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Sato H, Kato A, Adachi H, Takahashi K, Arai H, Ito M, Namba F, Takahashi T. High oxygen exposure's impact on newborn mice: Temporal changes observed via micro-computed tomography. Exp Lung Res 2024; 50:127-135. [PMID: 38973401 DOI: 10.1080/01902148.2024.2375099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024]
Abstract
INTRODUCTION Bronchopulmonary dysplasia (BPD) impacts life expectancy and long-term quality of life. Currently, BPD mouse models exposed to high oxygen are frequently used, but to reevaluate their relevance to human BPD, we attempted an assessment using micro-computed tomography (µCT). METHODS Newborn wildtype male mice underwent either 21% or 95% oxygen exposure for 4 days, followed until 8 wk. Weekly µCT scans and lung histological evaluations were performed independently. RESULTS Neonatal hyperoxia for 4 days hindered lung development, causing alveolar expansion and simplification. Histologically, during the first postnatal week, the exposed group showed a longer mean linear intercept, enlarged alveolar area, and a decrease in alveolar number, diminishing by week 4. Weekly µCT scans supported these findings, revealing initially lower lung density in newborn mice, increasing with age. However, the high-oxygen group displayed higher lung density initially. This difference diminished over time, with no significant contrast to controls at 3 wk. Although no significant difference in total lung volume was observed at week 1, the high-oxygen group exhibited a decrease by week 2, persisting until 8 wk. CONCLUSION This study highlights µCT-detected changes in mice exposed to high oxygen. BPD mouse models might follow a different recovery trajectory than humans, suggesting the need for further optimization.
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Affiliation(s)
- Himeko Sato
- Department of Pediatrics, Akita University Graduate School of Medicine, Akita City, Japan
| | - Akie Kato
- Department of Pediatrics, Akita University Graduate School of Medicine, Akita City, Japan
| | - Hiroyuki Adachi
- Department of Pediatrics, Akita University Graduate School of Medicine, Akita City, Japan
| | - Kiichi Takahashi
- Department of Neonatology, Akita Red Cross Hospital, Akita City, Japan
| | - Hirokazu Arai
- Department of Neonatology, Akita Red Cross Hospital, Akita City, Japan
| | - Masato Ito
- Department of Pediatrics, Akita University Graduate School of Medicine, Akita City, Japan
| | - Fumihiko Namba
- Department of Pediatrics, Saitama Medical Center, Saitama Medical University, Kawagoe City, Japan
| | - Tsutomu Takahashi
- Department of Pediatrics, Akita University Graduate School of Medicine, Akita City, Japan
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Weckerle J, Mayr CH, Fundel-Clemens K, Lämmle B, Boryn L, Thomas MJ, Bretschneider T, Luippold AH, Huber HJ, Viollet C, Rist W, Veyel D, Ramirez F, Klee S, Kästle M. Transcriptomic and Proteomic Changes Driving Pulmonary Fibrosis Resolution in Young and Old Mice. Am J Respir Cell Mol Biol 2023; 69:422-440. [PMID: 37411041 DOI: 10.1165/rcmb.2023-0012oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023] Open
Abstract
Bleomycin-induced pulmonary fibrosis in mice mimics major hallmarks of idiopathic pulmonary fibrosis. Yet in this model, it spontaneously resolves over time. We studied molecular mechanisms of fibrosis resolution and lung repair, focusing on transcriptional and proteomic signatures and the effect of aging. Old mice showed incomplete and delayed lung function recovery 8 weeks after bleomycin instillation. This shift in structural and functional repair in old bleomycin-treated mice was reflected in a temporal shift in gene and protein expression. We reveal gene signatures and signaling pathways that underpin the lung repair process. Importantly, the downregulation of WNT, BMP, and TGFβ antagonists Frzb, Sfrp1, Dkk2, Grem1, Fst, Fstl1, and Inhba correlated with lung function improvement. Those genes constitute a network with functions in stem cell pathways, wound, and pulmonary healing. We suggest that insufficient and delayed downregulation of those antagonists during fibrosis resolution in old mice explains the impaired regenerative outcome. Together, we identified signaling pathway molecules with relevance to lung regeneration that should be tested in-depth experimentally as potential therapeutic targets for pulmonary fibrosis.
