Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.

Cellular-state information between generations of developing cells may be propagated via regulatory regions. We report consistent patterns of gain and loss of DNase I-hypersensitive sites (DHSs) as cells progress from embryonic stem cells (ESCs) to terminal fates. DHS patterns alone convey rich information about cell fate and lineage relationships distinct from information conveyed by gene expression. Developing cells share a proportion of their DHS landscapes with ESCs; that proportion decreases continuously in each cell type as differentiation progresses, providing a quantitative benchmark of developmental maturity. Developmentally stable DHSs densely encode binding sites for transcription factors involved in autoregulatory feedback circuits. In contrast to normal cells, cancer cells extensively reactivate silenced ESC DHSs and those from developmental programs external to the cell lineage from which the malignancy derives. Our results point to changes in regulatory DNA landscapes as quantitative indicators of cell-fate transitions, lineage relationships, and dysfunction.

Spectral clustering of single-cell multi-omics data on multilayer graphs.

MOTIVATION: Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. RESULTS: We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. AVAILABILITY AND IMPLEMENTATION: The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum. All public data used in the article are accurately cited and described in Materials and Methods and in Supplementary Information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods.

Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to map complete DNA methylomes. These include whole-genome bisulfite sequencing (WGBS, MethylC-seq, or BS-seq), reduced-representation bisulfite sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. These methods yield largely comparable results but differ significantly in extent of genomic CpG coverage, resolution, quantitative accuracy, and cost, at least while using current algorithms to interrogate the data. None of these existing methods provides single-CpG resolution, comprehensive genome-wide coverage, and cost feasibility for a typical laboratory. We introduce methylCRF, a novel conditional random fields-based algorithm that integrates methylated DNA immunoprecipitation (MeDIP-seq) and methylation-sensitive restriction enzyme (MRE-seq) sequencing data to predict DNA methylation levels at single-CpG resolution. Our method is a combined computational and experimental strategy to produce DNA methylomes of all 28 million CpGs in the human genome for a fraction (<10%) of the cost of whole-genome bisulfite sequencing methods. methylCRF was benchmarked for accuracy against Infinium arrays, RRBS, WGBS sequencing, and locus-specific bisulfite sequencing performed on the same human embryonic stem cell line. methylCRF transformation of MeDIP-seq/MRE-seq was equivalent to a biological replicate of WGBS in quantification, coverage, and resolution. We used conventional bisulfite conversion, PCR, cloning, and sequencing to validate loci where our predictions do not agree with whole-genome bisulfite data, and in 11 out of 12 cases, methylCRF predictions of methylation level agree better with validated results than does whole-genome bisulfite sequencing. Therefore, methylCRF transformation of MeDIP-seq/MRE-seq data provides an accurate, inexpensive, and widely accessible strategy to create full DNA methylomes.

Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm.

DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. This includes one of the most widely applied technologies for measuring DNA methylation: methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been unavailable. Here, we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes, and combines MeDIP-seq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MRE-seq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities.

ALEA: a toolbox for allele-specific epigenomics analysis.

The assessment of expression and epigenomic status using sequencing based methods provides an unprecedented opportunity to identify and correlate allelic differences with epigenomic status. We present ALEA, a computational toolbox for allele-specific epigenomics analysis, which incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. ALEA provides a customizable pipeline of command line tools for allele-specific analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. The pipeline has been validated using human and hybrid mouse ChIP-seq and RNA-seq data. AVAILABILITY: The package, test data and usage instructions are available online at http://www.bcgsc.ca/platform/bioinfo/software/alea CONTACT: : mkarimi1@interchange.ubc.ca or sjones@bcgsc.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Short cell cycle duration is a phenotype of human epidermal stem cells.

