Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers

[64Cu]Cu-PEG-FUD peptide for noninvasive and sensitive detection of murine pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease resulting in irreversible scarring within the lungs. However, the lack of biomarkers that enable real-time assessment of disease activity remains a challenge in providing efficient clinical decision-making and optimal patient care in IPF. Fibronectin (FN) is highly expressed in fibroblastic foci of the IPF lung where active extracellular matrix (ECM) deposition occurs. Functional upstream domain (FUD) tightly binds the N-terminal 70-kilodalton domain of FN that is crucial for FN assembly. In this study, we first demonstrate the capacity of PEGylated FUD (PEG-FUD) to target FN deposition in human IPF tissue ex vivo. We subsequently radiolabeled PEG-FUD with 64Cu and monitored its spatiotemporal biodistribution via μPET/CT imaging in mice using the bleomycin-induced model of pulmonary injury and fibrosis. We demonstrated [64Cu]Cu-PEG-FUD uptake 3 and 11 days following bleomycin treatment, suggesting that radiolabeled PEG-FUD holds promise as an imaging probe in aiding the assessment of fibrotic lung disease activity.

A WNT mimetic with broad spectrum FZD-specificity decreases fibrosis and improves function in a pulmonary damage model

BACKGROUND

Wnt/β-catenin signaling is critical for lung development and AT2 stem cell maintenance in adults, but excessive pathway activation has been associated with pulmonary fibrosis, both in animal models and human diseases such as idiopathic pulmonary fibrosis (IPF). IPF is a detrimental interstitial lung disease, and although two approved drugs limit functional decline, transplantation is the only treatment that extends survival, highlighting the need for regenerative therapies.

METHODS

Using our antibody-based platform of Wnt/β-catenin modulators, we investigated the ability of a pathway antagonist and pathway activators to reduce pulmonary fibrosis in the acute bleomycin model, and we tested the ability of a WNT mimetic to affect alveolar organoid cultures.

RESULTS

A WNT mimetic agonist with broad FZD-binding specificity (FZD1,2,5,7,8) potently expanded alveolar organoids. Upon therapeutic dosing, a broad FZD-binding specific Wnt mimetic decreased pulmonary inflammation and fibrosis and increased lung function in the bleomycin model, and it impacted multiple lung cell types in vivo.

CONCLUSIONS

Our results highlight the unexpected capacity of a WNT mimetic to effect tissue repair after lung damage and support the continued development of Wnt/β-catenin pathway modulation for the treatment of pulmonary fibrosis.

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Sfrp1 inhibits lung fibroblast invasion during transition to injury-induced myofibroblasts

BACKGROUND

Fibroblast-to-myofibroblast conversion is a major driver of tissue remodelling in organ fibrosis. Distinct lineages of fibroblasts support homeostatic tissue niche functions, yet their specific activation states and phenotypic trajectories during injury and repair have remained unclear.

METHODS

We combined spatial transcriptomics, multiplexed immunostainings, longitudinal single-cell RNA-sequencing and genetic lineage tracing to study fibroblast fates during mouse lung regeneration. Our findings were validated in idiopathic pulmonary fibrosis patient tissues in situ as well as in cell differentiation and invasion assays using patient lung fibroblasts. Cell differentiation and invasion assays established a function of SFRP1 in regulating human lung fibroblast invasion in response to transforming growth factor (TGF)β1.

MEASUREMENTS AND MAIN RESULTS

We discovered a transitional fibroblast state characterised by high Sfrp1 expression, derived from both Tcf21-Cre lineage positive and negative cells. Sfrp1 + cells appeared early after injury in peribronchiolar, adventitial and alveolar locations and preceded the emergence of myofibroblasts. We identified lineage-specific paracrine signals and inferred converging transcriptional trajectories towards Sfrp1 + transitional fibroblasts and Cthrc1 + myofibroblasts. TGFβ1 downregulated SFRP1 in noninvasive transitional cells and induced their switch to an invasive CTHRC1+ myofibroblast identity. Finally, using loss-of-function studies we showed that SFRP1 modulates TGFβ1-induced fibroblast invasion and RHOA pathway activity.

CONCLUSIONS

Our study reveals the convergence of spatially and transcriptionally distinct fibroblast lineages into transcriptionally uniform myofibroblasts and identifies SFRP1 as a modulator of TGFβ1-driven fibroblast phenotypes in fibrogenesis. These findings are relevant in the context of therapeutic interventions that aim at limiting or reversing fibroblast foci formation.

Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis

The progression of idiopathic pulmonary fibrosis (IPF) is diverse and unpredictable. We identified and validated a new biomarker for IPF progression. To identify a candidate gene to predict progression, we assessed differentially expressed genes in patients with advanced IPF compared with early IPF and controls in three lung sample cohorts. Candidate gene expression was confirmed using immunohistochemistry and Western blotting of lung tissue samples from an independent IPF clinical cohort. Biomarker potential was assessed using an enzyme-linked immunosorbent assay of serum samples from the retrospective validation cohort. We verified that the final candidate gene reflected the progression of IPF in a prospective validation cohort. In the RNA-seq comparative analysis of lung tissues, CD276, COL7A1, CTSB, GLI2, PIK3R2, PRAF2, IGF2BP3, and NUPR1 were up-regulated, and ADAMTS8 was down-regulated in the samples of advanced IPF. Only CTSB showed significant differences in expression based on Western blotting (n = 12; p < 0.001) and immunohistochemistry between the three groups of the independent IPF cohort. In the retrospective validation cohort (n = 78), serum CTSB levels were higher in the progressive group (n = 25) than in the control (n = 29, mean 7.37 ng/mL vs. 2.70 ng/mL, p < 0.001) and nonprogressive groups (n = 24, mean 7.37 ng/mL vs. 2.56 ng/mL, p < 0.001). In the prospective validation cohort (n = 129), serum CTSB levels were higher in the progressive group than in the nonprogressive group (mean 8.30 ng/mL vs. 3.00 ng/mL, p < 0.001). After adjusting for baseline FVC, we found that CTSB was independently associated with IPF progression (adjusted OR = 2.61, p < 0.001). Serum CTSB levels significantly predicted IPF progression (AUC = 0.944, p < 0.001). Serum CTSB level significantly distinguished the progression of IPF from the non-progression of IPF or healthy control.

Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with single-cell RNA sequencing and induced a multilineage circuit of fibrogenic cell states in hPCLS. We showed that these cell states were highly similar to the in vivo cell circuit in a multicohort lung cell atlas from patients with pulmonary fibrosis. Using micro-CT-staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state, and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the hPCLS model provided evidence that the basaloid cell state was derived from alveolar type 2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in hPCLS, and antifibrotic drug treatments showed highly cell type-specific effects. Our work provides an experimental framework for perturbational single-cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration, and pathology. We further demonstrate that hPCLS offer an avenue for scalable, high-resolution drug testing to accelerate antifibrotic drug development and translation.

Meta-Analysis of COVID-19 BAL Single-Cell RNA Sequencing Reveals Alveolar Epithelial Transitions and Unique Alveolar Epithelial Cell Fates

Single-cell RNA sequencing (scRNA-seq) of BAL cells has provided insights into coronavirus disease (COVID-19). However, reports have been limited by small patient cohorts. We performed a meta-analysis of BAL scRNA-seq data from healthy control subjects (n = 13) and patients with COVID-19 (n = 20), sourced from six independent studies (167,280 high-quality cells in total). Consistent with the source reports, increases in infiltrating leukocyte subtypes were noted, several with type I IFN signatures and unique gene expression signatures associated with transcellular chemokine signaling. Noting dramatic reductions of inferred NKX2-1 and NR4A1 activity in alveolar epithelial type II (AT-II) cells, we modeled pseudotemporal AT-II-to-AT-I progression. This revealed changes in inferred AT-II cell metabolic activity, increased transitional cells, and a previously undescribed AT-I state. This cell state was conspicuously marked by the induction of genes of the epidermal differentiation complex, including the cornified envelope protein SPRR3 (small proline-rich protein 3), upregulation of multiple KRT (keratin) genes, inferred mitochondrial dysfunction, and cell death signatures including apoptosis and ferroptosis. Immunohistochemistry of lungs from patients with COVID-19 confirmed upregulation and colocalization of KRT13 and SPRR3 in the distal airspaces. Forced overexpression of SPRR3 in human alveolar epithelial cells ex vivo did not activate caspase-3 or upregulate KRT13, suggesting that SPRR3 marks an AT-I cornification program in COVID-19 but is not sufficient for phenotypic changes.

Identification of metabolism-related gene signature in lung adenocarcinoma

AIM

Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases.

METHODS

TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established.

RESULTS

A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature.

CONCLUSION

In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

Mass spectrometry-based autoimmune profiling reveals predictive autoantigens in idiopathic pulmonary fibrosis

Autoimmunity plays a role in certain types of lung fibrosis, notably connective tissue disease-associated interstitial lung disease (CTD-ILD). In idiopathic pulmonary fibrosis (IPF), an incurable and fatal lung disease, diagnosis typically requires clinical exclusion of autoimmunity. However, autoantibodies of unknown significance have been detected in IPF patients. We conducted computational analysis of B cell transcriptomes in published transcriptomics datasets and developed a proteomic Differential Antigen Capture (DAC) assay that captures plasma antibodies followed by affinity purification of lung proteins coupled to mass spectrometry. We analyzed antibody capture in two independent cohorts of IPF and CTL-ILD patients over two disease progression time points. Our findings revealed significant upregulation of specific immunoglobulins with V-segment bias in IPF across multiple cohorts. We identified a predictive autoimmune signature linked to reduced transplant-free survival in IPF, persisting over time. Notably, autoantibodies against thrombospondin-1 were associated with decreased survival, suggesting their potential as predictive biomarkers.

Identification and immunological characterization of PLA2G2A and cell death-associated molecular clusters in idiopathic pulmonary fibrosis

AIMS

Idiopathic pulmonary fibrosis (IPF) is a severe pulmonary interstitial pneumonia. Our study focuses on the role of PLA2 enzyme in the IPF to explore a more effective diagnosis and treatment mechanism of IPF.

MAIN METHODS

Transcriptome data of IPF from GEO database and bleomycin-induced pulmonary fibrosis mice were analyzed to identify PLA2 enzyme and their metabolite, lysophosphatidylcholines 18:0, in IPF. Based on PLA2G2A and PLA2G2D / PLA2G2A-associated cell death genes (PCDs), the consensus clustering analysis was used to identify the subtypes of IPF and the correlation between PLA2G2A and prognosis was analyzed. The machine learning (ML) models and artificial neural network (ANN) model was used to validate the diagnostic accuracy of PLA2s and PCDs in diagnosing IPF. The gene and protein expression of NLRP3, GSDMD, and CASP-1 was estimated in recombinant PLA2G2A protein induced MLE-12 cells.

KEY FINDINGS

The expression of PLA2G2D, PLA2G2A, and LPC18 significantly changed in IPF. Furtherly, PLA2G2A has a significant correlation with poor patient prognosis, which could predict the 2 or 3-years mortality rates of IPF. Two subtypes of IPF patients, identified based on PCDs, showed significant different immunoinfiltration. Recombinant PLA2G2A protein could induce the pyrotosis in the MLE-12 cell. The generalized linear model and ANN model of PLA2s or PCDs accurate diagnosis IPF.

SIGNIFICANCE

PLA2G2A is the most robustly associated gene with IPF among the PLA2s, which demonstrates a potential in diagnosing and prognostic value in IPF, and provides a foundation for further understanding and breakthroughs in IPF diagnosis and treatment.

Increased lipocalin-2 expression in pulmonary inflammation and fibrosis

Introduction

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive interstitial lung disease with dismal prognosis. The underlying pathogenic mechanisms are poorly understood, resulting in a lack of effective treatments. However, recurrent epithelial damage is considered critical for disease initiation and perpetuation, via the secretion of soluble factors that amplify inflammation and lead to fibroblast activation and exuberant deposition of ECM components. Lipocalin-2 (LCN2) is a neutrophil gelatinase-associated lipocalin (NGAL) that has been suggested as a biomarker of kidney damage. LCN2 has been reported to modulate innate immunity, including the recruitment of neutrophils, and to protect against bacterial infections by sequestering iron.

Methods

In silico analysis of publicly available transcriptomic datasets; ELISAs on human IPF patients' bronchoalveolar lavage fluids (BALFs); bleomycin (BLM)-induced pulmonary inflammation and fibrosis and LPS-induced acute lung injury (ALI) in mice: pulmonary function tests, histology, Q-RT-PCR, western blot, and FACS analysis.

Results and discussion

Increased LCN2 mRNA expression was detected in the lung tissue of IPF patients negatively correlating with respiratory functions, as also shown for BALF LCN2 protein levels in a cohort of IPF patients. Increased Lcn2 expression was also detected upon BLM-induced pulmonary inflammation and fibrosis, especially at the acute phase correlating with neutrophilic infiltration, as well as upon LPS-induced ALI, an animal model characterized by neutrophilic infiltration. Surprisingly, and non withstanding the limitations of the study and the observed trends, Lcn2-/- mice were found to still develop BLM- or LPS-induced pulmonary inflammation and fibrosis, thus questioning a major pathogenic role for Lcn2 in mice. However, LCN2 qualifies as a surrogate biomarker of pulmonary inflammation and a possible indicator of compromised pulmonary functions, urging for larger studies.

Decreased plasma cartilage acidic protein 1 in COVID-19

Cartilage acidic protein-1 (CRTAC1) is produced by several cell types, including Type 2 alveolar epithelial (T2AE) cells that are targeted by SARS-CoV2. Plasma CRTAC1 is known based on proteomic surveys to be low in patients with severe COVID-19. Using an ELISA, we found that patients treated for COVID-19 in an ICU almost uniformly had plasma concentrations of CRTAC1 below those of healthy controls. Magnitude of decrease in CRTAC1 distinguished COVID-19 from other causes of acute respiratory decompensation and correlated with established metrics of COVID-19 severity. CRTAC1 concentrations below those of controls were found in some patients a year after hospitalization with COVID-19, long COVID after less severe COVID-19, or chronic obstructive pulmonary disease. Decreases in CRTAC1 in severe COVID-19 correlated (r = 0.37, p = 0.0001) with decreases in CFP (properdin), which interacts with CRTAC1. Thus, decreases of CRTAC1 associated with severe COVID-19 may result from loss of production by T2AE cells or co-depletion with CFP. Determination of significance of and reasons behind decreased CRTAC1 concentration in a subset of patients with long COVID will require analysis of roles of preexisting lung disease, impact of prior acute COVID-19, age, and other confounding variables in a larger number of patients.

CRTAC1 enhances the chemosensitivity of non-small cell lung cancer to cisplatin by eliciting RyR-mediated calcium release and inhibiting Akt1 expression

Sensitivity to platinum-based combination chemotherapy is associated with a favorable prognosis in patients with non-small cell lung cancer (NSCLC). Here, our results obtained from analyses of the Gene Expression Omnibus database of NSCLC patients showed that cartilage acidic protein 1 (CRTAC1) plays a role in the response to platinum-based chemotherapy. Overexpression of CRTAC1 increased sensitivity to cisplatin in vitro, whereas knockdown of CRTAC1 decreased chemosensitivity of NSCLC cells. In vivo mouse experiments showed that CRTAC1 overexpression increased the antitumor effects of cisplatin. CRTAC1 overexpression promoted NFAT transcriptional activation by increasing intracellular Ca2+ levels, thereby inducing its regulated STUB1 mRNA transcription and protein expression, accelerating Akt1 protein degradation and, in turn, enhancing cisplatin-induced apoptosis. Taken together, the present results indicate that CRTAC1 overexpression increases the chemosensitivity of NSCLC to cisplatin treatment by inducing Ca2+-dependent Akt1 degradation and apoptosis, suggesting the potential of CRTAC1 as a biomarker for predicting cisplatin chemosensitivity. Our results further reveal that modulating the expression of CRTAC1 could be a new strategy for increasing the efficacy of cisplatin in chemotherapy of NSCLC patients.

