1 cells sequentially occupy concentric zones radiating from the parabronchial lumen. Our study explores the evolutionary space of lung diversification and lays the foundation for functional analysis of species-specific genetic determinants.

Adventitial fibroblasts direct smooth muscle cell-state transition in pulmonary vascular disease

Background

Pulmonary vascular remodeling is a progressive pathological process characterized by functional alterations within pulmonary artery smooth muscle cells (PASMCs) and adventitial fibroblasts (PAAFs). Mechanisms driving the transition to a diseased phenotype remain elusive.

Methods

We combined transcriptomic and proteomic profiling with phenotypic characterization of source-matched cells from healthy controls and individuals with idiopathic pulmonary arterial hypertension (IPAH). Bidirectional cellular crosstalk was examined using direct and indirect co-culture models, and phenotypic responses were assessed via transcriptome analysis.

Results

PASMC and PAAF undergo distinct phenotypic shifts during pulmonary vascular remodeling, with limited shared features, such as reduced mitochondrial content and hyperpolarization. IPAH-PASMC exhibit increased glycosaminoglycan production and downregulation of contractile machinery, while IPAH-PAAF display a hyperproliferative phenotype. We identified alterations in extracellular matrix components, including laminin and collagen, alongside pentraxin-3 and hepatocyte growth factor, as potential regulators of PASMC phenotypic transitions mediated by PAAF.

Conclusions

While PASMCs and PAAFs retain their core cellular identities, they acquire distinct disease-associated states. These findings provide new insights into the dynamic interplay of pulmonary vascular mesenchymal cells in disease pathogenesis.

Funding

This work was supported by Cardio-Pulmonary Institute EXC 2026 390649896 (GK) and Austrian Science Fund (FWF) grant I 4651-B (SC).

Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies

BACKGROUND

Single-cell transcriptomics has transformed our understanding of cellular diversity, yet noise from technical artifacts and low-quality cells can obscure key biological signals. A common practice is filtering out cells with a high percentage of mitochondrial RNA counts (pctMT), typically indicative of cell death. However, commonly used filtering thresholds, primarily derived from studies on healthy tissues, may be overly stringent for malignant cells, which often naturally exhibit higher baseline mitochondrial gene expression.

RESULTS

We examine nine public single-cell RNA-seq datasets from various cancers, including 441,445 cells from 134 patients, and public spatial transcriptomics data, assessing the viability of malignant cells with high pctMT. Our analysis reveals that malignant cells exhibit significantly higher pctMT than nonmalignant cells, without a notable increase in dissociation-induced stress scores. Malignant cells with high pctMT show metabolic dysregulation, including increased xenobiotic metabolism, relevant to therapeutic response. Analysis of pctMT in cancer cell lines further reveals links to drug resistance. We also observe associations between pctMT and malignant cell transcriptional heterogeneity, as well as patient clinical features.

CONCLUSIONS

This study provides insights into the functional characteristics of malignant cells with elevated pctMT, challenging current quality control practices in tumor single-cell RNA-seq analyses and offering potential improvements in data interpretation for future cancer studies.

Feature selection methods affect the performance of scRNA-seq data integration and querying

The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.

IL-33 is associated with alveolar dysfunction in patients with viral lower respiratory tract disease

