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

Abstract

Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package.

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Fig. 1: Overview of the Cell-ID approach.
Fig. 2: Cell-ID cell-type prediction of human CBMCs using preestablished marker lists.
Fig. 3: Performance of Cell-ID cell matching across scRNA-seq datasets from the same or different tissue of origin, within and across species.
Fig. 4: Performance of Cell-ID cell-to-cell matching across independent datasets from different single-cell omics technologies: scRNA-seq and scATAC-seq.

Data availability

All single-cell datasets used in this paper are publicly available (Supplementary Table 7). scRNA-seq datasets for human blood cells profiled by Cite-Seq17 and Reap-Seq18 were downloaded from the Gene Expression Omnibus (GEO) (accession numbers GSE100866 and GSE100501, respectively). Cell-type labels for these two datasets were obtained following the Multimodal Analysis vignette of the Seurat33 R package (https://satijalab.org/seurat/multimodal_vignette.html). Pancreas scRNA-seq datasets from Baron20, Muraro22 and Segerstolpe21, as well as their associated cell-type annotations were downloaded via the scRNA-seq59 R package as a SingleCellExperiment format R object. Plasschaert23 mouse and human and Montoro24 mouse airway epithelium scRNA-seq datasets, and their annotations were downloaded from GEO (GSE102580, GSE103354). Haber34 intestinal epithelium scRNA-seq dataset was downloaded from GEO accession code GSE92332. Olfactory epithelium scRNA-seq datasets from Fletcher36 and Wu35 were downloaded from GEO (GSE95601, GSE120199), and their cell-type annotations were obtained from the associated GitHub repositories: https://github.com/rufletch/p63-HBC-diff and https://www.stowers.org/research/publications/odr for Fletcher36 and Wu35, respectively. Tabula Muris39 10X and Smart-seq mouse scRNA-seq datasets were downloaded from https://tabula-muris.ds.czbiohub.org/. Gene activity score matrices from the Mouse sci-ATAC-seq atlas datasets from Cusanovich40 were obtained from http://atlas.gs.washington.edu/mouse-atac/data/, as provided by the authors and resulting from the aggregation of information across all differentially accessible chromatin sites linked to a target gene.

Code availability

Cell-ID is implemented as an R package and is available on GitHub (https://github.com/RausellLab/CelliD) under the GPL-3 open-source license. Complete documentation is provided with step-by-step procedures for MCA dimensionality reduction, per-cell gene signature extraction, cell-type prediction, label transferring across datasets and functional enrichment analysis. A development version of Cell-ID software is also available in Bioconductor (devel branch 3.13): https://bioconductor.org/packages/CelliD. In addition, R scripts to reproduce all figures in the paper are available on a dedicated GitHub repository (https://github.com/RausellLab/CellIDPaperScript).

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Acknowledgements

We thank the Laboratory of Clinical Bioinformatics and the Laboratory of Human Lymphohematopoiesis for helpful discussions and support. The Laboratory of Clinical Bioinformatics was partly supported by the French National Research Agency (ANR) ‘Investissements d’Avenir’ Program (grant no. ANR-10-IAHU-01). The Laboratory of Clinical Bioinformatics and the Laboratory of Human Lymphohematopoiesis were partly supported by Christian Dior Couture, Dior. We also thank G. Fuentes from The Visual Thinker LLP for the creation of the illustrations in Figs. 1–4.

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A.C. and A.R. conceived and designed research. A.C. performed research. A.C and L.M. contributed with materials/analysis tools. A.C. and A.R. analyzed data. A.C., E.S. and A.R. interpreted results. A.C., E.S. and A.R. wrote the paper. All authors read and approved the final draft of the paper.

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Correspondence to Antonio Rausell.

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Cortal, A., Martignetti, L., Six, E. et al. Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nat Biotechnol 39, 1095–1102 (2021). https://doi.org/10.1038/s41587-021-00896-6

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