Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research

Samuel Schepps; Jonathan Xu; Henry Yang; Jenna Mandel; Jaanvi Mehta; Julianna Tolotta; Nicole Baker; Volkan Tekmen; Neda Nikbakht; Paolo Fortina; Ignacia Fuentes; Bonnie LaFleur; Raymond J. Cho; Andrew P. South

doi:10.1515/cclm-2023-1245

Article Publicly Available

Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research

Samuel Schepps , Jonathan Xu , Henry Yang , Jenna Mandel , Jaanvi Mehta , Julianna Tolotta , Nicole Baker , Volkan Tekmen , Neda Nikbakht , Paolo Fortina , Ignacia Fuentes , Bonnie LaFleur , Raymond J. Cho and Andrew P. South

Published/Copyright: April 25, 2024

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From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 10

Abstract

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) are two emerging research technologies that uniquely characterize gene expression microenvironments on a cellular or subcellular level. The skin, a clinically accessible tissue composed of diverse, essential cell populations, serves as an ideal target for these high-resolution investigative approaches. Using these tools, researchers are assembling a compendium of data and discoveries in healthy skin as well as a range of dermatologic pathophysiologies, including atopic dermatitis, psoriasis, and cutaneous malignancies. The ongoing advancement of single-cell approaches, coupled with anticipated decreases in cost with increased adoption, will reshape dermatologic research, profoundly influencing disease characterization, prognosis, and ultimately clinical practice.

Keywords: spatial transcriptomics; gene expression; skin; single cell transcriptomics

Introduction

Aims

As research methodologies evolve towards an increasingly complex future, it is important to track their impact as they develop. Here we focus on two emerging technologies: single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), methodologies that can measure the gene expression of samples at increasing cellular resolution. This review aims to examine how these technologies are shaping our knowledge and the future research in the field of dermatology and using recent examples from a broad range of skin conditions.

Overview of single-cell RNA sequencing

ScRNA-seq, a successor to so-called “bulk” RNA sequencing, is a technique that obtains transcriptomic data from single cells, identifying and classifying cell subpopulations based on molecular similarity [1]. First demonstrated in 2009 [2], most scRNA-seq approaches share several basic steps depicted in Figure 1. A single-cell suspension must be generated from the healthy or lesional tissue of interest. This is typically accomplished by a blood draw or mechanical and enzymatic digestion of solid tissues. Specific cell types may then be isolated via antibody staining and fluorescence-activated cell sorting (FACS) or another, similar, method. For instance, a macrophage population could be isolated using a fluorescence-conjugated antibody to CD68; several distinct antibodies could simultaneously isolate multiple such subpopulations. Viable cells are separated from dead or dying cells and cellular and tissue debris and single cells must then be physically separated from one another, typically using an emulsification process. Individual cells are then lysed, and a barcoded cDNA library specific to each cell is generated from the resulting RNA. Thousands of such libraries, each corresponding to a single cell, can then be sequenced and analyzed in aggregate using established analysis and visualization pipelines [3].

Figure 1:

An overview of sc-RNA seq. (1) First, single cells are isolated and separated from tissue samples. (2) Next, cDNA libraries are prepared from the transcriptomes of isolated cells. Then, generated cDNA libraries are sequenced and the data is normalized and filtered. (3) Finally, results are analyzed and visualized, often using tools based on Uniform Manifold Approximation and Projection (UMAP) or t-distributed Stochastic Neighbor Embedding (t-SNE).

This basic method has already spawned variants and subvariants allowing for niche use and integration alongside other emerging technologies [1, 4]. For example, Smart-seq uses template-switching technology to sequence full length mRNAs from single cells, not just the 5′ or 3′ ends, in order to improve transcriptome coverage and characterize alternative splicing [5]. Another method, Drop-seq, utilizes nanoliter-scale aqueous droplets to co-encapsulate each cell with a distinct barcode that specifies each transcript’s cell of origin, allowing for large-scale library generation of multiple cell types that is both faster and cheaper, due to its larger scalability and faster throughput compared with microfluidics-based methods [6]. Additionally, another microfluidic-based method, seq-well, houses cells in individual capture beads that are separated into wells sealed with a semipermeable membrane. Beads are then removed and cells lysed to isolate the mRNA [7, 8], allowing for large-scale analysis from a relatively small sample with minimal cross-contamination risk [9].

