The endoplasmic reticulum (ER) is an organelle consisting of a network of membranous structures essential for various cellular activities, making its homeostasis critical for proper cell function. The composition of its membrane can be easily affected by various cellular stressors, triggering ER stress response. Therefore, conducting a detailed structural and compositional analysis of ER is crucial. However, due to resolution limits, analyzing the ER composition in situ remains difficult. Here, we propose a dual-modality imaging and analysis method integrating stimulated Raman scattering (SRS) and structured illumination microscopy (SIM) for imaging the lipid and protein contents of ER structures. With super-resolved structural guidance provided by SIM, the protein/lipid ratio was quantified for ER using multispectral SRS imaging. The spatial mapping of ER compositions in a single cell revealed subcellular diversity in the protein and lipid ratios in the ER structures, which significantly altered under ER stress.
KEYWORDS: Image restoration, Signal to noise ratio, Mitochondria, Light sources and illumination, Reconstruction algorithms, Microscopy, Deep learning, Model based design, Photobleaching, Photonics
Structured illumination microscopy (SIM) has been widely used in live-cell superresolution (SR) imaging. However, conventional physical model-based SIM SR reconstruction algorithms are prone to artifacts in handling raw images with low signal-to-noise ratios (SNRs). Deep-learning (DL)-based methods can address this challenge but may lead to degradation and hallucinations. By combining the physical inversion model with a total deep variation (TDV) regularization, we propose a hybrid restoration method (TDV-SIM) that outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining resolutions. We demonstrate the performance superiority of TDV-SIM in restoring actin filaments, endoplasmic reticulum, and mitochondrial cristae from extremely low SNR raw images. Thus TDV-SIM represents the ideal method for prolonged live-cell SR imaging with minimal exposure and photodamage. Overall, TDV-SIM proves the power of integrating model-based reconstruction methods with DL ones, possibly leading to the rapid exploration of similar strategies in high-fidelity reconstructions of other microscopy methods.
The emergence of super-resolution (SR) fluorescence microscopy has rejuvenated the search for new cellular sub-structures. However, SR fluorescence microscopy achieves high contrast at the cost of the lack of a holistic view of their interacting partners and surrounding environment. Thus we develop SR fluorescence-assisted diffraction computational tomography (SR-FACT), which combines label-free three-dimensional optical diffraction tomography (ODT) with two-dimensional fluorescence Hessian structured illumination microscopy. The ODT module is capable of resolving mitochondria, lipid droplets, the nuclear membrane, chromosomes, the tubular endoplasmic reticulum and lysosomes. Using dual-mode correlated live cell imaging for prolonged period of time, we observe the dynamics of a novel subcellular structure named dark-vacuole bodies. These works demonstrate the unique capabilities of SR-FACT, which suggest its wide applicability in cell biology in general.
In this presentation we report a new 3D scanned DSLM. The system combined 1) two-photon excitation, 2) scanning along the illumination axis (x-axis) using tunable acoustic gradient lens (TAG) to stretch the Rayleigh range [5], 3) scanning vertically to the illumination axis (y-axis) by one galvo mirror to create light sheet. 4) scanning along Z-axis to do fast 3D imaging by another galvo mirror. The image plane was kept aligned with the fast z-axis scanned light sheet plane by an electric tunable lens (ETL) as described in ref. 6. The light sheet can be tailored to any shape between 50×50 μm2 and more than 500×500 μm2 with constant thickness limited by diffraction and fast imaging rates limited by the detector. The tailorable illumination area allows multi-scale field of view (FOV), and is consequently capable of imaging cells, tissue and live animals in one setup.
Conference Committee Involvement (6)
Advanced Optical Imaging Technologies VII
12 October 2024 | Nantong, Jiangsu, China
Advanced Optical Imaging Technologies VI
14 October 2023 | Beijing, China
Second Conference on Biomedical Photonics and Cross-Fusion (BPC2023)
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