Deep neural network automated segmentation of cellular structures in volume electron microscopy

CLCC1 promotes hepatic neutral lipid flux and nuclear pore complex assembly

Imbalances in lipid storage and secretion lead to the accumulation of hepatocyte lipid droplets (LDs) (i.e., hepatic steatosis). Our understanding of the mechanisms that govern the channeling of hepatocyte neutral lipids towards cytosolic LDs or secreted lipoproteins remains incomplete. Here, we performed a series of CRISPR-Cas9 screens under different metabolic states to uncover mechanisms of hepatic neutral lipid flux. Clustering of chemical-genetic interactions identified CLIC-like chloride channel 1 (CLCC1) as a critical regulator of neutral lipid storage and secretion. Loss of CLCC1 resulted in the buildup of large LDs in hepatoma cells and knockout in mice caused liver steatosis. Remarkably, the LDs are in the lumen of the ER and exhibit properties of lipoproteins, indicating a profound shift in neutral lipid flux. Finally, remote homology searches identified a domain in CLCC1 that is homologous to yeast Brl1p and Brr6p, factors that promote the fusion of the inner and outer nuclear envelopes during nuclear pore complex assembly. Loss of CLCC1 lead to extensive nuclear membrane herniations, consistent with impaired nuclear pore complex assembly. Thus, we identify CLCC1 as the human Brl1p/Brr6p homolog and propose that CLCC1-mediated membrane remodeling promotes hepatic neutral lipid flux and nuclear pore complex assembly.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.

Constitutive Endolysosomal Perforation in Neurons allows Induction of α-Synuclein Aggregation by Internalized Pre-Formed Fibrils

The endocytic pathway is both an essential route of molecular uptake in cells and a potential entry point for pathology-inducing cargo. The cell-to-cell spread of cytotoxic aggregates, such as those of α-synuclein (α-syn) in Parkinson's Disease (PD), exemplifies this duality. Here we used a human iPSC-derived induced neuronal model (iNs) prone to death mediated by aggregation in late endosomes and lysosomes of endogenous α-syn, seeded by internalized pre-formed fibrils of α-syn (PFFs). This PFF-mediated death was not observed with parental iPSCs or other non-neuronal cells. Using live-cell optical microscopy to visualize the read out of biosensors reporting endo-lysosome wounding, we discovered that up to about 10% of late endosomes and lysosomes in iNs exhibited spontaneous constitutive perforations, regardless of the presence of internalized PFFs. This wounding, absent in parental iPSCs and non-neuronal cells, corresponded to partial damage by nanopores in the limiting membranes of a subset of endolysosomes directly observed by volumetric focused ion beam scanning electron microscopy (FIB-SEM) in iNs and in CA1 pyramidal neurons from mouse brain, and not found in iPSCs or in other non-neuronal cells in culture or in mouse liver and skin. We suggest that the compromised limiting membranes in iNs and neurons in general are the primary conduit for cytosolic α-syn to access PFFs entrapped within endo-lysosomal lumens, initiating PFF-mediated α-syn aggregation. Significantly, eradicating the intrinsic endolysosomal perforations in iNs by inhibiting the endosomal Phosphatidylinositol-3-Phosphate/Phosphatidylinositol 5-Kinase (PIKfyve kinase) using Apilimod or Vacuolin-1 markedly reduced PFF-induced α-syn aggregation, despite PFFs continuing to enter the endolysosomal compartment. Crucially, this intervention also diminished iN death associated with PFF incubation. Our results reveal the surprising presence of intrinsically perforated endo-lysosomes in neurons, underscoring their crucial early involvement in the genesis of toxic α-syn aggregates induced by internalized PFFs. This discovery offers a basis for employing PIKfyve kinase inhibition as a potential therapeutic strategy to counteract synucleinopathies.

Electron Microscopy Techniques for 3D Plant ER Imaging

The endoplasmic reticulum (ER) forms an extensive network in plant cells. In leaf cells and vacuolated root cells it is mainly restricted to the cortex, whereas in the root meristem the cortical and cytoplasmic ER takes up a large volume throughout the entire cell. Only 3D electron microscopy provides sufficient resolution to understand the spatial organization of the ER in the root. Here we present two protocols for 3D EM imaging of the ER across a range of scales. For large-scale ER structure analysis, we describe selective ER staining with ZIO that allows for automated or semi-automated ER segmentation. For smaller regions of ER, we describe high-pressure freezing, which enables almost instantaneous fixation of plant tissues but without organelle specific staining. These fixation and staining techniques are suitable for a range of imaging modalities, including serial sections, array tomography, serial block face-scanning electron microscopy (SBF-SEM), or focused ion beam (FIB) SEM.

Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy

Accurate delineation of plant cell organelles from electron microscope images is essential for understanding subcellular behaviour and function. Here we develop a deep-learning pipeline, called the organelle segmentation network (OrgSegNet), for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei and vacuoles. OrgSegNet was evaluated on a large manually annotated dataset collected from 19 plant species and achieved state-of-the-art segmentation performance. We defined three digital traits (shape complexity, electron density and cross-sectional area) to track the quantitative features of individual organelles in 2D images and released an open-source web tool called Plantorganelle Hunter for quantitatively profiling subcellular morphology. In addition, the automatic segmentation method was successfully applied to a serial-sectioning scanning microscope technique to create a 3D cell model that offers unique views of the morphology and distribution of these organelles. The functionalities of Plantorganelle Hunter can be easily operated, which will increase efficiency and productivity for the plant science community, and enhance understanding of subcellular biology.