Previously, researchers were limited to sequencing the total RNA isolated from a piece of tissue (bulk RNA-sequencing), losing information about cellular composition or architecture. Thus, scRNA-seq represents a singularly powerful method of resolving the expression environment of a given sample, exploring cell-level processes in both healthy and diseased tissue. For instance, whereas disease-driving transcriptional change in a small subset of immune cells would be obscured within bulk sequencing, scRNA-seq enables us to identify dramatic changes in those few cells that may drive pathologic states. Furthermore, complementary techniques can layer additional phenotypic information over single-cell data already ascertained via scRNA-seq. For example, expression levels of specific surface proteins in a given cell population can be measured using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method that uses labeled antibodies with binding specificity for target surface proteins to measure their expression within an scRNA-seq sample [10]. What scRNA-seq lacks is a description of tissue architecture and while ligand-receptor binding and downstream signaling partners can be inferred from individual cells to suggest proximity or directional interaction [1, 3, 9], microenvironmental niches and cell-cell proximity remain unknown.

Overview of spatial transcriptomics

ST captures complexities and interactions in the microenvironmental organization of cells that are missed by scRNA-seq. Multiple methods are available to gather ST data, including in situ hybridization (ISH) and in situ sequencing (ISS), both of which involve targeting known RNA sequences with a fluorescent probe for direct quantification (ISH) or subsequent sequencing via cDNA generation and amplification (ISS). Another method, in situ capture (ISC), provides an unbiased profile of a spatial transcriptome by capturing transcripts directly from tissue samples and sequencing ex situ using next-generation sequencing (NGS), which utilizes positionally barcoded RT primers to hybridize extracted mRNA and generate a cDNA library [11]. In all of these cases, sections of tissue are mounted onto glass slides where histological data are first collected and used to anchor transcriptional information to a physical location.

One approach to ST is to utilize oligonucleotide barcodes combined with antibody tags or ISH probes on formalin-fixed paraffin-embedded (FFPE) samples to precisely quantify the gene and protein expression of the sample [12]. The probes are hybridized to the immobilized sample RNA and each probe contains a photo-cleavable linker released upon exposure to UV light which is then collected and counted or sequenced using NGS, avoiding the bias introduced by amplification, reverse transcription, or library preparation. Ultraviolet light can be directed precisely to regions of the sample such that transcriptomes and associated protein markers can be segmented [12]. These data are analyzed using a variety of analytic tools and can be integrated with bulk and/or scRNA-seq to provide a comprehensive transcriptional profile of a given tissue [1, 11]. For example, by spatially representing and integrating different cell populations [13, 14], single-cell data sets can be used for deconvolution of spatial data which often is limited to areas larger than a single cell. Such techniques have already identified expression clusters unique to patients with a common disease and localized these clusters within tissues. These approaches also identify the relative clonality of cell subpopulations, such as dominant malignant T cell populations in patients with cutaneous T-cell lymphoma (CTCL) [15].

Spatial profiling technologies do require correction of batch effects [16] and most forms of the technology capture and barcode cells within a predetermined area and consequently, the data’s resolution may be much larger than a single cell [17]. One of the greatest limitations at this time is the overall cost and labor [18]. Tradeoffs remain between efficacy and efficiency of RNA binding/detection, as well as between efficiency and spatial resolution [11].

Skin: unraveling complexity

The skin is an organ serving vastly different functions, including thermal regulation, external sensation, and innate immunity [16, 18]. Critical to these ends, the skin’s multiple layers are comprised of heterogeneous cell types, each with their own transcriptomic profiles and environmental interactions [9]. By resolving transcriptomes to the cellular and subcellular level, scRNA-seq and ST offer insights into the function of specific skin cell populations for both homeostatic and disease processes [1, 3, 9]. Single-cell RNA-sequencing and ST data also power computational approaches to resolve complex-cell interactions in the cutaneous microenvironment [19].