Modeling fibrotic alveolar transitional cells with pluripotent stem cell-derived alveolar organoids

Repeated injury of the lung epithelium is proposed to be the main driver of idiopathic pulmonary fibrosis (IPF). However, available therapies do not specifically target the epithelium and human models of fibrotic epithelial damage with suitability for drug discovery are lacking. We developed a model of the aberrant epithelial reprogramming observed in IPF using alveolar organoids derived from human-induced pluripotent stem cells stimulated with a cocktail of pro-fibrotic and inflammatory cytokines. Deconvolution of RNA-seq data of alveolar organoids indicated that the fibrosis cocktail rapidly increased the proportion of transitional cell types including the KRT5 - /KRT17 + aberrant basaloid phenotype recently identified in the lungs of IPF patients. We found that epithelial reprogramming and extracellular matrix (ECM) production persisted after removal of the fibrosis cocktail. We evaluated the effect of the two clinically approved compounds for IPF, nintedanib and pirfenidone, and found that they reduced the expression of ECM and pro-fibrotic mediators but did not completely reverse epithelial reprogramming. Thus, our system recapitulates key aspects of IPF and is a promising system for drug discovery.

Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases

Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even though they have been recognized for several years, there are still areas of research debate. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up. The intricate nature of ILDs and the accompanying data have led to an increasing adoption of artificial intelligence (AI) techniques, primarily on imaging data but also in genetic data, spirometry and lung diffusion, among others. In this literature review, we describe the most prominent applications of AI in ILDs presented approximately within the last five years. We roughly stratify these studies in three categories, namely: (i) screening, (ii) diagnosis and classification, (iii) prognosis.

Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status

BACKGROUND

There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease.

RESULTS

We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease.

CONCLUSIONS

The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

An update in club cell biology and its potential relevance to chronic obstructive pulmonary disease

Club cells are found in human small airways where they play an important role in immune defense, xenobiotic metabolism, and repair after injury. Over the past few years, data from single-cell RNA sequencing (scRNA-seq) studies has generated new insights into club cell heterogeneity and function. In this review, we integrate findings from scRNA-seq experiments with earlier in vitro, in vivo, and microscopy studies and highlight the many ways club cells contribute to airway homeostasis. We then discuss evidence for loss of club cells or club cell products in the airways of patients with chronic obstructive pulmonary disease (COPD) and discuss potential mechanisms through which this might occur.

An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers

Pulmonary fibrosing diseases are in the very epicenter of biomedical research both due to their increasing prevalence and their association with SARS-CoV-2 infections. Research of idiopathic pulmonary fibrosis, the most lethal among the interstitial lung diseases, is in need for new biomarkers and potential disease targets, a goal that could be accelerated using machine learning techniques. In this study, we have used Shapley values to explain the decisions made by an ensemble learning model trained to classify samples to an either pulmonary fibrosis or steady state based on the expression values of deregulated genes. This process resulted in a full and a laconic set of features capable of separating phenotypes to an at least equal degree as previously published marker sets. Indicatively, a maximum increase of 6% in specificity and 5% in Mathew's correlation coefficient was achieved. Evaluation with an additional independent dataset showed our feature set having a greater generalization potential than the rest. Ultimately, the proposed gene lists are expected not only to serve as new sets of diagnostic marker elements, but also as a target pool for future research initiatives.

Fibrinogen on extracellular vesicles derived from polyhexamethylene guanidine phosphate-exposed mice induces inflammatory effects via integrin β

Polyhexamethylene guanidine phosphate (PHMG-p), used as a humidifier disinfectant, causes interstitial lung disease, obliterative bronchiolitis, and lung fibrosis; however, little is known about its effect on intercellular interactions. Extracellular vesicles (EVs), which carry diverse compounds including proteins, RNA, and DNA to mediate cell-to-cell communication through their paracrine effects, have been highlighted as novel factors in lung fibrogenesis. This study aimed to identify the effect of proteins on small EVs (sEVs) from bronchoalveolar lavage fluid (BALF) of the recipient cells after PHMG-p exposure. A week after intratracheal administration of PHMG-p, sEVs were isolated from BALF of tissue showing overexpressed inflammatory and fibrosis markers. To investigate the role of sEVs in inflammation, naïve macrophages were cultured with sEVs, which induced their activation. To identify sEV proteins that are associated with these responses, proteomics analysis was performed. In the gene ontology analysis, coagulation, fibrinolysis, and hemostasis were associated with the upregulated proteins in sEVs. The highest increase was observed in fibrinogen levels, which was also related to those gene ontologies. We validated role of exosomal fibrinogen in inflammation using recombinant fibrinogen and an inhibitor of the integrin, which is the binding receptor for fibrinogen. Overall, we elucidated that increased fibrinogen levels in the early sEVs-PHMG activated inflammatory response during early fibrosis. These results suggest that sEVs from the BALF of PHMG-p-exposed mice could aggravate fibrogenesis by activating naïve macrophages via various proteins in the sEVs, Furthermore, this finding will be broadening the spectrum of communicating mediators.

