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Background: Disabling pansclerotic morphea (DPM) is a rare systemic inflammatory disorder, characterized by poor wound healing, fibrosis, cytopenias, hypogammaglobulinemia, and squamous-cell carcinoma. The cause is unknown, and mortality is high., Methods: We evaluated four patients from three unrelated families with an autosomal dominant pattern of inheritance of DPM. Genomic sequencing independently identified three heterozygous variants in a specific region of the gene that encodes signal transducer and activator of transcription 4 ( STAT4 ). Primary skin fibroblast and cell-line assays were used to define the functional nature of the genetic defect. We also assayed gene expression using single-cell RNA sequencing of peripheral-blood mononuclear cells to identify inflammatory pathways that may be affected in DPM and that may respond to therapy., Results: Genome sequencing revealed three novel heterozygous missense gain-of-function variants in STAT4 . In vitro, primary skin fibroblasts showed enhanced interleukin-6 secretion, with impaired wound healing, contraction of the collagen matrix, and matrix secretion. Inhibition of Janus kinase (JAK)-STAT signaling with ruxolitinib led to improvement in the hyperinflammatory fibroblast phenotype in vitro and resolution of inflammatory markers and clinical symptoms in treated patients, without adverse effects. Single-cell RNA sequencing revealed expression patterns consistent with an immunodysregulatory phenotype that were appropriately modified through JAK inhibition., Conclusions: Gain-of-function variants in STAT4 caused DPM in the families that we studied. The JAK inhibitor ruxolitinib attenuated the dermatologic and inflammatory phenotype in vitro and in the affected family members. (Funded by the American Academy of Allergy, Asthma, and Immunology Foundation and others.)., (Copyright © 2023 Massachusetts Medical Society.)

Cell-cell interactions shape cellular function and ultimately organismal phenotype. Interacting cells can sense their mutual distance using combinations of ligand-receptor pairs, suggesting the existence of a spatial code, i.e., signals encoding spatial properties of cellular organization. However, this code driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans' body. Leveraging a 3D atlas of C. elegans' cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells across the whole body. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Armingol et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Multiphoton microscopy is a powerful technique for deep in vivo imaging in scattering samples. However, it requires precise, sample-dependent increases in excitation power with depth in order to generate contrast in scattering tissue, while minimizing photobleaching and phototoxicity. We show here how adaptive imaging can optimize illumination power at each point in a 3D volume as a function of the sample's shape, without the need for specialized fluorescent labeling. Our method relies on training a physics-based machine learning model using cells with identical fluorescent labels imaged in situ. We use this technique for in vivo imaging of immune responses in mouse lymph nodes following vaccination. We achieve visualization of physiologically realistic numbers of antigen-specific T cells (~2 orders of magnitude lower than previous studies), and demonstrate changes in the global organization and motility of dendritic cell networks during the early stages of the immune response. We provide a step-by-step tutorial for implementing this technique using exclusively open-source hardware and software.