A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Abstract

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete understanding of potentially druggable immune mediators of disease. To advance this, we present a comprehensive multi-omic blood atlas in patients with varying COVID-19 severity and compare with influenza, sepsis and healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity revealed cells, their inflammatory mediators and networks as potential therapeutic targets, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Tensor and matrix decomposition of the overall dataset revealed feature groupings linked with disease severity and specificity. Our systems-based integrative approach and blood atlas will inform future drug development, clinical trial design and personalised medicine approaches for COVID-19.

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury

“…Regulatory mechanisms were largely undetectable apart from IL-10R expression in severe areas (associating with T cell modules) and complement regulatory genes ( CD46, CD55, CD59 ) in milder areas. Together with our data, these studies of lung immune pathology contrast with peripheral blood data where CD8 lymphopaenia and exhaustion is a feature of severe COVID-19 infection 39 and may highlight dissimilarities in tissue and systemic responses 8 .…”

“…The immune contributors and biological pathways associated with the severe alveolar injury therefore remain unclear. A greater understanding of the host response to COVID-19 within the lung and correlation to DAD would complement the increasing knowledge of both tissue and blood-based immune profiles 8 .…”

An immunodominant NP105–113-B*07:02 cytotoxic T cell response controls viral replication and is associated with less severe COVID-19 disease

“…To take advantage of the results from our SmartSeq2 scRNA-seq, we first compared TCR sequences from our four convalescent patients with COVID-19 with prepandemic TCR sequences from healthy donors, published by Lineburg et al 10 , Nguyen et al 7 and another study cohort, COMBAT 14 . The COMBAT dataset represents a comprehensive multi-omic blood atlas encompassing acute patients with varying COVID-19 severity (41 mild and 36 severe), and 10 healthy volunteers (prepandemic), using bulk TCR sequencing and CITE-Seq, which combines single-cell gene expression and cell-surface protein expression.…”

“…Normalized single-cell gene expression data for T cells from the COMBAT dataset (level 2 subsets a and b) 14 was annotated with specific T cell subtypes according to COMBAT multimodal analysis, COMBAT TCR chain information and patient metadata. Any cells without both a CD8 + multimodal major cell type classification and TCR chain information were excluded from further analysis.…”

Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions

“…We recently demonstrated that an artificial-intelligence (AI) screening test (CURIAL-1.0) can rapidly detect COVID-19 amongst patients being admitted to hospital, by recognising SARS-CoV-2-induced abnormalities in routinely collected data 20 . A strength of our approach is the use of readily available blood test, blood gas & physiological measurements which are typically collected within 1h of presentation to hospitals in high- and middle-income countries, without requiring patient exposure to ionising radiation 21,22 . Explainability analyses revealed that features most informative to predictions were components of the Full Blood Count (FBC) and vital signs (Basophil count, Eosinophil count and Oxygen requirements), offering promise for clinically-guided optimisation to reduce prediction time.…”