A lentiviral toolkit to monitor airway epithelial cell differentiation using bioluminescence.

Basal cells are adult stem cells in the airway epithelium and regenerate differentiated cell populations, including the mucosecretory and ciliated cells that enact mucociliary clearance. Human basal cells can proliferate and produce differentiated epithelium in vitro. However, studies of airway epithelial differentiation mostly rely on immunohistochemical or immunofluorescence-based staining approaches, meaning that a dynamic approach is lacking, and quantitative data is limited. Here, we use a lentiviral reporter gene approach to transduce primary human basal cells with bioluminescence reporter constructs to monitor airway epithelial differentiation longitudinally. We generated three constructs driven by promoter sequences from the TP63, MUC5AC and FOXJ1 genes to quantitatively assess basal cell, mucosecretory cell and ciliated cell abundance, respectively. We validated these constructs by tracking differentiation of basal cells in air-liquid interface and organoid ('bronchosphere') cultures. Transduced cells also responded appropriately to stimulation with interleukin 13 (IL-13; to increase mucosecretory differentiation and mucus production) and IL-6 (to increase ciliated cell differentiation). These constructs represent a new tool for monitoring airway epithelial cell differentiation in primary epithelial and/or induced pluripotent stem cell (iPSC) cell cultures.

Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance.

The morphology and distribution of airway tree abnormalities enable diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. Furthermore, the segmentation of a complete airway tree is challenging as the intensity, scale/size and shape of airway segments and their walls change across generations. The existing classical techniques either provide an undersegmented or oversegmented airway tree, and manual intervention is required for optimal airway tree segmentation. The recent development of deep learning methods provides a fully automatic way of segmenting airway trees; however, these methods usually require high GPU memory usage and are difficult to implement in low computational resource environments. Therefore, in this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost the segmentation performance of the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52%, and 85.80%; (B) outperforms the baseline models by 2.89%, 3.86%, and 3.87% on average; and (C) produces maximum segmentation performance gain by 14.11%, 9.28%, and 12.70% for individual cases when (1) nnU-Net with instant normalisation and leaky ReLU; (2) nnU-Net with batch normalisation and ReLU; and (3) modified dilated U-Net are used respectively. Our proposed method outperformed the state-of-the-art airway segmentation approaches. Furthermore, our proposed technique has low RAM and GPU memory usage, and it is GPU memory-efficient and highly flexible, enabling it to be deployed on any 2D deep learning model.

11ß hydroxysteroid dehydrogenase type 1 transgenic mesenchymal stem cells attenuate inflammation in models of sepsis.

Background: Human bone marrow mesenchymal stem cell (MSC) administration reduces inflammation in pre-clinical models of sepsis and sepsis-related lung injury, however clinical efficacy in patients has not yet been demonstrated. We previously showed that Alveolar Macrophage (AM) 11ß-hydroxysteroid dehydrogenase type-1 (HSD-1) autocrine signalling is impaired in critically ill sepsis patients, which promotes inflammatory injury. Administration of transgenic MSCs (tMSCs) which overexpress HSD-1 may enhance the anti-inflammatory effects of local glucocorticoids and be more effective at reducing inflammation in sepsis than cellular therapy alone. Methods: MSCs were transfected using a recombinant lentiviral vector containing the HSD-1 and GPF transgenes under the control of a tetracycline promoter. Thin layer chromatography assessed HSD-1 reductase activity in tMSCs. Mesenchymal stem cell phenotype was assessed by flow cytometry and bi-lineage differentiation. HSD-1 tMSCs were co-cultured with LPS-stimulated monocyte-derived macrophages (MDMs) from healthy volunteers prior to assessment of pro-inflammatory cytokine release. HSD-1 tMSCs were administered intravenously to mice undergoing caecal ligation and puncture (CLP). Results: MSCs were transfected with an efficiency of 91.1%, and maintained an MSC phenotype. Functional HSD-1 activity was demonstrated in tMSCs, with predominant reductase cortisol activation (peak 8.23 pM/hour/100,000 cells). HSD-1 tMSC co-culture with LPS-stimulated MDMs suppressed TNFα and IL-6 release. Administration of transgene activated HSD-1 tMSCs in a murine model of CLP attenuated neutrophilic inflammation more effectively than transgene inactive tMSCs (medians 0.403 v 1.36 × 106/ml, p = 0.033). Conclusion: The synergistic impact of HSD-1 transgene expression and MSC therapy attenuated neutrophilic inflammation in a mouse model of peritoneal sepsis more effectively than MSC therapy alone. Future studies investigating the anti-inflammatory capacity of HSD-1 tMSCs in models of sepsis-related direct lung injury and inflammatory diseases are required.