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Affiliation(s)
| | | | | | - Bärbel Lämmle
- Global Computational Biology and Digital Sciences, and
| | | | | | - Tom Bretschneider
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany; and
| | - Andreas H Luippold
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany; and
| | | | | | - Wolfgang Rist
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany; and
| | - Daniel Veyel
- Department of Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany; and
| | - Fidel Ramirez
- Global Computational Biology and Digital Sciences, and
| | - Stephan Klee
- Department of Immunology and Respiratory Disease Research
| | - Marc Kästle
- Department of Immunology and Respiratory Disease Research
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Buccardi M, Ferrini E, Pennati F, Vincenzi E, Ledda RE, Grandi A, Buseghin D, Villetti G, Sverzellati N, Aliverti A, Stellari FF. A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy. Respir Res 2023; 24:126. [PMID: 37161569 PMCID: PMC10170869 DOI: 10.1186/s12931-023-02432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 04/24/2023] [Indexed: 05/11/2023] Open
Abstract
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory μCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.
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Affiliation(s)
- Martina Buccardi
- Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | - Erica Ferrini
- Department of Veterinary Science, University of Parma, Parma, Italy
| | - Francesca Pennati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico Di Milano, Milan, Italy
| | - Elena Vincenzi
- Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
- Camelot Biomedical System S.R.L, Via Al Ponte Reale 2/20, 16124, Genoa, Italy
| | | | - Andrea Grandi
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | - Davide Buseghin
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Gino Villetti
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | | | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico Di Milano, Milan, Italy
| | - Franco Fabio Stellari
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy.
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Zaw Thin M, Moore C, Snoeks T, Kalber T, Downward J, Behrens A. Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice. Nat Protoc 2023; 18:990-1015. [PMID: 36494493 DOI: 10.1038/s41596-022-00769-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/09/2022] [Indexed: 12/14/2022]
Abstract
X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4-8 min, and data analysis typically requires 10-30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents.
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Affiliation(s)
- May Zaw Thin
- Cancer Stem Cell Laboratory, Institute of Cancer Research, London, UK. .,Adult Stem Cell Laboratory, The Francis Crick Institute, London, UK.
| | - Christopher Moore
- Oncogene Biology Laboratory, The Francis Crick Institute, London, UK
| | - Thomas Snoeks
- Imaging Research Facility, The Francis Crick Institute, London, UK
| | - Tammy Kalber
- Centre for Advanced Biomedical Imaging (CABI), University College London, London, UK
| | - Julian Downward
- Oncogene Biology Laboratory, The Francis Crick Institute, London, UK. .,Lung Cancer Group, Division of Molecular Pathology, Institute of Cancer Research, London, UK.
| | - Axel Behrens
- Cancer Stem Cell Laboratory, Institute of Cancer Research, London, UK.,Adult Stem Cell Laboratory, The Francis Crick Institute, London, UK.,Department of Surgery and Cancer, Imperial College London, London, UK.,Cancer Research UK Convergence Science Centre, Imperial College London, London, UK
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Luisi JD, Lin JL, Ochoa LF, McAuley RJ, Tanner MG, Alfarawati O, Wright CW, Vargas G, Motamedi M, Ameredes BT. Semi-automated micro-computed tomography lung segmentation and analysis in mouse models. MethodsX 2023; 10:102198. [PMID: 37152666 PMCID: PMC10154963 DOI: 10.1016/j.mex.2023.102198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023] Open
Abstract
Computed Tomography (CT) is a standard clinical tool utilized to diagnose known lung pathologies based on established grading methods. However, for preclinical trials and toxicity investigations in animal models, more comprehensive datasets are typically needed to determine discriminative features between experimental treatments, which oftentimes require analysis of multiple images and their associated differential quantification using manual segmentation methods. Furthermore, for manual segmentation of image data, three or more readers is the gold standard of analysis, but this requirement can be time-consuming and inefficient, depending on variability due to reader bias. In previous papers, microCT image manual segmentation was a valuable tool for assessment of lung pathology in several animal models; however, the manual segmentation approach and the commercial software used was typically a major rate-limiting step. To improve the efficiency, the semi-manual segmentation method was streamlined, and a semi-automated segmentation process was developed to produce:•Quantifiable segmentations: using manual and semi-automated analysis methods for assessing experimental injury and toxicity models,•Deterministic results and efficiency through automation in an unbiased and parameter free process, thereby reducing reader variance, user time, and increases throughput in data analysis,•Cost-Effectiveness: portable with low computational resource demand, based on a cross-platform open-source ImageJ program.
