Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset

Metabolic dysregulation contributes to the development of dysferlinopathy

Dysferlin is a transmembrane protein that plays a prominent role in membrane repair of damaged muscle fibers. Accordingly, mutations in the dysferlin gene cause progressive muscular dystrophies, collectively referred to as dysferlinopathies for which no effective treatment exists. Unexpectedly, experimental approaches that successfully restore membrane repair fail to prevent a dystrophic phenotype, suggesting that additional, hitherto unknown dysferlin-dependent functions contribute to the development of the pathology. Our experiments revealed an altered metabolic phenotype in dysferlin-deficient muscles, characterized by (1) mitochondrial abnormalities and elevated death signaling and (2) increased glucose uptake, reduced glycolytic protein levels, and pronounced glycogen accumulation. Strikingly, elevating mitochondrial volume density and muscle glycogen accelerates disease progression; whereas, improvement of mitochondrial function and recruitment of muscle glycogen with exercise ameliorated functional parameters in a mouse model of dysferlinopathy. Collectively, our results not only shed light on a metabolic function of dysferlin but also imply new therapeutic avenues aimed at promoting mitochondrial function and normalizing muscle glycogen to ameliorate dysferlinopathies, complementing efforts that target membrane repair.

Morphometric study of proximal tubular cell mitochondria using TEM images in renal diseases

The kidney is rich in mitochondria, and any alterations or damage to tubular cell mitochondria play an important role in renal metabolic activities and the pathogenesis of various kidney diseases. Quantitative analysis of mitochondrial concentration, size, and shape is essential for understanding mitochondrial biology in renal disorders. This study assessed mitochondrial morphometric parameters of the proximal convoluted tubular cell adjacent to the glomerulus in different renal disorders and investigated how they correlated with serum creatinine. A total of 65 kidney biopsy cases received by the transmission electron microscope (TEM) laboratory for diagnosis were included in the study. TEM images of glutaraldehyde-osmium tetroxide fixed epoxy-resin embedded 70 nm thick sections were used for the evaluation of (i) minor axis(MinX) (ii) major axis(MajX) (iii) Area, (iv)Perimeter, (v) Aspect ratio and (vi) Roundness of mitochondria in renal tubular cells using QuPath software. Mitochondrial density (MDensity), % of mitochondrial space (MSpace), and mitochondrial surface density (MSDensity) in the cytoplasm of tubular space were estimated for each sample. Serum creatinine showed good negative correlations with MSpace and MSDensity, and elongation of mitochondria was more in renal disorder in comparison to normal histology, which indicated the variation of mitochondrial concentration and shape in proximal tubular cells could be important features in the renal function disorder.

Mitochondrial complexity is regulated at ER-mitochondria contact sites via PDZD8-FKBP8 tethering

Mitochondria-ER membrane contact sites (MERCS) represent a fundamental ultrastructural feature underlying unique biochemistry and physiology in eukaryotic cells. The ER protein PDZD8 is required for the formation of MERCS in many cell types, however, its tethering partner on the outer mitochondrial membrane (OMM) is currently unknown. Here we identify the OMM protein FKBP8 as the tethering partner of PDZD8 using a combination of unbiased proximity proteomics, CRISPR-Cas9 endogenous protein tagging, Cryo-electron tomography, and correlative light-electron microscopy. Single molecule tracking reveals highly dynamic diffusion properties of PDZD8 along the ER membrane with significant pauses and captures at MERCS. Overexpression of FKBP8 is sufficient to narrow the ER-OMM distance, whereas independent versus combined deletions of these two proteins demonstrate their interdependence for MERCS formation. Furthermore, PDZD8 enhances mitochondrial complexity in a FKBP8-dependent manner. Our results identify a novel ER-mitochondria tethering complex that regulates mitochondrial morphology in mammalian cells.

