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

Ahern, David J; Ai, Zhichao; Ainsworth, Mark; Allan, Chris; Allcock, Alice; Angus, Brian; Ansari, Meshal; Arancibia‐Cárcamo, Carolina V.; Aschenbrenner, Dominik; Attar, Moustafa; Baillie, J Kenneth; Barnes, Eleanor; Bashford-Rogers, Rachael; Bashyal, Archana; Beer, Sally; Berridge, G.; Beveridge, Amy; Bibi, Sagida; Bicanic, Tihana; Blackwell, Luke; Bowness, Paul; Brent, Andrew; Brown, Andrew; Broxholme, John; Buck, David; Burnham, Katie L; Byrne, Helen; Camara, Susana; Candido-Ferreira, Ivan; Charles, P. David; Chen, Wentao; Chen, Yi‐Ling; Chong, Amanda Y; Clutterbuck, Elizabeth A.; Coles, Mark; Conlon, Christopher P.; Cornall, Richard J.; Cribbs, Adam P.; Curion, Fabiola; Davenport, Emma E; Davidson, Neil; Davis, Simon; Dendrou, Calliope A.; Dequaire, Julie; Dib, Lea; Docker, James; Dold, Christina; Dong, Tao; Downes, Damien J.; Drakesmith, Hal; Dunachie, Susanna; Duncan, David A.; Eijsbouts, Chris; Esnouf, Robert; Espinosa, Alexis; Etherington, Rachel; Fairfax, Benjamin P.; Fairhead, Rory; Fang, Hai; Fassih, Shayan; Felle, Sally; Mendoza, Maria Fernandez; Ferreira, Ricardo Melo; Fischer, Román; Foord, Thomas; Forrow, Aden; Frater, John; Fries, Anastasia; Sánchez, Verónica; Garner, Lucy C.; Geeves, Clementine; Georgiou, Dominique; Godfrey, Leila; Golubchik, Tanya; Vazquez, Maria Gomez; Green, Angie; Harper, Hong; Harrington, Heather A.; Heilig, Raphael; Hester, Svenja; Hill, Jennifer; Hinds, C. J.; Hird, Clare; Ho, Ling-Pei; Hoekzema, Renee S.; Hollis, Benjamin; Hughes, Jim R.; Hutton, Paula; Jackson-Wood, Matthew A.; Jainarayanan, Ashwin Kumar; James-Bott, Anna; Jansen, Kathrin; Jeffery, Katie; Jones, Elizabeth; Jostins, Luke; Kerr, Georgina; Kim, David; Klenerman, Paul; Knight, Julian C.; Kumar, Vinod; Sharma, Piyush Kumar; Kurupati, Prathiba; Kwok, Andrew; Lee, Angela; Linder, Aline; Lockett, Teresa; Lonie, Lorne; Lopopolo, Maria; Lukoseviciute, Martyna; Luo, Jian; Marinou, Spyridoula; Marsden, Brian D.; Martinez, Jose; Matthews, Philippa C.; Mazurczyk, Michalina; McGowan, Simon J.; McKechnie, Stuart; Mead, Adam J.; Mentzer, Alexander J.; Mi, Yuxin; Monaco, Claudia; Montadon, Ruddy; Napolitani, Giorgio; Nassiri, Isar; Novak, Alex; O’Brien, Darragh P.; O’Connor, Daniel; O’Donnell, Denise; Ogg, Graham; Overend, Lauren; Park, Inhye; Pavord, Ian; Peng, Yanchun; Penkava, Frank; Pinho, Mariana Pereira; Perez, Elena; Pollard, Andrew J.; Powrie, Fiona; Psaila, Bethan; Quan, T Phuong; Repapi, Emmanouela; Revale, Santiago; Silva-Reyes, Laura; Richard, Jean-Baptiste; Rich-Griffin, Charlotte; Ritter, Thomas; Rollier, Christine S.; Rowland, Matthew; Ruehle, Fabian; Salio, Mariolina; Sansom, Stephen N.; Peres, Raphael Sanches; Delgado, Alberto Santos; Sauka‐Spengler, Tatjana; Schweßinger, Ron; Scozzafava, Giuseppe; Screaton, Gavin; Seigal, Anna; Semple, Malcolm G; Sergeant, Martin J.; Karali, Christina Simoglou; Sims, David; Skelly, Donal; Sławiński, Hubert; Sobrinodiaz, Alberto; Sousos, Nikolaos; Stafford, Lizzie; Stockdale, Lisa; Strickland, Marie; Sumray, Otto; Sun, Bo; Taylor, Chelsea; Taylor, Stephen; Taylor, A; Thongjuea, Supat; Thraves, Hannah; Todd, John; Tomić, Adriana; Tong, Orion; Trebes, Amy; Trzupek, Dominik; Tucci, Felicia Anna; Turtle, Lance; Udalova, Irina A.; Uhlig, Holm H.; Grinsven, Erinke van; Vendrell, Iolanda; Verheul, Marije K.; Voda, Alexandru; Wang, Guanlin; Wang, Lihui; Wang, Dapeng; Watkinson, Peter; Watson, Robert; Weinberger, Michael; Whalley, Justin P.; Witty, Lorna; Wray, Katherine; Xue, Luzheng; Yeung, Hing‐Yuen; Yin, Zixi; Young, Rebecca; Youngs, Jonathan; Zhang, Ping; Zurke, Yasemin-Xiomara

doi:10.1016/j.cell.2022.01.012

Cited by 225 publications

(200 citation statements)