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Vincenzi E, Fantazzini A, Basso C, Barla A, Odone F, Leo L, Mecozzi L, Mambrini M, Ferrini E, Sverzellati N, Stellari FF. A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models. Respir Res 2022; 23:308. [DOI: 10.1186/s12931-022-02236-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/15/2022] [Indexed: 11/13/2022] Open
Abstract
AbstractIdiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings.
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Camara JA, Pujol A, Jimenez JJ, Donate J, Ferrer M, Vande Velde G. Lung Volume Calculation in Preclinical MicroCT: A Fast Geometrical Approach. J Imaging 2022; 8:jimaging8080204. [PMID: 35893082 PMCID: PMC9330811 DOI: 10.3390/jimaging8080204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/08/2022] [Accepted: 07/18/2022] [Indexed: 12/04/2022] Open
Abstract
In this study, we present a time-efficient protocol for thoracic volume calculation as a proxy for total lung volume. We hypothesize that lung volume can be calculated indirectly from this thoracic volume. We compared the measured thoracic volume with manually segmented and automatically thresholded lung volumes, with manual segmentation as the gold standard. A linear regression formula was obtained and used for calculating the theoretical lung volume. This volume was compared with the gold standard volumes. In healthy animals, thoracic volume was 887.45 mm3, manually delineated lung volume 554.33 mm3 and thresholded aerated lung volume 495.38 mm3 on average. Theoretical lung volume was 554.30 mm3. Finally, the protocol was applied to three animal models of lung pathology (lung metastasis and transgenic primary lung tumor and fungal infection). In confirmed pathologic animals, thoracic volumes were: 893.20 mm3, 860.12 and 1027.28 mm3. Manually delineated volumes were 640.58, 503.91 and 882.42 mm3, respectively. Thresholded lung volumes were 315.92 mm3, 408.72 and 236 mm3, respectively. Theoretical lung volume resulted in 635.28, 524.30 and 863.10.42 mm3. No significant differences were observed between volumes. This confirmed the potential use of this protocol for lung volume calculation in pathologic models.
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Affiliation(s)
- Juan Antonio Camara
- Preclinical Therapeutics Core, University of California San Francisco, San Francisco, CA 94158, USA
- Correspondence: ; Tel.: +1-628-6293-555
| | - Anna Pujol
- Onna Therapeutics, 08028 Barcelona, Spain;
| | - Juan Jose Jimenez
- Preclinical Imaging Platform, Vall d’Hebron Institute of Research, 08035 Barcelona, Spain; (J.J.J.); (J.D.)
| | - Jaime Donate
- Preclinical Imaging Platform, Vall d’Hebron Institute of Research, 08035 Barcelona, Spain; (J.J.J.); (J.D.)
