Quantitative proteogenomic profiling of epidermal barrier formation in vitro

Abstract

Background: The barrier function of the epidermis is integral to personal well-being, and defects in the skin barrier are associated with several widespread diseases. Currently there is a limited understanding of system-level proteomic changes during epidermal stratification and barrier establishment.

Objective: Here we report the quantitative proteogenomic profile of an in vitro reconstituted epidermis at three time points of development in order to characterize protein changes during stratification.

Methods: The proteome was measured using data-dependent "shotgun" mass spectrometry and quantified with statistically validated label-free proteomic methods for 20 replicates at each of three time points during the course of epidermal development.

Results: Over 3600 proteins were identified in the reconstituted epidermis, with more than 1200 of these changing in abundance over the time course. We also collected and discuss matched transcriptomic data for the three time points, allowing alignment of this new dataset with previously published characterization of the reconstituted epidermis system.

Conclusion: These results represent the most comprehensive epidermal-specific proteome to date, and therefore reveal several aspects of barrier formation and skin composition. The limited correlation between transcript and protein abundance underscores the importance of proteomic analysis in developing a full understanding of epidermal maturation.

Keywords: Barrier; Epidermal differentiation; Proteomics; Skin equivalent.

Figure 1

Summary of results from pilot study. A) Bar chart of protein identifications at each time point for unfractionated (orange) and fractionated (blue) samples. B) Venn diagram of protein identifications for the pilot study at each time point. C) Venn diagram of combined protein identifications for fractionated (OGE, grey area) and unfractionated samples (red area). D) Power analysis of quantification based on the standard deviation measured in the pilot study. Shown are the number of biological replicates needed vs. detectable differences in normalized spectral index (given power = 0.8 and p = 0.05).

Figure 2

Schematic diagram of quantification software. The user provides a protein result file (ProtXML) and desired FDR cutoff. The program then automatically extracts and compiles fragment ion intensities, returning log₂(SI_N) values.

Figure 3

A) Distributions of protein abundances (averaged across time points) for all proteins identified (blue area) and those not identified in the pilot experiment (red area). B) Venn diagram of all protein identifications at a 1% FDR for all three time points studied.

Figure 4

A) Correlation plots of unfractionated vs. fractionated SI_N values for days 3, 10, and 18 respectively. B) Heatmap of SI_N protein quantification for each time point. Results were filtered for proteins which were quantified in at least 2/3 time points. Missing data were replaced with a minimum value and appear as blocks of solid green in the image.

Figure 5

K-means clusters of protein abundances. Missing SI_N values were imputed using 100 knearest neighbors, and scaled prior to clustering. Cluster 2 contains proteins for which imputation failed at Day 3, indicating very sparse abundance data at this time point.

Figure 6

Abundance trajectories for selected proteins related to Desmosomes and Tight Junctions. SI_N values at Day 3 were normalized to baseline to demonstrate increasing or decreasing abundance over the time course.

Figure 7

Heatmap of proteins with matching rank-order normalization profiles in proteomic and transcriptomic datasets. General trajectory trend and related biological process enrichments are shown to the right.