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Cited by 431Single-cell and spatial transcriptomicsHair Growth and DisordersSkin and Cellular Biology Research

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Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity

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Joost, Simon, Zeisel, Amit, Jacob, Tina, Sun, Xiaoyan, La Manno, Gioele, Lönnerberg, Peter, Linnarsson, Sten, Kasper, Maria·2016·Cell Systems
DOI 10.1016/j.cels.2016.08.010PMC PMC5052454
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Abstract

The murine epidermis with its hair follicles represents an invaluable model system for tissue regeneration and stem cell research. Here we used single-cell RNA-sequencing to reveal how cellular heterogeneity of murine telogen epidermis is tuned at the transcriptional level. Unbiased clustering of 1,422 single-cell transcriptomes revealed 25 distinct populations of interfollicular and follicular epidermal cells. Our data allowed the reconstruction of gene expression programs during epidermal differentiation and along the proximal-distal axis of the hair follicle at unprecedented resolution. Moreover, transcriptional heterogeneity of the epidermis can essentially be explained along these two axes, and we show that heterogeneity in stem cell compartments generally reflects this model: stem cell populations are segregated by spatial signatures but share a common basal-epidermal gene module. This study provides an unbiased and systematic view of transcriptional organization of adult epidermis and highlights how cellular heterogeneity can be orchestrated in vivo to assure tissue homeostasis.

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Graphical abstract
Graphical abstract CC BY-NC-ND · PubMed Central
Figure 1 Defining the Main Epidermal Cell Populations (A) Overview of the experimental workflow. (B) Illustrated microanatomy and compartmentalization of the murine epidermis including HF and SG, colored according to main populations (C). (C) Identity and marker genes of cell populations defined during first-level clustering. (D) Epidermal cell transcriptomes (n = 1,422) visualized with t-distributed stochastic neighbor embedding (t-SNE), colored according to unsupervised (first level) clustering (C). (E) Expression of group-specific marker genes projected onto the t-SNE map. (F) Immunostaining or single-molecule FISH for group-specific genes. Protein or mRNA (symbols italics) expression is pseudocolored corresponding to groups shown in (C). Cell nuclei are shown in white. Scale bars, 20 μm. See also Figure S2J. (G) Hierarchical clustering (Ward’s linkage) of gene expression data averaged over each group.
Figure 1 Defining the Main Epidermal Cell Populations (A) Overview of the experimental workflow. (B) Illustrated microanatomy and compartmentalization of the murine epidermis including HF and SG, colored according to main populations (C). (C) Identity and marker genes of cell populations defined during first-level clustering. (D) Epidermal cell transcriptomes (n = 1,422) visualized with t-distributed stochastic neighbor embedding (t-SNE), colored according to unsupervised (first level) clustering (C). (E) Expression of group-specific marker genes projected onto the t-SNE map. (F) Immunostaining or single-molecule FISH for group-specific genes. Protein or mRNA (symbols italics) expression is pseudocolored corresponding to groups shown in (C). Cell nuclei are shown in white. Scale bars, 20 μm. See also Figure S2J. (G) Hierarchical clustering (Ward’s linkage) of gene expression data averaged over each group.CC BY-NC-ND · PubMed Central
Figure 2 Subclustering of Epidermal Cell Populations (A–D) Subclustering (second-level clustering) of epidermal cells from the IFE basal (A), upper HF (B), outer bulge (C), and inner bulge (D) compartments. Upper panel: projection of subpopulations onto the t-SNE map of the full dataset introduced in Figure 1D. Lower panel: barplots showing the expression of marker genes per subpopulation. Each bar represents a single cell, and the black line indicates the average expression over each subpopulation. (E) Selection of immuno- and single-molecule FISH (symbols italics) stainings to visualize subpopulation localization within the tissue. Arrowheads highlight the position of the populations: IFE BI (filled arrowhead)/BII (empty arrowhead); uHF I (filled arrowhead)/II (empty arrowhead); OB III (filled arrowhead; dashed line marks lower end of KRT15 gap). HS, hair shaft. SG, sebaceous gland. CH, club hair. Scale bars, 10 μm. See also Figure S3L. (F) Identity and marker genes of cell populations defined during second-level clustering. (G) Summary of the approximate location of each defined subpopulation in the IFE, SG, and HF.
