Single-cell immunology of SARS-CoV-2 infection

Tian, Yuan; Carpp, Lindsay N.; Miller, Helen; Zager, Michael; Newell, Evan W.; Gottardo, Raphaël

doi:10.1038/s41587-021-01131-y

Cited by 94 publications

(105 citation statements)

References 149 publications

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“…Exploring publicly available studies of either COVID-19 samples or healthy controls, we accumulated a collection of 14 studies with scRNA-seq measurements, representing a total of 3.46M cells from 639 individuals. Data from 11 of the studies was obtained from a recently published collection of standardized single-cell sequencing datasets 59 . We performed unsupervised atomic sketch integration, yielding a harmonized collection in which we annotated 30 cell states ( Fig.…”

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

View full text Add to dashboard Cite

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“…These transitioning expanded T cell clones may represent unannotated SARS-CoV-2-specific T cells. Interferon- activated naïve T cells are a novel cell population and add to the growing recognition from single cell analyses that seemingly homogeneous cell populations, in this case the naïve T cell pool, may be more heterogeneous than previously recognized (Nguyen et al, 2018; Tian et al, 2021). Furthermore, this suggests that traditional nomenclature based on cell surface markers may be inadequate.…”

Section: Discussionmentioning

confidence: 99%

Clonal dynamics of SARS-CoV-2-specific T cells in children and adults with COVID-19

Khoo

Jackson

Phetsouphanh

et al. 2022

Preprint

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Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV-2-specific naive T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. More naive interferon-activated CD4+ T cells were recruited into the memory compartment and recovery was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection.

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“…Another example of the power of rapidly dissecting immune signatures using single-cell multiomics technologies is observed in the rapid advancement of characterization of acute respiratory syndrome coronavirus (SARS-CoV-2). Throughout the COVID-19 pandemic outbreak, a large body of work was accumulated by the immunology community (Tian et al, 2022) to define the cells and pathways involved in SARS-CoV-2 pathology. Implementing cutting-edge genomic technologies will improve our ability to study this deadly pathogen, better understand the role of the immune system in the pathology and to design better therapies.…”

Section: Future: Consensual Systems-level Immunologymentioning

confidence: 99%