Applications of sc-RNA-seq and spatial transcriptomics in dermatological research

We now discuss applications of these technologies in recent dermatological research. The list cannot be exhaustive; however, it provides insight into the range, depth, and scope of data and experimentation.

Healthy skin

Many of the earliest uses of scRNA-seq and ST in dermatology, particularly scRNA-seq, characterized healthy skin and its development, contributing to a so-called “single cell atlas of skin”. These efforts are providing transcriptional granularity to sub populations of skin cells in vivo and documenting how this transcriptional network changes as the skin responds to homeostatic shifts. As far back as 2006, Jensen and Watt [20] used an early version of scRNA-seq to identify leucine-rich repeats and immunoglobulin-like domains 1 (LRIG1) as a marker of human epidermal stem cells, helping to reveal its roles in regulating keratinocyte proliferation, epidermal growth factor (EGF) sensitivity, ERK MAPK activity, and Myc transcription. Watt and colleagues subsequently used scRNA-seq to characterize the epidermal microenvironment and its effects on stem cells, focusing on interactions that direct cell fate [21]. Later studies have also used scRNA-seq to characterize the development of a stratified epidermis, including newly described transition states and changes over time in cell-cell communication among keratinocytes, the major cell type that forms the epidermal barrier [22]. Reports have also described differences between human-skin-equivalent organoids grown in culture and skin samples taken in vivo [23]. Cheng et al. more broadly used scRNA-seq to profile human keratinocytes from distinct anatomic cutaneous sites [24], both revealing high inflammatory tone in resting scalp keratinocytes and also re-identifying from their data the classic histopathologic epidermal layers. Characterization of healthy skin through these methods can then be contrasted with diseased skin. Through such comparisons, Thrane et al. [25] identified keratinocyte subpopulations that were amplified in lesional skin from both psoriasis and zinc-deficiency dermatitis, suggesting a partially shared pathogenesis.

Combinations of scRNA-seq and ST have been used in several studies to classify dermal fibroblasts, yielding differing, heterogeneous, and seemingly mutually exclusive interpretations of subpopulations [26], [27], [28], [29], [30], conflicts that were later reconciled through advances in computational analysis. Meta-analyses of multiple single-cell datasets have now resolved cutaneous fibroblasts into 10 distinct fibroblast populations [31]. The major subpopulations, listed in descending order of prevalence, were as follows: A, dermal and ECM homeostasis defined by ELN, MMP2, QPCT, and SFRP2 expression; B, immune surveillance and pro-inflammatory processes defined by APOE, C7, CYGB, and IGFBP7 expression; and C, specialized subpopulations such as dermal papilla cells and dermo-hypodermal junction fibroblasts defined by DKK3, TNMD, TNN, and SFRP1 expression [31].

Aging skin

Single-cell RNA-seq and ST have already furthered our molecular understanding of the cutaneous aging process. One study used scRNA-seq to investigate eyelid skin from healthy patients of different ages to identify numerous transcriptional alterations throughout all 11 identified cell types, greatest in fibroblasts. Changes associated with aging included the upregulation of MMP2, involved in extracellular matrix (ECM) remodeling, and the downregulation of genes associated with cell development and anti-inflammatory processes. Importantly, the HES1 transcription factor in fibroblasts and the KLF9 transcription factor in keratinocytes were both inhibited in aged skin, ostensibly promoting inflammation and cellular senescence. Notably, successful re-activation of HES1 via genetic or pharmacologic methods resulted in reversal of aspects of the senescence phenotype in aged dermal fibroblasts [32].

Another group used scRNA-seq to determine that macrophage polarization increased with aging, alongside a 50 % increase in macrophage quantity that could be traced to changes in the monocytic developmental lineage [33]. Aged macrophages expressed a pro-inflammatory, M1 phenotype associated with a downregulation of ECM protein expression in dermal fibroblasts, perhaps contributing to skin thinning during aging. Recently, Wang et al. developed regulatory networks from scRNA-seq data of skin of different ages and identified basal cells, spinous cells, mitotic cells, and fibroblasts as containing the most significant transcriptomic changes associated with aging. The CTCF and RAD21 transcription factors were attenuated in keratinocytes of aging skin; downregulating these factors replicated some cellular aging phenotypes [34].