Figure 2 Subclustering of Epidermal Cell Populations (A–D) Subclustering (second-level clustering) of epidermal cells from the IFE basal (A), upper HF (B), outer bulge (C), and inner bulge (D) compartments. Upper panel: projection of subpopulations onto the t-SNE map of the full dataset introduced in Figure 1D. Lower panel: barplots showing the expression of marker genes per subpopulation. Each bar represents a single cell, and the black line indicates the average expression over each subpopulation. (E) Selection of immuno- and single-molecule FISH (symbols italics) stainings to visualize subpopulation localization within the tissue. Arrowheads highlight the position of the populations: IFE BI (filled arrowhead)/BII (empty arrowhead); uHF I (filled arrowhead)/II (empty arrowhead); OB III (filled arrowhead; dashed line marks lower end of KRT15 gap). HS, hair shaft. SG, sebaceous gland. CH, club hair. Scale bars, 10 μm. See also Figure S3L. (F) Identity and marker genes of cell populations defined during second-level clustering. (G) Summary of the approximate location of each defined subpopulation in the IFE, SG, and HF.CC BY-NC-ND · PubMed Central
Figure 3 Reconstruction of the Epidermal Differentiation Process (A) Pseudotemporal ordering of IFE cells (n = 536) in t-SNE space, using a minimum spanning tree. The longest path through the graph is highlighted and cells are colored according to first-level clustering. (B) Validation of pseudotemporal ordering of IFE cells using the known basal (Krt14), mature (Krt10), and terminally differentiated (Lor) cell stage markers and Mt4, a transient marker defined in this study. Upper panel: gene expression in IFE cells plotted along pseudotime and fitted with a cubic smoothing spline (black line). Lower panel: gene expression projected onto the t-SNE map shown in (A). (C) “Rolling wave” plot showing the spline-smoothed expression pattern of pseudotime-dependent genes (n = 1,627) clustered into eight groups (I–VIII) and ordered according to their peak expression. (D) “Rolling wave” plot showing the spline-smoothed expression pattern of the 30 most significantly differentiation-related transcription factors (TFs). TFs were ordered according to group membership (left) and peak expression as shown in (C). P-values for pseudotime dependency are shown on the right. Red line marks Bonferroni-corrected significance threshold of 0.001. TFs marked in bold have not been previously described as relevant for epidermal stratification. (E) Expression of differentiation-related genes in all epidermal subpopulations defined by either first- or second-level clustering. Bars show the percentage of genes expressed over baseline with 95% posterior probability (negative binomial regression model) in each of the populations for every differentiation group (I–VIII). Populations where the pseudotime model is not applicable are shaded gray. (F) Position of epidermal cells from each subpopulation plotted on the differentiation axis (defined by highest Pearson correlation). Populations where the pseudotime model is not applicable are colored light gray. (G) Summary illustrating the differentiation status of cells in the HF and IFE.
