Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

Cortal, Akira; Martignetti, Loredana; Six, Emmanuelle; Rausell, Antonio

doi:10.1038/s41587-021-00896-6

Cited by 113 publications

(129 citation statements)

References 53 publications

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…23A and S24B). These results were validated by using the independent method of Cell-ID [ 64 ] (Additional file 1 : Supplementary methods and Additional file 3 : Fig. S29).…”

Section: Resultsmentioning

confidence: 86%

“…S16-S19). Additionally, using the Cell-ID method [ 64 ], we found that 34 GWAS-identified genes score of individual cells were higher detected in CD16+ monocytes and memory CD8+T cells (Additional file 3 : Fig. S20).…”

Section: Resultsmentioning

confidence: 99%

“…In addition, we found that ABO + megakaryocytes had a significantly higher inflammatory cytokine score than that in ABO − cells by using two independent methods of AddModuleScore in Seurat [ 30 ] and Cell-ID [ 64 ] ( P < 0.001, Additional file 3 : Fig. S25A-B, S26A-B and S29).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Qiu

Deng

et al. 2022

Genome Med

View full text Add to dashboard Cite

Background Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. Methods We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. Results We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. Conclusions We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

show abstract

“…23A and S24B). These results were validated by using the independent method of Cell-ID [ 64 ] (Additional file 1 : Supplementary methods and Additional file 3 : Fig. S29).…”

Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Qiu

Deng

et al. 2022

Genome Med

View full text Add to dashboard Cite

show abstract

“…Next, the package CelliD 23 , which provides a convenient and scalable workflow to define single-cell gene signatures, is used to define per-cell gene signatures. Briefly, user-defined variable genes are used to embed the dataset into low dimensional space by multiple correspondence analysis (MCA).…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of cell-cell communication at single-cell resolution

Wilk

Shalek

Holmes

et al. 2022

Preprint

View full text Add to dashboard Cite

Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. Here we present Scriabin: a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data to validate that the recovered edges are biologically meaningful. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche as well as previously unappreciated modes of intercellular communication. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease.

show abstract

“…Using SingleR, the authors identified a novel disease-associated macrophage subgroup between monocyte-derived and alveolar macrophages. Cortal et al proposed a clustering-free multivariate statistical method named Cell-ID for gene signature extraction and cell identification (Cortal et al, 2021). Cell-ID first performs a dimensionality reduction on the cell-by-gene expression matrix using the multiple correspondence analysis (MCA).…”

Section: Identification Of Cell Typesmentioning

confidence: 99%

Analysis and Visualization of Spatial Transcriptomic Data

Liu¹,

Li²,

Zhang³

2022

Front. Genet.

View full text Add to dashboard Cite

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

show abstract

Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

Cited by 113 publications

References 53 publications

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Comparative analysis of cell-cell communication at single-cell resolution

Analysis and Visualization of Spatial Transcriptomic Data

Contact Info

Product

Resources

About