Wound healing

Wound healing represents a $10–20B annual expenditure for the US healthcare system alone [35] and thorough exploration of transcriptional changes and characterization of cell-cell interactions might illuminate new therapies. A 2014 scRNA-seq study of skin wound-healing identified expression-level differences affecting cell migration in response to mechanical injury [36]. Later studies focused on fibroblast transcriptomic evolution during wound healing. A recent study used scRNA-seq to analyze fibroblasts transfected with an anti-miR-200b oligonucleotide that augmented natural changes during wound healing. The miRNA knockdown produced a vasculogenic, endothelial-like fibroblast state that promoted wound healing [37]. A detailed study in mice showed that activated macrophages, as identified by their expression of Msr1, were localized to the wound center. Timp1, an enzyme involved in ECM remodeling, was expressed by fibroblasts in the healing basal dermis, while Thbs2, a protein involved in interactions between the cell and the ECM, was predominantly expressed by fibroblasts at the scar apex [38].

Several in vivo scRNA-seq studies in mice have implicated Wnt signaling as central to a regenerative fibroblast phenotype in different wound healing mechanisms. For example, Mascharak et al. [39] integrated scRNA-seq as part of a multi-omics approach to identify divergent fibroblast expression pathways associated with wound healing. A pro-fibrotic, scarring pathway inhibited healing, while an alternative, regenerative pathway associated with Wnt signaling activation. Trps1 was nominated as a key mediator of Wnt signaling in the regenerative pathway [39].

Another study identified a distinct transcriptional signature in fibroblasts in large compared to small wounds [40]. Fibroblasts in the center of large wounds are distinct from those on the periphery, which express genes associated with scar formation, such as Dlk1, Sca1, and Mest. Wound-center fibroblasts expressed higher levels of Runx1 and retinoid-binding proteins, like Crabp1 and Fabp5. The study also found Runx1 to regulate the regenerative capacity of fibroblasts, implying a latent and tunable capacity of fibroblasts to modify wound healing. Meanwhile, Phan et al. used scRNA-seq to identify Lef1, another Wnt pathway regulator, as key to a pro-regenerative fibroblast phenotype [41]. Macrophage-mediated phagocytosis of the Wnt inhibitor SFRP4 directed aberrant activity of myofibroblasts in the wound, leading to fibrotic scarring [42]. Other studies have characterized specific transcriptomic alterations in mucosal associated invariant T cells [43], monocytes [44], Langerhans cells [45], and melanocytes [46] in the context of wound healing.

Cutaneous malignancy

Skin is the most prevalent site of cancer development [47] and offers the opportunity to genetically profile a normal tissue as it progresses through tissue damage and inflammation, into pre-cancerous lesions, and on to full-blown malignancy [48]. Single-cell and ST analyses are ideal to identify the key cell types and interactions in this progression and have already determined spatially localized subpopulations of cells germane to the major sub-types of skin cancer. Early studies have phenotypically characterized and identified tumor heterogeneity, tumor and immune cell subpopulations, and profiled cellular communication in the tumor microenvironment. Exploration of tumor-immune cell dynamics and cell signaling pathways, as well as intratumoral heterogeneity, has revealed novel therapeutic targets and continues to provide insight into how we can harness the body’s immune system to treat cancer.

Melanoma

A 2016 study by Tirosh et al. first used scRNA-seq to show that cells of malignant melanoma exist on a continuum between two transcriptionally distinct states (MITF-high to AXL-high), challenging a binary model [49]. MITF-high cells responded to mitogen-activated protein kinase targeted therapy, and AXL-high cells were associated with treatment resistance. Later, another group used scRNA-seq to identify a transcriptomic signature of melanomas that exhibit immune checkpoint inhibitor resistance, rationalizing second-line regimens [50].

