Logo MSM

Call: +1.631.470.9640
Mon - Fri 10:00 am - 02:00 pm EST

Contact Us

Logo MSM Logo MSM Logo MSM

20 May 2021: Review Articles

Applications of Single-Cell Sequencing in Dermatology

Dandan Zou 1ABCDEF , Jue Qi 1BC , Wenjuan Wu 1AEG , Dan Xu 1BCF , Ying Tu 1F , Tongyun Liu 1BC , Juan Zhang 1BF , Xing Li 2F , Fengyan Lu 3F , Li He 1ADG*

DOI: 10.12659/MSM.931862

Med Sci Monit 2021; 27:e931862


ABSTRACT: Single-cell sequencing (SCS) is a promising new technique used to assess the genomics, transcriptomics, epigenetics, and other multi-omics at the single-cell level. In addition to elucidating the immune microenvironment and revealing the pathomechanisms of disease and drug resistance, SCS can profile the actual state of an individual cell and identify a novel cell type and differentiation trajectories, which cannot be achieved by bulk tissue sequencing technique. SCS technique serves as powerful tools to explore more meaningful biomarkers of diagnosis, prognosis, and new therapeutic targets in clinical practice. The SCS technique has been widely applied in the field of dermatology. In this review, we summarize the advances of SCS in dermatology.

Keywords: Cellular Microenvironment, Dermatology, Gene Expression Regulation, Genetic Heterogeneity, Molecular Targeted Therapy, Single-Cell Analysis


Single-cell sequencing (SCS) is a new technology for high-throughput sequencing analysis of genome, transcriptome, and epigenome at the level of a single cell. Almost all tissues and organs are composed of various types of cells. Compared to bulk sequencing of multicellular samples, SCS can determine the expression pattern of a precise gene in thousands of single cells, and comprehend the function and gene expression status of individual cells and the interaction between cells. In addition, SCS can identify new/rare cell populations, explore their function, and solve the problem of cellular heterogeneity, which could not be achieved by bulk sequencing. Since the first application of high-throughput single-cell whole-transcriptome sequencing in 2009 by Tang et al [1], the SCS technique is becoming a powerful tool for making breakthroughs in life science and clinical medical research. Now the SCS technique has been employed in single-cell genome sequencing (scDNA-seq), single-cell transcriptome sequencing (scRNA-seq), single-cell DNA methylation sequencing, single-cell histone modification sequencing, and single-cell chromatin structure sequencing (scATAC-seq). The main workflow includes: single-cell suspension preparation, single-cell capture and labeling, library preparation and sequencing, and data analysis. The commonly used platforms are Smart-Seq2, 10x Genomics, and Fluidigm C1.

Skin is the largest organ of the human body, with a complex structure and multiple cell types, exerting multiple functions such as permeability barrier, immunity, antimicrobial defense, and metabolism. The interactions between cells maintain cutaneous homeostasis and involve in the pathogenesis of diseases. Now SCS technique has been widely used in the analysis of both normal skin and diseased skin to understand the cutaneous biology, wound healing, pathogenesis, and progression of skin disorders. Here, we comprehensively review the progress of SCS applications in the field of dermatology.

Normal Skin And Hair Follicles

NORMAL SKIN: It is generally assumed that the same type of cells exerts the same function. However, results of SCS defy our conventional wisdom. Using scRNA-seq to analyze the transcriptional characteristics of keratinocytes (KCs), Cheng et al [2] showed that the KC subpopulations and inflammatory genes expression differed in the scalp, trunk, and foreskin. In particular, inflammation-related markers such as S100A7, S1008, S100A9, and IFI27 are upregulated in normal scalp interfollicular KCs, likely due to inflammation and pruritus in the scale, while in the foreskin, levels of proliferation-related transcripts are upregulated accompanied by increased IL1βhiCCL3hiCD14+ macrophages, which may play a role in immune surveillance.

In the traditional view, dermal fibroblasts are classified into papillary and reticular fibroblasts. However, analyses with SCS technique revealed that dermal fibroblasts consist of at least 4–7 different subpopulations in skin samples from the dorsal forearm, the female breast, and the abdomen [3–5]. Utilizations of spatial transcriptomics and RNAScope in situ hybridization show variation in spatial distribution of fibroblast subpopulations, suggesting that different fibroblast subpopulations may play distinct roles in extracellular matrix (ECM) production, inflammation, and epidermal remodeling. Recent studies demonstrated that the potential interactions of aged fibroblasts with other cells were significantly reduced in comparison to young fibroblasts [6]. Moreover, 3 macrophage and 6 dendritic cell (DCs) subpopulations, with distinct gene expression profiles, were identified in healthy human skin by scRNA-seq [7]. Identification of subpopulations of cells can improve our understanding of cutaneous biology.

HAIR FOLLICLES: Hair follicles (HFs) are complex skin appendages with strong self-renewal and regenerative capacity, and their evolution and regeneration involve many cell populations. The outer root sheath (ORS) is composed of 2 transcriptionally distinct cell types with different abilities to interact with stromal cells, while the inner root sheath (IRS) is generated from transcriptionally uncommitted progenitor cells and coordinate hair production with ORS. Although bulk sequencing can assess expression levels of genes, it cannot differentiate cell types. In contrast, the scRNA-seq technique can identify 25 different cell populations, including previously unknown cells, based on cell marker genes [8]. In addition, using scRNA-seq and single-molecule mRNA fluorescence in situ hybridization (smFISH), Joost et al mapped the gene expression characteristics and spatial positioning of full-thickness mouse skin during hair growth and rest, and identified 56 epithelial and stromal cell populations [9]. Moreover, Wang et al [10] showed that Oncostatin M (an endogenous inhibitor) was mainly produced by TREM2+ macrophages in the early telogen phase, which maintain hair follicle stem cells (HFSCs) quiescence by negative regulation of the hair growth process induced by the JAK-STAT pathway, while TREM2+ macrophages decreased after the telogen phase. These results indicate that scRNA-seq is useful to identify different cell subpopulations in hair follicles.