Transfer learning improves performance in volumetric electron microscopy organelle segmentation across tissues

Motivation

Volumetric electron microscopy (VEM) enables nanoscale resolution three-dimensional imaging of biological samples. Identification and labeling of organelles, cells, and other structures in the image volume is required for image interpretation, but manual labeling is extremely time-consuming. This can be automated using deep learning segmentation algorithms, but these traditionally require substantial manual annotation for training and typically these labeled datasets are unavailable for new samples.

Results

We show that transfer learning can help address this challenge. By pretraining on VEM data from multiple mammalian tissues and organelle types and then fine-tuning on a target dataset, we segment multiple organelles at high performance, yet require a relatively small amount of new training data. We benchmark our method on three published VEM datasets and a new rat liver dataset we imaged over a 56×56×11 μ m volume measuring 7000×7000×219 px using serial block face scanning electron microscopy with corresponding manually labeled mitochondria and endoplasmic reticulum structures. We further benchmark our approach against the Segment Anything Model 2 and MitoNet in zero-shot, prompted, and fine-tuned settings.

Availability and implementation

Our rat liver dataset's raw image volume, manual ground truth annotation, and model predictions are freely shared at github.com/Xrioen/cross-tissue-transfer-learning-in-VEM.

Redirecting glucose flux during in vitro expansion generates epigenetically and metabolically superior T cells for cancer immunotherapy

Cellular therapies are living drugs whose efficacy depends on persistence and survival. Expansion of therapeutic T cells employs hypermetabolic culture conditions to promote T cell expansion. We show that typical in vitro expansion conditions generate metabolically and functionally impaired T cells more reliant on aerobic glycolysis than those expanding in vivo. We used dichloroacetate (DCA) to modulate glycolytic metabolism during expansion, resulting in elevated mitochondrial capacity, stemness, and improved antitumor efficacy in murine T cell receptor (TCR)-Tg and human CAR-T cells. DCA-conditioned T cells surprisingly show no elevated intratumoral effector function but rather have improved engraftment. DCA conditioning decreases reliance on glucose, promoting usage of serum-prevalent physiologic carbon sources. Further, DCA conditioning promotes metabolic flux from mitochondria to chromatin, resulting in increased histone acetylation at key longevity genes. Thus, hyperglycemic culture conditions promote expansion at the expense of metabolic flexibility and suggest pharmacologic metabolic rewiring as a beneficial strategy for improvement of cellular immunotherapies.

Segment Anything for Microscopy

Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models.

Efficient cell-wide mapping of mitochondria in electron microscopic volumes using webKnossos

Recent technical advances in volume electron microscopy (vEM) and artificial-intelligence-assisted image processing have facilitated high-throughput quantifications of cellular structures, such as mitochondria, that are ubiquitous and morphologically diversified. A still often-overlooked computational challenge is to assign a cell identity to numerous mitochondrial instances, for which both mitochondrial and cell membrane contouring used to be required. Here, we present a vEM reconstruction procedure (called mito-SegEM) that utilizes virtual-path-based annotation to assign automatically segmented mitochondrial instances at the cellular scale, therefore bypassing the requirement of membrane contouring. The embedded toolset in webKnossos (an open-source online annotation platform) is optimized for fast annotation, visualization, and proofreading of cellular organelle networks. We demonstrate the broad applications of mito-SegEM on volumetric datasets from various tissues, including the brain, intestine, and testis, to achieve an accurate and efficient reconstruction of mitochondria in a use-dependent fashion.

VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles

Cochlear hair cells are essential for hearing, and their stereocilia bundles are critical for mechanotransduction. However, analyzing the 3D morphology of these bundles can be challenging due to their complex organization and the presence of other cellular structures in the tissue. To address this, we developed VASCilia (Vision Analysis StereoCilia), a Napari plugin suite that automates the analysis of 3D confocal microscopy datasets of phalloidin-stained cochlear hair cell bundles. VASCilia includes five deep learning-based models that streamline the analysis process, including: (1) Z-Focus Tracker (ZFT) for selecting relevant slices in a 3D image stack; (2) PCPAlignNet (Planar Cell Polarity Alignment Network) for automated orientation of image stacks; (3) a segmentation model for identifying and delineating stereocilia bundles; (4) a tonotopic Position Prediction tool; and (5) a classification tool for identifying hair cell subtypes. In addition, VASCilia provides automated computational tools and measurement capabilities. Using VASCilia, we found that the total actin content of stereocilia bundles (as measured by phalloidin staining) does not necessarily increase with bundle height, which is likely due to differences in stereocilia thickness and number. This novel biological finding demonstrates the power of VASCilia in facilitating detailed quantitative analysis of stereocilia. VASCilia also provides a user-friendly interface that allows researchers to easily navigate and use the tool, with the added capability to reload all their analyses for review or sharing purposes. We believe that VASCilia will be a valuable resource for researchers studying cochlear hair cell development and function, addressing a longstanding need in the hair cell research community for specialized deep learning-based tools capable of high-throughput image quantitation. We have released our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation (https://github.com/ucsdmanorlab/Napari-VASCilia). This dataset comprises a total of 502 inner and 1,703 outer hair cell bundles annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to accelerate advances within the hearing research community.

Deep generative modeling of annotated bacterial biofilm images

Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN. Synthetic datasets can significantly improve the training of computer vision models for automated biofilm analysis, as demonstrated with the application of Mask R-CNN detection model. The approach represents a key advance in the field of biofilm research, offering a scalable solution for generating high-quality training data and working with different strains of microorganisms at different stages of formation. Terabyte-scale datasets can be easily generated on personal computers. A web application is provided for the on-demand generation of biofilm images.

Chronic undernutrition impairs renal mitochondrial respiration accompanied by intense ultrastructural damage in juvenile rats

This study investigated whether chronic undernutrition alters the mitochondrial structure and function in renal proximal tubule cells, thus impairing fluid transport and homeostasis. We previously showed that chronic undernutrition downregulates the renal proximal tubules (Na++K+)ATPase, the main molecular machine responsible for fluid transport and ATP consumption. Male rats received a multifactorial deficient diet, the so-called Regional Basic Diet (RBD), mimicking those used in impoverished regions worldwide, from weaning to a juvenile age (3 months). The diet has a low content (8 %) of poor-quality proteins, low lipids, and no vitamins compared to control (CTR). We investigated citrate synthase activity, mitochondrial respiration (oxygraphy) in phosphorylating and non-phosphorylating conditions with different substrates/inhibitors, potential across the internal membrane (Δψ), and anion superoxide/H2O2 formation. The data were correlated with ultrastructural alterations evaluated using transmission electron microscopy (TEM) and focused ion beam scanning electron microscopy (FIB-SEM). Citrate synthase activity decreased (∼50 %) in RBD rats, accompanied by a similar reduction in respiration in non-phosphorylating conditions, maximum respiratory capacity, and ATP synthesis. The Δψ generation and its dissipation after carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone remained unmodified in the survival mitochondria. H2O2 production increased (∼100 %) after Complex II energization. TEM demonstrated intense matrix vacuolization and disruption of cristae junctions in a subpopulation of RBD mitochondria, which was also demonstrated in the 3D analysis of FIB-SEM tomography. In conclusion, chronic undernutrition impairs mitochondrial functions in renal proximal tubules, with profound alterations in the matrix and internal membrane ultrastructure that culminate with the compromise of ATP supply for transport processes.

Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging

Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the model computational constraints, Cloud Computing (CC) is integrated to operate with the DL models. Recent articles on DL-based MRI have not discussed datasets specific to different diseases, which makes it difficult to build the specific DL model. Thus, the article systematically explores a tutorial approach, where we first discuss a classification taxonomy of medical imaging datasets. Next, we present a case-study on AD MRI classification using the DL methods. We analyse three distinct models-Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach-for classification and predictive outcomes. In addition, we designed a novel framework that offers insight into how various layers interact with the dataset. Our architecture comprises an input layer, a cloud-based layer responsible for preprocessing and model execution, and a diagnostic layer that issues alerts after successful classification and prediction. According to our simulations, CNN outperformed other models with a test accuracy of 99.285%, followed by VGG-16 with 85.113%, while the ensemble model lagged with a disappointing test accuracy of 79.192%. Our cloud Computing framework serves as an efficient mechanism for medical image processing while safeguarding patient confidentiality and data privacy.

Simultaneous detection of membrane contact dynamics and associated Ca2+ signals by reversible chemogenetic reporters

Membrane contact sites (MCSs) are hubs allowing various cell organelles to coordinate their activities. The dynamic nature of these sites and their small size hinder analysis by current imaging techniques. To overcome these limitations, we here design a series of reversible chemogenetic reporters incorporating improved, low-affinity variants of splitFAST, and study the dynamics of different MCSs at high spatiotemporal resolution, both in vitro and in vivo. We demonstrate that these versatile reporters suit different experimental setups well, allowing one to address challenging biological questions. Using these probes, we identify a pathway in which calcium (Ca2+) signalling dynamically regulates endoplasmic reticulum-mitochondria juxtaposition, characterizing the underlying mechanism. Finally, by integrating Ca2+-sensing capabilities into the splitFAST technology, we introduce PRINCESS (PRobe for INterorganelle Ca2+-Exchange Sites based on SplitFAST), a class of reporters to simultaneously detect MCSs and measure the associated Ca2+ dynamics using a single biosensor.

DELE1 maintains muscle proteostasis to promote growth and survival in mitochondrial myopathy

Mitochondrial dysfunction causes devastating disorders, including mitochondrial myopathy, but how muscle senses and adapts to mitochondrial dysfunction is not well understood. Here, we used diverse mouse models of mitochondrial myopathy to show that the signal for mitochondrial dysfunction originates within mitochondria. The mitochondrial proteins OMA1 and DELE1 sensed disruption of the inner mitochondrial membrane and, in response, activated the mitochondrial integrated stress response (mt-ISR) to increase the building blocks for protein synthesis. In the absence of the mt-ISR, protein synthesis in muscle was dysregulated causing protein misfolding, and mice with early-onset mitochondrial myopathy failed to grow and survive. The mt-ISR was similar following disruptions in mtDNA maintenance (Tfam knockout) and mitochondrial protein misfolding (CHCHD10 G58R and S59L knockin) but heterogenous among mitochondria-rich tissues, with broad gene expression changes observed in heart and skeletal muscle and limited changes observed in liver and brown adipose tissue. Taken together, our findings identify that the DELE1 mt-ISR mediates a similar response to diverse forms of mitochondrial stress and is critical for maintaining growth and survival in early-onset mitochondrial myopathy.

Making the most of bioimaging data through interdisciplinary interactions

The increasing technical complexity of all aspects involving bioimages, ranging from their acquisition to their analysis, has led to a diversification in the expertise of scientists engaged at the different stages of the discovery process. Although this diversity of profiles comes with the major challenge of establishing fruitful interdisciplinary collaboration, such collaboration also offers a superb opportunity for scientific discovery. In this Perspective, we review the different actors within the bioimaging research universe and identify the primary obstacles that hinder their interactions. We advocate that data sharing, which lies at the heart of innovation, is finally within reach after decades of being viewed as next to impossible in bioimaging. Building on recent community efforts, we propose actions to consolidate the development of a truly interdisciplinary bioimaging culture based on open data exchange and highlight the promising outlook of bioimaging as an example of multidisciplinary scientific endeavour.