Characterizations of the tumor microenvironment have also addressed melanoma detection. Through ST, Kiuru et al. discovered marked S100A8 elevation in keratinocytes derived from melanoma samples, implicating epidermal damage as a possible harbinger of melanoma development [51]. Spatial transcriptomics have also probed the immune microenvironment of melanoma metastases, identifying IGLL5-predominant lymphoid tissue in close proximity to the tumor cells, while upregulated CD74 lymphoid tissue was additionally found further away [52]. Melanoma positive lymph nodes also displayed elevated PMEL expression, with elevated SPP1 within the primary tumor as well as some of the surrounding, peri-lesional tissue. A separate multi-omics study of brain metastases found spatial restriction of plasma cell-dominated lymphoid aggregates and identified a dysfunctional, checkpoint-activated CD8⁺ compartment [53]. This high resolution landscape provides far greater insight into response and response to the immunotherapeutic approaches that have now standard of care for advanced melanoma.

Squamous cell carcinoma

Cutaneous squamous cell carcinoma (SCC), the second most common skin malignancy affecting individuals in the United States [47], is a keratinocyte malignancy characterized by disrupted tissue polarity, aberrant differentiation, and eventual invasion into the underlying dermis. In 2020, Ji et al. combined scRNA-seq and ST to compare SCC with normal skin and identified four distinct keratinocyte populations in the tumor [54]. Three epidermal keratinocyte populations were consistent with those in normal skin, while unique, SCC-specific keratinocytes localized to the leading edge of the tumor in a fibrovascular niche and were proposed to play a role in stromal interactions, promoting tumorigenesis [54]. Similar data were reported in head and neck SCC, suggesting a distinct tumor-edge population whose abundance might inform both prognosis and therapy [55].

More recently, ST analysis of sun-protected skin compared to sun-damaged skin and the SCC precursor lesion actinic keratosis (AK) from matched individuals localized the greatest transcriptional change to the dermal compartment [56]. Fewer changes were seen in the keratinocyte compartment from which SCCs arise. These ST data thus suggest a closer examination of the tumor microenvironment to more accurately trace SCC initiation and progression in the skin [56].

Cutaneous T-cell lymphomas

CTCLs are a heterogeneous group of localized lymphomas of which mycosis fungoides (MF) is the most common. CTCLs can also progress to blood involvement, as seen in Sezary syndrome [57]. Efforts to characterize the microenvironments of these tumors using scRNA-seq and ST have furthered the clinical characterization, diagnosis, prognosis, and treatment of CTCLs [15, 58]. For example, Borcherding et al. [59] used these tools in 2019 to associate CTCL cellular heterogeneity with disease prognosis and staging. Specifically, FOXP3, a marker expressed by malignant T cells during clonal expansion in CTCL, proved the most important early predictor of disease stage and progression [59]. Contemporaneously, Gaydosik et al. used scRNA-seq to characterize the various lymphocytes involved in advanced-stage CTCL [60]. In doing so, they identified a transcriptomic signature for CTCL lymphocytes, composed of genes promoting cell-cycle progression, cellular proliferation, and cell survival. However, they also identified patient-specific heterogeneity in the effector vs. exhausted CD8⁺ T cells infiltrating tumor.

Heterogeneity was also probed by Buus et al. in a study that used scRNA-seq to divide CTCL cells from patients with SS into several subpopulations, which displayed differing sensitivities to standard HDAC-inhibitor therapies [61]. Gaydosik et al. used scRNA-seq to further explore heterogeneity between T cell clonotypes of a single tumor [15]. Interestingly, while CTCL cases were invariably composed of multiple clonotypes, the dominant clonotypes shared a common expression pattern between patients. This pattern consisted of genes involved in oxidative phosphorylation, de novo nucleotide synthesis, adherens junction remodeling, and FAT2 signaling cascades. Similar heterogeneity was found in Sezary cancers, though no major overlaps between the expression profiles of the dominant clonotypes were detected. Non-clonal T lymphocytes were also characterized and as in other studies, found to exhibit a predominantly anti-inflammatory, exhausted phenotype potentially allowing tumor progression.