Recently, Ge et al [11] employed scRNA-seq to elucidate the dermal role in the development of hair follicles. They found that although the aged HFSCs were reduced in numbers and activity, they maintained their HFSC identity within the bulge. Young HFSCs, but not aged HFSCs, regenerate HFs when the skin is injured. However, aged HFSCs can regenerate HFs when supported with young dermis, while young HFSCs fail to regenerate HFs when combined with aged dermis. This study points to the importance of the stem cell niche as a key modulator of stem cell behavior.

Wound Healing

Cutaneous wound healing is a complex process. Basal cells are essential for maintaining the integrity of skin and promoting wound healing. Haensel et al [12] used scRNA-seq combined with RNA in situ detection (RNAScope) and fluorescence lifetime imaging microscopy (FLIM) to dynamically monitor cell states during skin homeostasis and wound healing. In their study, 3 non-proliferative states and 1 proliferative state with different metabolic profiles were identified in the basal layer during homeostasis, and were spatially separated in wound epithelialization. In wound healing, the inflammatory genes Cxcl2, Ccl2, and Ccl7 and epithelial-to-mesenchymal transition (EMT)-related genes Snai2 and Vim are upregulated in basal cells. Guerrero-Juarez et al [13] revealed the wound fibroblast diversity and myeloid-derived adipocyte progenitors by scRNA-seq analysis of cells from mouse wound dermis 12 days after wounding. They distinguished 3 distinct fibroblast-to-myofibroblast differentiation trajectories and 12 fibroblast subpopulations, and predicted fibroblasts differentiation trajectories via pseudo-time and RNA velocity analysis. In addition, their studies showed that differentiation of myeloid-originated myofibroblasts to adipocytes benefits wound repair. Other studies also demonstrated that generation of diverse ECM-producing myofibroblasts within wounds by adipocyte-derived cells contributes to skin wound healing [14]. Moreover, scRNA-seq technique reveals that skin adipocyte lipolysis plays a key role in regulating inflammation of efficient macrophage after skin injury in genetic mouse models. Furthermore, Guerrero-Juarez et al [15] used scRNA-seq to analyze the healing ability of fibroblasts from young and old mice, and found that old mice with slow healing had more fibroblasts, whereas fast-healing old mice had more immune cells in the wounded site. This result highlights the possibility of personalized treatment of cutaneous wounds in the future.

Inflammatory and Allergic Skin Diseases

PSORIASIS: Psoriasis, one of the top 10 recalcitrant chronic diseases listed by the WHO, is an immune-mediated chronic inflammatory skin disease with unclear pathogenesis. Over the last decade, scRNA-seq has advanced the progress of understanding of the pathogenesis of psoriasis. Cheng et al [2] used scRNA-seq to analyze the heterogeneity of KCs in different parts of 9 normal and 3 psoriatic skin samples. They found more proliferation-related and cell signaling-related KC subpopulations in psoriasis skin lesions. The inflammatory transcription level of S100, IFI27, and PI3 and cell division are significantly enhanced both in basal, spinous, and granular KCs. More CD1C+CD301A+ myeloid DCs are enriched, while fewer macrophages and Langerhans cells (LCs) are enriched in the epidermis, suggesting that the proliferation and recruitment of DCs further promote the activation of effector lymphoid and myeloid cells to provoke the development of psoriasis.

In addition, a large body of evidence indicates the association of psoriasis with abnormal metabolism. Lou et al [16] analyzed psoriatic epidermis with scRNA-seq and revealed high expression levels of the arginine biosynthesis rate-limiting enzyme, Arginosuccinate synthetase1 (ASS1), in basal KCs, suggesting that basal KCs are the source of arginine in the skin and contribute to epidermal hyperproliferation. Correspondingly, arginase inhibitors improve hyperplasia and inflammation in the epidermis of mouse psoriasis-like models.

ATOPIC DERMATITIS: Atopic dermatitis (AD) is also a very common inflammatory skin disease. He et al [17] performed scRNA-seq on 39 042 single-cell skin samples from 5 AD patients and 7 healthy controls to construct a comprehensive single-cell transcriptome map of all cell types from healthy skin and from AD lesional and non-lesional skin. The results revealed the changes in composition and specific gene expression, and identified a unique fibroblast subpopulation, COL6A5+COL18A1+ fibroblasts, in AD lesional skin. COL6A5 is an AD susceptibility gene, which can cause AD through the formation of unstable heterotrimers, resulting in abnormal fibroblast adhesion, collagen synthesis and metabolism, and barrier disruption, while COL18A1/endostatin can promote allergen sensitization and inflammation by regulating LCs and T cells.

DRUG ERUPTION: Drug-induced hypersensitivity syndrome (DiHS) is a fatal systemic drug reaction with a high mortality rate, which is characterized by acute extensive skin lesions, multiple-organ damage, eosinophilia, and human herpesviruses (HHV6b) reactivation. Kim et al [18] performed scRNA-seq, single-cell T cell receptor sequencing (scTCR-seq) on skin and blood samples from a patient with DIHS (induced by sulfamethyisoxazole/trimethoprim and uncontrolled by high doses of hormones) and 5 healthy volunteers. They sequenced 4676 single cells from DiHS skin and 13 542 cells from healthy volunteer skin. A total of 14 cell clusters were clustered by Unsupervised t-SNE. Extensive transcriptomic alterations were found in immune and nonimmune cell types, particularly in lymphocyte clusters. The pathway enrichment analysis with differently expressed genes (DEGs) showed that the JAK-STAT signaling pathway was most active, and the DNA from HHV6b was found to be highly enriched in CD4+ TCM. TCR chain sequencing by scTCR-seq showed significant differences in T cells, high expression of chemokine receptors in lymphocytes, and upregulation of genes related to the JAK-STAT signaling pathway in the patient with drug hypersensitivity reaction syndrome. Based on the results, tofacitinib (an inhibitor of JAK1 and JAK3) was given to the patient. In addition to the improvements in clinical condition, treatment with tofacitinib reduced CCR4+CCR10+CD4+ and CD8+ T cells as assessed by scRNA-seq. Finally, studies using a lymphocyte transformation test (LTT) to simulate drug-induced immune responses in vitro confirmed that tofatinib and antiviral drugs inhibited CD4+ T cell proliferation and downregulated JAK-STAT pathway-related genes. These results suggest the usefulness of scRNA-seq in identification of potential therapeutic target and the therapeutic utility of JAK inhibitors and antivirals for DiHS.

