Understanding cells as integrated systems is a challenge central to modern biology. While different microscopy approaches may be used to probe diverse aspects of biological organization, each method presents limitations which ultimately restrict a view into unified cellular organization. For example, while fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and can damage cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images. We then demonstrate that this same method can be used to predict immunofluorescence from electron micrograph inputs, extending the method to a wider range of bioimaging applications. The various imaging methods currently used to capture details of cellular organization all present restrictions with respect to expense, spatio-temporal resolution, and sample perturbation. Fluorescence microscopy permits imaging of specific proteins and structures of interest by labeling them specifically; but it requires advanced instrumentation and time-consuming sample preparation. Critically, samples are subject to significant phototoxicity and photobleaching, creating a tradeoff between data quality and time scales available for live cell imaging 1 , 2 . Furthermore, the number of simultaneous fluorescent tags is restricted by both spectrum saturation and cell health, limiting the number of parallel labels that can be imaged together. Transmitted light microscopy, e.g., bright-field, phase, DIC, etc., in contrast, is relatively low-cost and label/dye-free, with greatly reduced phototoxicity 3 and simplified sample preparation. Although valuable information about cellular organization is apparent in transmitted light images; they lack the clear contrast of fluorescence labeling, a limitation also present in other widely used microscopy modalities. Electron micrographs, for example, contain a rich set of biological detail about subcellular structure, but often require tedious expert interpretation. A method that could combine the clarity of fluorescence microscopy with the relative simplicity and modest cost of other imaging techniques would present a groundbreaking tool for biological insight into the integrated organization of subcellular structures. Convolutional neural networks (CNNs) capture non-linear relationships over large spatial areas of images, resulting in vastly improved performance for image recognition tasks as compared to classical machine learning methods. Here, we present a CNN-based tool, employing a U-Net architecture 4 ( Supplementary Fig. 1, see Methods) to model gene-edited hiPS cell lines, developed image-based assays, and recorded the high replicate data sets suitable for modeling and without whom this work would not have been possible. We especially thank the Allen Institute for Cell Science Gene Editing, Assay Development, Microscopy, and Pipeline teams for providing cell lines and images of different transmitted-light imaging modalities, and particularly Kaytlyn Gerbin, Angel Nelson, and Haseeb Malik for performing the cardiomyocyte differentiation and culture, Winnie Leung, Joyce Tang, Melissa Hendershott and Nathalie Gaudreault for gathering the additional time series, CAAX-labeled, cardiomyocyte, HEK-293, and HT-1080 data. We would like to thank the Allen Institute for Cell Science Animated Cell team and Thao Do specifically for providing her expertise in figure preparation. We thank Daniel Fernandes for developing an early proof of concept 2D version of the model. We would like to thank members of the Allen Institute for Brain Science Synapse Biology department for preparing samples and providing images that were the basis for training the conjugate array tomography data. These contributions were absolutely critical for model development. Cardiomyocyte and hiPSC data in this publication were derived from cells in the Allen Cell Collection, a collection of fluorescently labeled hiPSCs derived from the parental WTC line provided by Bruce R.
Ounkomol et al. REF then used a modified U-net structure to predict 3D fluorescent images using 3D bright-field data.
Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy
bioRxiv
Biology