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

Electrochemical immunosensor based on AuNPs/ERGO@CNT nanocomposites by one-step electrochemical co-reduction for sensitive detection of P-glycoprotein in serum

P-glycoprotein (P-gp), a transmembrane glycoprotein widely expressed on the surface of various cells, is highly associated with multidrug resistance (MDR) that heralds the malignant progress of disease after drug treatment. Notably, there have been reported that serum P-gp is a potential marker for assessing the progression of disease resistance. Currently, there are few methods for point-of-care serum P-gp detection. In this study, we proposed a gold nanoparticles/electrochemically reduced graphene oxide@carbon nanotube (AuNPs/ERGO@CNT) modified immunosensor based on a one-step electrochemical co-reduction method. The limit of detection (LOD) of our constructed electrochemical immunosensor for P-gp detection reached 0.13 ng/mL, and the detection results in serum were consistent with ELISA. The developed immunosensor is expected to provide a scientific basis for the clinical application of serum P-gp monitoring and integrated medicine.

Somatostatin Receptor PET/MR Imaging of Inflammation in Patients With Large Vessel Vasculitis and Atherosclerosis

BACKGROUND

Assessing inflammatory disease activity in large vessel vasculitis (LVV) can be challenging by conventional measures.

OBJECTIVES

We aimed to investigate somatostatin receptor 2 (SST2) as a novel inflammation-specific molecular imaging target in LVV.

METHODS

In a prospective, observational cohort study, in vivo arterial SST2 expression was assessed by positron emission tomography/magnetic resonance imaging (PET/MRI) using 68Ga-DOTATATE and 18F-FET-βAG-TOCA. Ex vivo mapping of the imaging target was performed using immunofluorescence microscopy; imaging mass cytometry; and bulk, single-cell, and single-nucleus RNA sequencing.

RESULTS

Sixty-one participants (LVV: n = 27; recent atherosclerotic myocardial infarction of ≤2 weeks: n = 25; control subjects with an oncologic indication for imaging: n = 9) were included. Index vessel SST2 maximum tissue-to-blood ratio was 61.8% (P < 0.0001) higher in active/grumbling LVV than inactive LVV and 34.6% (P = 0.0002) higher than myocardial infarction, with good diagnostic accuracy (area under the curve: ≥0.86; P < 0.001 for both). Arterial SST2 signal was not elevated in any of the control subjects. SST2 PET/MRI was generally consistent with 18F-fluorodeoxyglucose PET/computed tomography imaging in LVV patients with contemporaneous clinical scans but with very low background signal in the brain and heart, allowing for unimpeded assessment of nearby coronary, myocardial, and intracranial artery involvement. Clinically effective treatment for LVV was associated with a 0.49 ± 0.24 (standard error of the mean [SEM]) (P = 0.04; 22.3%) reduction in the SST2 maximum tissue-to-blood ratio after 9.3 ± 3.2 months. SST2 expression was localized to macrophages, pericytes, and perivascular adipocytes in vasculitis specimens, with specific receptor binding confirmed by autoradiography. SSTR2-expressing macrophages coexpressed proinflammatory markers.

CONCLUSIONS

SST2 PET/MRI holds major promise for diagnosis and therapeutic monitoring in LVV. (PET Imaging of Giant Cell and Takayasu Arteritis [PITA], NCT04071691; Residual Inflammation and Plaque Progression Long-Term Evaluation [RIPPLE], NCT04073810).

Epithelial senescence in idiopathic pulmonary fibrosis is propagated by small extracellular vesicles

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease that affects 3 million people worldwide. Senescence and small extracellular vesicles (sEVs) have been implicated in the pathogenesis of IPF, although how sEVs promote disease remains unclear. Here, we profile sEVs from bronchial epithelial cells and determine small RNA (smRNA) content.

METHODS

Conditioned media was collected and sEVs were isolated from normal human bronchial epithelial cells (NHBEs) and IPF-diseased human bronchial epithelial cells (DHBEs).

RESULTS

Increased sEV release from DHBEs compared to NHBEs (n = 4; p < 0.05) was detected by nanoparticle tracking analysis. NHBEs co-cultured with DHBE-derived sEVs for 72 h expressed higher levels of SA-β-Gal and γH2AX protein, p16 and p21 RNA and increased secretion of IL6 and IL8 proteins (all n = 6-8; p < 0.05). sEVs were also co-cultured with healthy air-liquid interface (ALI) cultures and similar results were observed, with increases in p21 and p16 gene expression and IL6 and IL8 (basal and apical) secretion (n = 6; p < 0.05). Transepithelial electrical resistance (TEER) measurements, a reflection of epithelial barrier integrity, were decreased upon the addition of DHBE-derived sEVs (n = 6; p < 0.05). smRNA-sequencing identified nineteen significantly differentially expressed miRNA in DHBE-derived sEVs compared to NHBE-derived sEVs, with candidate miRNAs validated by qPCR (all n = 5; p < 0.05). Four of these miRNAs were upregulated in NHBEs co-cultured with DHBE-derived sEVs and three in healthy ALI cultures co-cultured with DHBE-derived sEVs (n = 3-4; p < 0.05).

