Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Hyperspectral compressive microscopy based on structured light sheet and deep convolutional neural network

International audienceWe describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization

Hyperspectral compressive microscopy based on structured light sheet and deep convolutional neural network

International audienceWe describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Hyperspectral compressive microscopy based on structured light sheet and deep convolutional neural network

International audienceWe describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization

NRF2 shortage in human skin fibroblasts dysregulates matrisome gene expression and affects collagen fibrillogenesis

International audienceNRF2 is a master regulator of anti-oxidative response that was recently proposed as a potential regulator of extracellular matrix (ECM) gene expression. Fibroblasts are major ECM producers in all connective tissues including dermis. A better understanding of NRF2-mediated ECM regulation in skin fibroblasts is thus of great interest for skin homeostasis maintenance and aging protection. Here, we investigate the impact of NRF2 downregulation on matrisome gene expression and ECM deposits in human primary dermal fibroblasts. RNA-seq-based transcriptome analysis of NRF2 silenced dermal fibroblasts shows that ECM genes are the most regulated gene sets, highlighting the relevance of the NRF2-mediated matrisome program in these cells. Using complementary light and electron microscopy methods, we show that NRF2 deprivation in dermal fibroblasts results in reduced collagen I biosynthesis and impacts collagen fibril deposition. Moreover, we identify ZNF469, a putative transcriptional regulator of collagen biosynthesis, as a novel target of NRF2. Both ZNF469 silenced fibroblasts and fibroblasts derived from Brittle Corneal Syndrome patients carrying mutations in ZNF469 show reduced collagen I gene expression. Our study shows that NRF2 orchestrates matrisome expression in human skin fibroblasts through direct or indirect transcriptional mechanisms that could be prioritized to target dermal ECM homeostasis in health and disease