Interleukin (IL)-33 is released following tissue damage, causing airway inflammation and remodelling via reduced IL-33 (IL-33red)/serum stimulation-2 (ST2) and oxidised IL-33 (IL-33ox)/receptor for advanced glycation end products (RAGE)/epidermal growth factor receptor (EGFR) pathways. This study aimed to identify associations of IL-33 with clinical outcomes and pathological mechanisms during viral lower respiratory tract disease (LRTD). Ultra-sensitive immunoassays were developed to measure IL-33red, IL-33ox and IL-33/sST2 complexes in samples from patients hospitalised with COVID-19. Immunohistochemistry and multiomics were used to characterise lung samples. Elevated IL-33 in the airway and IL-33/sST2 complex in the circulation correlated with poor clinical outcomes (death, need for intensive care or mechanical ventilation). IL-33 was localised to airway epithelial and endothelial barriers, whereas IL1RL1 was expressed on aerocytes, alveolar endothelial cells specialised for gaseous exchange. IL-33 increased expression of mediators of neutrophilic inflammation, immune cell infiltration, interferon signalling and coagulation in endothelial cell cultures. Endothelial IL-33 signatures were strongly related with signatures associated with viral LRTD. Increased IL-33 release following respiratory viral infections is associated with poor clinical outcomes and might contribute to alveolar dysfunction. Although this does not show a causal relationship with disease, these results provide a rationale to evaluate pathological roles for IL-33 in viral LRTD.

Cellular blueprint of healthy and diseased human epiglottis and subglottis-a study of the Canadian Airways Research (CARE) group

BACKGROUND

The larynx consists of the supraglottis, glottis, and subglottis and each differ in tissue composition, lymphatic drainage, ability to counter infections, and response to injuries. However, the cellular mechanisms driving laryngeal homoeostasis remain largely unexplored. As a result, understanding disease pathogenesis within the larynx including idiopathic subglottic stenosis (iSGS) and intubation-related traumatic stenosis has been challenging. Here, we sought to characterise the cellular processes governing laryngeal health and disease.

METHODS

As part of the prospective Canadian Airways Research (CARE) iSGS study, we characterised 122,004 high-quality transcriptomes using single nucleus RNA-sequencing to profile 11 human epiglottis and 17 human subglottis biopsies across three different conditions: control, iSGS, and intubation-related traumatic stenosis to define cell populations and pathways associated with disease. We validated our results using cohort-level bulk transcriptomics using 114 human epiglottis and 121 human subglottis.

FINDINGS

We defined the single-cell taxonomy of the human subglottis and epiglottis using single-nucleus sequencing in both healthy and disease states. Mechanistically, we discovered the presence of unique epithelial and fibroblast progenitor subsets within the control subglottis but not within the anatomically adjacent epiglottis. The uncontrolled proliferation of these cellular subsets exhibited skewed sex hormone signalling and orchestrated a fibro-inflammatory cascade. We leveraged cohort-level bulk transcriptomics to define hallmarks of iSGS associated with disease covariates and introduced the first biomarker associated with recurrent relapse. Longitudinal sampling demonstrated that the subglottic microenvironment in patients with iSGS is changing dynamically with and without therapeutic intervention.

INTERPRETATION

Together, our data refines our understanding of laryngeal biology, nominates candidate compounds for iSGS treatment, and serves as a transformative platform for future clinical investigations to further precision laryngology.

FUNDING

This study was funded by a grant from the American Laryngology Association (#1082), an Academic Medical Organisation of Southwestern Ontario innovation fund grant (INN21-016), grant support from the Departments of Otolaryngology-Head and Neck Surgery at University of Toronto, Canada and Western University, Canada. ACN was supported by the Wolfe Surgical Research Professorship in the Biology of Head and Neck Cancers Fund. PYFZ was supported by a Vanier Canada Graduate Scholarship and PSI Foundation fellowship.

Multimodal learning for mapping genotype-phenotype dynamics

How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, uncovered potential cross-tissue biomarkers and provided contextualized embeddings to investigate the polyfunctionality of genes shown for the von Willebrand factor (VWF) gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.

Comparative anatomy of respiratory bronchioles and lobular structures in mammals

Rodents are widely used to study the toxicity of chemicals; however, differences between species indicate that the results from rodents are not always directly transferable to humans. The health of workers exposed to various chemicals and particulates at high doses or for long periods is at risk. Respiratory bronchioles and lobular structures, which are demarcated by interlobular septa, are key sites for occupational lung diseases such as pneumoconiosis; however, these structures vary among animal species. Understanding these differences is crucial for studying the pathology of human occupational lung diseases. However, there is a lack of reviews focusing on these structures in different species. This review explores the lung anatomy of various mammals and its functional importance in disease to connect animal studies with human occupational lung diseases. Our results indicate that artiodactyls, especially small pig breeds and goats, are ideal for research because their respiratory bronchioles and lobular structures are similar to those of humans. This review aims to enhance the use of experimental animal data and improve our understanding of human occupational lung diseases, thereby facilitating early disease detection, treatment, and prevention.

uHAF: a unified hierarchical annotation framework for cell type standardization and harmonization

SUMMARY

In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community.

