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Mitochondria play a critical role in generating energy to support the entire lifecycle of biological cells, yet it is still unclear how their morphological structures evolve to regulate their functionality. Conventional fluorescence microscopy can only provide ~300 nm resolution, which is insufficient to visualize mitochondrial cristae. Here, we developed an enhanced squaraine variant dye (MitoESq-635) to study the dynamic structures of mitochondrial cristae in live cells with a superresolution technique. The low saturation intensity and high photostability of MitoESq-635 make it ideal for long-term, high-resolution (stimulated emission depletion) STED nanoscopy. We performed time-lapse imaging of the mitochondrial inner membrane over 50 min (3.9 s per frame, with 71.5 s dark recovery) in living HeLa cells with a resolution of 35.2 nm. The forms of the cristae during mitochondrial fusion and fission can be clearly observed. Our study demonstrates the emerging capability of optical STED nanoscopy to investigate intracellular physiological processes with nanoscale resolution for an extended period of time. Live cell imaging of mitochondrial cristae is challenged by the unsuitability of current fluorescent probes and high phototoxicity. Here the authors develop a squarine variant probe (MitoESq-635) that is capable of longitudinal imaging of cristae with STED with minimal phototoxicity.

Lipid membranes are found in most intracellular organelles, and their heterogeneities play an essential role in regulating the organelles’ biochemical functionalities. Here we report a Spectrum and Polarization Optical Tomography (SPOT) technique to study the subcellular lipidomics in live cells. Simply using one dye that universally stains the lipid membranes, SPOT can simultaneously resolve the membrane morphology, polarity, and phase from the three optical-dimensions of intensity, spectrum, and polarization, respectively. These high-throughput optical properties reveal lipid heterogeneities of ten subcellular compartments, at different developmental stages, and even within the same organelle. Furthermore, we obtain real-time monitoring of the multi-organelle interactive activities of cell division and successfully reveal their sophisticated lipid dynamics during the plasma membrane separation, tunneling nanotubules formation, and mitochondrial cristae dissociation. This work suggests research frontiers in correlating single-cell super-resolution lipidomics with multiplexed imaging of organelle interactome. Lipid membranes are heterogeneous and dynamically regulated in cells. Here the authors report a Spectrum and Polarisation Optical Tomography (SPOT) method where they use Nile Red dye to resolve membrane morphology, polarity and phase in cells.

A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy. The resolution of fluorescence microscopy is increased by incorporating prior information into deconvolution algorithms.

Fluorescence polarization microscopy images both the intensity and orientation of fluorescent dipoles and plays a vital role in studying molecular structures and dynamics of bio-complexes. However, current techniques remain difficult to resolve the dipole assemblies on subcellular structures and their dynamics in living cells at super-resolution level. Here we report polarized structured illumination microscopy (pSIM), which achieves super-resolution imaging of dipoles by interpreting the dipoles in spatio-angular hyperspace. We demonstrate the application of pSIM on a series of biological filamentous systems, such as cytoskeleton networks and λ-DNA, and report the dynamics of short actin sliding across a myosin-coated surface. Further, pSIM reveals the side-by-side organization of the actin ring structures in the membrane-associated periodic skeleton of hippocampal neurons and images the dipole dynamics of green fluorescent protein-labeled microtubules in live U2OS cells. pSIM applies directly to a large variety of commercial and home-built SIM systems with various imaging modality. Polarization microscopy has been combined with single-molecule localization, but it’s often limited in either speed or resolution. Here the authors present polarized Structured Illumination Microscopy (pSIM), a method that uses polarized laser excitation to measure dye orientation during fast super-resolution live cell imaging.