BACKGROUND: A traditional view is that stem cells (SCs) divide slowly. Meanwhile, both embryonic and pluripotent SCs display a shorter cell cycle duration (CCD) in comparison to more committed progenitors (CPs). METHODS: We examined the in vitro proliferation and cycling behavior of somatic adult human cells using live cell imaging of passage zero keratinocytes and single-cell RNA sequencing. RESULTS: We found two populations of keratinocytes: those with short CCD and protracted near exponential growth, and those with long CCD and terminal differentiation. Applying the ergodic principle, the comparative numbers of cycling cells in S phase in an enriched population of SCs confirmed a shorter CCD than CPs. Further, analysis of single-cell RNA sequencing of cycling adult human keratinocyte SCs and CPs indicated a shortening of both G1 and G2M phases in the SC. CONCLUSIONS: Contrary to the pervasive paradigm, SCs progress through cell cycle more quickly than more differentiated dividing CPs. Thus, somatic human adult keratinocyte SCs may divide infrequently, but divide rapidly when they divide. Additionally, it was found that SC-like proliferation persisted in vitro.

A single-cell atlas of IL-23 inhibition in cutaneous psoriasis distinguishes clinical response.

Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of interleukin-23 (IL-23) signaling, but the genetic mechanisms underlying clinical responses remain poorly understood. Using single-cell transcriptomics, we profiled immune cells isolated from lesional psoriatic skin before and during IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was strongly attenuated. In contrast, poorly responsive patients were distinguished by persistent activation of IL-17-producing T (T17) cells, a mechanism distinct from alternative cytokine signaling or resistance isolated to epidermal keratinocytes. Even in IL-23 blockade-responsive patients, we detected a recurring set of recalcitrant, disease-specific transcriptional abnormalities. This irreversible immunological state may necessitate ongoing IL-23 inhibition. Spatial transcriptomic analyses also suggested that successful IL-23 blockade requires dampening of >90% of IL-17-induced response in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. Collectively, our data establish a patient-level paradigm for dissecting responses to immunomodulatory treatments.

Isolation of human cutaneous immune cells for single-cell RNA sequencing.

Single-cell RNA sequencing (scRNA-seq) allows for high-resolution analysis of transcriptionally dysregulated cell subpopulations in inflammatory diseases. However, it can be challenging to properly isolate viable immune cells from human skin for scRNA-seq due to its barrier properties. Here, we present a protocol to isolate high-viability human cutaneous immune cells. We describe steps for obtaining and enzymatically dissociating a skin biopsy specimen and isolating immune cells using flow cytometry. We then provide an overview of downstream computational techniques to analyze sequencing data. For complete details on the use and execution of this protocol, please refer to Cook et al. (2022)1 and Liu et al. (2022).2.

GLUT3 promotes macrophage signaling and function via RAS-mediated endocytosis in atopic dermatitis and wound healing.

The facilitative GLUT1 and GLUT3 hexose transporters are expressed abundantly in macrophages, but whether they have distinct functions remains unclear. We confirmed that GLUT1 expression increased after M1 polarization stimuli and found that GLUT3 expression increased after M2 stimulation in macrophages. Conditional deletion of Glut3 (LysM-Cre Glut3fl/fl) impaired M2 polarization of bone marrow-derived macrophages. Alternatively activated macrophages from the skin of patients with atopic dermatitis showed increased GLUT3 expression, and a calcipotriol-induced model of atopic dermatitis was rescued in LysM-Cre Glut3fl/fl mice. M2-like macrophages expressed GLUT3 in human wound tissues as assessed by transcriptomics and costaining, and GLUT3 expression was significantly decreased in nonhealing, compared with healing, diabetic foot ulcers. In an excisional wound healing model, LysM-Cre Glut3fl/fl mice showed significantly impaired M2 macrophage polarization and delayed wound healing. GLUT3 promoted IL-4/STAT6 signaling, independently of its glucose transport activity. Unlike plasma membrane-localized GLUT1, GLUT3 was localized primarily to endosomes and was required for the efficient endocytosis of IL-4Rα subunits. GLUT3 interacted directly with GTP-bound RAS in vitro and in vivo through its intracytoplasmic loop domain, and this interaction was required for efficient STAT6 activation and M2 polarization. PAK activation and macropinocytosis were also impaired without GLUT3, suggesting broader roles for GLUT3 in the regulation of endocytosis. Thus, GLUT3 is required for efficient alternative macrophage polarization and function, through a glucose transport-independent, RAS-mediated role in the regulation of endocytosis and IL-4/STAT6 activation.

A Novel Potential Role for Monocytes Revealed by Single Cell Analysis of Immunotherapy Induced Immune Related Adverse Events.