AVAILABILITY AND IMPLEMENTATION

uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

An aging bone marrow exacerbates lung fibrosis by fueling profibrotic macrophage persistence

Pulmonary fibrosis is an incurable disease that manifests with advanced age. Yet, how hematopoietic aging influences immune responses and fibrosis progression remains unclear. Using heterochronic bone marrow transplant mouse models, we found that an aged bone marrow exacerbates lung fibrosis irrespective of lung tissue age. Upon lung injury, there was an increased accumulation of monocyte-derived alveolar macrophages (Mo-AMs) driven by cell-intrinsic hematopoietic aging. These Mo-AMs exhibited an enhanced profibrotic profile and stalled maturation into a homeostatic, tissue-resident phenotype. This delay was shaped by cell-extrinsic environmental signals such as reduced pulmonary interleukin-10 (IL-10), perpetuating a profibrotic macrophage state. We identified regulatory T cells (Tregs) as critical providers of IL-10 upon lung injury that promote Mo-AM maturation and attenuate fibrosis progression. Our study highlights the impact of an aging bone marrow on lung immune regulation and identifies Treg-mediated IL-10 signaling as a promising target to mitigate fibrosis and promote tissue repair.

scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to distinguish malignant cells from their normal counterparts using a data- and knowledge-driven strategy. To develop the tool, multiple cancer datasets were collected, and the initially annotated malignant cells were calibrated using nine carefully curated pan-cancer gene signatures, resulting in over 400,000 single-cell transcriptomes for training. The union of differentially expressed genes across datasets was taken as the features for model construction to comprehensively capture tumor transcriptional diversity. scMalignantFinder outperformed existing automated methods across two gold-standard and eleven patient-derived scRNA-seq datasets. The capability to predict malignancy probability empowers scMalignantFinder to capture dynamic characteristics during tumor progression. Furthermore, scMalignantFinder holds the potential to annotate malignant regions in tumor spatial transcriptomics. Overall, we provide an efficient tool for detecting heterogeneous malignant cell populations.

Caveolin Scaffolding Domain (CSD) Peptide LTI-2355 Modulates the Phagocytic and Synthetic Activity of Lung-Derived Myeloid Cells in Idiopathic Pulmonary Fibrosis (IPF) and Post-Acute Sequelae of COVID Fibrosis (PASC-F)

Rationale: The role of the innate immune system in idiopathic pulmonary fibrosis (IPF) remains poorly understood. However, a functional myeloid compartment is required to remove dying cells and cellular debris, as well as to mediate innate immune responses against pathogens. Aberrant macrophage activity has been described in patients with post-acute sequelae of COVID fibrosis (PASC-F), and caveolin scaffolding domain (CSD) peptides have been found to attenuate inflammation and fibrosis in mouse lung injury models. Therefore, we examined, for the first time, the effects of CSD peptide LTI-2355 on the functional and synthetic properties of human myeloid cells isolated from lung explant tissue of donor lungs as well as IPF and PASC-F lung explant tissue. Methods and Results: CD45+ myeloid cells isolated from lung explant tissue from IPF and PASC-F patients exhibited an impaired capacity to clear autologous dead cells and cellular debris. The uptake of pathogen-coated bioparticles was impaired in myeloid cells from both fibrotic patient groups independent of the type of pathogen, highlighting an intrinsic functional cell impairment. LTI-2355 improved the phagocytic activity of both IPF and PASC-F myeloid cells, and this improvement was paired with decreased proinflammatory and pro-fibrotic synthetic activity. LTI-2355 was also shown to primarily target CD206-expressing IPF and PASC-F myeloid cells. Conclusions: Primary myeloid cells from IPF and PASC-F patients exhibit dysfunctional phagocytic and synthetic properties that are modulated by LTI-2355. LTI-2355 treatment of IPF myeloid cells resulted in significantly reduced sCD163, IFN-α2, IFN-γ, IL-2, IL-10, IL-12p40, and MMP-1 in the cell supernatant. This study highlights an additional mechanism of action of the CSD peptide in the treatment of IPF and progressive fibrotic lung disease.