Human SARS-CoV-2 challenge uncovers local and systemic response dynamics.

The COVID-19 pandemic is an ongoing global health threat, yet our understanding of the dynamics of early cellular responses to this disease remains limited1. Here in our SARS-CoV-2 human challenge study, we used single-cell multi-omics profiling of nasopharyngeal swabs and blood to temporally resolve abortive, transient and sustained infections in seronegative individuals challenged with pre-Alpha SARS-CoV-2. Our analyses revealed rapid changes in cell-type proportions and dozens of highly dynamic cellular response states in epithelial and immune cells associated with specific time points and infection status. We observed that the interferon response in blood preceded the nasopharyngeal response. Moreover, nasopharyngeal immune infiltration occurred early in samples from individuals with only transient infection and later in samples from individuals with sustained infection. High expression of HLA-DQA2 before inoculation was associated with preventing sustained infection. Ciliated cells showed multiple immune responses and were most permissive for viral replication, whereas nasopharyngeal T cells and macrophages were infected non-productively. We resolved 54 T cell states, including acutely activated T cells that clonally expanded while carrying convergent SARS-CoV-2 motifs. Our new computational pipeline Cell2TCR identifies activated antigen-responding T cells based on a gene expression signature and clusters these into clonotype groups and motifs. Overall, our detailed time series data can serve as a Rosetta stone for epithelial and immune cell responses and reveals early dynamic responses associated with protection against infection.

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently. Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models.

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

Airway injury induces alveolar epithelial and mesenchymal responses mediated by macrophages.

Acute injury in the airways or the lung activates local progenitors and stimulates changes in cell-cell interactions to restore homeostasis, but it is not appreciated how more distant niches are impacted. We utilized mouse models of airway-specific epithelial injury to examine secondary tissue-wide alveolar, immune, and mesenchymal responses. Single-cell transcriptomics and in vivo validation revealed transient, tissue-wide proliferation of alveolar type 2 (AT2) progenitor cells after club cell-specific ablation. The AT2 cell proliferative response was reliant on alveolar macrophages (AMs) via upregulation of Spp1 which encodes the secreted factor Osteopontin. A previously uncharacterized mesenchymal population we termed Mesenchymal Airway/Adventitial Niche Cell 2 (MANC2) also exhibited dynamic changes in abundance and a pro-fibrotic transcriptional signature after club cell ablation in an AM-dependent manner. Overall, these results demonstrate that acute airway damage can trigger distal lung responses including altered cell-cell interactions that may contribute to potential vulnerabilities for further dysregulation and disease.

Lentiviral expression of wild-type LAMA3A restores cell adhesion in airway basal cells from children with epidermolysis bullosa.

The hallmark of epidermolysis bullosa (EB) is fragile attachment of epithelia due to genetic variants in cell adhesion genes. We describe 16 EB patients treated in the ear, nose, and throat department of a tertiary pediatric hospital linked to the United Kingdom's national EB unit between 1992 and 2023. Patients suffered a high degree of morbidity and mortality from laryngotracheal stenosis. Variants in laminin subunit alpha-3 (LAMA3) were found in 10/15 patients where genotype was available. LAMA3 encodes a subunit of the laminin-332 heterotrimeric extracellular matrix protein complex and is expressed by airway epithelial basal stem cells. We investigated the benefit of restoring wild-type LAMA3 expression in primary EB patient-derived basal cell cultures. EB basal cells demonstrated weak adhesion to cell culture substrates, but could otherwise be expanded similarly to non-EB basal cells. In vitro lentiviral overexpression of LAMA3A in EB basal cells enabled them to differentiate in air-liquid interface cultures, producing cilia with normal ciliary beat frequency. Moreover, transduction restored cell adhesion to levels comparable to a non-EB donor culture. These data provide proof of concept for a combined cell and gene therapy approach to treat airway disease in LAMA3-affected EB.