CONCLUSIONS

This data demonstrates that DHBE-derived sEVs transfer senescence to neighbouring healthy cells, promoting the disease state in IPF.

Novel diagnostic techniques in interstitial lung disease

Research into novel diagnostic techniques and targeted therapeutics in interstitial lung disease (ILD) is moving the field toward increased precision and improved patient outcomes. An array of molecular techniques, machine learning approaches and other innovative methods including electronic nose technology and endobronchial optical coherence tomography are promising tools with potential to increase diagnostic accuracy. This review provides a comprehensive overview of the current evidence regarding evolving diagnostic methods in ILD and to consider their future role in routine clinical care.

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,283,972 single cells from 556 samples and 318 patients across 29 datasets, including our dataset capturing cells with low mRNA content. We stratify patients into immune-deserted, B cell, T cell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identify cellular components associated with tumor histology and genotypes. We then focus on the analysis of tissue-resident neutrophils (TRNs) and uncover distinct subpopulations that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. Finally, we show that a TRN-derived gene signature is associated with anti-programmed cell death ligand 1 (PD-L1) treatment failure.

The Vasculature in Pulmonary Fibrosis

Purpose of Review

The current paradigm of idiopathic pulmonary fibrosis (IPF) pathogenesis involves recurrent injury to a sensitive alveolar epithelium followed by impaired repair responses marked by fibroblast activation and deposition of extracellular matrix. Multiple cell types are involved in this response with potential roles suggested by advances in single-cell RNA sequencing and lung developmental biology. Notably, recent work has better characterized the cell types present in the pulmonary endothelium and identified vascular changes in patients with IPF.

Recent Findings

Lung tissue from patients with IPF has been examined at single-cell resolution, revealing reductions in lung capillary cells and expansion of a population of vascular cells expressing markers associated with bronchial endothelium. In addition, pre-clinical models have demonstrated a fundamental role for aging and vascular permeability in the development of pulmonary fibrosis.

Summary

Mounting evidence suggests that the endothelium undergoes changes in the context of fibrosis, and these changes may contribute to the development and/or progression of pulmonary fibrosis. Additional studies will be needed to further define the functional role of these vascular changes.

Analysis of CRSsNP Proteome Using a Highly Multiplexed Approach in Nasal Mucus

BACKGROUND

Chronic rhinosinusitis without nasal polyps (CRSsNP) represents a phenotype of CRS, whose immunological mechanisms are still unclear. So far there are neither suitable biomarkers to determine the course of the disease nor an individual therapy.

OBJECTIVE

The purpose of this study was to characterize the CRSsNP endotype by identifying and validating non-invasive proteomic biomarkers.

METHODS

A highly-multiplexed proteomic array consisting of antibodies against 2000 proteins was used to identify proteins that are differentially expressed in the nasal mucus of the CRSsNP and control groups (n = 7 per group). The proteins identified to be most differentially expressed were validated in matched nasal mucus samples using western blots and enzyme-linked immunosorbent assay (ELISA). Validation was also done in a second cohort using western blots (CRSsNP n = 25, control n = 23) and ELISA (n = 30 per group). Additionally, immunohistochemistry in CRSsNP and control tissue samples was performed to characterize the selected proteins further.

RESULTS

Out of the 2000 proteins examined, 7 from the most differentially expressed proteins were chosen to be validated. The validation results showed that 4 proteins were significantly upregulated in CRSsNP mucus, including macrophage inflammatory protein-1beta (MIP-1β), resistin, high mobility group box 1 (HMGB1), and forkhead box protein 3 (FOXP3). Cartilage acidic protein 1 (CRTAC1) was not significantly upregulated. Two proteins were significantly downregulated including scavenger receptor class F member 2 (SCARF2) and P-selectin. All proteins selected are mainly associated with inflammation, cell proliferation/differentiation, apoptosis and cell-cell or cell-matrix interaction.

CONCLUSION

Proteomic analysis of CRSsNP and control mucus has confirmed known and revealed novel disease-associated proteins that could potentially serve as a new biosignature for CRSsNP. Analysis of the associated pathways will specify endotypes of CRSsNP and will lead to an improved understanding of the pathophysiology of CRSsNP. Furthermore, our data contribute to the development of a reproducible, non-invasive, and quantitative "liquid biopsy" for rhinosinusitis.

ScRNA-seq expression of IFI27 and APOC2 identifies four alveolar macrophage superclusters in healthy BALF