| | - Marina Ferrer
- Gnotobiotics Core Facility, University of California San Francisco, San Francisco, CA 94158, USA;
| | - Greetje Vande Velde
- Biomedical MRI/MoSAIC, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3001 Leuven, Belgium;
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8
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The importance of routine quality control for reproducible pulmonary measurements by in vivo micro-CT. Sci Rep 2022; 12:9695. [PMID: 35690601 PMCID: PMC9188608 DOI: 10.1038/s41598-022-13477-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/13/2022] [Indexed: 11/08/2022] Open
Abstract
Micro-computed tomography (CT) imaging provides densitometric and functional assessment of lung diseases in animal models, playing a key role either in understanding disease progression or in drug discovery studies. The generation of reliable and reproducible experimental data is strictly dependent on a system's stability. Quality controls (QC) are essential to monitor micro-CT performance but, although QC procedures are standardized and routinely employed in clinical practice, detailed guidelines for preclinical imaging are lacking. In this work, we propose a routine QC protocol for in vivo micro-CT, based on three commercial phantoms. To investigate the impact of a detected scanner drift on image post-processing, a retrospective analysis using twenty-two healthy mice was performed and lung density histograms used to compare the area under curve (AUC), the skewness and the kurtosis before and after the drift. As expected, statistically significant differences were found for all the selected parameters [AUC 532 ± 31 vs. 420 ± 38 (p < 0.001); skewness 2.3 ± 0.1 vs. 2.5 ± 0.1 (p < 0.001) and kurtosis 4.2 ± 0.3 vs. 5.1 ± 0.5 (p < 0.001)], confirming the importance of the designed QC procedure to obtain a reliable longitudinal quantification of disease progression and drug efficacy evaluation.
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Le HQ, Hill MA, Kollak I, Keck M, Schroeder V, Wirth J, Skronska‐Wasek W, Schruf E, Strobel B, Stahl H, Herrmann FE, Campos AR, Li J, Quast K, Knebel D, Viollet C, Thomas MJ, Lamb D, Garnett JP. An EZH2-dependent transcriptional complex promotes aberrant epithelial remodelling after injury. EMBO Rep 2021; 22:e52785. [PMID: 34224201 PMCID: PMC8339687 DOI: 10.15252/embr.202152785] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Unveiling the molecular mechanisms of tissue remodelling following injury is imperative to elucidate its regenerative capacity and aberrant repair in disease. Using different omics approaches, we identified enhancer of zester homolog 2 (EZH2) as a key regulator of fibrosis in injured lung epithelium. Epithelial injury drives an enrichment of nuclear transforming growth factor-β-activated kinase 1 (TAK1) that mediates EZH2 phosphorylation to facilitate its liberation from polycomb repressive complex 2 (PRC2). This process results in the establishment of a transcriptional complex of EZH2, RNA-polymerase II (POL2) and nuclear actin, which orchestrates aberrant epithelial repair programmes. The liberation of EZH2 from PRC2 is accompanied by an EZH2-EZH1 switch to preserve H3K27me3 deposition at non-target genes. Loss of epithelial TAK1, EZH2 or blocking nuclear actin influx attenuates the fibrotic cascade and restores respiratory homeostasis. Accordingly, EZH2 inhibition significantly improves outcomes in a pulmonary fibrosis mouse model. Our results reveal an important non-canonical function of EZH2, paving the way for new therapeutic interventions in fibrotic lung diseases.
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Affiliation(s)
- Huy Q Le
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Matthew A Hill
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
- University of BathBathUK
| | - Ines Kollak
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Martina Keck
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Victoria Schroeder
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Johannes Wirth
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Wioletta Skronska‐Wasek
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Eva Schruf
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Benjamin Strobel
- Drug Discovery SciencesBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Heiko Stahl
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Franziska E Herrmann
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | | | - Jun Li
- Immunology and Respiratory Disease Research DepartmentBoehringer Ingelheim Pharmaceuticals, IncRidgefieldCTUSA
| | - Karsten Quast
- Global Computational Biology and Digital SciencesBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Dagmar Knebel
- Global Computational Biology and Digital SciencesBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Coralie Viollet
- Global Computational Biology and Digital SciencesBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Matthew J Thomas
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
- University of BathBathUK
| | - David Lamb
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - James P Garnett
- Lung Repair & Regeneration DepartmentBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
- Translational and Clinical Research InstituteNewcastle UniversityNewcastleUK
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