Mitochondria are crucial organelles closely associated with cellular metabolism and function. Mitochondrial DNA (mtDNA) encodes a variety of transcripts and proteins essential for cellular function. However, the interaction between the inner membrane (IM) and mtDNA remains elusive due to the limitations in spatiotemporal resolution offered by conventional microscopy and the absence of suitable in vivo probes specifically targeting the IM. Here, we have developed a novel fluorescence probe called HBmito Crimson, characterized by exceptional photostability, fluorogenicity within lipid membranes, and low saturation power. We successfully achieved over 500 frames of low-power stimulated emission depletion microscopy (STED) imaging to visualize the IM dynamics, with a spatial resolution of 40 nm. By utilizing dual-color imaging of the IM and mtDNA, it has been uncovered that mtDNA tends to habitat at mitochondrial tips or branch points, exhibiting an overall spatially uniform distribution. Notably, the dynamics of mitochondria are intricately associated with the positioning of mtDNA, and fusion consistently occurs in close proximity to mtDNA to minimize pressure during cristae remodeling. In healthy cells, >66% of the mitochondria are Class III (i.e., mitochondria >5 μm or with >12 cristae), while it dropped to <18% in ferroptosis. Mitochondrial dynamics, orchestrated by cristae remodeling, foster the even distribution of mtDNA. Conversely, in conditions of apoptosis and ferroptosis where the cristae structure is compromised, mtDNA distribution becomes irregular. These findings, achieved with unprecedented spatiotemporal resolution, reveal the intricate interplay between cristae and mtDNA and provide insights into the driving forces behind mtDNA distribution.HBmito Crimson has the ability to localize specifically on the inner mitochondrial membrane and emit light, while its superior photostability enables STED imaging.

Skeletal muscle atrophy is a complication of obesity, partially induced by impaired mitophagy. This study investigates whether Berberine(BBR) protects mice from obese skeletal muscle atrophy and the underlying molecular mechanism. Twenty C57BL/6 mice were fed a high-fat diet until they weighed more than 20% of the average body weight of the control group. The mice were then divided into two groups and gavaged with BBR or vehicle for 8 weeks. 10 mice were used as controls. Fasting blood glucose was measured, an oral glucose tolerance test was performed, and the mice were measured for grip strength and exercise capacity. H&E and Oil Red O staining were used to observe the pathological changes of skeletal muscle. MURF1, FBXO32, BAX, BCL2, P62, LC3 and mitophagy receptor FUNDC1 were observed in mice. BBR was intervened in C2C12 myotubes. The role of FUNDC1 was verified by RNA interference. We found that BBR treatment increased grip strength and improved muscle function. BBR not only reduced weight gain, excessive lipid accumulation and hyperlipidemia, but also ameliorated obesity-induced skeletal muscle atrophy and apoptosis. BBR promoted autophagy and increased FUNDC1 protein expression. The same positive effects were observed after BBR intervening on C2C12 myotubes, whereas FUNDC1 RNA interference attenuated the anti-skeletal muscle atrophy effect of BBR. These results suggest that BBR ameliorated obesity-induced skeletal muscle atrophy in mice by modulating the skeletal muscle mitophagy receptor FUNDC1, which may be a potential therapeutic target for obesity-induced skeletal muscle atrophy.

Studying organelles’ interactome at system level requires simultaneous observation of subcellular compartments and tracking their dynamics. Conventional multicolor approaches rely on specific fluorescence labeling, where the number of resolvable colors is far less than the types of organelles. Here, we use a lipid-specific dye to stain all the membrane-associated organelles and spinning-disk microscopes with an extended resolution of ~143 nm for high spatiotemporal acquisition. Due to the chromatic polarity sensitivity, high-resolution ratiometric images well reflect the heterogeneity of organelles. With deep convolutional neuronal networks, we successfully segmented up to 15 subcellular structures using one laser excitation. We further show that transfer learning can predict both 3D and 2D datasets from different microscopes, different cell types, and even complex systems of living tissues. We succeeded in resolving the 3D anatomic structure of live cells at different mitotic phases and tracking the fast dynamic interactions among six intracellular compartments with high robustness. The authors demonstrate an AI-enhanced imaging method combining lipid-specific dyes and super-resolution microscopy enables simultaneous tracking of up to 15 organelles in living cells, revealing dynamic interactions across diverse biological systems.