Epithelial stem and progenitor cells of the upper airway.

The upper airway acts as a conduit for the passage of air to the respiratory system and is implicated in several chronic diseases. Whilst the cell biology of the distal respiratory system has been described in great detail, less is known about the proximal upper airway. In this review, we describe the relevant anatomy of the upper airway and discuss the literature detailing the identification and roles of the progenitor cells of these regions.

Health State Utilities Associated with False-Positive Cancer Screening Results.

INTRODUCTION: Early cancer detection can significantly improve patient outcomes and reduce mortality rates. Novel cancer screening approaches, including multi-cancer early detection tests, have been developed. Cost-utility analyses will be needed to examine their value, and these models require health state utilities. The purpose of this study was to estimate the disutility (i.e., decrease in health state utility) associated with false-positive cancer screening results. METHODS: In composite time trade-off interviews using a 1-year time horizon, UK general population participants valued 10 health state vignettes describing cancer screening with true-negative or false-positive results. Each false-positive vignette described a common diagnostic pathway following a false-positive result suggesting lung, colorectal, breast, or pancreatic cancer. Every pathway ended with a negative result (no cancer detected). The disutility of each false positive was calculated as the difference between the true-negative and each false-positive health state, and because of the 1-year time horizon, each disutility can be interpreted as a quality-adjusted life-year decrement associated with each type of false-positive experience. RESULTS: A total of 203 participants completed interviews (49.8% male; mean age = 42.0 years). The mean (SD) utility for the health state describing a true-negative result was 0.958 (0.065). Utilities for false-positive health states ranged from 0.847 (0.145) to 0.932 (0.059). Disutilities for false positives ranged from - 0.031 to - 0.111 (- 0.041 to - 0.111 for lung cancer; - 0.079 for colorectal cancer; - 0.031 to - 0.067 for breast cancer; - 0.048 to - 0.088 for pancreatic cancer). CONCLUSION: All false-positive results were associated with a disutility. Greater disutility was associated with more invasive follow-up diagnostic procedures, longer duration of uncertainty regarding the eventual diagnosis, and perceived severity of the suspected cancer type. Utility values estimated in this study would be useful for economic modeling examining the value of cancer screening procedures.

The Role of Smoking Status in Making Risk-Informed Diagnostic Decisions in the Lung Cancer Pathway: A Qualitative Study of Health Care Professionals and Patients.

BACKGROUND: Lung cancer clinical guidelines and risk tools often rely on smoking history as a significant risk factor. However, never-smokers make up 14% of the lung cancer population, and this proportion is rising. Consequently, they are often perceived as low-risk and may experience diagnostic delays. This study aimed to explore how clinicians make risk-informed diagnostic decisions for never-smokers. METHODS: Qualitative interviews were conducted with 10 lung cancer diagnosticians, supported by data from interviews with 20 never-smoker lung cancer patients. The data were analyzed using a framework analysis based on the Model of Pathways to Treatment framework and data-driven interpretations. RESULTS: Participants described 3 main strategies for making risk-informed decisions incorporating smoking status: guidelines, heuristics, and potential harms. Clinicians supplemented guidelines with their own heuristics for never-smokers, such as using higher thresholds for chest X-ray. Decisions were easier for patients with high-risk symptoms such as hemoptysis. Clinicians worried about overinvestigating never-smoker patients, particularly in terms of physical and psychological harms from invasive procedures or radiation. To minimize unnecessary anxiety about lung cancer risk, clinicians made efforts to downplay this. Conversely, some patients found that this caused process harms such as delays and miscommunications. CONCLUSION: Improved guidance and methods of risk differentiation for never-smokers are needed to avoid diagnostic delays, overreassurance, and clinical pessimism. This requires an improved evidence base and initiatives to increase awareness among clinicians of the incidence of lung cancer in never-smokers. As the proportion of never-smoker patients increases, this issue will become more urgent. HIGHLIGHTS: Smoking status is the most common risk factor used by clinicians to guide decision making, and guidelines often focus on this factor.Some clinicians also use their own heuristics for never-smokers, and this becomes particularly relevant for patients with lower risk symptoms.Clinicians are also concerned about the potential harms and risks associated with deploying resources on diagnostics for never-smokers.Some patients find it difficult to decide whether or not to go ahead with certain procedures due to efforts made by clinicians to downplay the risk of lung cancer.Overall, the study highlights the complex interplay between smoking history, clinical decision making, and patient anxiety in the context of lung cancer diagnosis and treatment.

Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review.

Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.

Mortality surrogates in combined pulmonary fibrosis and emphysema.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) with coexistent emphysema, termed combined pulmonary fibrosis and emphysema (CPFE) may associate with reduced forced vital capacity (FVC) declines compared to non-CPFE IPF patients. We examined associations between mortality and functional measures of disease progression in two IPF cohorts. METHODS: Visual emphysema presence (>0% emphysema) scored on computed tomography identified CPFE patients (CPFE/non-CPFE: derivation cohort n=317/n=183, replication cohort n=358/n=152), who were subgrouped using 10% or 15% visual emphysema thresholds, and an unsupervised machine-learning model considering emphysema and interstitial lung disease extents. Baseline characteristics, 1-year relative FVC and diffusing capacity of the lung for carbon monoxide (D LCO) decline (linear mixed-effects models), and their associations with mortality (multivariable Cox regression models) were compared across non-CPFE and CPFE subgroups. RESULTS: In both IPF cohorts, CPFE patients with ≥10% emphysema had a greater smoking history and lower baseline D LCO compared to CPFE patients with <10% emphysema. Using multivariable Cox regression analyses in patients with ≥10% emphysema, 1-year D LCO decline showed stronger mortality associations than 1-year FVC decline. Results were maintained in patients suitable for therapeutic IPF trials and in subjects subgrouped by ≥15% emphysema and using unsupervised machine learning. Importantly, the unsupervised machine-learning approach identified CPFE patients in whom FVC decline did not associate strongly with mortality. In non-CPFE IPF patients, 1-year FVC declines ≥5% and ≥10% showed strong mortality associations. CONCLUSION: When assessing disease progression in IPF, D LCO decline should be considered in patients with ≥10% emphysema and a ≥5% 1-year relative FVC decline threshold considered in non-CPFE IPF patients.

Early human lung immune cell development and its role in epithelial cell fate.

Studies of human lung development have focused on epithelial and mesenchymal cell types and function, but much less is known about the developing lung immune cells, even though the airways are a major site of mucosal immunity after birth. An unanswered question is whether tissue-resident immune cells play a role in shaping the tissue as it develops in utero. Here, we profiled human embryonic and fetal lung immune cells using scRNA-seq, smFISH, and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including innate lymphoid cells, natural killer cells, myeloid cells, and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes expressing high levels of cytotoxicity genes and the presence of mature B lymphocytes, including B-1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the presence and diversity of immune cells during development, we also investigated their possible effect on epithelial maturation. We found that IL-1ß drives epithelial progenitor exit from self-renewal and differentiation to basal cells in vitro. In vivo, IL-1ß-producing myeloid cells were found throughout the lung and adjacent to epithelial tips, suggesting that immune cells may direct human lung epithelial development.

Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study.

BACKGROUND: Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. METHODS AND FINDINGS: For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. CONCLUSIONS: We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

Automated airway quantification associates with mortality in idiopathic pulmonary fibrosis.