Figure 3 Reconstruction of the Epidermal Differentiation Process (A) Pseudotemporal ordering of IFE cells (n = 536) in t-SNE space, using a minimum spanning tree. The longest path through the graph is highlighted and cells are colored according to first-level clustering. (B) Validation of pseudotemporal ordering of IFE cells using the known basal (Krt14), mature (Krt10), and terminally differentiated (Lor) cell stage markers and Mt4, a transient marker defined in this study. Upper panel: gene expression in IFE cells plotted along pseudotime and fitted with a cubic smoothing spline (black line). Lower panel: gene expression projected onto the t-SNE map shown in (A). (C) “Rolling wave” plot showing the spline-smoothed expression pattern of pseudotime-dependent genes (n = 1,627) clustered into eight groups (I–VIII) and ordered according to their peak expression. (D) “Rolling wave” plot showing the spline-smoothed expression pattern of the 30 most significantly differentiation-related transcription factors (TFs). TFs were ordered according to group membership (left) and peak expression as shown in (C). P-values for pseudotime dependency are shown on the right. Red line marks Bonferroni-corrected significance threshold of 0.001. TFs marked in bold have not been previously described as relevant for epidermal stratification. (E) Expression of differentiation-related genes in all epidermal subpopulations defined by either first- or second-level clustering. Bars show the percentage of genes expressed over baseline with 95% posterior probability (negative binomial regression model) in each of the populations for every differentiation group (I–VIII). Populations where the pseudotime model is not applicable are shaded gray. (F) Position of epidermal cells from each subpopulation plotted on the differentiation axis (defined by highest Pearson correlation). Populations where the pseudotime model is not applicable are colored light gray. (G) Summary illustrating the differentiation status of cells in the HF and IFE.CC BY-NC-ND · PubMed Central
Figure 4 Defining Spatial Gene Expression Signatures (A) Pseudospatial ordering of basal cells (n = 486) in t-SNE space, using a minimum spanning tree. The longest path through the graph is highlighted and cells are colored according to second-level clustering. (B) Validation of pseudospatial ordering of basal cells using known and new IFE basal (Krt14), upper HF (Krt79), Gli1+ outer bulge (Aspn), general outer bulge (Postn), and inner bulge (Krt6a) markers. Upper panel: gene expression in basal cells plotted along the pseudospace trajectory and fitted with a cubic smoothing spline (black line). Lower panel: gene expression projected onto the t-SNE map shown in (A). (C) “Rolling wave” plot showing the spline-smoothed expression pattern of pseudospace-dependent genes (n = 547) clustered into eight groups (I-VIII) and ordered according to their peak expression. (D) “Rolling wave” plot showing the spline-smoothed expression pattern of the 30 most significant spatially expressed TFs. TFs were ordered according to group membership and peak expression as shown in (C). P-values for pseudospace dependency are shown on the right. Red line marks Bonferroni-corrected significance threshold of 0.001. TFs marked in bold have not been previously described as relevant for cellular heterogeneity along the proximal-distal axis. (E) Peak positions of basal cell populations and IB I (defined in second-level clustering) on the spatial axis visualized by kernel density estimation. The organization of the cell populations confirms their spatial positioning in IFE and HF along the proximal-distal axis. (F) Summary illustrating spatial signatures in epidermal cell populations.
Figure 4 Defining Spatial Gene Expression Signatures (A) Pseudospatial ordering of basal cells (n = 486) in t-SNE space, using a minimum spanning tree. The longest path through the graph is highlighted and cells are colored according to second-level clustering. (B) Validation of pseudospatial ordering of basal cells using known and new IFE basal (Krt14), upper HF (Krt79), Gli1+ outer bulge (Aspn), general outer bulge (Postn), and inner bulge (Krt6a) markers. Upper panel: gene expression in basal cells plotted along the pseudospace trajectory and fitted with a cubic smoothing spline (black line). Lower panel: gene expression projected onto the t-SNE map shown in (A). (C) “Rolling wave” plot showing the spline-smoothed expression pattern of pseudospace-dependent genes (n = 547) clustered into eight groups (I-VIII) and ordered according to their peak expression. (D) “Rolling wave” plot showing the spline-smoothed expression pattern of the 30 most significant spatially expressed TFs. TFs were ordered according to group membership and peak expression as shown in (C). P-values for pseudospace dependency are shown on the right. Red line marks Bonferroni-corrected significance threshold of 0.001. TFs marked in bold have not been previously described as relevant for cellular heterogeneity along the proximal-distal axis. (E) Peak positions of basal cell populations and IB I (defined in second-level clustering) on the spatial axis visualized by kernel density estimation. The organization of the cell populations confirms their spatial positioning in IFE and HF along the proximal-distal axis. (F) Summary illustrating spatial signatures in epidermal cell populations.CC BY-NC-ND · PubMed Central
