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

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.

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. 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.

Downregulation of MALAT1 is a hallmark of tissue and peripheral proliferative T cells in COVID-19.

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.

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.

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

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.

Development and Application of a Semi quantitative Scoring Method for Ultrastructural Assessment of Acute Stress in Pancreatic Islets.

BACKGROUND: Pancreas and islet transplantation outcomes are negatively impacted by injury to the endocrine cells from acute stress during donor death, organ procurement, processing, and transplant procedures. Here, we report a novel electron microscopy scoring system, the Newcastle Pancreas Endocrine Stress Score (NPESS). METHODS: NPESS was adapted and expanded from our previously validated method for scoring pancreatic exocrine acinar cells, yielding a 4-point scale (0-3) classifying ultrastructural pathology in endocrine cell nuclei, mitochondria, endoplasmic reticulum, cytoplasmic vacuolization, and secretory granule depletion, with a maximum additive score of 15. We applied NPESS in a cohort of deceased organ donors after brainstem (DBD) and circulatory (DCD) death with a wide range of cold ischemic times (3.6-35.9 h) including 3 donors with type 1 and 3 with type 2 diabetes to assess islets in situ (n = 30) in addition to pancreata (n = 3) pre- and postislet isolation. RESULTS: In DBD pancreata, NPESS correlated with cold ischemic time (head: r = 0.55; P = 0.02) and mirrored exocrine score (r = 0.48; P = 0.01). When stratified by endocrine phenotype, cells with granules of heterogeneous morphology had higher scores than α, ß, and δ cells (P < 0.0001). Cells of mixed endocrine-exocrine morphology were observed in association with increased NPESS (P = 0.02). Islet isolation was associated with improved NPESS (in situ: 8.39 ± 0.77 [Mean ± SD]; postisolation: 5.44 ± 0.31; P = 0.04). CONCLUSIONS: NPESS provides a robust method for semiquantitative scoring of subcellular ultrastructural changes in human pancreatic endocrine cells in situ and following islet isolation with utility for unbiased evaluation of acute stress in organ transplantation research.