Depth-enhanced high-throughput microscopy by compact PSF engineering.

High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.

DeepOM: single-molecule optical genome mapping via deep learning.

MOTIVATION: Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optical genome mapping utilizing deep learning is presented, termed DeepOM. Utilization of a convolutional neural network, trained on simulated images of labeled DNA molecules, improves the success rate in the alignment of DNA images to genomic references. RESULTS: The method is evaluated on acquired images of human DNA molecules stretched in nano-channels. The accuracy of the method is benchmarked against state-of-the-art commercial software Bionano Solve. The results show a significant advantage in alignment success rate for molecules shorter than 50 kb. DeepOM improves the yield, sensitivity, and throughput of optical genome mapping experiments in applications of human genomics and microbiology. AVAILABILITY AND IMPLEMENTATION: The source code for the presented method is publicly available at https://github.com/yevgenin/DeepOM.