Figure 5 Modeling Transcriptional Heterogeneity Using Space and Time Signatures (A) Pseudospacetime: matrix showing each cell’s (dots) identity along the differentiation- and spatial-axis, in which both axes were divided into 15 equally sized bins. The numbers of genes expressed over baseline (95% posterior probability, negative binomial regression model) for each bin are shown in barplots (upper and left panels). Cells with expression patterns that could not be placed along the differentiation- and spatial-axes are presented in a separated bar to the right. (B) The pseudospacetime positions of cells from each cell population defined by either first- or second-level clustering, visualized as percentage of cells per bin. (C) The number of genes expressed over baseline (95% posterior probability) for the additional signatures used for modeling the transcriptomes of all cells (including SG-related and immune populations). (D) Model accuracy for the model (including all signature model predictors) in comparison with model accuracy based on either grouping cells according to the first- or second-level clustering or after shuffling the model-predictor matrix (negative control). The model accuracy was computed as the ratio of explained molecules (present in both the simulated and observed) to the sum of explained and unexplained molecules. For each model, the mean and SD of the model accuracy over each group are shown. See Figure S6D for results of each individual cell population. (E) Percentage of molecules (averaged over all cells) explained by models of increasing complexity. The explained molecules are indicated in green, underexplained in red, and overexplained in blue.
Figure 5 Modeling Transcriptional Heterogeneity Using Space and Time Signatures (A) Pseudospacetime: matrix showing each cell’s (dots) identity along the differentiation- and spatial-axis, in which both axes were divided into 15 equally sized bins. The numbers of genes expressed over baseline (95% posterior probability, negative binomial regression model) for each bin are shown in barplots (upper and left panels). Cells with expression patterns that could not be placed along the differentiation- and spatial-axes are presented in a separated bar to the right. (B) The pseudospacetime positions of cells from each cell population defined by either first- or second-level clustering, visualized as percentage of cells per bin. (C) The number of genes expressed over baseline (95% posterior probability) for the additional signatures used for modeling the transcriptomes of all cells (including SG-related and immune populations). (D) Model accuracy for the model (including all signature model predictors) in comparison with model accuracy based on either grouping cells according to the first- or second-level clustering or after shuffling the model-predictor matrix (negative control). The model accuracy was computed as the ratio of explained molecules (present in both the simulated and observed) to the sum of explained and unexplained molecules. For each model, the mean and SD of the model accuracy over each group are shown. See Figure S6D for results of each individual cell population. (E) Percentage of molecules (averaged over all cells) explained by models of increasing complexity. The explained molecules are indicated in green, underexplained in red, and overexplained in blue.CC BY-NC-ND · PubMed Central
Figure 6 Single-Cell Analyses of Epidermal Stem Cell Populations (A) Percentage of basal (pseudotime ≤300) and non-basal cells, in each population of cells expressing Lgr5, Cd34, Gli1, Lgr6, Lrig1, or Krt14, respectively. For basal cells, the percentage and the number of cells per total cells are given. (B) Selection of all basal cells. Right panel: projection of all basal cells (pseudotime ≤300; with and without SCM expression) onto t-SNE space, colored according to the defined cell compartments (first- and second-level clustering). Left panel: illustration summarizing the location of the compartments. (C) Mapping of basal cells to the t-SNE map defined in (B) according to the expression of SCMs, for each marker gene respectively. (D) Percentage of basal cells that do not express any of the SCMs Lgr5, Cd34, Gli1, Lgr6, Lrig1, or Krt14 (in red). (E) Density of basal cells with (gray) and without (red) SCM expression along the pseudotime axis. (F) Projection of the basal cells that did not express any SCMs (red) onto the t-SNE map defined in (B). (G) Heatmap of 44 genes that are differentially expressed between SCM+ and SCM− basal cells. Negative binomial regression was used to define specific SCM+ and SCM– gene expression signatures (i.e., the additional number of molecules expressed for each gene if a cell belongs to the SCM+ or SCM– group). For each gene, the group-specific expression in SCM+ and SCM– cells as well as the difference between both groups is shown (median number of molecules).