OBJECTIVES: The study examined whether quantified airway metrics associate with mortality in idiopathic pulmonary fibrosis (IPF). METHODS: In an observational cohort study (n = 90) of IPF patients from Ege University Hospital, an airway analysis tool AirQuant calculated median airway intersegmental tapering and segmental tortuosity across the 2nd to 6th airway generations. Intersegmental tapering measures the difference in median diameter between adjacent airway segments. Tortuosity evaluates the ratio of measured segmental length against direct end-to-end segmental length. Univariable linear regression analyses examined relationships between AirQuant variables, clinical variables, and lung function tests. Univariable and multivariable Cox proportional hazards models estimated mortality risk with the latter adjusted for patient age, gender, smoking status, antifibrotic use, CT usual interstitial pneumonia (UIP) pattern, and either forced vital capacity (FVC) or diffusion capacity of carbon monoxide (DLco) if obtained within 3 months of the CT. RESULTS: No significant collinearity existed between AirQuant variables and clinical or functional variables. On univariable Cox analyses, male gender, smoking history, no antifibrotic use, reduced DLco, reduced intersegmental tapering, and increased segmental tortuosity associated with increased risk of death. On multivariable Cox analyses (adjusted using FVC), intersegmental tapering (hazard ratio (HR) = 0.75, 95% CI = 0.66-0.85, p < 0.001) and segmental tortuosity (HR = 1.74, 95% CI = 1.22-2.47, p = 0.002) independently associated with mortality. Results were maintained with adjustment using DLco. CONCLUSIONS: AirQuant generated measures of intersegmental tapering and segmental tortuosity independently associate with mortality in IPF patients. Abnormalities in proximal airway generations, which are not typically considered to be abnormal in IPF, have prognostic value. CLINICAL RELEVANCE STATEMENT: Quantitative measurements of intersegmental tapering and segmental tortuosity, in proximal (second to sixth) generation airway segments, independently associate with mortality in IPF. Automated airway analysis can estimate disease severity, which in IPF is not restricted to the distal airway tree. KEY POINTS: • AirQuant generates measures of intersegmental tapering and segmental tortuosity. • Automated airway quantification associates with mortality in IPF independent of established measures of disease severity. • Automated airway analysis could be used to refine patient selection for therapeutic trials in IPF.

Lung viral infection modelling in a bioengineered whole-organ.

Lung infections are one of the leading causes of death worldwide, and this situation has been exacerbated by the emergence of COVID-19. Pre-clinical modelling of viral infections has relied on cell cultures that lack 3D structure and the context of lung extracellular matrices. Here, we propose a bioreactor-based, whole-organ lung model of viral infection. The bioreactor takes advantage of an automated system to achieve efficient decellularization of a whole rat lung, and recellularization of the scaffold using primary human bronchial cells. Automatization allowed for the dynamic culture of airway epithelial cells in a breathing-mimicking setup that led to an even distribution of lung epithelial cells throughout the distal regions. In the sealed bioreactor system, we demonstrate proof-of-concept for viral infection within the epithelialized lung by infecting primary human airway epithelial cells and subsequently injecting neutrophils. Moreover, to assess the possibility of drug screening in this model, we demonstrate the efficacy of the broad-spectrum antiviral remdesivir. This whole-organ scale lung infection model represents a step towards modelling viral infection of human cells in a 3D context, providing a powerful tool to investigate the mechanisms of the early stages of pathogenic infections and the development of effective treatment strategies for respiratory diseases.

Phenotyping of lymphoproliferative tumours generated in xenografts of non-small cell lung cancer.

Background: Patient-derived xenograft (PDX) models involve the engraftment of tumour tissue in immunocompromised mice and represent an important pre-clinical oncology research method. A limitation of non-small cell lung cancer (NSCLC) PDX model derivation in NOD-scid IL2Rgammanull (NSG) mice is that a subset of initial engraftments are of lymphocytic, rather than tumour origin. Methods: The immunophenotype of lymphoproliferations arising in the lung TRACERx PDX pipeline were characterised. To present the histology data herein, we developed a Python-based tool for generating patient-level pathology overview figures from whole-slide image files; PATHOverview is available on GitHub (https://github.com/EpiCENTR-Lab/PATHOverview). Results: Lymphoproliferations occurred in 17.8% of lung adenocarcinoma and 10% of lung squamous cell carcinoma transplantations, despite none of these patients having a prior or subsequent clinical history of lymphoproliferative disease. Lymphoproliferations were predominantly human CD20+ B cells and had the immunophenotype expected for post-transplantation diffuse large B cell lymphoma with plasma cell features. All lymphoproliferations expressed Epstein-Barr-encoded RNAs (EBER). Analysis of immunoglobulin light chain gene rearrangements in three tumours where multiple tumour regions had resulted in lymphoproliferations suggested that each had independent clonal origins. Discussion: Overall, these data suggest that B cell clones with lymphoproliferative potential are present within primary NSCLC tumours, and that these are under continuous immune surveillance. Since these cells can be expanded following transplantation into NSG mice, our data highlight the value of quality control measures to identify lymphoproliferations within xenograft pipelines and support the incorporation of strategies to minimise lymphoproliferations during the early stages of xenograft establishment pipelines.