Autoimmune Dermatosis

VITILIGO: Vitiligo is an autoimmune skin disease that seriously affects the appearance of patients. Studies have concluded that vitiligo is not solely related to melanocytes, but is the result of a combination of cell populations across the entire layer of the skin. Lin et al [19] performed scRNA-seq to analyze 11 000 cells of skin samples from 2 patients with stable non-segmental vitiligo and 2 healthy controls. They found that the stem cell subpopulation in vitiligo lesions was significantly lower than that in normal skin, while the fibroblast subpopulation was absent or insufficient in number, and differentiated into myofibroblasts with high expression of α-smooth muscle actin, along with excessive accumulation of intracellular reactive oxygen species. In addition, the volume of fibroblasts and adhesion proteins increased, exhibiting an aging phenotype, leading to dysfunctions in skin nourishment and repair. Although the general proportion of KCs did not differ, 4 subpopulations of KCs at the end of cell differentiation were significantly higher in vitiligo skin than in normal skin. KC subpopulation 5 highly expresses KRT15, POSTN, COL17, and S100A2 genes, which are related to intercellular connection, cell adhesion, and cytoskeleton function; KC subpopulation 6 highly expresses cellular differentiation, oxidative stress-related genes, SFRP1, GJB family, SOX9, and KRT6; KC subpopulation 10 highly expresses cell homeostasis and cell cycle-related genes PTTG1, CENPF, HMGB2, TOP2A, UBE2C. The characteristics of these cell subpopulations suggest that they are associated with vitiligo occurrence and progression.

To investigate the signaling networks that contribute to the pathogenesis of vitiligo, Strassner et al [20] used a modified suction blistering technique to isolate skin cells directly from 6 vitiligo subjects with active disease, and analyzed more than 18 000 multiple cell populations, including melanocytes, KCs, and immune cells. Their results showed an IFN-γ signature, increased CD8+ T cell, and 87 ligand-receptor pairs, including CXCR6 and CXCL16, which can promote vitiligo progression. Importantly, LCs were found to express many genes that are associated with vitiligo, suggesting that LCs play an essential role in the development of vitiligo.

ALOPECIA AREATA: Alopecia areata (AA) is a disorder of T lymphocyte-mediated autoimmune hair loss. The “normal appearance” of smooth and non-inflammatory skin in the affected area contrasts sharply with the complex immune activity occurring at the HFs. The loss of immune privilege of HFs resulting in an autoimmune attack contributes to the development of AA. Borcherding et al [21] applied scRNA-seq and scTCR-seq to study 18 231 immune cells from the skin and lymph nodes of the C3H/HeJ AA mouse model and control group to explore the role of the complex skin immune ecosystem, microenvironment, and other cells in the pathogenesis of AA. They found that CD11b+ CCR2+ myeloid DCs (mDCs) enriched the JAK/STAT signal, and the expression of MHC-I and MHC-II genes in APCs increased, indicating an increased immune reactivity and loss of immune privilege in AA. Moreover, CD4+ and CD8+ T cells in AA were clonotypically expanded, and TCR sequences were extensively shared between different cell clusters in AA, but not in the control group, suggesting an antigen-specific immune response in AA. Results in the animal model were validated by scRNA-seq on 2416 T cells from human AA lesions. Based on differentially upregulated genes in CD4+ and CD8+ T cells in a murine AA model, the authors used a 15-gene signature (eg, TAP1, PSMB9) to predict human AA. The results showed that CD4+ and CD8+ T cell gene signatures had equivalent performance, with an overall accuracy of 87.5%, sensitivity of 90%, and a specificity of 83.3%.

LUPUS ERYTHEMATOSUS: Systemic lupus erythematosus (SLE) is a common, intractable autoimmune disease that can extensively affect the skin, kidneys, joints, blood system, and central nervous system. Lupus nephritis (LN) is a leading cause of mortality in SLE patients. Der et al [22] implemented scRNA-seq on 5000 cells from skin samples from healthy controls, nonlesional, and non-sun-exposed skin of LN patients, and 15 000 kidney cells. Interestingly, they found upregulation of the type I IFN response pathway in KCs and the kidney of LN patients. Goel et al [23] demonstrated that immune cells were not extensively responsive to IFN-λ, but the KCs and mesangial cells responded to IFN-λ, and produced chemokines to induce immune cell recruitment and to promote systemic immune dysfunction in SLE. Because IFN-λ and type I IFN share some functional similarities, new treatment measures for the shared elements of type I and type III IFN may be effective for SLE patients.

SCLERODERMA: Systemic scleroderma (SSc) is a chronic connective tissue disease with extensive hardening of the skin and connective tissue. It is generally believed that the pathogenesis is mainly the abnormal activation of fibroblasts, leading to scarring of skin and internal organs. However, the first event in most SSc patients is microvascular injury, not fibrosis, suggesting that vascular endothelial injury may contribute to the pathogenesis of SSc. Apostolidis et al [24] used scRNA-seq to analyze the endothelial cells of SSc skin lesions and healthy skin. They found that the endothelial cells of SSc highly expressed VWF, THBS1, APLNR, and HSPG2, which are mainly related to vascular injury and activation, ECM generation, and negative regulation of inhibiting angiogenesis. Both APLNR and HSPG2 are 2 main creators of SSc endothelial cell injury as assessed by IHC, immunofluorescence, and qPCR.