Most recently, researchers have used ST to explore the CTCL microenvironment. An scRNA-seq study from the Koralov group traced the clonal evolution of heterogeneity between T cell populations involved in CTCL in skin and blood [62]. Their lineage tracing suggested that the skin microenvironment preferentially promoted a rapidly expanding malignant state, distinct from mutational identity. In a separate study of granulomatous slack skin (a very rare form of CTCL), three clusters of pathogenic macrophages were identified. The first, a CD163⁺/CD206⁺ cluster, demonstrated a tumor-associated, M2-like phenotype, with upregulation of genes involved in T cell inflammatory identity and tumor progression, such as ZFP36, CCL2, TNFAIP6, and KLF2. The second, an APOC1⁺/APOE⁺ cluster, demonstrated a phenotype distinct from both M1 and M2 macrophages, with upregulation of genes involved in lipid metabolism. The third, a D11c⁺/LYZ⁺ cluster demonstrated an M1-like phenotype, with upregulation of gene programs involved in ECM and tissue remodeling, including MMP9 and MMP1. The characterization of these macrophage clusters thus provides a theoretical framework for MMP9 inhibitor therapy counteracting the cutaneous relaxation of granulomatous slack skin [58]. A second study used ST to characterize MF with large cell transformation, with a particular focus on CD30⁺ lesional areas, which indicate localized large cell transformation. The study found that M2 macrophages and fibroblasts represented the predominant stromal cell types in these regions, and that genes involved in ECM remodeling and degradation, oxidative stress, and M2 macrophage markers were associated with a malignant phenotype [63].

Inflammatory conditions

Inflammatory conditions such as psoriasis (PV) and atopic dermatitis (AD) remain among the most clinically prevalent and researched diseases in dermatology. Both conditions were thus the logical targets of the earliest scRNA-seq and ST studies, deepening the sophisticated genetic understanding of these diseases that ushered in the current era of biological medications.

Psoriasis

PV is an inflammatory skin disease driven primarily by the overactivity of skin T lymphocytes. Dysfunctional T cells overstimulated by IL-23 appear to release pathogenic levels of cytokines, creating a pro-inflammatory environment enriched in diverse cell populations and immune gene expression [64]. One early study reported elevation in keratinocyte pro-inflammatory gene expression, including upregulation of S100A7, S100A8, S100A9, and IFI27. Notably, this inflammatory pattern was also identified in healthy control scalp keratinocytes, revealing a mechanism likely contributing to the frequent itch and scale at this anatomic site [24]. Other scRNA-seq profiles have pointed to CXCL13 as a specific PV biomarker whose elevation correlates with disease severity [65, 66]. The Liu et al. study was also able to transcriptomically differentiate the inflammatory state between psoriatic skin-resident CD8⁺ cells and those infiltrating melanomas [65]. Another report integrating flow cytometry with scRNA-seq identified a CD14-expression dendritic cell (DC) subset co-producing IL-1B and IL-23A, key pathological reprogrammers of T17 cells in the skin [67]. Single-cell studies have also re-examined the heterogeneity of skin-resident T cells in PV. For example, Cook et al. showed that inflammation-suppressing genes, including the tristetraprolin family of RNA binding proteins, are diminished in more inflamed skin-resident T cells, creating a continuum of pathogenicity on the single cell level [68].

Approaches beyond reverse-emulsion-droplet-based scRNA-seq have been used to refine psoriatic T cell subpopulations. Using Seq-Well S³ (“Second strand synthesis”)³, researchers identified a sub-cluster of psoriatic skin T cells expressing NR4A1, a transcription factor previously associated with T cell dysfunction and aberrant development [69, 70]. A recent study by Schabitz et al. [71] was among the first to use ST to analyze PV, reporting surprisingly low abundance of pathogenic cytokine transcripts such as IFNG, IL13, and IL17A adjacent to skin T cells. A different study implicated SHP2 in the promotion of myeloid cell aggregation in PV [72]. Approaches integrating scRNA-seq and ST have also identified novel circuits from epidermal to dendritic cells as potential contributors to pathogenesis [73].

High-resolution profiles have also suggested new roles for keratinocytes in PV. For instance, Schwingen et al. [74] proposed a pro-inflammatory role for lipid metabolism in keratinocytes, with downregulation of pro-inflammatory genes (e.g., CCL20, IL8, and S100A7) in the absence of cholesterol [24]. Another recent study integrating ST with scRNA-seq found that IL-26-expressing T_H17 cells induced basal keratinocytes in psoriatic lesions to produce TGF-β1. TGF-β1 was hypothesized then play a feedback role, enhancing differentiation of IL-17A producing cells [75]. ST has also been used to differentiate disease mechanisms in mild and severe disease [76] and order specific immune-spatial interactions in progression of disease [77].

