Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

Background Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease. Methods Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns. Findings Forty patients (32M:8F, age: 22–98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury. Interpretation The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established

Ex Vivo Lung Perfusion: A Platform for Donor Lung Assessment, Treatment and Recovery

Lung transplantation offers a lifesaving therapy for patients with end-stage lung disease but its availability is presently limited by low organ utilization rates with donor lungs frequently excluded due to unsuitability at assessment. When transplantation does occur, recipients are then vulnerable to primary graft dysfunction (PGD), multitudinous short-term complications, and chronic lung allograft dysfunction. The decision whether to use donor lungs is made rapidly and subjectively with limited information and means many lungs that might have been suitable are lost to the transplant pathway. Compared to static cold storage (SCS), ex vivo lung perfusion (EVLP) offers clinicians unrivalled opportunity for rigorous objective assessment of donor lungs in conditions replicating normal physiology, thus allowing for better informed decision-making in suitability assessments. EVLP additionally offers a platform for the delivery of intravascular or intrabronchial therapies to metabolically active tissue aiming to treat existing lung injuries. In the future, EVLP may be employed to provide a pre-transplant environment optimized to prevent negative outcomes such as primary graft dysfunction (PGD) or rejection post-transplant

Ex Vivo Lung Perfusion: A Platform for Donor Lung Assessment, Treatment and Recovery

Lung transplantation offers a lifesaving therapy for patients with end-stage lung disease but its availability is presently limited by low organ utilization rates with donor lungs frequently excluded due to unsuitability at assessment. When transplantation does occur, recipients are then vulnerable to primary graft dysfunction (PGD), multitudinous short-term complications, and chronic lung allograft dysfunction. The decision whether to use donor lungs is made rapidly and subjectively with limited information and means many lungs that might have been suitable are lost to the transplant pathway. Compared to static cold storage (SCS), ex vivo lung perfusion (EVLP) offers clinicians unrivalled opportunity for rigorous objective assessment of donor lungs in conditions replicating normal physiology, thus allowing for better informed decision-making in suitability assessments. EVLP additionally offers a platform for the delivery of intravascular or intrabronchial therapies to metabolically active tissue aiming to treat existing lung injuries. In the future, EVLP may be employed to provide a pre-transplant environment optimized to prevent negative outcomes such as primary graft dysfunction (PGD) or rejection post-transplant

The trajectory of COVID-19 cardiopulmonary disease: insights from an autopsy study of community-based, pre-hospital deaths

Background Post mortem examination of lung and heart tissue has been vital to developing an understanding of COVID-19 pathophysiology; however studies to date have almost uniformly used tissue obtained from hospital-based deaths where individuals have been exposed to major medical and pharmacological interventions. Methods In this study we investigated patterns of lung and heart injury from 46 community-based, pre-hospital COVID-19-attributable deaths who underwent autopsy. Results The cohort comprised 22 females and 24 males, median age 64 years (range 19–91) at time of death with illness duration range 0–23 days. Comorbidities associated with poor outcomes in COVID-19 included obesity (body mass index >30 kg·m−2) in 19 out of 46 cases (41.3%). Diffuse alveolar damage in its early exudative phase was the most common pattern of lung injury; however significant heterogeneity was identified with bronchopneumonia, pulmonary oedema consistent with acute cardiac failure, pulmonary thromboembolism and microthrombosis also identified and often in overlapping patterns. Review of clinical records and next of kin accounts suggested a combination of unexpectedly low symptom burden, rapidly progressive disease and psychosocial factors may have contributed to a failure of hospital presentation prior to death. Conclusions Identifying such advanced acute lung injury in community-based deaths is extremely unusual and raises the question why some with severe COVID-19 pneumonitis were not hospitalised. Multiple factors including low symptom burden, rapidly progressive disease trajectories and psychosocial factors provide possible explanations

Identification of a protein expression signature distinguishing early from organising diffuse alveolar damage in COVID-19 patients

Diffuse alveolar damage (DAD) is the histological expression of acute respiratory distress syndrome and characterises lung pathology due to infection with SARS-CoV-2, and other respiratory pathogens of clinical significance. DAD reflects a time-dependent immunopathological process, progressing from an early/exudative stage through to an organising/fibrotic stage, yet within an individual these different stages of DAD may coexist. Understanding the progression of DAD is central to the development of new therapeutics to limit progressive lung damage. Here, we applied highly multiplexed spatial protein profiling to autopsy lung tissues derived from 27 patients who died from COVID-19 and identified a protein signature (ARG1, CD127, GZMB, IDO1, Ki67, phospho-PRAS40 (T246) and VISTA) that distinguishes early DAD from late DAD with good predictive accuracy. These proteins warrant further investigation as potential regulators of DAD progression

OPTIMAL: An OPTimised Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

Funder: Medical Research CouncilFunder: UK Research and Innovation; doi: http://dx.doi.org/10.13039/100014013Funder: JGW Patterson Foundation; doi: http://dx.doi.org/10.13039/100010089Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates. FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists