COVID‐19: searching for clues among other respiratory viruses

Nguyen-Robertson, Catriona V.; Haque, Ashraful; Mintern, Justine D.; Flamme, Anne Camille La

doi:10.1111/imcb.12336

Cited by 2 publications

(1 citation statement)

References 16 publications

(17 reference statements)

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“…8c, ISG clusters 1-05). Our results indicate the activation of the innate immunological pathway, including several previously characterized genes (SERPINB9, CKAP4, CCL2 and SPHK1) [52][53][54][55] , in C1Q low macrophages to inhibit SARS-CoV-2 replication and entry. The failure to subsequently regulate and dampen this innate response resulted in unchecked host immune responses and collateral tissue damage for C1Q low macrophages, while C1Q high macrophages have elevated complement cascade activation (for example, LGALS3BP 56 ) and express genes correlating with mild rather than severe COVID-19 symptoms (for example, SIGLEC1, ref.…”

Section: Generation Of a Multi-omic Covid-19 Lung Molecular Atlassupporting

confidence: 57%

Robust single-cell matching and multimodal analysis using shared and distinct features

et al. 2023

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The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

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Section: Generation Of a Multi-omic Covid-19 Lung Molecular Atlassupporting

confidence: 57%

Robust single-cell matching and multimodal analysis using shared and distinct features

et al. 2023

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Robust Single-cell Matching and Multi-modal Analysis Using Shared and Distinct Features Reveals Orchestrated Immune Responses

Zhu

Chen

Bai

et al. 2021

Preprint

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The ability to align individual cellular information from multiple experimental sources, techniques and systems is fundamental for a true systems-level understanding of biological processes. While single-cell transcriptomic studies have transformed our appreciation for the complexities and contributions of diverse cell types to disease, they can be limited in their ability to assess protein-level phenotypic information and beyond. Therefore, matching and integrating single-cell datasets which utilize robust protein measurements across multiple modalities is critical for a deeper understanding of cell states, and signaling pathways particularly within their native tissue context. Current available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely upon a large number of shared features across datasets for mutual Nearest Neighbor (mNN) matching. This approach is unsuitable when applied to single-cell proteomic datasets, due to the limited number of parameters simultaneously accessed, and lack of shared markers across these experiments. Here, we introduce a novel cell matching algorithm, Matching with pARtIal Overlap (MARIO), that takes into account both shared and distinct features, while consisting of vital filtering steps to avoid sub-optimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multi-modal methods, including spatial techniques, and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via CODEX imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid via CITE-seq. This cross-platform integrative analysis enabled the identification of unique orchestrated immune responses within the lung of complement-expressing macrophages and their impact on the local tissue microenvironment. MARIO thus provides an analytical framework for unified analysis of single-cell data for a comprehensive understanding of the underlying biological system.

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COVID‐19: searching for clues among other respiratory viruses

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Cited by 2 publications

References 16 publications

Robust single-cell matching and multimodal analysis using shared and distinct features

Robust single-cell matching and multimodal analysis using shared and distinct features

Robust Single-cell Matching and Multi-modal Analysis Using Shared and Distinct Features Reveals Orchestrated Immune Responses

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