References 166 publications

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“…While single-cell sequencing technologies are capable of measuring RNA transcripts and surface proteins in thousands of single cells, cytometry-based techniques can measure both extracellular and intracellular proteins in millions of cells. As our bridge integration procedure should enable the mapping of CyTOF profiles onto scRNA-seq datasets, we obtained a collection of CyTOF datasets spanning 119 individuals and 5,170,249 total cells 62 . We used our previously collected CITE-seq dataset of 161,764 PBMC from healthy donors as a multi-omic bridge 4 .…”

Section: Resultsmentioning

confidence: 99%

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

Hao

Stuart

Kowalski

et al. 2022

Preprint

219

163

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Mapping single-cell sequencing profiles to comprehensive reference datasets represents a powerful alternative to unsupervised analysis. Reference datasets, however, are predominantly constructed from single-cell RNA-seq data, and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to harmonize single-cell datasets across modalities by leveraging a multi-omic dataset as a molecular bridge. Each cell in the multi-omic dataset comprises an element in a 'dictionary', which can be used to reconstruct unimodal datasets and transform them into a shared space. We demonstrate that our procedure can accurately harmonize transcriptomic data with independent single cell measurements of chromatin accessibility, histone modifications, DNA methylation, and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to substantially improve computational scalability, and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach aims to broaden the utility of single-cell reference datasets and facilitate comparisons across diverse molecular modalities. Availability: Installation instructions, documentations, and vignettes are available at http://www.satijalab.org/seurat

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Section: Resultsmentioning

confidence: 99%

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

Hao

Stuart

Kowalski

et al. 2022

Preprint

219

163

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“…Finally, we applied SepstratifieR to two COVID-19 cohorts: the COVID-19 Multi-Omic Blood Atlas (COMBAT) 26 and the Deutsche COVID-19 Omics Initiative (DeCOI; Supplementary Table 1 ). 27 In both instances, whole-transcriptome analysis showed a clear separation between patients and controls ( Figure 6A and Supplementary Figure 6A ).…”

Section: Resultsmentioning

confidence: 99%

An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression

Cano-Gamez

Burnham

Goh

et al. 2022

Preprint

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Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of deaths globally each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole blood transcriptomics for stratification of patients with severe infection by integrating data from 3,149 samples of sepsis patients and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 19-gene signature. Finally, we built a machine learning framework, SepstratifieR, to deploy SRSq in sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, thus bringing us closer to precision medicine in infection.

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“…To test the validity of our results, we first sought further evidence of increased immature neutrophils in SRS1. We re-analysed whole blood bulk RNA-seq and mass cytometric (CyTOF) immunophenotyping data from a cohort of 36 all cause sepsis patients (42 samples), who were previously recruited to the same study and included as part of a bioresource from the COMBAT consortium to compare with COVID-19 patient samples (cohort 2)(26) (Fig. 1A, Table S11).…”

Section: Resultsmentioning

confidence: 99%

“…Healthy volunteers and sepsis patients were recruited into the same studies as in cohort 1 under the same criteria and ethics and were analysed previously as part of a COVID-19 centric bioresource (26).…”

Section: Methodsmentioning

confidence: 99%

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Identification of deleterious neutrophil states and altered granulopoiesis in sepsis

Kwok

Allcock

Ferreira

et al. 2022

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Sepsis is a condition of high mortality arising from dysregulation of the host immune response. Gene expression studies have identified multiple immune endotypes but gaps remain in our understanding of the underlying biology and heterogeneity. We used single-cell multi-omics to profile 272,993 cells across 48 whole blood samples from 26 sepsis patients (9 with paired convalescent samples), 6 healthy controls and 7 post-surgery patients. We identified immature neutrophil populations specific to sepsis and demonstrated the immunosuppressive nature of sepsis neutrophils in vitro. An IL1R2+ neutrophil state was expanded in a transcriptomic sepsis endotype associated with increased early mortality (sepsis response signature 1, SRS1), together with enrichment of the response to IL-1 pathway in mature neutrophils, marking IL-1 out as a potential target for immunotherapy in SRS1 sepsis patients. We confirmed the expansion of immature neutrophils, specifically IL1R2+ neutrophils, in SRS1 in additional cohorts of patients (n = 906 RNA-sequencing samples, n = 41 CyTOF samples). Neutrophil changes persisted in convalescence, implicating disrupted granulopoiesis. Our findings establish a cellular immunological basis for transcriptomically defined sepsis endotypes and emphasise the relevance of granulopoietic dysfunction in sepsis, identifying opportunities for precision medicine approaches to the condition.

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

Cited by 225 publications

References 166 publications

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

Dictionary learning for integrative, multimodal, and scalable single-cell analysis

An immune dysfunction score for stratification of patients with acute infection based on whole blood gene expression

Identification of deleterious neutrophil states and altered granulopoiesis in sepsis

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