Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy
| Autor: | Sharmishtaa Seshamani, Mary M. Maleckar, Chawin Ounkomol, Forrest Collman, Gregory R. Johnson |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Materials science Fibrosarcoma Induced Pluripotent Stem Cells Three dimensional fluorescence Fluorescent Antibody Technique Image processing Biochemistry Convolutional neural network Article law.invention 03 medical and health sciences Imaging Three-Dimensional 0302 clinical medicine law Microscopy Image Processing Computer-Assisted Fluorescence microscope Humans Molecular Biology Cells Cultured Label free food and beverages Cell Biology Fluorescence Cellular Structures Microscopy Electron HEK293 Cells 030104 developmental biology Microscopy Fluorescence Electron microscope Biological system 030217 neurology & neurosurgery Biotechnology |
| Zdroj: | Nature Methods. 15:917-920 |
| ISSN: | 1548-7105 1548-7091 |
| DOI: | 10.1038/s41592-018-0111-2 |
| Popis: | Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron micrographs. |
| Databáze: | OpenAIRE |
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