HSV-1: Herpes simplex virus-1 (HSV-1) is one of the viruses with the highest infection rate in the world, affecting 45%–88% of the global population [25]. Although 80% of the viruses are static latent infection, they can replicate in large numbers and cause various diseases after activation. Wyler et al [26] utilized scRNA-seq to accurately detect about 12 000 primary normal human dermal fibroblasts (NHDFs) infected with HSV-1 in vitro at different early stages (1, 3, and 5 h after infection). They also showed that the main cause of cell heterogeneity is cell cycle and infection processes. Cells in the S/G2/M phases are more susceptible to viral infection, and the virus replicates faster in these cells. In addition, expression of NRF2 is positively correlated with viral transcripts through RNA velocity analysis, while NRF2 can slow down the process of infection activation. Thus, activation level of NRF2 can serve as a marker of resistance to HSV-1 infection. Bardoxolone methyl and Sulforaphane, 2 known NRF2 agonists, can reduce the mRNA expression of the early virus gene UL29 and late virus gene UL6, indicating the decline of the virus’s ability to replicate. These results suggest that scRNA-seq is useful to detect early HSV-1 infection and to identify therapeutic targets.

The combination of scRNA-seq and live-cell imaging techniques revealed that during HSV-1 infection, only a few cells become highly infected, largely due to shutting down the signal transduction process that protects against the virus [27]. Authors also showed that viral gene expression level in infected cells at G2 phase is very low. Viral gene expression is negatively correlated with cell cycle score. Interestingly, highly infected cells undergo cell reprogramming, which involves the recruitment of β-catenin into the host nucleus and virus replication regions required for late viral gene expression and progeny production. Other studies using SCS demonstrated that lncRNA plays an important role in host cells infected by HSV. Silencing lncRNA MAMDC2-AS1 can reduce HSV-1 immediate early (IE) genes expression in human host cells to limit HSV-1 infection [28].

HPV: Human papillomavirus (HPV) infection is also highly prevalent worldwide; it can induce warts and cancers. HPV-related tumors are highly correlated with HPV16. Lukowski et al [29] used SCS on KCs from K14E7 transgenic mice, a precancerous hyperproliferative epithelium model, to explore the relationship between HPV16 infection and cutaneous squamous cell carcinoma (cSCC). They observed that the expression of E7 is mainly associated with basal KCs, while E7 can upregulate multiple cell cycle- and proliferation-related genes. The increased E7 transcript copy number is positively correlated with the expression level of E7-induced genes.

Persistent HPV infection of skin can occur in patients with chronic immunosuppression such as after organ transplantation. These patients usually have more severe symptoms and are more likely to develop tumors. Devitt et al [30] implemented HPV genotyping and scRNA-seq to analyze 3 wart lesion samples and normal skin of an immunosuppressed patient with HPV infection, and found that the HPV transcripts of different lesions in the same patient are heterogeneous. Although more than 92% of cells in each lesion express HPV78, the markers associated with skin barrier function (ARL4A, MT2A) and inflammation (FOS and JUN) were upregulated in 2 out of 3 lesions. Moreover, the expression levels of stress keratins such as KRT6A and MT1G genes were upregulated in 1 lesion. Previously, Zhussupbekova et al [31] showed that KRT6A could modify the local immune environment and alter the virus expression, and MT1G was upregulated in precancerous lesions with HPV16 infection. In addition, the proportion of cells in G1 phase was higher, and that in S phase was lower in 1 lesion, suggesting these cells may be precancerous cells.

SCS technique has also been used to study human cytomegalovirus (HMCV) [32] and human immunodeficiency virus (HIV) [33]. These studies are of potential significance for further exploration of the interaction between virus infection and host in the pathogenesis of viral infections, as well as for the development of virus vaccines and immunotherapy.

BACTERIA: Skin-resident immune cells play an important role in antimicrobial defense. Conventional dendritic cells (cDCs), including cDC1s and cDC2s, are the main antigen-presenting cells (APCs), with dual functions of activating immune response and immune tolerance. Janela et al [34] used single-cell mRNA sequencing to reveal the interactions between skin APC subsets and other immune cells during Propionibacterium acnes (P. acnes) infection. They found that EpCAM+CD59+ ly-6D+cDC1s, a subpopulation of cDC1s, can secrete VEGF-α to recruit neutrophils to the inflammatory site and to regulate the immune response induced by P. acnes in the dermis of mice. Thus, SCS technique can differentiate the roles of different cDCs subsets in regulating innate immunity.

Innate lymphoid cells (ILCs), which are tissue-resident cells, exert antimicrobial properties. The mechanisms of ILC action are not clear yet. Studies using scRNA-seq demonstrated that ILCs in different sites are highly heterogeneous in the skin [35]. Persistence of ILCs in the skin relies on IL-7 and thymic stromal lymphopoietin, while its localization is determined by cytokines secreted by epithelial cells and chemokine receptor CCR6. Interactions of ILCs and HFs and sebaceous glands regulate host-microbe symbiosis. Deficiency of ILCs results in sebaceous hyperplasia with an increase in antibacterial lipids to inhibit the symbiosis of gram-positive bacteria.


Tumors cells lose their normal growth control and proliferate abnormally. To adapt to the complex tumor microenvironment (TME) in the host, the tumor cells change constantly, resulting in many gene mutations and exhibition of unique pathogenic phenotypes, termed intratumoral heterogeneity. The development of scRNA-seq has provided a powerful tool for exploring the heterogeneity, occurrence, and development of tumors, the mechanism of drug resistance, and new treatment strategies.

