Peer review of: Single cell transcriptomics of human epidermis identifies basal stem cell transition states (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
In this manuscript by Wang et al. the authors utilize Single-Cell-RNA-Sequencing by 10X genomics 
to explore cellular heterogeneity of human neonatal foreskin epidermis. To my knowledge this is 
one of the first single-cell-RNA-seq analysis of the epidermis of neonatal human skin. Neonatal 
human skin is difficult to acquire with foreskin epidermis being the most practical to gather. 
Furthermore, neonatal mammalian skin is very different than adult tissue. This is because neonatal 
skin is at a point of generating new skin to grow in conjunction with the organism compared to 
resting adult skin, making the analysis performed by the authors critically important. 
The authors analyzed their single cell data sets with multiple computational pipelines, including; 
Seurat, Monocle, and SoptSC. These analyses have identified multiple populations of basal-
progenitor cells in neonatal epidermis in addition to the differentiated layers such as the spinous 
keratinocytes. These clusters are interesting but expected to be found in Single-Cell-RNA-seq 
experiments. Importantly, the authors validate their initial analysis by using novel SoptSC 
software packages that produces investigate cell-cell interactions in addition to the Cellular 
Entropy Estimator. This result was highly interesting because it adds an additional dimension to 
RNA-velocity analysis, which was also performed, to reveal the potential for epidermal progenitor 
differentiation along a trajectory. 
The authors used the multiple biocomputational pipelines to identify new genes (epigenetic- 
UHRF1/Hells and Pttg1, Rrm2, Ass1, Col17a1 etc) to generate a novel molecular map of the basal 
layer of the skin. In addition, the authors tested whether the identified molecular factors were 
critical for the formation of the establishment of skin equivalents in culture, which revealed their 
importance in the formation of healthy epidermis. 
This manuscript is well written, and the data is novel with multiple detailed computational analysis 
performed in a statistically rigorous manner for Single Cell RNA seq data. The manuscript is of very 
high quality and is ready for publication. 
 
 
 
Reviewer #2: 
Remarks to the Author: 
The paper by Shuxiong Wang et al. describes a comprehensive single-cell transcriptomics and 
computational analysis of cellular heterogeneity in human interfollicular epidermis. The authors 
find stable functional heterogeneity that correlates with spatial heterogeneity and is further 
corroborated by functional analyses. The authors identify HELLS and PPTG as novel factors 
involved in epidermal homeostasis and differentiation. 
While the paper has great potential, I propose that the following issues are addressed before 
publication: 
 
Major 
• The paper describes droplet-based scRNA-seq of primary cells from five donors. However, many 
of the presented analyses are based on a single donor. Supplementary Figure 4 indicates some 
variability in the cell clustering from different donors. For example, the GRN cluster is not detected 
in all of the donors. In my opinion, it would be important to show that the key findings from 
analyses of Library 3 are reproducible across donors. These include the expression of key marker 
genes (highlighted in Figure 2A) and the results of the pseudotime analysis. 
• On page 9 the authors state that “SoptSC unbiasedly reconstructed a putative BAS-SPN-GRN 
keratinocyte differentiation trajectory (Figure 4B)”. However, the figure does not clearly show GRN 
being the end point of the trajectory. Instead, based on 4A-B it seems that some of the GRN cells 
in fact precede SPN cells in pseudotime. In my opinion, the figure could be improved by addition of 
a panel visualizing pseudotime vs. cell identities (using the colour scheme from panel A). 
 
Minor 

• The cell-cell network inference analysis is rather difficult to interpret from Figure 3. From the 
heatmaps it would seem that the Basal cell cohort expresses on average the highest levels of 
genes of each pathway, yet based on the circos plots they are not among the active clusters. 
Could the authors please explain this discrepancy? Would it be make sense to show the expression 
of ligands and receptors separately? Finally, in the methods section it is suggested that the circos 
plots have directionality indicated by arrows. However, the figure is much too small to see such 
detail. 
• Combining entropy and RNA velocity is an elegant and innovative approach for delineating 
differentiation trajectories. Unfortunately, panel 4E is too small for seeing the direction of the 
vectors. Please improve this as it is central information. (The same applies also to Fig. 5E) 
• P. 18 methods: “. For downstream analyses, we kept cells which met the following filtering 
criteria per biological replicate per condition: <6000 UMI/cell, and <10% mitochondrial gene 
expression. “ Is this UMI filter correct? From Supplementary Figure 2 it is evident that a significant 
fraction of cells has more than 6000 UMIs. 
• Figure 1C and 1G legends are identical – in the interest of clarity it would be good to point out 
the different clustering methods used for defining the cohorts 
• Figure 1H does not seem to be cited in the text 
• In the results section supervised clustering in Seurat is mentioned. But the methods section 
describes unsupervised clustering. Which one was used in fact? 
• Typos: P.20 “circus plots” –> circos plots 
 