ARL13B controls male reproductive tract physiology through primary and Motile Cilia

ARL13B is a small regulatory GTPase that controls ciliary membrane composition in both motile cilia and non-motile primary cilia. In this study, we investigated the role of ARL13B in the efferent ductules, tubules of the male reproductive tract essential to male fertility in which primary and motile cilia co-exist. We used a genetically engineered mouse model to delete Arl13b in efferent ductule epithelial cells, resulting in compromised primary and motile cilia architecture and functions. This deletion led to disturbances in reabsorptive/secretory processes and triggered an inflammatory response. The observed male reproductive phenotype showed significant variability linked to partial infertility, highlighting the importance of ARL13B in maintaining a proper physiological balance in these small ducts. These results emphasize the dual role of both motile and primary cilia functions in regulating efferent duct homeostasis, offering deeper insights into how cilia related diseases affect the male reproductive system.

The synaptic vesicle cluster as a controller of pre- and postsynaptic structure and function

The synaptic vesicle cluster (SVC) is an essential component of chemical synapses, which provides neurotransmitter-loaded vesicles during synaptic activity, at the same time as also controlling the local concentrations of numerous exo- and endocytosis cofactors. In addition, the SVC hosts molecules that participate in other aspects of synaptic function, from cytoskeletal components to adhesion proteins, and affects the location and function of organelles such as mitochondria and the endoplasmic reticulum. We argue here that these features extend the functional involvement of the SVC in synapse formation, signalling and plasticity, as well as synapse stabilization and metabolism. We also propose that changes in the size of the SVC coalesce with changes in the postsynaptic compartment, supporting the interplay between pre- and postsynaptic dynamics. Thereby, the SVC could be seen as an 'all-in-one' regulator of synaptic structure and function, which should be investigated in more detail, to reveal molecular mechanisms that control synaptic function and heterogeneity.

Microbial genotoxin-elicited host DNA mutations related to mitochondrial dysfunction, a momentous contributor for colorectal carcinogenesis

Gut microbe dysbiosis increases repetitive inflammatory responses, leading to an increase in the incidence of colorectal cancer. Recent studies have revealed that specific microbial species directly instigate mutations in the host nucleus DNA, thereby accelerating the progression of colorectal cancer. Given the well-established role of mitochondrial dysfunction in promoting colorectal cancer, it is reasonable to postulate that gut microbes may induce mitochondrial gene mutations, thereby inducing mitochondrial dysfunction. In this review, we focus on gut microbial genotoxins and their known and potential targets in mitochondrial genes. Consequently, we propose that targeted disruption of genotoxin transport pathways may effectively reduce the rate of mitochondrial gene mutations and yield substantial benefits for the prevention of colorectal carcinogenesis.

PDZD8-FKBP8 tethering complex at ER-mitochondria contact sites regulates mitochondrial complexity

Mitochondria-ER membrane contact sites (MERCS) represent a fundamental ultrastructural feature underlying unique biochemistry and physiology in eukaryotic cells. The ER protein PDZD8 is required for the formation of MERCS in many cell types, however, its tethering partner on the outer mitochondrial membrane (OMM) is currently unknown. Here we identified the OMM protein FKBP8 as the tethering partner of PDZD8 using a combination of unbiased proximity proteomics, CRISPR-Cas9 endogenous protein tagging, Cryo-Electron Microscopy (Cryo-EM) tomography, and correlative light-EM (CLEM). Single molecule tracking revealed highly dynamic diffusion properties of PDZD8 along the ER membrane with significant pauses and capture at MERCS. Overexpression of FKBP8 was sufficient to narrow the ER-OMM distance, whereas independent versus combined deletions of these two proteins demonstrated their interdependence for MERCS formation. Furthermore, PDZD8 enhances mitochondrial complexity in a FKBP8-dependent manner. Our results identify a novel ER-mitochondria tethering complex that regulates mitochondrial morphology in mammalian cells.

A practical guide to bioimaging research data management in core facilities

Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.

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.