MELANOMA: Melanoma is a type of skin malignant tumor with high rates of invasion, metastasis, and recurrence, and the highest mortality rate among skin cancers. In recent years, both targeted therapy and immunotherapy have helped melanoma patients, but because of the high heterogeneity of melanoma, the outcome is still not satisfactory, and more effective specific therapeutic targets are needed. Tirosh et al [36] analyzed the heterogeneity of tumor cells and their interactions with TME in 19 cases of melanoma using scRNA-seq. Two groups of tumor cells – high MITF subpopulations and low MITF/high AXL subpopulations – were identified. Importantly, high AXL tumor cell subpopulations were associated with resistance to RAF and MEK inhibition. Tumors with more CAF enrichment expressed the AXL-high signature, while the MITF-high tumors were negatively correlated with CAF abundance. In addition, expression of key T cell co-inhibitory receptors such as PD-1, TIM-3, and CTLA-4 suggests that T cells in melanoma present a state of exhaustion and may be used as targets for immunotherapy. Su et al [37] utilized single-cell proteomics and metabolic analysis on BRAFV600E mutant melanoma cancer cell lines cultured for 0, 1, 3, and 5 days following stimulation of melanoma cells from drug-responsive state to drug-tolerant state. The results showed that the key transcription factor MITF, metabolism regulators HIF1 and P-AMPK, proliferation marker Ki67, and glucose metabolism changed significantly at different time points. Three-dimensionality reduction algorithms (FLOW-MAP, t-SNE, and PHATE) were used to visualize the changes in multiple substances at the single-cell level at different time points after stimulation. Two distinct paths and MITF were identified as key molecular drivers for the bifurcation of the trajectory. This single-cell multi-omics study has greatly deepened our understanding of the transformation of tumor cells resistance, leading to the development of new therapies for melanoma.

Recently, Davidson et al [38] used scRNA-seq to investigate changes in TME and lymph node status during the development of melanoma in mice. They found that T cells recruited from the lymph nodes were immediately activated and expanded, in addition to exhibition of exhaustion state in the late stage of the tumor. They also identified 3 distinct functional populations of tumor stroma (S1–S3) with different immunity, proliferation, and contractility functions. S1 and S2 cells predominated in the early stage, and S3 populations were largely restricted in the late stage. Cell-cell communication analysis showed that complement Component C3 was specifically expressed in the immune population, and its cleavage product C3a played an important role in regulating immune populations. The combination of C3a and C3aR interferes immune infiltration and inhibits tumor growth, suggesting that stromal cells are important in regulating disease immune function.

CUTANEOUS T CELL LYMPHOMAS CTCL: Cutaneous T cell lymphomas (CTCLs), including mycosis fungoides (MF), Sézary syndrome (SS), and other types, are T cell lymphomas with skin damage as the main manifestation. Although most CTCLs lesions are limited to the skin, advanced-stage CTCLs can also involve system organs and cause death. So far, treatment regimens are limited. Gaydosik et al [39] analyzed 14 056 CD3+ T lymphocytes from patients with advanced-stage CTCL and healthy human skin by scRNA-seq. They observed that the cellular profiles were similar among individual normal skin samples, whereas CTCL samples were highly heterogeneous, especially in lymphocytes, and that different lymphocyte populations activated specific tumor-related pathways. High expression levels of PCNA, ATP5C1, and NUSPA1 in lymphocytes can be used as biomarkers for the diagnosis of CTCL. In advanced CTCLs, multiple co-inhibitor receptors were expressed in malignant and reactive lymphocytes, causing lymphocytes dysfunction. Understanding the heterogeneous expression of co-inhibitory receptors may be critical for individualized therapy in advanced CTCL patients.

SS is a highly malignant CTCL, affecting both the circulatory system and the skin, with a low 5-year survival rate. Borcherding et al [40] used scRNA-seq and scTCR-seq analyses on malignant CD4+ T cells (CD3+CD4+CD5brightSSChi, SS cells) of SS patients and normal CD4+ T cells (CD3+CD4+CD5intSSCint) of healthy controls, revealing that CD26 (DPP4) expression was downregulated while CD70 expression was upregulated in SS cells. And scTCR-seq showed that normal cells had 4007 clonotypes, while 97.3% of SS cells had only 1 single clonotype, TRBV14 and TRAV9-2. Although malignant cells showed a single clonal state, multiple malignant subpopulations were identified, and their different functions were enriched by cell trajectory and pseudo-time analysis. FOXP3 is an important marker of early SS. Thus, analysis of differential expression of malignant cells and normal cells in different states can predict the clinical stage of CTCLs. A combination of 19 DEGs with artificial intelligence (AI) can achieve 80% accuracy for the diagnosis and staging of CTCL.

CSCC AND BCC: Cutaneous squamous cell carcinoma (cSCC) is the second most common skin malignancy and the fatality rate is also second to melanoma. Although it can be cured by surgical resection, up to 4% of cSCCs develop nodal metastases. So far, no effective medication is available for this disease. In search of therapeutic targets for cSCC, Ji et al [41] used a combination of scRNA-seq with spatial transcriptomics (ST) and multiplexed ion beam imaging (MIBI) to construct single-cell transcriptome profiles and spatial features for revealing cSCC cellular heterogeneity, microenvironment, and immune mechanisms. They identified a cluster of tumor-specific keratinocyte (TSK) unique in cSCC, consisting of 2.7–13.8% of all tumor cells. The marker genes of TSK subpopulations were mainly associated with cellular movement, ECM disassembly, and epithelial-mesenchymal transition (EMT), and other biomarkers such as VIM and ITGA5, indicating the invasive behavior. In addition, genes linked to type I interferon response, glycolysis, and hypoxia were upregulated in cSCC. ST analysis showed that TSKs residing within a fibrovascular niche in leading edges of tumors were enriched in cancer-associated fibroblast (CAF) and endothelial transcripts. As for the immune landscape, despite the large differences in spatial composition, CD8 T cells, Tregs, macrophages, and CD4 T cells are highly correlated. They also observed co-localization of Tregs and CD8 T cells using MIBI and a positive correlation of the proportion of CD8 T cells with co-localization. In addition, fibroblasts, macrophages, and Tregs were the most abundant at the tumor-stroma border, whereas CD8 T cells and neutrophils were distributed farther away from the tumor, suggesting that Tregs may prevent effector lymphocytes from entering the tumor to suppress the immune response. Finally, scRNA-seq results of a mouse model of the xenograft tumor showed high consistency with human cSCC samples. Identification of TSK-specific co-essential gene network, including ITGB1, FERMT1, CD151, ARPC2, and HSP90B1, in vivo by CRISPR/Cas9 screening would be helpful to discover potential therapeutic targets for cSCC.

