Inflammation-mediated fibroblast activation and immune dysregulation in collagen VII-deficient skin.

Inflammation is known to play a critical role in all stages of tumorigenesis; however, less is known about how it predisposes the tissue microenvironment preceding tumor formation. Recessive dystrophic epidermolysis bullosa (RDEB), a skin-blistering disease secondary to COL7A1 mutations and associated with chronic wounding, inflammation, fibrosis, and cutaneous squamous cell carcinoma (cSCC), models this dynamic. Here, we used single-cell RNA sequencing (scRNAseq) to analyze gene expression patterns in skin cells from a mouse model of RDEB. We uncovered a complex landscape within the RDEB dermal microenvironment that exhibited altered metabolism, enhanced angiogenesis, hyperproliferative keratinocytes, infiltration and activation of immune cell populations, and inflammatory fibroblast priming. We demonstrated the presence of activated neutrophil and Langerhans cell subpopulations and elevated expression of PD-1 and PD-L1 in T cells and antigen-presenting cells, respectively. Unsupervised clustering within the fibroblast population further revealed two differentiation pathways in RDEB fibroblasts, one toward myofibroblasts and the other toward a phenotype that shares the characteristics of inflammatory fibroblast subsets in other inflammatory diseases as well as the IL-1-induced inflammatory cancer-associated fibroblasts (iCAFs) reported in various cancer types. Quantitation of inflammatory cytokines indicated dynamic waves of IL-1α, TGF-ß1, TNF, IL-6, and IFN-γ concentrations, along with dermal NF-κB activation preceding JAK/STAT signaling. We further demonstrated the divergent and overlapping roles of these cytokines in inducing inflammatory phenotypes in RDEB patients as well as RDEB mouse-derived fibroblasts together with their healthy controls. In summary, our data have suggested a potential role of inflammation, driven by the chronic release of inflammatory cytokines such as IL-1, in creating an immune-suppressed dermal microenvironment that underlies RDEB disease progression.

Lack of evidence for increased transcriptional noise in aged tissues.

Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.

The human body contains hundreds of different cell types which vary greatly in shape and size despite all sharing the same genetic material. This is because each cell switches on, or 'expresses', a unique set of genes that gives them a specific identity, such as becoming a nerve or a muscle cell. Recent studies have shown that cells in some tissues tend to lose their identity with age, and activate some of the genes that define them less strongly. This results in seemingly identical cells expressing the same genes in a more variable way, a phenomenon commonly referred to as noise. A technique called single-cell RNA sequencing is typically used to measure the activity of genes in individual cells, and has been used to study the role of noise in a wide range of aging tissues. However, the results of these studies have been analyzed using different computational methods, making it difficult to make comparisons between tissues and organisms. This has led to an ongoing debate about whether increased noise is a signature feature of aging, and if it is experienced throughout the body or restricted to certain cell types. To overcome this, Ibáñez-Solé, Ascensión et al. developed two new computational tools for analyzing noise and changes in cell identity: these were then applied to seven unique sequencing datasets which had been collected from various tissues in humans and mice at different ages. While there were some differences in the level of noise between young and old cells, these changes were not consistent across tissues and organisms. In contrast, other features associated with aging were consistently found in each of the sequencing datasets. The role of noise in aging has been gaining increasingly more attention in the scientific literature. However, the findings of Ibáñez-Solé, Ascensión et al. suggest that this phenomenon is not a hallmark of the aging process, and that the field should focus on other factors that reduce the health of tissues and cells as organisms get older. The computational approach they developed could also be used to evaluate the role of noise in other contexts, such as certain diseases.

Challenges and Opportunities for the Translation of Single-Cell RNA Sequencing Technologies to Dermatology.

Skin is a complex and heterogeneous organ at the cellular level. This complexity is beginning to be understood through the application of single-cell genomics and computational tools. A large number of datasets that shed light on how the different human skin cell types interact in homeostasis-and what ceases to work in diverse dermatological diseases-have been generated and are publicly available. However, translation of these novel aspects to the clinic is lacking. This review aims to summarize the state-of-the-art of skin biology using single-cell technologies, with a special focus on skin pathologies and the translation of mechanistic findings to the clinic. The main implications of this review are to summarize the benefits and limitations of single-cell analysis and thus help translate the emerging insights from these novel techniques to the bedside.

BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs.