Alveolar macrophages (AMs) reside on the luminal surface of the airways and alveoli, ensuring proper gas exchange by ingesting cellular debris and pathogens, and regulating inflammatory responses. Therefore, understanding the heterogeneity and diverse roles played by AMs, interstitial macrophages, and recruited monocytes is critical for treating airway diseases. We performed single-cell RNA sequencing on 113,213 bronchoalveolar lavage cells from four healthy and three uninflamed cystic fibrosis subjects and identified two MARCKS+LGMN+IMs, FOLR2+SELENOP+ and SPP1+PLA2G7+ IMs, monocyte subtypes, DC1, DC2, migDCs, plasmacytoid DCs, lymphocytes, epithelial cells, and four AM superclusters (families) based on the gene expression of IFI27 and APOC2 These four AM families have at least eight distinct functional members (subclusters) named after their differentially expressed gene(s): IGF1, CCL18, CXCL5, cholesterol, chemokine, metallothionein, interferon, and small-cluster AMs. Interestingly, the chemokine cluster further divides with each subcluster selectively expressing a unique combination of chemokines. One of the most striking observations, besides the heterogeneity, is the conservation of AM family members in relatively equal ratio across all AM superclusters and individuals. Transcriptional data and TotalSeq technology were used to investigate cell surface markers that distinguish resident AMs from recruited monocytes. Last, other AM datasets were projected onto our dataset. Similar AM superclusters and functional subclusters were observed, along with a significant increase in chemokine and IFN AM subclusters in individuals infected with COVID-19. Overall, functional specializations of the AM subclusters suggest that there are highly regulated AM niches with defined programming states, highlighting a clear division of labor.

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study

BACKGROUND

COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications.

METHODS

In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.

FINDINGS

We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile.

INTERPRETATION

A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study.

FUNDING

Eric and Wendy Schmidt.

Longitudinal Serum Proteome Characterization of COVID-19 Patients With Different Severities Revealed Potential Therapeutic Strategies

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation.

BAL Transcriptomes Characterize Idiopathic Pulmonary Fibrosis Endotypes With Prognostic Impact

BACKGROUND

Given the plethora of pathophysiologic mechanisms described in idiopathic pulmonary fibrosis (IPF), we hypothesize that the mechanisms driving fibrosis in IPF may be different from one patient to another.

RESEARCH QUESTION

Do IPF endotypes exist and are they associated with outcome?

STUDY DESIGN AND METHODS

Using a publicly available gene expression dataset retrieved from BAL samples of patients with IPF and control participants (GSE70867), we clustered IPF samples based on a dimension reduction algorithm specifically designed for -omics data, called DDR Tree. After clustering, gene set enrichment analysis was performed for functional annotation, associations with clinical variables and prognosis were investigated, and differences in transcriptional regulation were determined using motif enrichment analysis. The findings were validated in three independent publicly available gene expression datasets retrieved from IPF blood samples.

RESULTS

One hundred seventy-six IPF samples from three centers were clustered in six IPF clusters, with distinct functional enrichment. Although clinical characteristics did not differ between the clusters, one cluster conferred worse sex-age-physiology score-corrected survival, whereas another showed a numeric trend toward worse survival (P = .08). The first was enriched for increased epithelial and innate and adaptive immunity signatures, whereas the other showed important telomere and mitochondrial dysfunction, loss of proteostasis, and increased myofibroblast signatures. The existence of these two endotypes, including the impact on survival of the immune endotype, was validated in three independent validation cohorts. Finally, we identified transcription factors regulating the expression of endotype-specific survival-associated genes.

INTERPRETATION

Gene expression-based endotyping in IPF is feasible and can inform clinical evolution. As endotype-specific pathways and survival-associated transcription factors are identified, endotyping may open up the possibility of endotype-tailored therapy.

Mapping the cardiac vascular niche in heart failure

The cardiac vascular and perivascular niche are of major importance in homeostasis and during disease, but we lack a complete understanding of its cellular heterogeneity and alteration in response to injury as a major driver of heart failure. Using combined genetic fate tracing with confocal imaging and single-cell RNA sequencing of this niche in homeostasis and during heart failure, we unravel cell type specific transcriptomic changes in fibroblast, endothelial, pericyte and vascular smooth muscle cell subtypes. We characterize a specific fibroblast subpopulation that exists during homeostasis, acquires Thbs4 expression and expands after injury driving cardiac fibrosis, and identify the transcription factor TEAD1 as a regulator of fibroblast activation. Endothelial cells display a proliferative response after injury, which is not sustained in later remodeling, together with transcriptional changes related to hypoxia, angiogenesis, and migration. Collectively, our data provides an extensive resource of transcriptomic changes in the vascular niche in hypertrophic cardiac remodeling.

The cardiac vascular niche is of major importance in homeostasis and disease, but knowledge of its complexity in response to injury remains limited. Here we combine lineage tracing with single cell RNA sequencing to show alterations in fibroblasts, endothelial and mural cells in hypertrophic remodeling.

Consensus Gene Co-Expression Network Analysis Identifies Novel Genes Associated with Severity of Fibrotic Lung Disease

Idiopathic pulmonary fibrosis (IPF) is a severe fibrotic lung disease characterized by irreversible scarring of the lung parenchyma leading to dyspnea, progressive decline in lung function, and respiratory failure. We analyzed lung transcriptomic data from independent IPF cohorts using weighted gene co-expression network analysis (WGCNA) to identify gene modules based on their preservation status in these cohorts. The consensus gene modules were characterized by leveraging existing clinical and molecular data such as lung function, biological processes, pathways, and lung cell types. From a total of 32 consensus gene modules identified, two modules were found to be significantly correlated with the disease, lung function, and preserved in other IPF datasets. The upregulated gene module was enriched for extracellular matrix, collagen metabolic process, and BMP signaling while the downregulated module consisted of genes associated with tube morphogenesis, blood vessel development, and cell migration. Using a combination of connectivity-based and trait-based significance measures, we identified and prioritized 103 "hub" genes (including 25 secretory candidate biomarkers) by their similarity to known IPF genetic markers. Our validation studies demonstrate the dysregulated expression of CRABP2, a retinol-binding protein, in multiple lung cells of IPF, and its correlation with the decline in lung function.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

