Abstract
.Significance: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.Aim: The objective of our work was to determine how successfully deep learning methods perform on fluorescence prediction that depends on structural and/or a functional relationship between input labels and output labels.Approach: We present a virtual-fluorescence-staining method based on deep neural networks (VirFluoNet) to transform co-registered images of cells into subcellular compartment-specific molecular fluorescence labels in the same field-of-view. An algorithm based on conditional generative adversarial networks was developed and trained on microscopy datasets from breast-cancer and bone-osteosarcoma cell lines: MDA-MB-231 and U2OS, respectively. Several established performance metrics—the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural-similarity-index (SSIM)—as well as a novel performance metric, the tolerance level, were measured and compared for the same algorithm and input data.Results: For the MDA-MB-231 cells, F-actin signal performed the fluorescent antibody staining of vinculin prediction better than phase-contrast as an input. For the U2OS cells, satisfactory metrics of performance were archieved in comparison with ground truth. MAE is , 0.017, 0.012; PSNR is ; and SSIM is for 4′,6-diamidino-2-phenylindole/hoechst, endoplasmic reticulum, and mitochondria prediction, respectively, from channels of nucleoli and cytoplasmic RNA, Golgi plasma membrane, and F-actin.Conclusions: These findings contribute to the understanding of the utility and limitations of deep learning image-regression to predict fluorescence microscopy datasets of biological cells. We infer that predicted image labels must have either a structural and/or a functional relationship to input labels. Furthermore, the approach introduced here holds promise for modeling the internal spatial relationships between organelles and biomolecules within living cells, leading to detection and quantification of alterations from a standard training dataset.
Highlights
Microscopy techniques, the family of epifluorescence modalities, are workhorses of modern cell and molecular biology that enable microscale spatial insight
Does model prediction performance depend on the image modality and subcellular labels selected for training and prediction? Second, do errors in predicted images contribute to the likelihood of misinterpreting biology based on the image predictions? To address these questions, we developed a Deep convolutional neural networks (DCNNs)-based computational microscopy technique employing a customized conditional generative adversarial network that models the relationships between optical signals acquired using any imaging modality or fluorescence channel, assuming the signals are co-registered
We describe the results from all implementations in three categories: (1) AF: refocusing out-of-focus images; fluorescence prediction from (2) phase contrast images and (3) fluorescence images, and (4) error quantification
Summary
Microscopy techniques, the family of epifluorescence modalities, are workhorses of modern cell and molecular biology that enable microscale spatial insight. Phase-based microscopy techniques such as brightfield, phase contrast, differential interference contrast, digital holography, Fourier ptychography, and optical diffraction tomography,[1,2,3,4,5,6,7] among other modalities, have the potential to visualize the subcellular structure. Fluorescence-based techniques, on the other hand, excite fluorophores, which act as labels to spatially localize biological molecules and structures within cells These imaging techniques, especially fluorescence approaches, involve time-consuming preparation steps and costly reagents, introduce the possibility of signal bias due to photobleaching, and in time-lapse vital imaging, allow for possible misinterpretation of cell behavior due to gradual accumulation of sublethal damage from intense ultraviolet and other wavelengths used to excite molecular labels.[8] microscopy of cells is challenging due to the inherent trade-offs in sample preservation, image quality, and data acquisition time and the variability between labeling experiments
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