Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy

Abstract

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulations of cells and organelles in high resolution noninvasively. However, stochastic noise inherently restricts the upper bonds of optical interrogation quality and exacerbates the observation fidelity in encountering joint demand of high frame rate, long-term, and low photobleaching and phototoxicity. Here, we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing SNR by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi accomplished efficacious denoising requiring no clean data as references and no compromise of spatiotemporal resolution on diverse imaging systems. The computationally 15-fold multiplied photon budget in a standard confocal microscope by DeepSeMi allows for recording organelle interactions in four colors and high-frame-rate across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames, all faithfully and sample-friendly. Through comprehensive validations across various cells and species over various instruments, we prove DeepSeMi is a versatile tool for reliably and bio-friendly breaking the shot-noise limit, facilitating automated analysis of massive data about cell migrations and organelle interactions.