Bronchoscopic Probe-Based Confocal Laser Endomicroscopy to Diagnose Diffuse Parenchymal Lung Diseases.

Background: Diagnosis of diffuse parenchymal lung disease (DPLD) is based on clinical evaluation, radiological imaging and histology. However, additional techniques are warranted to improve diagnosis. Aims and objective: Probe based confocal laser endomicroscopy (pCLE) allows real time in vivo visualisation of the alveolar compartment during bronchoscopy based on autofluorescence of elastic fibres. We used pCLE (Cellvizio®, Mauna Kea Technology. Inc, Paris, France) to characterise alveolar patterns in patients with different types of DPLD. Methods: In this pilot study we included 42 therapy naive patients (13 female, age 72.6 +/- 2.3 years), who underwent bronchoscopy for workup of DPLD. pCLE images were obtained during rigid bronchoscopy in affected lung segments according to HR-CT scan, followed by cryobiopsies in the identical area. Diagnoses were made by a multidisciplinary panel. The description of pCLE patterns was based on the degree of distortion of the hexagonal alveolar pattern, the density of alveolar structures, the presence of consolidations or loaded alveolar macrophages (AM). The assessment was performed by 2 investigators blinded for the final diagnosis. Results: The normal lung showed a typical alveolar loop pattern. In amiodarone lung disease loaded AM were predominant. COP showed characteristic focal consolidations. IPF was characterized by significant distortion and destruction, NSIP showed significant increase in density, and chronic HP presented with consolidations, mild distortion and density. Conclusion: pCLE shows potential as an adjunctive bronchoscopic imaging technique in the differential diagnosis of DPLD. Structured and quantitative analysis of the images is required.

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

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

Automated evaluation of probe-based confocal laser endomicroscopy in the lung.

RATIONALE: Probe-based confocal endomicroscopy provides real time videos of autoflourescent elastin structures within the alveoli. With it, multiple changes in the elastin structure due to different diffuse parenchymal lung diseases have previously been described. However, these evaluations have mainly relied on qualitative evaluation by the examiner and manually selected parts post-examination. OBJECTIVES: To develop a fully automatic method for quantifying structural properties of the imaged alveoli elastin and to perform a preliminary assessment of their diagnostic potential. METHODS: 46 patients underwent probe-based confocal endomicroscopy, of which 38 were divided into 4 groups categorizing different diffuse parenchymal lung diseases. 8 patients were imaged in representative healthy lung areas and used as control group. Alveolar elastin structures were automatically segmented with a trained machine learning algorithm and subsequently evaluated with two methods developed for quantifying the local thickness and structural connectivity. MEASUREMENTS AND MAIN RESULTS: The automatic segmentation algorithm performed generally well and all 4 patient groups showed statistically significant differences with median elastin thickness, standard deviation of thickness and connectivity compared to the control group. CONCLUSION: Alveoli elastin structures can be quantified based on their structural connectivity and thickness statistics with a fully-automated algorithm and initial results highlight its potential for distinguishing parenchymal lung diseases from normal alveoli.