SUMMARY Perturbations in the transcriptional programs specifying epidermal differentiation cause diverse skin pathologies ranging from impaired barrier function to inflammatory skin disease. However, the global scope and organization of this complex cellular program remain undefined. Here we report single-cell RNA sequencing profiles of 92,889 human epidermal cells from 9 normal and 3 inflamed skin samples. Transcriptomics-derived keratinocyte subpopulations reflect classic epidermal strata but also sharply compartmentalize epithelial functions such as cell-cell communication, inflammation, and WNT pathway modulation. In keratinocytes, ~12% of assessed transcript expression varies in coordinate patterns, revealing undescribed gene expression programs governing epidermal homeostasis. We also identify molecular fingerprints of inflammatory skin states, including S100 activation in the interfollicular epidermis of normal scalp, enrichment of a CD1C+CD301A+ myeloid dendritic cell population in psoriatic epidermis, and IL1βhi CCL3hiCD14+ monocyte-derived macrophages enriched in foreskin. This compendium of RNA profiles provides a critical step toward elucidating epidermal diseases of development, differentiation, and inflammation.
Keratinocyte differentiation requires intricately coordinated spatiotemporal expression changes that specify epidermis structure and function. This article utilizes single-cell RNA-seq data from 22,338 human foreskin keratinocytes to reconstruct the transcriptional regulation of skin development and homeostasis genes, organizing them by differentiation stage and also into transcription factor (TF)–associated modules. We identify groups of TFs characterized by coordinate expression changes during progression from the undifferentiated basal to the differentiated state and show that these TFs also have concordant differential predicted binding enrichment in the super-enhancers previously reported to turn over between the two states. The identified TFs form a core subset of the regulators controlling gene modules essential for basal and differentiated keratinocyte functions, supporting their nomination as master coordinators of keratinocyte differentiation. Experimental depletion of the TFs ZBED2 and ETV4, both predicted to promote the basal state, induces differentiation. Furthermore, our single-cell RNA expression analysis reveals preferential expression of antioxidant genes in the basal state, suggesting keratinocytes actively suppress reactive oxygen species to maintain the undifferentiated state. Overall, our work demonstrates diverse computational methods to advance our understanding of dynamic gene regulation in development.
Cytosine methylation is a ubiquitous modification in mammalian DNA generated and maintained by several DNA methyltransferases (DNMTs) with partially overlapping functions and genomic targets. To systematically dissect the factors specifying each DNMT’s activity, we engineered combinatorial knock-in of human DNMT genes in Komagataella phaffii, a yeast species lacking endogenous DNA methylation. Time-course expression measurements captured dynamic network-level adaptation of cells to DNMT3B1-induced DNA methylation stress and showed that coordinately modulating the availability of S-adenosyl methionine (SAM), the essential metabolite for DNMT-catalyzed methylation, is an evolutionarily conserved epigenetic stress response, also implicated in several human diseases. Convolutional neural networks trained on genome-wide CpG-methylation data learned distinct sequence preferences of DNMT3 family members. A simulated annealing interpretation method resolved these preferences into individual flanking nucleotides and periodic poly(A) tracts that rotationally position highly methylated cytosines relative to phased nucleosomes. Furthermore, the nucleosome repeat length defined the spatial unit of methylation spreading. Gene methylation patterns were similar to those in mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription relative to control, respectively, in the absence of mammalian readers of DNA methylation. Introducing controlled epigenetic perturbations in yeast thus enabled characterization of fundamental genomic features directing specific DNMT3 proteins.
New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.
Background: Keratinocyte differentiation requires intricately coordinated spatiotemporal expression changes that specify epidermis structure and function. Results: This paper utilizes single-cell RNA-seq data from 22,338 human foreskin keratinocytes to reconstruct the transcriptional regulation of skin development and homeostasis genes, organizing them by differentiation stage and also into transcription factor (TF)-associated modules. We identify groups of TFs characterized by coordinate expression changes during progression from the undifferentiated basal to the differentiated state and show that these TFs also have concordant differential predicted binding enrichment in the super-enhancers previously reported to turn over between the two states. The identified TFs form a core subset of the regulators controlling gene modules essential for basal and differentiated keratinocyte functions, supporting their nomination as master coordinators of keratinocyte differentiation. Experimental depletion of the TFs ZBED2 and ETV4, both predicted to promote the basal state, induces differentiation. Furthermore, our single-cell RNA expression analysis reveals preferential expression of antioxidant genes in the basal state, suggesting keratinocytes actively suppress reactive oxygen species to maintain the undifferentiated state. Finally, we perform in-silico lineage tracing demonstrating transcriptomic correspondence of basal cell carcinoma (BCC) to a basal keratinocyte subpopulation and squamous cell carcinoma (SCC) to a rapidly proliferating stage that is intermediate between the basal and differentiated cell states, implicating distinct candidate cells of origin for these cancers. Conclusion: Our work demonstrates diverse computational methods integrating single-cell and bulk RNA expression data to advance our understanding of dynamic gene regulation in normal development and in disease.
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