Immunotherapies that block inhibitory checkpoint receptors on T cells have been increasingly used in the treatment of various skin tumors. However, whether the target T cells are derived from existing tumor-infiltrating lymphocyte regeneration or the recruitment of new T cells recruitment is still unclear. Using scRNA-seq and scTCR-seq, Yost et al. [42] comprehensively analyzed tumor-infiltrating T lymphocyte (TILs) phenotypes and clonal dynamics in patients with basal cell carcinoma (BCC) and cSCC before and after anti-PD-1 treatment. The cell map showed significant heterogeneity among malignant cells in BCC patients, but the TME of the same immune cell population was relatively consistent. A total of 577 differentially expressed genes including Ras were identified. The analysis of TILs showed that the clonally expanded cells were highly enriched with exhausted CD8+ T cells and expression of tumor-specific markers, including CD39 and CD103. Anti-PD-1 treatment mainly affects CD8+ T cells. Replacement of the clone pool of exhausted CD8+ T cells with novel expanded TCR clones suggest that the regeneration ability of T cells in tumors is limited, and that the response to checkpoint blockade relies on the expansion of new specific T cell clones just entering tumors. However, whether the source of new T cell clones is from lymph nodes or around the tumor remains unknown.

Miao et al [43] also used scRNA-seq and lineage tracing to study the mechanisms of recurrence after immunotherapy in a mouse model of skin squamous cell carcinoma (SCC). They found that 2% of tumor-initiating stem cells (tSCs) in tumor cells were highly resistant to T cell immunotherapy. tSCs selectively containing CD80 (a surface ligand previously identified on immune cells) could evade immune surveillance to survive immunotherapy, becoming the source of tumor recurrence. CD80 is activated only in the tSCs subset that responds positively to TGF-β, and its expression can be reduced by blocking TGF-signaling. The CD80 tSCs can directly suppress cytotoxic T cells to attack tumors by binding to cytotoxic T cell antigen-4 (CTLA4). On the contrary, CTLA4- or TGF-β-blocking antibodies, and CD80 inhibition can weaken the function of tSCs to reduce tumor recurrence. This study provides a potential way to improve immunotherapy for SCC.

OTHER SKIN TUMORS: Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasm predominantly affecting children and easily invades the skin, bones, lungs and other organs. To characterize the cellular and molecular landscape of LCH lesions, Halbritter et al [44] performed scRNA-seq and ATAC-seq on the skin, bone, and lymph node tissues of primary LCH. A wide range of cell heterogeneity was discovered, and 14 subsets with different degrees of differentiation, proliferation, and immune status were identified. The genetic characteristics associated with neoplastic (cell cycle, DNA replication, MYC) and inflammatory (immunity, inflammation, macrophages, DCs) phenotypes were identified in LCH cells. LCH cells displayed both neoplastic and inflammatory characteristics of LCH. Results of this study demonstrated that using SCS technique can define the transcriptomics and epigenomics characteristics of different cell subgroups in LCH, which not only provides new insights of LCH, but also potentiates the development of personalized treatment for LCH.


SCS technology has a variety of applications in dermatology, such as aiding in the study of skin development in both healthy and diseased conditions with the precision of single-cell resolution, discovering new subsets and functions of KCs, HFs, and immune cells, elucidating the pathogenesis of various skin diseases, revealing the interaction between pathogenic microorganisms and hosts in cases of infectious dermatoses, and researching the heterogeneity, development, drug resistance mechanism, and prognosis of skin cancers. Although SCS technology has now covered genomics, transcriptomics, and epigenomics, application of genomics and epigenomics in skin research are still rare. Most of the studies are based on single-omics studies. The functional state of cells depends on the complex inter-regulation of different molecules (including DNA, RNA, and proteins). Therefore, simultaneously acquiring multi-omics features of a single cell is necessary for comprehending the “identity”. Single-cell multi-omics methods can perform high-throughput analysis in genomics, transcriptomics, epigenomics, and proteomics of single cells at different omics levels. Explication of the relationship between the genomics, epigenetic modification, and gene expression can provide new methods and perspectives for in-depth analysis of the behavior and mechanisms and their relationship with the organism of individual cells in a population of cells, which will play an increasingly essential role in basic research and precision medicine [45].

SCS requires dissociating tissues into single-cell liquid, resulting in the loss of spatial information about where the cells are located, but spatial transcriptomics solves this technical challenge perfectly by integrating high-throughput mRNA analysis of tissue sections with morphological background of histological section [46]. Hence, SCS technology provides a powerful tool for constructing cellular profiles of skin tissues to reveal the mechanisms of complex skin diseases, and to explore new strategies for diagnosis, prevention, and therapy. Undoubtedly, the single-cell multi-genomics sequencing technique can advance our knowledge of both normal and diseased skin, and is a valuable approach for diagnosis and treatment of skin disorders.