FAST-EM array tomography: a workflow for multibeam volume electron microscopy

Elucidating the 3D nanoscale structure of tissues and cells is essential for understanding the complexity of biological processes. Electron microscopy (EM) offers the resolution needed for reliable interpretation, but the limited throughput of electron microscopes has hindered its ability to effectively image large volumes. We report a workflow for volume EM with FAST-EM, a novel multibeam scanning transmission electron microscope that speeds up acquisition by scanning the sample in parallel with 64 electron beams. FAST-EM makes use of optical detection to separate the signals of the individual beams. The acquisition and 3D reconstruction of ultrastructural data from multiple biological samples is demonstrated. The results show that the workflow is capable of producing large reconstructed volumes with high resolution and contrast to address biological research questions within feasible acquisition time frames.

DELE1 promotes translation-associated homeostasis, growth, and survival in mitochondrial myopathy

Mitochondrial dysfunction causes devastating disorders, including mitochondrial myopathy. Here, we identified that diverse mitochondrial myopathy models elicit a protective mitochondrial integrated stress response (mt-ISR), mediated by OMA1-DELE1 signaling. The response was similar following disruptions in mtDNA maintenance, from knockout of Tfam, and mitochondrial protein unfolding, from disease-causing mutations in CHCHD10 (G58R and S59L). The preponderance of the response was directed at upregulating pathways for aminoacyl-tRNA biosynthesis, the intermediates for protein synthesis, and was similar in heart and skeletal muscle but more limited in brown adipose challenged with cold stress. Strikingly, models with early DELE1 mt-ISR activation failed to grow and survive to adulthood in the absence of Dele1, accounting for some but not all of OMA1's protection. Notably, the DELE1 mt-ISR did not slow net protein synthesis in stressed striated muscle, but instead prevented loss of translation-associated proteostasis in muscle fibers. Together our findings identify that the DELE1 mt-ISR mediates a stereotyped response to diverse forms of mitochondrial stress and is particularly critical for maintaining growth and survival in early-onset mitochondrial myopathy.

A spatial map of hepatic mitochondria uncovers functional heterogeneity shaped by nutrient-sensing signaling

In the liver, mitochondria are exposed to different concentrations of nutrients due to their spatial positioning across the periportal and pericentral axis. How the mitochondria sense and integrate these signals to respond and maintain homeostasis is not known. Here, we combine intravital microscopy, spatial proteomics, and functional assessment to investigate mitochondrial heterogeneity in the context of liver zonation. We find that periportal and pericentral mitochondria are morphologically and functionally distinct; beta-oxidation is elevated in periportal regions, while lipid synthesis is predominant in the pericentral mitochondria. In addition, comparative phosphoproteomics reveals spatially distinct patterns of mitochondrial composition and potential regulation via phosphorylation. Acute pharmacological modulation of nutrient sensing through AMPK and mTOR shifts mitochondrial phenotypes in the periportal and pericentral regions, linking nutrient gradients across the lobule and mitochondrial heterogeneity. This study highlights the role of protein phosphorylation in mitochondrial structure, function, and overall homeostasis in hepatic metabolic zonation. These findings have important implications for liver physiology and disease.

Automatic enhancement preprocessing for segmentation of low quality cell images

We present a novel automatic preprocessing and ensemble learning technique for the segmentation of low-quality cell images. Capturing cells subjected to intense light is challenging due to their vulnerability to light-induced cell death. Consequently, microscopic cell images tend to be of low quality and it causes low accuracy for semantic segmentation. This problem can not be satisfactorily solved by classical image preprocessing methods. Therefore, we propose a novel approach of automatic enhancement preprocessing (AEP), which translates an input image into images that are easy to recognize by deep learning. AEP is composed of two deep neural networks, and the penultimate feature maps of the first network are employed as filters to translate an input image with low quality into images that are easily classified by deep learning. Additionally, we propose an automatic weighted ensemble learning (AWEL), which combines the multiple segmentation results. Since the second network predicts segmentation results corresponding to each translated input image, multiple segmentation results can be aggregated by automatically determining suitable weights. Experiments on two types of cell image segmentation confirmed that AEP can translate low-quality cell images into images that are easy to segment and that segmentation accuracy improves using AWEL.