Extracellular Lipids in the Lung and Their Role in Pulmonary Fibrosis

Lipids are major actors and regulators of physiological processes within the lung. Initial research has described their critical role in tissue homeostasis and in orchestrating cellular communication to allow respiration. Over the past decades, a growing body of research has also emphasized how lipids and their metabolism may be altered, contributing to the development and progression of chronic lung diseases such as pulmonary fibrosis. In this review, we first describe the current working model of the mechanisms of lung fibrogenesis before introducing lipids and their cellular metabolism. We then summarize the evidence of altered lipid homeostasis during pulmonary fibrosis, focusing on their extracellular forms. Finally, we highlight how lipid targeting may open avenues to develop therapeutic options for patients with lung fibrosis.

Emerging Roles of Airway Epithelial Cells in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a fatal disease with incompletely understood aetiology and limited treatment options. Traditionally, IPF was believed to be mainly caused by repetitive injuries to the alveolar epithelium. Several recent lines of evidence, however, suggest that IPF equally involves an aberrant airway epithelial response, which contributes significantly to disease development and progression. In this review, based on recent clinical, high-resolution imaging, genetic, and single-cell RNA sequencing data, we summarize alterations in airway structure, function, and cell type composition in IPF. We furthermore give a comprehensive overview on the genetic and mechanistic evidence pointing towards an essential role of airway epithelial cells in IPF pathogenesis and describe potentially implicated aberrant epithelial signalling pathways and regulation mechanisms in this context. The collected evidence argues for the investigation of possible therapeutic avenues targeting these processes, which thus represent important future directions of research.

The Impact of Inflammatory Immune Reactions of the Vascular Niche on Organ Fibrosis

Inflammation is a type of defense response against tissue damage, and can be mediated by lymphocytes and macrophages. Fibrosis is induced by tissue injury and inflammation, which leads to an increase in fibrous connective tissue in organs and a decrease in organ parenchyma cells, finally leading to organ dysfunction or even failure. The vascular niche is composed of endothelial cells, pericytes, macrophages, and hematopoietic stem cells. It forms a guiding microenvironment for the behavior of adjacent cells, and mainly exists in the microcirculation, including capillaries. When an organ is damaged, the vascular niche regulates inflammation and affects the repair of organ damage in a variety of ways, such as via its angiocrine function and transformation of myofibroblasts. In this paper, the main roles of vascular niche in the process of organ fibrosis and its mechanism of promoting the progress of fibrosis through inflammatory immunoregulation are summarized. It was proposed that the vascular niche should be regarded as a new therapeutic target for organ fibrosis, suggesting that antifibrotic effects could be achieved by regulating macrophages, inhibiting endothelial-mesenchymal transition, interfering with the angiocrine function of endothelial cells, and inhibiting the transformation of pericytes into myofibroblasts, thus providing new ideas for antifibrosis drug research.

Fibromine is a multi-omics database and mining tool for target discovery in pulmonary fibrosis

Idiopathic pulmonary fibrosis is a lethal lung fibroproliferative disease with limited therapeutic options. Differential expression profiling of affected sites has been instrumental for involved pathogenetic mechanisms dissection and therapeutic targets discovery. However, there have been limited efforts to comparatively analyse/mine the numerous related publicly available datasets, to fully exploit their potential on the validation/creation of novel research hypotheses. In this context and towards that goal, we present Fibromine, an integrated database and exploration environment comprising of consistently re-analysed, manually curated transcriptomic and proteomic pulmonary fibrosis datasets covering a wide range of experimental designs in both patients and animal models. Fibromine can be accessed via an R Shiny application (http://www.fibromine.com/Fibromine) which offers dynamic data exploration and real-time integration functionalities. Moreover, we introduce a novel benchmarking system based on transcriptomic datasets underlying characteristics, resulting to dataset accreditation aiming to aid the user on dataset selection. Cell specificity of gene expression can be visualised and/or explored in several scRNA-seq datasets, in an effort to link legacy data with this cutting-edge methodology and paving the way to their integration. Several use case examples are presented, that, importantly, can be reproduced on-the-fly by a non-specialist user, the primary target and potential user of this endeavour.

Disease pathology in fibrotic interstitial lung disease: is it all about usual interstitial pneumonia?

The interstitial pneumonias comprise a diverse group of diseases that are typically defined by their cause (either idiopathic or non-idiopathic) and their distinct histopathological features, for which radiology, in the form of high-resolution CT, is often used as a surrogate. One trend, fuelled by the failure of conventional therapies in a subset of patients and the broad-spectrum use of antifibrotic therapies, has been the focus on the progressive fibrosing phenotype of interstitial lung disease. The histological pattern, known as usual interstitial pneumonia, is the archetype of progressive fibrosis. However, it is clear that progressive fibrosis is not exclusive to this histological entity. Techniques including immunohistochemistry and single-cell RNA sequencing are providing pathogenetic insights and, if integrated with traditional histopathology, are likely to have an effect on the pathological classification of interstitial lung disease. This review, which focuses on the histopathology of interstitial lung disease and its relationship with progressive fibrosis, asks the question: is it all about usual interstitial pneumonia?