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 231 bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and High Performance Computer systems. We use BigMPI4py to speed-up the search for germ line specific de novo DNA methylated/unmethylated motifs from the 59 whole genome bisulfite sequencing DNA methylation samples from 27 human tissues of the ENCODE project. We developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores allowing us an efficient identification of a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues and finally, the discovery of germ layer specific DNA demethylation motifs. Our results point out that DNA methylation signal provide a higher degree of information for the demethylated state than for the methylated state. BigMPI4py is available at https://https://www.arauzolab.org/tools/bigmpi4py and https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis.

FOntCell: Fusion of Ontologies of Cells.

High-throughput cell-data technologies such as single-cell RNA-seq create a demand for algorithms for automatic cell classification and characterization. There exist several cell classification ontologies with complementary information. However, one needs to merge them to synergistically combine their information. The main difficulty in merging is to match the ontologies since they use different naming conventions. Therefore, we developed an algorithm that merges ontologies by integrating the name matching between class label names with the structure mapping between the ontology elements based on graph convolution. Since the structure mapping is a time consuming process, we designed two methods to perform the graph convolution: vectorial structure matching and constraint-based structure matching. To perform the vectorial structure matching, we designed a general method to calculate the similarities between vectors of different lengths for different metrics. Additionally, we adapted the slower Blondel method to work for structure matching. We implemented our algorithms into FOntCell, a software module in Python for efficient automatic parallel-computed merging/fusion of ontologies in the same or similar knowledge domains. FOntCell can unify dispersed knowledge from one domain into a unique ontology in OWL format and iteratively reuse it to continuously adapt ontologies with new data endlessly produced by data-driven classification methods, such as of the Human Cell Atlas. To navigate easily across the merged ontologies, it generates HTML files with tabulated and graphic summaries, and interactive circular Directed Acyclic Graphs. We used FOntCell to merge the CELDA, LifeMap and LungMAP Human Anatomy cell ontologies into a comprehensive cell ontology. We compared FOntCell with tools used for the alignment of mouse and human anatomy ontologies task proposed by the Ontology Alignment Evaluation Initiative (OAEI) and found that the Fß alignment accuracies of FOntCell are above the geometric mean of the other tools; more importantly, it outperforms significantly the best OAEI tools in cell ontology alignment in terms of Fß alignment accuracies.

Human Dermal Fibroblast Subpopulations Are Conserved across Single-Cell RNA Sequencing Studies.

On the basis of their differential location within the dermis and of discrete changes in gene and protein expression, two major fibroblast subtypes (papillary and reticular) have traditionally been distinguished. In the last 3 years, a number of research groups have begun to address transcriptomic heterogeneity of human skin cells at the single-cell level by determining mRNA levels of expressed genes through single-cell RNA sequencing technologies. However, the outcome of single-cell RNA sequencing studies is thus far confusing. Very little overlap was found in fibroblast subpopulations, which also varied in number and composition in each dataset. After a careful reappraisal of the transcriptomic data of 13,823 human adult dermal fibroblasts that have been sequenced to date, we show that fibroblasts may robustly be assigned to three major types (axes A‒C), which in turn are composed of 10 major subtypes (clusters), which we denominated A1‒A4, B1 and B2, and C1‒C4. These computationally determined axes and clusters represent the major fibroblast types and subtypes in adult healthy human skin across different datasets, accounting for 92.5% of the sequenced fibroblasts. They thus may provide the basis to improve our understanding of dermal homeostasis and cellular function at the transcriptomic level.

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing.

Background: The advent of single-cell RNA sequencing (scRNAseq) and additional single-cell omics technologies have provided scientists with unprecedented tools to explore biology at cellular resolution. However, reaching an appropriate number of good quality reads per cell and reasonable numbers of cells within each of the populations of interest are key to infer relevant conclusions about the underlying biology of the dataset. For these reasons, scRNAseq studies are constantly increasing the number of cells analysed and the granularity of the resultant transcriptomics analyses. Methods: We aimed to identify previously described fibroblast subpopulations in healthy adult human skin by using the largest dataset published to date (528,253 sequenced cells) and an unsupervised population-matching algorithm. Results: Our reanalysis of this landmark resource demonstrates that a substantial proportion of cell transcriptomic signatures may be biased by cellular stress and response to hypoxic conditions. Conclusions: We postulate that careful design of experimental conditions is needed to avoid long processing times of biological samples. Additionally, computation of large datasets might undermine the extent of the analysis, possibly due to long processing times.

