Biology subjects

Plikus, M.

Publications and source records attributed to Plikus, M..

3 recordsLinked to original sources

bioRxiv2026

Single-cell characterization of skin response to a bite by West Nile virus-infected mosquito reveals fibroblast-mediated barrier to transmission

Cutaneous events at the mosquito bite site that determine orthoflavivirus transmission efficiency remain largely uncharacterized. Here, we report single-cell RNA-sequencing of skin from immunocompetent mice bitten by West Nile virus-infected mosquitoes, capturing early response at a critical transmission bottleneck. Fibroblasts were the dominant cells exposed to infectious saliva. While neutrophils and Folr2+ macrophages diminished, monocyte-derived and antigen-presenting macrophages, and lymphocytes became more abundant. Cell-cell communication analysis revealed the central roles of fibroblasts and myeloid cells in induced immune-related signaling. Transcriptional profiling defined cell type-specific responses to infectious bite integrating immune modulation, skin repair and metabolic remodeling. Using in vivo gene silencing, we demonstrated that fibroblast-expressed LRRC15 (leucine rich repeat-containing 15) functions as a cutaneous restriction factor, limiting viral replication in skin, viral dissemination to draining lymph nodes, and disease severity. Collectively, our analyses provide cellular and molecular understanding of bite-initiated arboviral transmission, establishing skin-resident fibroblasts as frontline defender cells. HIGHLIGHTSO_LISingle-cell profiling captures early cutaneous response to West Nile virus-infected mosquito bites with cell type resolution. C_LIO_LISkin-resident fibroblasts are the dominant frontline cells exposed to infectious mosquito saliva. C_LIO_LIInfectious bite reconfigures cutaneous immune cells, and activates multi-directional signaling between immune and structural skin cell types. C_LIO_LIBite-induced fibroblast-expressed LRRC15 restricts viral transmission and attenuates disease severity. C_LI

zoology↗

bioRxiv2024

A single cell and spatial genomics atlas of human skin fibroblasts in health and disease

Fibroblasts are critical cells that shape the architecture and cellular ecosystems in multiple tissues. Understanding fibroblast heterogeneity and their spatial context in health and disease has enormous clinical relevance. In this study, we constructed a spatially-resolved atlas of human skin fibroblasts from healthy skin and 23 skin disorders. We define 6 major skin fibroblast populations in health and a further three skin disease-specific fibroblast subtypes, and demonstrate the fibroblast composition in different types of skin disease. We characterise a human-specific fibroblastic reticular cell (FRC)-like subtype in the skin perivascular niche and postulate their origin from prenatal skin lymphoid tissue organiser (LTo)-like cells. We also show that inflammatory myofibroblasts (IL11+MMP1+CXCL5+IL7R+) are a conserved fibroblast subtype in inflammatory disorders and cancers across multiple human tissues. We provide a harmonised nomenclature for skin fibroblasts that integrates previous findings from human skin and other tissues.

cell biology↗

bioRxiv2022

Screening cell-cell communication in spatial transcriptomics via collective optimal transport

Spatial transcriptomic technologies and spatially annotated single cell RNA-sequencing (scRNA-seq) datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). How to incorporate the spatial information and complex biochemical processes in reconstructing CCC remains a major challenge. Here we present COMMOT to infer CCC in spatial transcriptomics, which accounts for the competition among different ligand and receptor species as well as spatial distances between cells. A novel collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. We introduce downstream analysis tools on spatial directionality of signalings and genes regulated by such signalings using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies, showing its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT reveals new CCCs during skin morphogenesis in a case study of human epidermal development. Both the method and the computational package have broad applications in inferring cell-cell interactions within spatial genomics datasets.

bioinformatics↗