We report the use of conditional generative adversarial network (cGAN) for restoring undersampled images captured in free-space angular-chirp-enhanced delay (FACED) microscopy. We show that this deep-learning approach allows the wider imaging field of view (FOV) along FACED axis, without substantially sacrificing the imaging resolution, photon-budget and speed even with lower density of scanning foci. This study could show the potential of further extending the applicability of FACED imaging to a wider range of biological applications that require extended FOV imaging.
We report an integrative unsupervised deep learning approach to translate the complex morphological information of cells into interpretable representations that can be generalizable for downstream single-cell analytics. The method, integrating the respective advantages of generative adversarial network and variational autoencoder, enables faithful prediction of biological processes based on cell morphology read out from different imaging modalities. We demonstrate the generalizability and scalability of this method in a diverse range of applications, including cellular responses to SARS-CoV-2 infection, cell-cycle progression imaged by high-throughput quantitative phase imaging (QPI), and cellular changes during epithelial to mesenchymal transition (EMT) captured by fluorescence imaging.
We report the use of high-throughput quantitative phase imaging (QPI) flow cytometry (based on multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM)) to investigate biophysical profiles of single cells infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This technique reveals the subtle biophysical heterogeneity of SARS-CoV-2 infection under the same multiplicity of infection. Furthermore, analyzing the label-free high-dimensional single-cell biophysical profiles (derived from multi-ATOM images) based on an unsupervised trajectory inference algorithm accurately recovers the infection progression over time. This study could offer biophysical insight of cellular morphogenesis of SARS-CoV-2 and shows the potential of label-free morphological profiling as a complementary drug discovery strategy for SARS-CoV-2.
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