Lipid peroxidation and mitochondrial damage impair insulin sensitivity in skeletal muscle. Sirtuin-1 (SIRT1) protects mitochondria and activates under energy restriction. Dapagliflozin (Dapa) is an antihyperglycaemic agent that belongs to the sodium-glucose cotransporter-2 (SGLT2) inhibitors. Evidence shows that Dapa can induce nutrient deprivation effects, providing additional metabolic benefits. This study investigates whether Dapa can trigger nutrient deprivation to activate SIRT1 and enhance insulin sensitivity in skeletal muscle. We treated diet-induced obese (DIO) mice with Dapa and measured metabolic parameters, lipid accumulation, oxidative stress, mitochondrial function, and glucose utilization in skeletal muscle. β-hydroxybutyric acid (β-HB) was intervened in C2C12 myotubes. The role of SIRT1 was verified by RNA interference. We found that Dapa treatment induced nutrient deprivation state and reduced lipid deposition and oxidative stress, improved mitochondrial function and glucose tolerance in skeletal muscle. The same positive effects were observed after β-HB intervening for C2C12 myotubes, and the promoting effects on glucose utilization were diminished by SIRT1 RNA interference. Thus, Dapa promotes a nutrient deprivation state and enhances skeletal muscle insulin sensitivity via SIRT1 activation. In this study, we identified a novel hypoglycemic mechanism of Dapa and the potential mechanistic targets.

Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and artifacts, while deep learning methods typically lack generalizability. We introduce 3Snet-CLID, a computational SR method which integrates a hybrid supervised/self-supervised deep learning network for signal-preserving denoising with direct Richardson–Lucy deconvolution. 3Snet-CLID’s per-pixel denoising strategy suppresses noise while maintaining signal distribution, mitigating artifacts, and enhancing robustness. The method achieves more than 5-fold resolution improvement on conventional microscopes, revealing diverse structures such as the mitochondrial outer membrane, endoplasmic reticulum, and nuclear pores in live and fixed cells under standard labeling. By overcoming key computational SR bottlenecks, 3Snet-CLID offers denoising capability and an accessible platform for high-fidelity nanoscale live-cell imaging. 3Snet-CLID integrates signal-preserving deep learning denoising with direct Richardson–Lucy deconvolution to achieve over 5-fold resolution improvement on standard microscopes, enabling artifact-free nanoscale imaging of live-cell organelles and structures.

Automated glaucoma detection from retinal fundus images plays a crucial role in facilitating early intervention and improving the management of this progressive ocular condition. Although convolutional neural networks (CNNs) have significantly advanced image analysis, current CNN-based models encounter two major limitations. First, they rely primarily on convolutional operations, which restrict the ability to capture cross-channel correlations effectively due to the channel-specific focus of these operations. Second, they often depend on fully-connected (FC) layers for classification, which can introduce unnecessary complexity and limit adaptability, potentially impacting overall classification performance. This study introduces the Modular Convolution-Involution Cascade Network (MCICNet), an innovative CNN architecture designed to address these challenges in the context of glaucoma detection. The model employs a combination of convolution and involution operations in a cascade structure, allowing for the effective capture of inter-channel dependencies within the feature extraction process. Furthermore, the classification phase integrates light gradient boosting machine (LightGBM) as a replacement for traditional FC layers, offering enhanced precision and generalization while reducing model complexity. Extensive experiments conducted on the LAG and ACRIMA datasets demonstrate that MCICNet achieves significant improvements compared to existing CNN and transformer-based models. The model attained a classification accuracy of 95.6% on the LAG dataset and 96.2% on ACRIMA, outperforming nine widely used CNN architectures (AlexNet, MobileNetV2, SqueezeNet, ResNet18, GoogLeNet, DenseNet121, EfficientNetB0, ShuffleNet, and VGG16), as well as three transformer-based models (ViT, MaxViT, and SwinT). Additionally, MCICNet showed superior performance over its variant without involution (MCICNet-NoInvolution). With only 0.9 million parameters, MCICNet demonstrates substantial efficiency in resource utilization alongside its high learning capability, establishing it as an advanced and computationally efficient solution for glaucoma detection.