Atopic dermatitis

Similarly to PV, AD’s pathogenesis appears driven by aberrant infiltrative T cell activity in lesional skin, amenable to high-resolution transcriptomic investigation [3]. Along with PV, AD represents one of the most studied diseases using ST and scRNA-seq [9]. Unlike PV, AD is driven by T_H2 and T_H22 cells, thus the driving cytokines are distinct [3]. He et al.’s landmark 2020 paper used scRNA-seq to identify unique cell subpopulations and biomarkers in lesional skin [28]. A CCL2 and CCL19 co-expressing subpopulation of COL6A5⁺COL18A1⁺ fibroblasts unique to AD lesions was identified alongside a LAMP3⁺ subset of DCs that expressed the CCR7 receptor for CCL19 receptor, a known atopic marker. Together, these findings indicate a role for fibroblast signaling to DCs and other immune cells to promote AD development.

A technical study contrasted scRNA-seq with bulk RNA sequencing in analysis of AD lesions [78]. Only 18 % of differentially expressed genes (DEGs) identified via scRNA-seq overlapped with those identified by bulk RNA sequencing, with many scRNA-seq-specific genes representing cell-specific markers, such as FABP5, which are significantly upregulated in keratinocytes compared to endothelial cells or pericytes. Meanwhile, those DEGs identified solely by bulk RNA sequencing may have been lost in scRNA-seq datasets because of low expression, pre-analytic filtering, and quality control. Single-cell RNA-seq applied to solid tissues such as the skin also requires longer mechanical and enzymatic digestion than bulk profiles, stressing cells and altering RNA profiles. Thus, scRNA-seq profiles are likely to benefit from cross-referencing and integration with bulk RNA-seq and ST (Figure 2).

Figure 2:

An overview of ST. Cellular transcriptomes may be captured either in situ using a targeted probe for biased, higher efficacy sequencing, or captured and barcoded in bulk and sequenced ex situ for an unbiased screen. In either case, raw data must be further processed, analyzed, and visualized.

Rojahn et al. integrated scRNA-seq and interstitial fluid proteomics to identify myeloid, not T cells, as the most enriched cell population in lesional AD. IL-13 and IL-22, in addition to being expressed in pathogenic T_H2 and T_H22 cells, were also detected in IL-26-expressing proliferating T cells and natural killer cells (NKs) [79]. Another scRNA-seq study tracing development of innate lymphoid cells (ILC) in AD found that many CRTH⁺ ILCs in lesional skin displayed innate infidelity and could co-express markers belonging to cells in the T_H2 lineage, such as GATA3 and IL13, or the T_H3/T_H17 lineage, including RORC, IL22, and IL26. These findings expand a possible role for ILCs in AD pathogenesis [80].

Clinicopathologically intermediate rashes

Mining the interface between well-defined conditions like atopic dermatitis and PV, studies have also sought to define the exact transcriptomic differences delineating such classes in human [81] and model systems [82], leading to the development of an algorithmic resource to place clinicopathologically ambiguous rashes along a bipolar spectrum, based on T cell transcriptomics [83–85]. Anecdotal reports suggest that such profiling can help match diseases to effective therapeutics in challenging cases [86].

Autoimmune skin disorders

Skin autoimmunities, because they have been less exhaustively studied than the autoinflammatory conditions discussed above, represent particularly compelling areas to interrogate using ST and scRNA-seq, especially as the findings can be compared with earlier studies of circulating immune cells in these disorders.