HuR/ELAVL1 drives malignant peripheral nerve sheath tumor growth and metastasis.

Cancer cells can develop a strong addiction to discrete molecular regulators, which control the aberrant gene expression programs that drive and maintain the cancer phenotype. Here, we report the identification of the RNA-binding protein HuR/ELAVL1 as a central oncogenic driver for malignant peripheral nerve sheath tumors (MPNSTs), which are highly aggressive sarcomas that originate from cells of the Schwann cell lineage. HuR was found to be highly elevated and bound to a multitude of cancer-associated transcripts in human MPNST samples. Accordingly, genetic and pharmacological inhibition of HuR had potent cytostatic and cytotoxic effects on tumor growth, and strongly suppressed metastatic capacity in vivo. Importantly, we linked the profound tumorigenic function of HuR to its ability to simultaneously regulate multiple essential oncogenic pathways in MPNST cells, including the Wnt/ß-catenin, YAP/TAZ, RB/E2F, and BET pathways, which converge on key transcriptional networks. Given the exceptional dependency of MPNST cells on HuR for survival, proliferation, and dissemination, we propose that HuR represents a promising therapeutic target for MPNST treatment.

Signal Integration and Transcriptional Regulation of the Inflammatory Response Mediated by the GM-/M-CSF Signaling Axis in Human Monocytes.

In recent years, the macrophage colony-stimulating factor (M-CSF) and granulocyte-macrophage CSF (GM-CSF) cytokines have been identified as opposing regulators of the inflammatory program. However, the two cytokines are simultaneously present in the inflammatory milieu, and it is not clear how cells integrate these signals. In order to understand the regulatory networks associated with the GM/M-CSF signaling axis, we analyzed DNA methylation in human monocytes. Our results indicate that GM-CSF induces activation of the inflammatory program and extensive DNA methylation changes, while M-CSF-polarized cells are in a less differentiated state. This inflammatory program is mediated via JAK2 associated with the GM-CSF receptor and the downstream extracellular signal-regulated (ERK) signaling. However, PI3K signaling is associated with a negative regulatory loop of the inflammatory program and M-CSF autocrine signaling in GM-CSF-polarized monocytes. Our findings describe the regulatory networks associated with the GM/M-CSF signaling axis and how they contribute to the establishment of the inflammatory program associated with monocyte activation.

NaviSE: superenhancer navigator integrating epigenomics signal algebra.

BACKGROUND: Superenhancers are crucial structural genomic elements determining cell fate, and they are also involved in the determination of several diseases, such as cancer or neurodegeneration. Although there are pipelines which use independent pieces of software to predict the presence of superenhancers from genome-wide chromatin marks or DNA-interaction protein binding sites, there is not yet an integrated software tool that processes automatically algebra combinations of raw data sequencing into a comprehensive final annotated report of predicted superenhancers. RESULTS: We have developed NaviSE, a user-friendly streamlined tool which performs a fully-automated parallel processing of genome-wide epigenomics data from sequencing files into a final report, built with a comprehensive set of annotated files that are navigated through a graphic user interface dynamically generated by NaviSE. NaviSE also implements an 'epigenomics signal algebra' that allows the combination of multiple activation and repression epigenomics signals. NaviSE provides an interactive chromosomal landscaping of the locations of superenhancers, which can be navigated to obtain annotated information about superenhancer signal profile, associated genes, gene ontology enrichment analysis, motifs of transcription factor binding sites enriched in superenhancers, graphs of the metrics evaluating the superenhancers quality, protein-protein interaction networks and enriched metabolic pathways among other features. We have parallelised the most time-consuming tasks achieving a reduction up to 30% for a 15 CPUs machine. We have optimized the default parameters of NaviSE to facilitate its use. NaviSE allows different entry levels of data processing, from sra-fastq files to bed files; and unifies the processing of multiple replicates. NaviSE outperforms the more time-consuming processes required in a non-integrated pipeline. Alongside its high performance, NaviSE is able to provide biological insights, predicting cell type specific markers, such as SOX2 and ZIC3 in embryonic stem cells, CDK5R1 and REST in neurons and CD86 and TLR2 in monocytes. CONCLUSIONS: NaviSE is a user-friendly streamlined solution for superenhancer analysis, annotation and navigation, requiring only basic computer and next generation sequencing knowledge. NaviSE binaries and documentation are available at: https://sourceforge.net/projects/navise-superenhancer/ .