Immune related adverse events (irAEs) are one of the leading causes of discontinuation of cancer immunotherapy treatment. Despite extensive research into the frequency and types of irAEs, little is known about the cell types and pathways through which these drugs cause the observed side effects. To identify cell types and pathways of interest, we have analyzed single cell sequencing data of PBMCs from patients who developed skin irAEs as a result of their immunotherapy treatment. Using Azimuth's cell type identification software for PBMCs and GSEA pathway analysis, we found macrophage cell populations and reactive oxygen species related pathways to be upregulated. These results provide important groundwork to build a complete picture of the mechanisms which cause irAEs and finding ways to more effectively treat them.

Defining Patient-Level Molecular Heterogeneity in Psoriasis Vulgaris Based on Single-Cell Transcriptomics.

Identifying genetic variation underlying human diseases establishes targets for therapeutic development and helps tailor treatments to individual patients. Large-scale transcriptomic profiling has extended the study of such molecular heterogeneity between patients to somatic tissues. However, the lower resolution of bulk RNA profiling, especially in a complex, composite tissue such as the skin, has limited its success. Here we demonstrate approaches to interrogate patient-level molecular variance in a chronic skin inflammatory disease, psoriasis vulgaris, leveraging single-cell RNA-sequencing of CD45+ cells isolated from active lesions. Highly psoriasis-specific transcriptional abnormalities display greater than average inter-individual variance, nominating them as potential sources of clinical heterogeneity. We find that one of these chemokines, CXCL13, demonstrates significant correlation with severity of lesions within our patient series. Our analyses also establish that genes elevated in psoriatic skin-resident memory T cells are enriched for programs orchestrating chromatin and CDC42-dependent cytoskeleton remodeling, specific components of which are distinctly correlated with and against Th17 identity on a single-cell level. Collectively, these analyses describe systematic means to dissect cell type- and patient-level differences in cutaneous psoriasis using high-resolution transcriptional profiles of human inflammatory disease.

Classification of human chronic inflammatory skin disease based on single-cell immune profiling.

Inflammatory conditions represent the largest class of chronic skin disease, but the molecular dysregulation underlying many individual cases remains unclear. Single-cell RNA sequencing (scRNA-seq) has increased precision in dissecting the complex mixture of immune and stromal cell perturbations in inflammatory skin disease states. We single-cell-profiled CD45+ immune cell transcriptomes from skin samples of 31 patients (7 atopic dermatitis, 8 psoriasis vulgaris, 2 lichen planus (LP), 1 bullous pemphigoid (BP), 6 clinical/histopathologically indeterminate rashes, and 7 healthy controls). Our data revealed active proliferative expansion of the Treg and Trm components and universal T cell exhaustion in human rashes, with a relative attenuation of antigen-presenting cells. Skin-resident memory T cells showed the greatest transcriptional dysregulation in both atopic dermatitis and psoriasis, whereas atopic dermatitis also demonstrated recurrent abnormalities in ILC and CD8+ cytotoxic lymphocytes. Transcript signatures differentiating these rash types included genes previously implicated in T helper cell (TH2)/TH17 diatheses, segregated in unbiased functional networks, and accurately identified disease class in untrained validation data sets. These gene signatures were able to classify clinicopathologically ambiguous rashes with diagnoses consistent with therapeutic response. Thus, we have defined major classes of human inflammatory skin disease at the molecular level and described a quantitative method to classify indeterminate instances of pathologic inflammation. To make this approach accessible to the scientific community, we created a proof-of-principle web interface (RashX), where scientists and clinicians can visualize their patient-level rash scRNA-seq-derived data in the context of our TH2/TH17 transcriptional framework.

A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity.

