Miniaturized High-Throughput and High-Resolution Platform for Continuous Live-Cell Monitoring via Lens-Free Imaging and Deep Learning.

Abstract

Monitoring the morphology and dynamics of both individual and collective cells is crucial for understanding the complexities of biological systems, investigating disease mechanisms, and advancing therapeutic strategies. However, traditional live-cell workstations that rely on microscopy often face inherent trade-offs between field of view (FOV) and resolution, making it difficult to achieve both high-throughput and high-resolution monitoring simultaneously. While existing lens-free imaging technologies enable high-throughput cell monitoring, they are often hindered by algorithmic complexity, long processing times that prevent real-time imaging, or insufficient resolution due to large sensor pixel sizes. To overcome these limitations, here an imaging platform is presented that integrates a custom-developed 500 nm pixel-size, 400-megapixel sensor with lens-free shadow imaging technology. This platform is capable of achieving imaging at a speed of up to 40s per frame, with a large FOV of 1 cm² and an imaging signal-to-noise ratio (SNR) of 42 dB, enabling continuous tracking of individual and cell populations throughout their entire lifecycle. By leveraging deep learning algorithms, the system accurately analyzes cell movement trajectories, while the integration of a K-means unsupervised clustering algorithm ensures precise evaluation of cellular activity. This platform provides an effective solution for high-throughput live-cell morphology monitoring and dynamic analysis.