ABSTRACT
Acne affects 1 in 10 people globally, often resulting in disfigurement. The disease involves excess production of lipids, particularly squalene, increased growth of Cutibacterium acnes, and a host inflammatory response with foamy macrophages. By combining single-cell and spatial RNA sequencing as well as ultrahigh-resolution Seq-Scope analyses of early acne lesions on back skin, we identified TREM2 macrophages expressing lipid metabolism and proinflammatory gene programs in proximity to hair follicle epithelium expressing squalene epoxidase. We established that the addition of squalene induced differentiation of TREM2 macrophages in vitro, which were unable to kill C. acnes. The addition of squalene to macrophages inhibited induction of oxidative enzymes and scavenged oxygen free radicals, providing an explanation for the efficacy of topical benzoyl peroxide in the clinical treatment of acne. The present work has elucidated the mechanisms by which TREM2 macrophages and unsaturated lipids, similar to their involvement in atherosclerosis, may contribute to the pathogenesis of acne.
Subject(s)
Acne Vulgaris , Squalene , Acne Vulgaris/drug therapy , Acne Vulgaris/etiology , Acne Vulgaris/pathology , Humans , Inflammation , Lipids , Macrophages/pathology , Membrane Glycoproteins , Receptors, Immunologic/therapeutic use , Squalene/therapeutic useABSTRACT
Langerhans cells (LCs) reside in the epidermis where they are poised to mount an antimicrobial response against microbial pathogens invading from the outside environment. To elucidate potential pathways by which LCs contribute to host defense, we mined published LC transcriptomes deposited in GEO and the scientific literature for genes that participate in antimicrobial responses. Overall, we identified 31 genes in LCs that encode proteins that contribute to antimicrobial activity, ten of which were cross-validated in at least two separate experiments. Seven of these ten antimicrobial genes encode chemokines, CCL1, CCL17, CCL19, CCL2, CCL22, CXCL14 and CXCL2, which mediate both antimicrobial and inflammatory responses. Of these, CCL22 was detected in seven of nine transcriptomes and by PCR in cultured LCs. Overall, the antimicrobial genes identified in LCs encode proteins with broad antibacterial activity, including against Staphylococcus aureus, which is the leading cause of skin infections. Thus, this study illustrates that LCs, consistent with their anatomical location, are programmed to mount an antimicrobial response against invading pathogens in skin.
Subject(s)
Antimicrobial Peptides/genetics , Epidermis/metabolism , Langerhans Cells/metabolism , Staphylococcal Skin Infections/genetics , Staphylococcus aureus/pathogenicity , Transcriptome , Cells, Cultured , Databases, Genetic , Epidermis/immunology , Epidermis/microbiology , Gene Expression Profiling , Host-Pathogen Interactions , Humans , Langerhans Cells/immunology , Langerhans Cells/microbiology , Staphylococcal Skin Infections/immunology , Staphylococcal Skin Infections/metabolism , Staphylococcal Skin Infections/microbiology , Staphylococcus aureus/immunologyABSTRACT
Granulomas are complex cellular structures composed predominantly of macrophages and lymphocytes that function to contain and kill invading pathogens. Here, we investigated the single-cell phenotypes associated with antimicrobial responses in human leprosy granulomas by applying single-cell and spatial sequencing to leprosy biopsy specimens. We focused on reversal reactions (RRs), a dynamic process whereby some patients with disseminated lepromatous leprosy (L-lep) transition toward self-limiting tuberculoid leprosy (T-lep), mounting effective antimicrobial responses. We identified a set of genes encoding proteins involved in antimicrobial responses that are differentially expressed in RR versus L-lep lesions and regulated by interferon-γ and interleukin-1ß. By integrating the spatial coordinates of the key cell types and antimicrobial gene expression in RR and T-lep lesions, we constructed a map revealing the organized architecture of granulomas depicting compositional and functional layers by which macrophages, T cells, keratinocytes and fibroblasts can each contribute to the antimicrobial response.
Subject(s)
Leprosy, Lepromatous/immunology , Leprosy, Tuberculoid/immunology , Mycobacterium leprae/immunology , Skin/immunology , Adolescent , Adult , Aged , Female , Fibroblasts/immunology , Fibroblasts/microbiology , Fibroblasts/pathology , Gene Expression Profiling , Host-Pathogen Interactions , Humans , Keratinocytes/immunology , Keratinocytes/microbiology , Keratinocytes/pathology , Leprosy, Lepromatous/genetics , Leprosy, Lepromatous/microbiology , Leprosy, Lepromatous/pathology , Leprosy, Tuberculoid/genetics , Leprosy, Tuberculoid/microbiology , Leprosy, Tuberculoid/pathology , Macrophages/immunology , Macrophages/microbiology , Macrophages/pathology , Male , Middle Aged , Mycobacterium leprae/pathogenicity , RNA-Seq , Single-Cell Analysis , Skin/microbiology , Skin/pathology , T-Lymphocytes/immunology , T-Lymphocytes/microbiology , T-Lymphocytes/pathology , TranscriptomeABSTRACT
High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S3 ("Second-Strand Synthesis"), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S3 increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S3 to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.