The homeostatic mechanisms that fail to restrain chronic tissue inflammation in diseases, such as psoriasis vulgaris, remain incompletely understood. We profiled transcriptomes and epitopes of single psoriatic and normal skin-resident T cells, revealing a gradated transcriptional program of coordinately regulated inflammation-suppressive genes. This program, which is sharply suppressed in lesional skin, strikingly restricts Th17/Tc17 cytokine and other inflammatory mediators on the single-cell level. CRISPR-based deactivation of two core components of this inflammation-suppressive program, ZFP36L2 and ZFP36, replicates the interleukin-17A (IL-17A), granulocyte macrophage-colony-stimulating factor (GM-CSF), and interferon gamma (IFNγ) elevation in psoriatic memory T cells deficient in these transcripts, functionally validating their influence. Combinatoric expression analysis indicates the suppression of specific inflammatory mediators by individual program members. Finally, we find that therapeutic IL-23 blockade reduces Th17/Tc17 cell frequency in lesional skin but fails to normalize this inflammatory-suppressive program, suggesting how treated lesions may be primed for recurrence after withdrawal of treatment.

Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data.

Recent advances in next generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. Count data of surface proteins from the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology pose new computational challenges, and there is currently a dearth of rigorous mathematical tools for analyzing the data. This work utilizes concepts and ideas from Riemannian geometry to remove batch effects between samples and develops a statistical framework for distinguishing positive signals from background noise. The strengths of these approaches are demonstrated on two independent CITE-seq data sets in mouse and human.

Single-Cell Profiling Reveals Divergent, Globally Patterned Immune Responses in Murine Skin Inflammation.

Inflammatory response heterogeneity has impeded high-resolution dissection of diverse immune cell populations during activation. We characterize mouse cutaneous immune cells by single-cell RNA sequencing, after inducing inflammation using imiquimod and oxazolone dermatitis models. We identify 13 CD45+ subpopulations, which broadly represent most functionally characterized immune cell types. Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and antigen-presenting cells (APCs). Oxazolone also induces Il4/Il13 expression in newly infiltrating basophils, and Il4ra and Ccl24, most prominently in APCs. In contrast, imiquimod broadly upregulates Il17/Il22 and Ccl4/Ccl5. A comparative analysis of single-cell inflammatory transcriptional responses reveals that APC response to oxazolone is tightly restricted by cell identity, whereas imiquimod enforces shared programs on multiple APC populations in parallel. These global molecular patterns not only contrast immune responses on a systems level but also suggest that the mechanisms of new sources of inflammation can eventually be deduced by comparison to known signatures.

Single-Cell Transcriptomics Reveals Spatial and Temporal Turnover of Keratinocyte Differentiation Regulators.

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.

A20 and ABIN1 Suppression of a Keratinocyte Inflammatory Program with a Shared Single-Cell Expression Signature in Diverse Human Rashes.

Genetic variation in the NF-κB inhibitors, ABIN1 and A20, increase risk for psoriasis. While critical for hematopoietic immune cell function, these genes are believed to additionally inhibit psoriasis by dampening inflammatory signaling in keratinocytes. We dissected ABIN1 and A20's regulatory role in human keratinocyte inflammation using an RNA sequencing-based comparative genomic approach. Here we show subsets of the IL-17 and tumor necrosis factor-α signaling pathways are robustly restricted by A20 overexpression. In contrast, ABIN1 overexpression inhibits these genes more modestly for IL-17, and weakly for tumor necrosis factor-α. Our genome-scale analysis also indicates that inflammatory program suppression appears to be the major transcriptional influence of A20/ABIN1 overexpression, without obvious influence on keratinocyte viability genes. Our findings thus enable dissection of the differing anti-inflammatory mechanisms of two distinct psoriasis modifiers, which may be directly exploited for therapeutic purposes. Importantly, we report that IL-17-induced targets of A20 show similar aberrant epidermal layer-specific transcriptional upregulation in keratinocytes from diseases as diverse as psoriasis, atopic dermatitis, and erythrokeratodermia variabilis, suggesting a contributory role for epidermal inflammation in a broad spectrum of rashes.

Emergence and Evolution of Mutational Hotspots in Sun-Damaged Skin.

In this issue, Albibas et al. investigate the mutational nature of p53-immunopositive patches, commonly observed in sun-damaged skin. p53-immunopositive patches have long been suspected to be lineal precursors to actinic keratoses and cutaneous squamous cell carcinomas. However, the mutations actually giving rise to p53-immunopositive patches, and their relationship to skin cancer, have never been defined. The considerable clinical and economic costs of monitoring and treating sun-damaged skin demand we better understand the evolution of these common premalignancies.