High-throughput microscopy (HTM) is an indispensable tool for cancerous research. However, conventional microscopes can achieve either a large field of-view (FOV) or high resolution, but not both simultaneously. Thus, this proposal aims to develop a new HTM approach, aiming to integrate the merits of computational optics and deep learning microscopy. Visualizing a large number of individual cells, HTM generates vast spatiotemporal information on cellular structures and behavioral dynamics under a multitude of physiological condition[1-3]. It computationally evaluates the whole aspect of such data, yielding unbiased, systematic representation of biological process[4,5], and hidden features in bulk or small population measurements. This proposal implements computational illumination approach, known as Fourier ptychographic microscopy (FPM)[10-12]. FPM collects low resolution image sequences and changes the position of a point-light source; each image has spatially-shifted spectrum information in Fourier space, where the amount of shift depends on the illumination angle. By numerically stitching image sequences, the Fourier space, including high frequency components, can be reconstructed, which enables FPM to achieve high spatial resolution with low magnification lenses. In our year 1 pilot study, we successful constructed an FPM system to detect sample pattern. The promising result shows both large field-of-view and high resolution. In addition, this proposal adopts deep learning technology to develop deep learning microscopy (DLM). Deep learning (DL) is making an impressive progress in its ability to derive information from large data sets, to the point where such techniques can outperform human analyses for many data sets[13,14]. DL is particularly potent in discovering intricate, hidden structure in high dimensional data sets, and has been adopted for solving quantum physics problem. This proposal will leverage DL power to analyze large images from FPM. We will develop a DLM platform with the capacities for i) high-resolution, wide FOV imaging; ii) molecular profiling on individual cells; and iii) automated imaging analyses (molecular expression, morphology, migration, propagation) on large numbers of single cells. The development of DLM will have unprecedented analytical power: imaging large number of individual cells at high spatial resolution, and automatically extracting multitude of cellular features. As such, we can envision that the DLM will be a transformative tool for cancer research. Potential applications include better monitoring anticancer drug responses, analyzing cellular heterogeneity in large section of tissues, and prospectively detecting cellular fate under various physiological perturbations.