An interactive deep learning-based approach reveals mitochondrial cristae topologies

The convolution of membranes called cristae is a critical structural and functional feature of mitochondria. Crista structure is highly diverse between different cell types, reflecting their role in metabolic adaptation. However, their precise three-dimensional (3D) arrangement requires volumetric analysis of serial electron microscopy and has therefore been limiting for unbiased quantitative assessment. Here, we developed a novel, publicly available, deep learning (DL)-based image analysis platform called Python-based human-in-the-loop workflow (PHILOW) implemented with a human-in-the-loop (HITL) algorithm. Analysis of dense, large, and isotropic volumes of focused ion beam-scanning electron microscopy (FIB-SEM) using PHILOW reveals the complex 3D nanostructure of both inner and outer mitochondrial membranes and provides deep, quantitative, structural features of cristae in a large number of individual mitochondria. This nanometer-scale analysis in micrometer-scale cellular contexts uncovers fundamental parameters of cristae, such as total surface area, orientation, tubular/lamellar cristae ratio, and crista junction density in individual mitochondria. Unbiased clustering analysis of our structural data unraveled a new function for the dynamin-related GTPase Optic Atrophy 1 (OPA1) in regulating the balance between lamellar versus tubular cristae subdomains.

Application of Super-resolution SPEED Microscopy in the Study of Cellular Dynamics

Super-resolution imaging techniques have broken the diffraction-limited resolution of light microscopy. However, acquiring three-dimensional (3D) super-resolution information about structures and dynamic processes in live cells at high speed remains challenging. Recently, the development of high-speed single-point edge-excitation subdiffraction (SPEED) microscopy, along with its 2D-to-3D transformation algorithm, provides a practical and effective approach to achieving 3D subdiffraction-limit information in subcellular structures and organelles with rotational symmetry. One of the major benefits of SPEED microscopy is that it does not rely on complex optical components and can be implemented on a standard, inverted epifluorescence microscope, simplifying the process of sample preparation and the expertise requirement. SPEED microscopy is specifically designed to obtain 2D spatial locations of individual immobile or moving fluorescent molecules inside submicrometer biological channels or cavities at high spatiotemporal resolution. The collected data are then subjected to postlocalization 2D-to-3D transformation to obtain 3D super-resolution structural and dynamic information. In recent years, SPEED microscopy has provided significant insights into nucleocytoplasmic transport across the nuclear pore complex (NPC) and cytoplasm-cilium trafficking through the ciliary transition zone. This Review focuses on the applications of SPEED microscopy in studying the structure and function of nuclear pores.

Three-dimensional ultrastructure analysis of organelles in injured motor neuron

Morphological analysis of organelles is one of the important clues for understanding the cellular conditions and mechanisms occurring in cells. In particular, nanoscale information within crowded intracellular organelles of tissues provide more direct implications when compared to analyses of cells in culture or isolation. However, there are some difficulties in detecting individual shape using light microscopy, including super-resolution microscopy. Transmission electron microscopy (TEM), wherein the ultrastructure can be imaged at the membrane level, cannot determine the whole structure, and analyze it quantitatively. Volume EM, such as focused ion beam/scanning electron microscopy (FIB/SEM), can be a powerful tool to explore the details of three-dimensional ultrastructures even within a certain volume, and to measure several parameters from them. In this review, the advantages of FIB/SEM analysis in organelle studies are highlighted along with the introduction of mitochondrial analysis in injured motor neurons. This would aid in understanding the morphological details of mitochondria, especially those distributed in the cell bodies as well as in the axon initial segment (AIS) in mouse tissues. These regions have not been explored thus far due to the difficulties encountered in accessing their images by conditional microscopies. Some mechanisms of nerve regeneration have also been discussed with reference to the obtained findings. Finally, future perspectives on FIB/SEM are introduced. The combination of biochemical and genetic understanding of organelle structures and a nanoscale understanding of their three-dimensional distribution and morphology will help to match achievements in genomics and structural biology.

Out With a Bang: Celebrating Global Chemical Biology

On November 8-10, 2022, 163 participants from all over the world gathered at the Campus Biotech in Geneva, Switzerland to share in the latest research in chemical biology. The fourth international symposium of the Swiss National Centres of Competence in Research (NCCR) Chemical Biology coincided with the end of this successful research consortium, and as such this event marked a celebration of the past 12 years of chemical biology research in Switzerland. The inspiring talks delivered by the 15 well-known scientists, balanced in gender, expertise, and geographic location, as well as the numerous poster presentations by junior scientists showcased the breadth of global chemical biology and the bright future ahead.

Pumping up the volume

The time and cost of annotating ground-truth images and network training are major challenges to utilizing machine learning to automate the mining of volume electron microscopy data. In this issue, Gallusser et al. (2023. J. Cell Biol.https://doi.org/10.1083/jcb.202208005) present a less computationally intense pipeline to detect a single type of organelle using a limited number of loosely annotated images.