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Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods

Nature Protocols volume 16, pages 2749–2764 (2021)Cite this article

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Abstract

Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification. Frequently encountered challenges are discussed, as well as strategies to address them. Guiding principles and specific recommendations for software tools and resources that can be used for each step are covered, and an R notebook is included to help run the recommended workflow. Basic familiarity with computer software is assumed, and basic knowledge of programming (e.g., in the R language) is recommended.

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Fig. 1: An annotated single-cell transcriptomic map.
Fig. 2: Cell annotation workflow.
Fig. 3: Automatic annotation results depend on the marker genes used.
Fig. 4: Refining cluster labels from automatic annotation.
Fig. 5: Visualizing well-known markers.
Fig. 6: How to identify and visualize a cell-type gradient.
Fig. 7: Batch correction.

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Data availability

The data used to generate this tutorial are openly available at the following sources. The sequence data used to generate Figs. 1 and 4 are available from MacParland et al.100 through the NCBI GEO accession GSE115469. The analyzed data from which the map was directly created can also be accessed interactively as the R package HumanLiver from https://github.com/BaderLab/HumanLiver. The sequence data used to generate Figs. 3 and 7 are available from 10x Genomics and can be downloaded from https://support.10xgenomics.com/single-cell-gene-expression/datasets. The sequence data used to generate Fig. 6 are available through the NCBI GEO accession GSE129788, as reported by Ximerakis et al.101. The analyzed data can be accessed interactively at http://shiny.baderlab.org/AgingMouseBrain/. The human bulk RNA-seq data used to generate the reference data set in the accompanying R code (https://github.com/BaderLab/CellAnnotationTutorial and https://codeocean.com/capsule/d67541eb-43f8-4cae-a258-5ef0069e5301/) are available from the Database of Immune Cell Expression and can be downloaded in R through the package ‘celldex’43 by the command DatabaseImmuneCellExpressionData(). The query data set used in the accompanying R code is available from 10x Genomics and can be downloaded from https://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz. The collection of PBMC marker genes used in the accompanying R code is available from Diaz-Mejia et al.37 with read data from NCBI Sequence Read Archive accession number SRX1723926. The supplementary data from the Diaz-Mejia et al. paper can be accessed at https://zenodo.org/record/3369934/#.X3CGN5NKjGI. The Gene Matrix Transposed (GMT) file used for pathway analysis in the accompanying R code can be downloaded from http://download.baderlab.org/EM_Genesets/current_release/Human/symbol/Pathways/Human_MSigdb_March_01_2021_symbol.gmt.

Code availability

An R script that implements the main workflow described in this proposal is available at https://github.com/BaderLab/CellAnnotationTutorial, and a version-controlled copy is available through Code Ocean at https://codeocean.com/capsule/d67541eb-43f8-4cae-a258-5ef0069e5301/.

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Acknowledgements

This project has been made possible in part by grant CZF2019-002429 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. We acknowledge Shirley Hui, Daniel Stueckmann and Ronald Xie for their help in reviewing the tutorial.

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Author notes

  1. These authors contributed equally: Zoe A. Clarke, Tallulah S. Andrews, Jawairia Atif, Delaram Pouyabahar.

Authors and Affiliations

  1. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Zoe A. Clarke, Delaram Pouyabahar, Brendan T. Innes & Gary D. Bader

  2. The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada

    Zoe A. Clarke, Tallulah S. Andrews, Delaram Pouyabahar, Brendan T. Innes & Gary D. Bader

  3. Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, Ontario, Canada

    Tallulah S. Andrews, Jawairia Atif & Sonya A. MacParland

  4. Department of Immunology, University of Toronto, Toronto, Ontario, Canada

    Tallulah S. Andrews, Jawairia Atif & Sonya A. MacParland

  5. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

    Sonya A. MacParland

  6. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Gary D. Bader

  7. Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada

    Gary D. Bader

Authors
  1. Zoe A. Clarke
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  2. Tallulah S. Andrews
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  3. Jawairia Atif
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  4. Delaram Pouyabahar
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  5. Brendan T. Innes
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  6. Sonya A. MacParland
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Contributions

Z.A.C., T.S.A., J.A., D.P. and S.A.M. initially wrote different sections of the document and collaboratively joined them together. Z.A.C., T.S.A., J.A., D.P. and B.T.I. designed the figures. Z.A.C. and G.D.B. organized the process and undertook major edits. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sonya A. MacParland or Gary D. Bader.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Åsa Björklund and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Heat map and UMAP visualizations.

An extension of Fig. 6 incorporating the visualization of marker genes for identified cell types as a heat map (a) and a Mllt11 expression overlaid on a UMAP plot (b). Mllt11 is expressed at various levels across clusters, suggesting a cell-type gradient across NRP, ImmN, NendC and mNEUR. Both plots are generated from scRNA-seq data from young and old mouse brains101.

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Supplementary Information

Supplementary Table 1.

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Clarke, Z.A., Andrews, T.S., Atif, J. et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc 16, 2749–2764 (2021). https://doi.org/10.1038/s41596-021-00534-0

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