1. Tang F, Barbacioru C, Wang Y: mRNA-Seq whole-transcriptome analysis of a single cell: Nat Methods, 2009; 6(5); 377-82

2. Cheng JB, Sedgewick AJ, Finnegan AI: Transcriptional programming of normal and inflamed human epidermis at single-cell resolution: Cell Rep, 2018; 25(4); 871-83

3. Tabib T, Morse C, Wang T, Chen W, Lafyatis R: SFRP2/DPP4 and FMO1/LSP1 define major fibroblast populations in human skin: J Invest Dermatol, 2018; 138(4); 802-10

4. Philippeos C, Telerman SB, Oules B: Spatial and single-Cell transcriptional profiling identifies functionally distinct human dermal fibroblast subpopulations: J Invest Dermatol, 2018; 138(4); 811-25

5. Vorstandlechner V, Laggner M, Kalinina P: Deciphering the functional heterogeneity of skin fibroblasts using single-cell RNA sequencing: FASEB J, 2020; 34(3); 3677-92

6. Sole-Boldo L, Raddatz G, Schutz S: Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming: Commun Biol, 2020; 3(1); 188

7. Xue D, Tabib T, Morse C, Lafyatis R: Transcriptome landscape of myeloid cells in human skin reveals diversity, rare populations and putative DC progenitors: J Dermatol Sci, 2020; 97(1); 41-49

8. Joost S, Zeisel A, Jacob T: Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity: Cell Syst, 2016; 3(3); 221-37.e9

9. Joost S, Annusver K, Jacob T: The molecular anatomy of mouse skin during hair growth and rest: Cell Stem Cell, 2020; 26(3); 441-57.e7

10. Wang ECE, Dai Z, Ferrante AW: A subset of TREM2(+) dermal macrophages secretes oncostatin M to maintain hair follicle stem cell quiescence and inhibit hair growth: Cell Stem Cell, 2019; 24(4); 654-69.e6

11. Ge Y, Miao Y, Gur-Cohen S: The aging skin microenvironment dictates stem cell behavior: Proc Natl Acad Sci USA, 2020; 117(10); 5339-50

12. Haensel D, Jin S, Sun P: Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics: Cell Rep, 2020; 30(11); 3932-47.e6

13. Guerrero-Juarez CF, Dedhia PH, Jin S: Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds: Nat Commun, 2019; 10(1); 650

14. Shook BA, Wasko RR, Mano O: Dermal adipocyte lipolysis and myofibroblast conversion are required for efficient skin repair: Cell Stem Cell, 2020; 26(6); 880-95.e6

15. Mahmoudi S, Mancini E, Xu L: Heterogeneity in old fibroblasts is linked to variability in reprogramming and wound healing: Nature, 2019; 574(7779); 553-58

16. Lou F, Sun Y, Xu Z: Excessive polyamine generation in keratinocytes promotes self-RNA sensing by dendritic cells in psoriasis: Immunity, 2020; 53(1); 204-16.e10

17. He H, Suryawanshi H, Morozov P: Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis: J Allergy Clin Immunol, 2020; 145(6); 1615-28

18. Kim D, Kobayashi T, Voisin B: Targeted therapy guided by single-cell transcriptomic analysis in drug-induced hypersensitivity syndrome: A case report: Nat Med, 2020; 26(2); 236-43

19. Lin F, Zhou M, Xu W: A single-cell transcriptome atlas of non-segmental vitiligo lesions: A preliminary study: Chinese Journal of Dermatology, 2020; 53(4); 259-65; [in Chinese]

20. Strassner JP, Rashighi M, Ahmed Refat M: Suction blistering the lesional skin of vitiligo patients reveals useful biomarkers of disease activity: J Am Acad Dermatol, 2017; 76(5); 847-55.e5

21. Borcherding N, Crotts SB, Ortolan LS: A transcriptomic map of murine and human alopecia areata: JCI Insight, 2020; 5(13); e137424

22. Der E, Ranabothu S, Suryawanshi H: Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis: JCI Insight, 2017; 2(9); e93009

23. Goel RR, Wang X, O’Neil LJ: Interferon lambda promotes immune dysregulation and tissue inflammation in TLR7-induced lupus: Proc Natl Acad Sci USA, 2020; 117(10); 5409-19

24. Apostolidis Sokratis A, Stifano G: Single cell RNA sequencing identifies HSPG2 and APLNR as markers of endothelial cell injury in systemic sclerosis skin: Front Immunol, 2018(9); 2191

25. James C, Harfouche M, Welton NJ: Herpes simplex virus: Global infection prevalence and incidence estimates, 2016: Bull World Health Organ, 2020; 98(5); 315-29

26. Wyler E, Franke V, Menegatti J: Single-cell RNA-sequencing of herpes simplex virus 1-infected cells connects NRF2 activation to an antiviral program: Nat Commun, 2019; 10(1); 4878

27. Drayman N, Patel P, Vistain L, Tay S: HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations: Elife, 2019; 15(8); e46339

28. Wang Y, Huang L, Wang Y: Single-cell RNA-sequencing analysis identifies host long noncoding RNA MAMDC2-AS1 as a co-factor for HSV-1 nuclear transport: Int J Biol Sci, 2020; 16(9); 1586-603

29. Lukowski SW, Tuong ZK, Noske K: Detection of HPV E7 transcription at single-cell resolution in epidermis: J Invest Dermatol, 2018; 138(12); 2558-67

30. Devitt K, Hanson SJ, Tuong ZK: Single-cell RNA sequencing reveals cell type-specific HPV expression in hyperplastic skin lesions: Virology, 2019; 537; 14-19

31. Zhussupbekova S, Sinha R, Kuo P: A mouse model of hyperproliferative human epithelium validated by keratin profiling shows an aberrant cytoskeletal response to injury: EBioMedicine, 2016; 9; 314-23