Fibroblast-derived osteoglycin promotes epithelial cell repair

There is an urgent need for innovative therapies targeting defective epithelial repair in chronic diseases like COPD. The mesenchymal niche is a critical regulator in epithelial stem cell activation, suggesting that their secreted factors are possible potent drug targets. Utilizing a proteomics-guided drug discovery strategy, we explored the lung fibroblast secretome to uncover impactful drug targets. Our lung organoid assays identified several regenerative ligands, with osteoglycin (OGN) showing the most profound effects. Transcriptomic analyses revealed that OGN enhances alveolar progenitor differentiation, detoxifies reactive oxygen species, and strengthens fibroblast-epithelial crosstalk. OGN expression was diminished in COPD patients and smoke-exposed mice. An active fragment of OGN (leucine-rich repeat regions 4-7) replicated full-length OGN's regenerative effects, significantly ameliorating elastase-induced lung injury in lung slices and improving lung function in vivo. These findings highlight OGN as a pivotal secreted protein for alveolar epithelial repair, positioning its active fragment as a promising therapeutic for COPD.

Sorted-Cell Proteomics Reveals an AT1-Associated Epithelial Cornification Phenotype and Suggests Endothelial Redox Imbalance in Human Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a neonatal lung disease characterized by inflammation and scarring leading to long-term tissue damage. Previous whole tissue proteomics identified BPD-specific proteome changes and cell type shifts. Little is known about the proteome-level changes within specific cell populations in disease. Here, we sorted epithelial (EPI) and endothelial (ENDO) cell populations based on their differential surface markers from normal and BPD human lungs. Using a low-input compatible sample preparation method (MicroPOT), proteins were extracted and digested into peptides and subjected to Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) proteome analysis. Of the 4,970 proteins detected, 293 were modulated in abundance or detection in the EPI population and 422 were modulated in ENDO cells. Modulation of proteins associated with actin-cytoskeletal function such as SCEL, LMO7, and TBA1B were observed in the BPD EPIs. Using confocal imaging and analysis, we validated the presence of aberrant multilayer-like structures comprising SCEL and LMO7, known to be associated with epidermal cornification, in the human BPD lung. This is the first report of accumulation of cornification-associated proteins in BPD. Their localization in the alveolar parenchyma, primarily associated with alveolar type 1 (AT1) cells, suggests a role in the BPD post-injury response. In the ENDOs, redox balance and mitochondrial function pathways were modulated. Alternative mRNA splicing and cell proliferative functions were elevated in both populations suggesting potential dysregulation of cell progenitor fate. This study characterized the proteome of epithelial and endothelial cells from the BPD lung for the first time, identifying population-specific changes in BPD pathogenesis.

New & Noteworthy

The study is the first to perform proteomics on sorted pulmonary epithelial and endothelial populations from BPD and age-matched control human donors. We identified an increase in cornification-associated proteins in BPD (e.g., SCEL and LMO7), and evidenced the presence of multilayered structures unique to BPD alveolar regions, associated with alveolar type 1 (AT1) cells. By changing the nature and/or biomechanical properties of the epithelium, these structures may alter the behavior of other alveolar cell types potentially contributing to the arrested alveolarization observed in BPD. Lastly, our data suggest the modulation of cell proliferation and redox homeostasis in BPD providing potential mechanisms for the reduced vascular growth associated with BPD.

Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas

Tuberculosis (TB) remains a major global health challenge, particularly in the context of multidrug-resistant (MDR) Mycobacterium tuberculosis (Mtb). Host-directed therapies (HDTs) have been proposed as adjunctive therapy to enhance immune control of infection. Recently, one such HDT, pharmacologic modulation of myeloid-derived suppressor cells (MDSCs), has been proposed to treat MDR-TB. While MDSCs have been well characterized in cancer, their role in TB pathogenesis remains unclear. To investigate whether MDSCs or other myeloid suppressor populations contribute to TB granuloma microenvironments (GME), we performed spatial transcriptional profiling and single-cell immunophenotyping on eighty-four granulomas in lung specimens from three individuals with active disease. Granulomas were histologically classified based on H&E staining, and transcriptional signatures were compared across regions of interest (ROIs) at different states of granuloma maturation. Our analysis revealed that immune suppression within granuloma was not primarily driven by classical MDSCs but rather by multiple myeloid cell subsets, including dendritic cells expressing indoleamine 2,3 dioxygenase-1 expressing (IDO1+ DCs). IDO1+ DCs were the most frequently observed suppressive myeloid cells, particularly in cellular regions, and their spatial proximity to activated T cells suggested localized immunosuppression. Importantly, granulomas at different stages contained distinct proportions of suppressor myeloid cells, with necrotic and cellular regions showing different myeloid phenotypes that may influence granuloma progression. Gene set enrichment analysis (GSEA) further indicated that elevated IDO1 expression was associated with a complex immune response that balanced suppressive signaling, immune activation, and cellular metabolism. These findings suggest that classical MDSCs, as defined in tumor microenvironments, likely play a minor role in TB, whereas IDO1+ DCs may be key regulators of immune suppression in granulomas influencing local Mtb control in infected lung. A deeper understanding of the role of IDO1+ suppressive myeloid cells in TB granulomas is essential to assessing their potential as therapeutic targets in TB treatment.

Integrative analysis of gene expression and chromatin dynamics multi-omics data in mouse models of bleomycin-induced lung fibrosis

BACKGROUND

Pulmonary fibrosis is a relentless and ultimately fatal lung disorder. Despite a wealth of research, the intricate molecular pathways that contribute to the onset of PF, especially the aspects related to epigenetic modifications and chromatin dynamics, continue to be elusive and not fully understood.

METHODS

Utilizing a bleomycin-induced pulmonary fibrosis model, we conducted a comprehensive analysis of the interplay between chromatin structure, chromatin accessibility, gene expression patterns, and cellular heterogeneity. Our chromatin structure analysis included 5 samples (2 control and 3 bleomycin-treated), while accessibility and expression analysis included 6 samples each (3 control and 3 bleomycin-treated).

RESULTS

We found that chromatin architecture, with its alterations in compartmentalization and accessibility, is positively correlated with genome-wide gene expression changes during fibrosis. The importance of immune system inflammation and extracellular matrix reorganization in fibrosis is underscored by these chromatin alterations. Transcription factors such as PU.1, AP-1, and IRF proteins, which are pivotal in immune regulation, are associated with an increased abundance of their motifs in accessible genomic regions and are correlated with highly expressed genes.

CONCLUSIONS

We identified 14 genes that demonstrated consistent changes in their expression, accessibility, and compartmentalization, suggesting their potential as promising targets for the development of treatments for lung fibrosis.

Single-cell RNA-seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-seq by genetic and contextual information

Single-cell RNA sequencing (scRNA-seq) data from complex human tissues have prevalent blood cell contamination during the sample preparation process. They may also comprise cells of different genetic makeups. We propose a new computational framework, Originator, which deciphers single cells by genetic origin and separates immune cells of blood contamination from those of expected tissue-resident cells. We demonstrate the accuracy of Originator at separating immune cells from the blood and tissue as well as cells of different genetic origins, using a variety of artificially mixed and real datasets, including pancreatic cancer and placentas as examples.