Vitiligo is an autoimmune skin disease that is characterized by the progressive destruction of melanocytes by autoreactive CD8+ T cells. Melanocyte destruction in active vitiligo is mediated by CD8+ T cells but why white patches in stable disease persist is poorly understood. The interaction between immune cells, melanocytes, and keratinocytes in situ in human skin has been difficult to study due to the lack of proper tools. Here, we combine non-invasive multiphoton microscopy (MPM) imaging and single-cell RNA sequencing (scRNA-seq) to identify distinct subpopulations of keratinocytes in lesional skin of stable vitiligo patients. We show that these keratinocytes are enriched in lesional vitiligo skin and differ in metabolism, an observation corroborated by both MPM and scRNA-seq. Systematic investigation of cell-cell communication show that CXCL is the prominent signaling change in this small population of keratinocytes, which secrete CXCL9 and CXCL10 to create local inflammatory cytokine loops with T cells to drive stable vitiligo persistence. Pseudotemporal dynamics analyses predict an alternative keratinocyte differentiation trajectory that generates this new population of keratinocytes in vitiligo skin. In summary, we couple advanced imaging with transcriptomics and bioinformatics to discover cellcell communication networks and keratinocyte cell states that perpetuate inflammation and prevent repigmentation.One Sentence SummaryCommunication between keratinocytes, immune cells, and melanocytes maintain depigmented patches in stable vitiligo.
The potential to differentiate between diseased and healthy tissue has been demonstrated through the extraction of morphological and functional metrics from label-free, two-photon images. Acquiring such images as fast as possible without compromising their diagnostic and functional content is critical for clinical translation of two-photon imaging. Computational restoration methods have demonstrated impressive recovery of image quality and important biological information. However, access to large clinical datasets has hampered advancement of denoising algorithms. Here, we seek to demonstrate the application of denoising algorithms on depth-resolved two-photon excited fluorescence (TPEF) images with specific focus on recovery of functional metabolic metrics. Datasets were generated through the collection of images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins from freshly excised rat cheek epithelium. Image datasets were patched across depth, generating 1012, 256-by-256 patches. A well-known Unet architecture was trained on 6628 low-signal-to-noise-ratio (SNR) patches from a previously collected large dataset and later retrained on a smaller 620 low-SNR patches dataset before being validated and evaluated on 88 and 304 low-SNR patches, respectively, using a structural similarity index measure (SSIM) loss function. We demonstrate models trained on larger datasets of human cervical tissue could be used to successfully restore metabolic metrics with an improvement in image quality when applied to rat cheek epithelium images. These results motivate further exploration of weight transfer for denoising of small clinical two-photon microscopy datasets.
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