32. Shnayder M, Nachshon A, Rozman B: Single cell analysis reveals human cytomegalovirus drives latently infected cells towards an anergic-like monocyte state: Elife, 2020; 9; e52168

33. Sannier G, Dube M, Kaufmann DE: Single-cell technologies applied to HIV-1 research: Reaching maturity: Front Microbiol, 2020; 11; 297

34. Janela B, Patel AA, Lau MC: A subset of type I conventional dendritic cells controls cutaneous bacterial infections through VEGFalpha-mediated recruitment of neutrophils: Immunity, 2019; 50(4); 1069-83.e8

35. Kobayashi T, Voisin B, Kim DY: Homeostatic control of sebaceous glands by innate lymphoid cells regulates commensal bacteria equilibrium: Cell, 2019; 176(5); 982-97.e6

36. Tirosh I, Izar B, Prakadan SM: Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq: Science, 2016; 352(6282); 189-96

37. Su Y, Ko ME, Cheng H: Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line: Nat Commun, 2020; 11(1); 2345

38. Davidson S, Efremova M, Riedel A: Single-Cell RNA sequencing reveals a dynamic stromal niche that supports tumor growth: Cell Rep, 2020; 31(7); 107628

39. Gaydosik AM, Tabib T, Geskin LJ: Single-cell lymphocyte heterogeneity in advanced cutaneous T-cell lymphoma skin tumors: Clin Cancer Res, 2019; 25(14); 4443-54

40. Borcherding N, Voigt AP, Liu V: Single-cell profiling of cutaneous T-cell lymphoma reveals underlying heterogeneity associated with disease progression: Clin Cancer Res, 2019; 25(10); 2996-3005

41. Ji AL, Rubin AJ, Thrane K: Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma: Cell, 2020; 182(2); 497-514.e2

42. Yost KE, Satpathy AT, Wells DK: Clonal replacement of tumor-specific T cells following PD-1 blockade: Nat Med, 2019; 25(8); 1251-59

43. Miao Y, Yang H, Levorse J: Adaptive immune resistance emerges from tumor-initiating stem cells: Cell, 2019; 177(5); 1172-86.e4

44. Halbritter F, Farlik M, Schwentner R: Epigenomics and single-cell sequencing define a developmental hierarchy in langerhans cell histiocytosis: Cancer Discov, 2019; 9(10); 1406-21

45. Song Y, Xu X, Wang W: Single cell transcriptomics: Moving towards multi-omics: Analyst, 2019; 144(10); 3172-89

46. Vickovic S, Eraslan G, Salmen F: High-definition spatial transcriptomics for in situ tissue profiling: Nat Methods, 2019; 16(10); 987-90

Coronavirus/Covid 19

22 September 2021 : Editorial

Editorial: Global Regulatory Initiatives Deliver Accelerated Approval of the First Bispecific Therapeutic M...

Med Sci Monit In Press; DOI: 10.12659/MSM.934854  

08 September 2021 : Clinical Research

Acceptance of COVID-19 Vaccination and Its Associated Factors Among Cancer Patients Attending the Oncology ...

Med Sci Monit In Press; DOI: 10.12659/MSM.932788  

16 July 2021 : Review article

Silent Hypoxemia in Patients with COVID-19 Pneumonia: A Review

Med Sci Monit In Press; DOI: 10.12659/MSM.930776  

09 July 2021 : Database Analysis

A Simple Clinical Prediction Tool for COVID-19 in Primary Care with Epidemiology: Temperature-Leukocytes-CT...

Med Sci Monit In Press; DOI: 10.12659/MSM.931467  

In Press

23 Sep 2021 : Animal Research

Forkhead Box Protein M1 Promotes Nasopharyngeal Carcinoma Cell Tumorigenesis Possibly via the Wnt/β-Catenin...

Med Sci Monit In Press; DOI: 10.12659/MSM.931970  

22 Sep 2021 : Editorial

Editorial: Global Regulatory Initiatives Deliver Accelerated Approval of the First Bispecific Therapeutic M...

Med Sci Monit In Press; DOI: 10.12659/MSM.934854  

22 Sep 2021 : Clinical Research

A Comparative Study of the Clinical Efficacy and Safety of Pneumatic Trabeculoplasty and Selective Laser Tr...

Med Sci Monit In Press; DOI: 10.12659/MSM.933454  

20 Sep 2021 : Clinical Research

Comparison of the Use of Magnetic Resonance Imaging of Partial Anterior Cruciate Ligament Tears Using Maxim...

Med Sci Monit In Press; DOI: 10.12659/MSM.932228  

Most Viewed

20 Mar 2020 : Clinical Research

Social Capital and Sleep Quality in Individuals Who Self-Isolated for 14 Days During the Coronavirus Diseas...

DOI :10.12659/MSM.923921

Med Sci Monit 2020; 26:e923921

15 Apr 2020 : Clinical Research

Psychological Impact and Coping Strategies of Frontline Medical Staff in Hunan Between January and March 20...

DOI :10.12659/MSM.924171

Med Sci Monit 2020; 26:e924171

05 May 2020 : Review article

An Evidence Based Perspective on mRNA-SARS-CoV-2 Vaccine Development

DOI :10.12659/MSM.924700

Med Sci Monit 2020; 26:e924700

26 Apr 2020 : Clinical Research

Comparison of Prevalence and Associated Factors of Anxiety and Depression Among People Affected by versus P...

DOI :10.12659/MSM.924609

Med Sci Monit 2020; 26:e924609

Your Privacy

We use cookies to ensure the functionality of our website, to personalize content and advertising, to provide social media features, and to analyze our traffic. If you allow us to do so, we also inform our social media, advertising and analysis partners about your use of our website, You can decise for yourself which categories you you want to deny or allow. Please note that based on your settings not all functionalities of the site are available. View our privacy policy.

Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750