Systemic lupus erythematosus

Cutaneous lupus erythematosus (CLE) is believed to arise from a complex mix of genetic and environmental factors. In 5–25 % cases, isolated skin disease progresses to systemic lupus erythematosus (SLE) [87], but cutaneous-only manifestations represent some of the most common manifestations of lupus [88]. Single cell transcriptomic studies have been particularly important in validating the importance of interferon overactivity in lupus, consistent with the now FDA-approved status of a monoclonal antibody inhibitor of type I interferon receptor subunit 1 [89]. Elevation of type I interferon response genes have been detected in both lesional keratinocytes and affected kidney tubules of affected individuals [90]. In lesional CLE, four types of fibrocytes were expanded, each of which demonstrated an upregulation of interferon response genes. CLE keratinocytes also showed increased expression of interferon response genes, as well of chemokine and chemokine receptor genes, particularly CCL20 [87]. Pediatric SLE blood samples profiled by scRNA-seq showed upregulation of interferon-stimulated genes in multiple immune cell types, including monocytes, CD4⁺ and CD8⁺ T cells, NK cells, conventional and plasmacytoid DCs, and B cells. These genes were found to differentiate SLE patients from healthy children and the abundance of some transcripts correlated with disease activity [91].

Outside the interferon pathway, scRNA-seq studies have identified six candidate pathogenic genes (D83, ELF4, ITPKB, RAB27A, RUNX3, ZMIZ1) upregulated in circulating B cells of SLE patients under the control of 12 distinct transcription factors (HNF1B, POU3F2, TFAP2A, ZNF740, EWSR1-FLI1, MAF, MAFA, NFIB, NR2C2 (var. 2), REL, TBX4, and TBX5) [92]. Single-cell RNA-seq has also profiled T-cell receptors and B cell receptors in lupus and more broadly characterized the identities and types of circulating immune cells unique to SLE [93].

The largest transcriptomic analysis of SLE has distinguished recurrent abnormalities in almost all major immune cell populations, albeit with a unifying decrease in the naïve CD4⁺ T cell population and an increase in the GZMH⁺ CD8⁺ T cell population in SLE [94]. The immunological effects of disease-modifying antirheumatic drugs, such as the anti-CD38 monoclonal antibody daratumumab in the treatment of refractory SLE, have also been tracked using scRNA-seq [95]. Treatment with daratumumab demonstrated reductions in plasma cell counts, interferon type I activity, and T-cell transcripts associated with chronic inflammation, potential measures of treatment efficacy [95].

Vitiligo

Vitiligo is an autoimmune condition driven by CD8⁺ reactivity against skin melanocytes, resulting in progressive pigmentation loss. A study of stable disease integrating scRNA-seq with noninvasive multiphoton microscopy imaging found enrichment of discrete keratinocyte subpopulations harboring upregulated oxidative phosphorylation pathways [96]. These keratinocytes specifically secreted increased CXCL9 and CXCL10, suggesting a role for these cytokines in vitiligo persistence.

Conclusions

Despite their relatively recent development, scRNA-seq and ST have already reframed dermatological research. These emerging technologies have played key roles in the comprehensive characterization of the microenvironments of developing, healthy, and aging skin [20, 26, 33], as well as identifying novel components critical to wound healing [39]. They are providing powerful insight into the disease mechanisms for a range of dermatologic conditions, including inflammation, autoimmunity, and cutaneous malignancies, both rare and common [15, 54, 74, 94].

Integration of these technologies with evolving computational, proteomic, and genomic methods will amplify the component methods beyond the sum of their parts. With improvements on the horizon that promise to lower cost, increase sensitivity, and increase throughput, without tradeoffs in efficacy or efficiency, we anticipate that scRNA-seq and ST will become a dominant tool in the research setting and influence clinical medicine by defining new disease subclasses and therapeutics.

Corresponding author: Andrew P. South, PhD, Department of Pharmacology, Physiology and Cancer Biology, Thomas Jefferson University, 233 S. 10th Street, BLSB 406, Philadelphia, PA19107, PA, USA; and International Federation of Clinical Chemistry Working Group on Single Cell and Spatial Transcriptomics, Milan, Italy, E-mail: andrew.south@jefferson.edu

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Received: 2023-11-03

Accepted: 2024-02-29

Published Online: 2024-04-25

Published in Print: 2024-09-25

Articles in the same Issue

https://doi.org/10.1515/cclm-2023-1245

Keywords for this article

spatial transcriptomics; gene expression; skin; single cell transcriptomics