Figure 6 Single-Cell Analyses of Epidermal Stem Cell Populations (A) Percentage of basal (pseudotime ≤300) and non-basal cells, in each population of cells expressing Lgr5, Cd34, Gli1, Lgr6, Lrig1, or Krt14, respectively. For basal cells, the percentage and the number of cells per total cells are given. (B) Selection of all basal cells. Right panel: projection of all basal cells (pseudotime ≤300; with and without SCM expression) onto t-SNE space, colored according to the defined cell compartments (first- and second-level clustering). Left panel: illustration summarizing the location of the compartments. (C) Mapping of basal cells to the t-SNE map defined in (B) according to the expression of SCMs, for each marker gene respectively. (D) Percentage of basal cells that do not express any of the SCMs Lgr5, Cd34, Gli1, Lgr6, Lrig1, or Krt14 (in red). (E) Density of basal cells with (gray) and without (red) SCM expression along the pseudotime axis. (F) Projection of the basal cells that did not express any SCMs (red) onto the t-SNE map defined in (B). (G) Heatmap of 44 genes that are differentially expressed between SCM+ and SCM− basal cells. Negative binomial regression was used to define specific SCM+ and SCM– gene expression signatures (i.e., the additional number of molecules expressed for each gene if a cell belongs to the SCM+ or SCM– group). For each gene, the group-specific expression in SCM+ and SCM– cells as well as the difference between both groups is shown (median number of molecules).CC BY-NC-ND · PubMed Central
Figure 7 Functional Signatures Expressed in Epidermal Subpopulations (A–C) Expression of genes linked to signaling pathways (A), cell adhesion (B), and extracellular matrix and basement membrane constituents (C) in each epidermal population (defined in either first- or second-level clustering). Shown is the median number of molecules expressed in each cell population (negative binomial regression model).
Figure 7 Functional Signatures Expressed in Epidermal Subpopulations (A–C) Expression of genes linked to signaling pathways (A), cell adhesion (B), and extracellular matrix and basement membrane constituents (C) in each epidermal population (defined in either first- or second-level clustering). Shown is the median number of molecules expressed in each cell population (negative binomial regression model).CC BY-NC-ND · PubMed Central

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Metadata

Authors
Joost, S., Zeisel, A., Jacob, T., Sun, X., La Manno, G., Lönnerberg, P., Linnarsson, S., & Kasper, M.
Affiliations
Karolinska Institutet (SE)
Corresponding author
  • Linnarsson, Sten
  • Kasper, Maria
ORCID
Venue
Cell Systems
Publisher
Elsevier BV
Published
2016-09
Volume
3
Issue
3
Pages
221-237.e9
ISSN
2405-4712 (ISSN-L)
DOI
10.1016/j.cels.2016.08.010
PMC
PMC5052454
OpenAlex
W2489812534
Cited by
429
References
73
Citations by year
2026: 42 · 2025: 35 · 2024: 45 · 2023: 40 · 2022: 55 · 2021: 45 · 2020: 68 · 2019: 39 · 2018: 40 · 2017: 18 · 2016: 1
FWCI
16.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell Systems (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Health Sciences › Medicine › Dermatology › Hair Growth and Disorders
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Skin and Cellular Biology Research
Keywords
epidermis, hair follicle, cellular heterogeneity, epidermal differentiation, telogen, interfollicular epidermis, proximal-distal axis, stem cell compartment, transcriptional organization, tissue homeostasis, spatial signatures, mouse skin
MeSH terms
Animals, Cell Differentiation, Epidermis, Female, Homeostasis, Mice, Inbred C57BL, Stem Cells, Hair Follicle, Mice, Transcriptome

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