Search

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., Funding: UK Research and Innovation/Medical Research Council through the UK Coronavirus Immunology Consortium, Barbour Foundation, General Sir John Monash Foundation, Newcastle University, JGW Patterson Foundation, Wellcome Trust., Competing Interests: Declaration of interests L Milross was supported by a General Sir John Monash Scholarship awarded by the General Sir John Monash Foundation and a Vice-Chancellor's Global Scholarship from Newcastle University in support of a Master of Research in Immunobiology at Newcastle University. A Thomson was supported by funding from the JGW Patterson Foundation. C. J. A. Duncan was supported by a Wellcome Clinical Research Career Development Fellowship (211153/Z/18/Z)., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization 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 "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood 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" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization 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 neighborhood analysis was influenced by the method used for finding neighboring 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 and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists., (© 2023 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

T cells play key protective but also pathogenic roles in COVID-19. We studied the expression of long non-coding RNAs (lncRNAs) in COVID-19 T-cell transcriptomes by integrating previously published single-cell RNA sequencing datasets. The long intergenic non-coding RNA MALAT1 was the most highly transcribed lncRNA in T cells, with Th1 cells demonstrating the lowest and CD8+ resident memory cells the highest MALAT1 expression, amongst CD4+ and CD8+ T-cells populations, respectively. We then identified gene signatures that covaried with MALAT1 in single T cells. A significantly higher number of transcripts correlated negatively with MALAT1 than those that correlated. Enriched functional annotations of the MALAT1- anti-correlating gene signature included processes associated with T-cell activation such as cell division, oxidative phosphorylation, and response to cytokine. The MALAT1 anti-correlating gene signature shared by both CD4+ and CD8+ T-cells marked dividing T cells in both the lung and blood of COVID-19 patients. Focussing on the tissue, we used an independent patient cohort of post-mortem COVID-19 lung samples and demonstrated that MALAT1 suppression was indeed a marker of MKI67+ proliferating CD8+ T cells. Our results reveal MALAT1 suppression and its associated gene signature are a hallmark of human proliferating T cells., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Immunology.)

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., Competing Interests: Conflicts of interest: The authors declare no conflict of interest., (Copyright ©The authors 2022.)

Lung transplantation is the optimal treatment for selected patients with end-stage chronic lung diseases. However, chronic lung allograft dysfunction remains the leading obstacle to improved long-term outcomes. Traditionally, lung allograft rejection has been considered primarily as a manifestation of cellular immune responses. However, in reality, an array of complex, interacting and multifactorial mechanisms contribute to its emergence. Alloimmune-dependent mechanisms, including T-cell-mediated rejection and antibody-mediated rejection, as well as non-alloimmune injuries, have been implicated. Moreover, a role has emerged for autoimmune responses to lung self-antigens in the development of chronic graft injury. The aim of this review is to summarise the immune processes involved in the pathogenesis of chronic lung allograft dysfunction, with advanced insights into the role of innate immune pathways and crosstalk between innate and adaptive immunity, and to identify gaps in current knowledge., Competing Interests: Conflict of interest: S. Bos has nothing to disclose. Conflict of interest: L. Milross has nothing to disclose. Conflict of interest: A.J. Filby has nothing to disclose. Conflict of interest: R. Vos reports grants from Research Foundation-Flanders (FWO), outside the submitted work. Conflict of interest: A.J. Fisher reports grants from GlaxoSmithKline, grants, personal fees and non-financial support from Mallinckrodt Pharmaceuticals, personal fees from Altavant, and grants from Pfizer, outside the submitted work., (Copyright ©The authors 2022.)

Background and Objective: Organ donation rates continue to be low in Australia compared with demand. Donation after circulatory death (DCD) has been an important strategy to increase donation rates, facilitated by advances in cardiopulmonary support in intensive care units (ICUs). However, DCD may harbour greater logistical challenges and unfavourable perceptions amongst some ICU healthcare professionals. The aim of this study was to evaluate and understand DCD perceptions at an Australian tertiary hospital., Methods: This descriptive exploratory study was conducted at an Australian tertiary hospital. Participants were recruited voluntarily for interview via email and word-of-mouth through the hospital's ICU network. The study used a mixed-methods approach; five close-ended questions were included in the form of Likert scales followed by a semistructured interview with open-ended questions designed to understand participants' perceptions of DCD. Interviews were recorded, transcribed, and thematically analysed., Results: Sixteen participants were interviewed including eight intensive care doctors, four donation specialist nursing coordinators (DSNCs), and four bedside nurses. Likert responses demonstrated clinicians' support for both DCD and donation after brain death (DBD). Thematic analysis of the transcripts yielded three overarching themes including 'Contextual and environmental influences on DCD decision-making', 'Personal difficulties faced by clinicians in DCD decision-making', and 'Family influences on DCD decision-making'. Significant geographical separation between donation and organ retrieval teams, incurring significant resource utilisation, impacted the donation team's decision-making around DCD, as did a perceived disruption of ICU care to facilitate donation especially for cases where successful DCD was identified to be unlikely., Conclusions: Overall, DCD was as acceptable to participants as DBD. However, the geographical separation of this centre meant that logistical barriers potentially impacted the DCD process. Open lines of communication with transplant centres, local resourcing, and a culture of education, experience, and leadership may facilitate the DCD programs where distant retrieval is commonplace., (Copyright © 2021 Australian College of Critical Care Nurses Ltd. Published by Elsevier Ltd. All rights reserved.)

Organ donation networks audit and report on national or regional organ donation performance, however there are inconsistencies in the metrics and definitions used, rendering comparisons difficult or inappropriate. This is despite multiple attempts exploring the possibility for convergently evolving audits so that collectives of donation networks might transparently share data and practice and then target system interventions. This paper represents a collaboration between the United Kingdom and Australian organ donation organisations which aimed to understand the intricacies of our respective auditing systems, compare the metrics and definitions they employ and ultimately assess their level of comparability. This point of view outlines the historical context underlying the development of the auditing tools, demonstrates their differences to the Critical Pathway proposed as a common tool a decade ago and presents a side-by-side comparison of donation definitions, metrics and data for the 2019 calendar year. There were significant differences in donation definition terminology, metrics and overall structure of the audits. Fitting the audits to a tiered scaffold allowed for reasonable comparisons however this required substantial effort and understanding of nuance. Direct comparison of international and inter-regional donation performance is challenging and would benefit from consistent auditing processes across organisations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Milross, Brown, Gladkis, Downes, Goodwin, Madden, McDonald, Barry, Opdam, Manara and Gardiner.)

The lungs are the main site that is affected in severe COVID-19, and post-mortem lung tissue provides crucial insights into the pathophysiology of severe disease. From basic histology to state-of-the-art multiparameter digital pathology technologies, post-mortem lung tissue provides snapshots of tissue architecture, and resident and inflammatory cell phenotypes and composition at the time of death. Contrary to early assumptions that COVID-19 in the lungs is a uniform disease, post-mortem findings have established a high degree of disease heterogeneity. Classic diffuse alveolar damage represents just one phenotype, with disease divisible by early and late progression as well as by pathophysiological process. A distinct lung tissue state occurs with secondary infection; extrapulmonary causes of death might also originate from a pathological process in the lungs linked to microthrombosis. This heterogeneity of COVID-19 lung disease must be recognised in the management of patients and in the development of novel treatment strategies., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)