 
 
Reviewer #3: 
Remarks to the Author: 
The authors of the submitted manuscript perform single cell RNAseq analysis of neonatal foreskin 
epidermal keratinocytes. Based on the analysis the authors propose the existence of four distinct 
populations of basal cells, and provide some evidence for heterogeneity within the basal layer of 
the epidermis based on expression of markers. However, the authors provide no quantification of 
their staining nor coevaluation of the proposed markers. The authors go on to infer based simply 
on RNA expression analysis that paracrine and autocrine signaling are essential for homeostasis 
without any validating data. They subsequently propose that cells are organized along discrete 
cellular trajectories with key transition states. Functional analysis of a couple of markers for 
different markers associated with the basal population forms the basis for proposing that this 
further support that cells are organized in a cellular hierarchy and that specific epigenetic 
regulators control progenitor behavior. 
 
Although the dataset could have some interest for the skin community, the manuscript appears 
somewhat premature, lacking general controls and quantification of the different described 
phenotypes and expression patterns. Several concerns with both the analyses and the 
interpretation of the resulting data are outlined below. 
 
Major comments 
First and foremost, the authors clearly state that they produced 5 libraries for single cell analyses, 
however, they have chosen to focus on one library from a single individual. Human samples are 
heterogenous by nature and focusing the analysis on only one sample raises major concerns 
whether the detected cell populations and differentially expressed genes are indeed representative 
and would stand up to further rigorous analyses of the additional samples analyzed. There are 
efficient algorithms that allow batch corrections between different samples thereby providing the 
basis for analysis of single cell samples isolated and analyzed from multiple individuals. It is simply 
not sufficient to base the entire study essentially on a single sample. 
 
The decision to proceed using SoptSC analyses is not entirely clear. It does identify an additional 
population of cells that are classified by the authors as basal, however, fails to distinguish 
melanocytes and Langerhans and erythrocytes as separate populations, while Seurat successfully 

clusters them separately, and so is arguably giving a better performance for this specific task. 
Regardless of the choice between clustering with Seurat or SoptSC Figure 1 is difficult to interpret. 
For example, the plots provided illustrating the expression profiles of markers associated with 
differentiation and progenitor status do not align between figure 1 D and H. It seems that there 
are many more cells expressing INV and K10 at high levels, as well as a more pronounced 
population of cells that overlap for K14 (a basal cell marker) and K10 (a differentiation marker) in 
plots presented in Figure 1H than in plots presented in Figure 1D. The same raw data on 
differential expression and levels of gene expression is presumably used to generate both plots, so 
it is unclear why such a discrepancy should be present. Naturally this could reflect the fact that the 
authors chose different color schemes to present the data. Figure 1 D displays expression data 
ranging from light yellow to red, while in figure H data is presented from light yellow through red 
to dark brown (with no scale bar for both). Therefore, as the data is presented at the moment it is 
difficult to interpret it in a meaningful way. 
 
The authors then performed differential expression analyses on the individual populations that are 
assigned the basal identity in SoptSC and demonstrate via IHC in Figure 2 E-H that markers are 
expressed in subset of cells within the foreskin epidermis. This analysis needs to be supported 
larger images showing a bigger area of the epidermis and also quantification of the analysis. 
Instead of zooming in on regions like the authors have done in E-H, it would be much more 
informative to display the staining similar to that for K14/K10 as in Figure 2D. Importantly, from 
Figure 2B it is evident that some of the markers identified are not exclusive to just one population 
of basal cells. Co-staining for the markers in figure 2 (E-H) is a requirement alongside 
quantification of the analysis. 
 
The resulted for the RNA analysis needs further support. Firstly, the proposed signaling analysis 
(Figure 3) does not provide evidence for autocrine or paracrine signaling within the epithelium. 
This provides the basis for hypothesizes that can then subsequently be tested functionally. 
Secondly, the data presented for pseudotime trajectories and velocity is not very strong and the 
provided supporting data based on knock down is currently not analyzed in a manner whereby the 
authors can relate this to the proposed trajectories. Importantly, the analyses include no 
quantification of phenotypes, expression analysis for markers identified in the in vivo sample, as 
well as data that support that the same population dynamics observed in the one in vivo samples 
is recapitulated in the in vitro skin reconstitution model system included here (Figure 4). 
 
In Figure 5 the authors use PTTG1 and RRM2 KD to assess their role in skin reconstitution assays 
and conclude that PTTG1 as a marker of BAS1 population is essential for epidermal homeostasis 
unlike the other BAS populations identified. Much more sophisticated analyses are required for 
such statements. Again, further characterization is a requirement and importantly this should be 
supported by analysis of replicate samples (currently n=1). 
 
 
Minor comments 
Supplementary figure 1: Labels for dyes are missing 
 
All figures - the figures are not display consistently e.g. order of cell populations, labels are 
missing, information needs to be obtained from previous figures.