Integrating cellular electron microscopy with multimodal data to explore biology across space and time

Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.

Adaptive t-vMF dice loss: An effective expansion of dice loss for medical image segmentation

Dice loss is widely used for medical image segmentation, and many improved loss functions have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter κ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMF Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve adaptive training based on the accuracy of each class. We evaluated binary segmentation datasets of CVC-ClinicDB and Kvasir-SEG, and multi-class segmentation datasets of Automated Cardiac Diagnosis Challenge and Synapse multi-organ segmentation. Through experiments conducted on four datasets using a five-fold cross-validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.

Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.

A spatial map of hepatic mitochondria uncovers functional heterogeneity shaped by nutrient-sensing signaling

In the liver, mitochondria are exposed to different concentrations of nutrients due to their spatial positioning across the periportal (PP) and pericentral (PC) axis. How these mitochondria sense and integrate these signals to respond and maintain homeostasis is not known. Here, we combined intravital microscopy, spatial proteomics, and functional assessment to investigate mitochondrial heterogeneity in the context of liver zonation. We found that PP and PC mitochondria are morphologically and functionally distinct; beta-oxidation was elevated in PP regions, while lipid synthesis was predominant in the PC mitochondria. In addition, comparative phosphoproteomics revealed spatially distinct patterns of mitochondrial composition and potential regulation via phosphorylation. Acute pharmacological modulation of nutrient sensing through AMPK and mTOR shifted mitochondrial phenotypes in the PP and PC regions, linking nutrient gradients across the lobule and mitochondrial heterogeneity. This study highlights the role of protein phosphorylation in mitochondrial structure, function, and overall homeostasis in hepatic metabolic zonation. These findings have important implications for liver physiology and disease.

Online citizen science with the Zooniverse for analysis of biological volumetric data

Public participation in research, also known as citizen science, is being increasingly adopted for the analysis of biological volumetric data. Researchers working in this domain are applying online citizen science as a scalable distributed data analysis approach, with recent research demonstrating that non-experts can productively contribute to tasks such as the segmentation of organelles in volume electron microscopy data. This, alongside the growing challenge to rapidly process the large amounts of biological volumetric data now routinely produced, means there is increasing interest within the research community to apply online citizen science for the analysis of data in this context. Here, we synthesise core methodological principles and practices for applying citizen science for analysis of biological volumetric data. We collate and share the knowledge and experience of multiple research teams who have applied online citizen science for the analysis of volumetric biological data using the Zooniverse platform ( www.zooniverse.org ). We hope this provides inspiration and practical guidance regarding how contributor effort via online citizen science may be usefully applied in this domain.

SKOOTS: Skeleton oriented object segmentation for mitochondria

The segmentation of individual instances of mitochondria from imaging datasets is informative, yet time-consuming to do by hand, sparking interest in developing automated algorithms using deep neural networks. Existing solutions for various segmentation tasks are largely optimized for one of two types of biomedical imaging: high resolution three-dimensional (whole neuron segmentation in volumetric electron microscopy datasets) or two-dimensional low resolution (whole cell segmentation of light microscopy images). The former requires consistently predictable boundaries to segment large structures, while the latter is boundary invariant but struggles with segmentation of large 3D objects without downscaling. Mitochondria in whole cell 3D EM datasets often occupy the challenging middle ground: large with ambiguous borders, limiting accuracy with existing tools. To rectify this, we have developed skeleton oriented object segmentation (SKOOTS); a new segmentation approach which efficiently handles large, densely packed mitochondria. We show that SKOOTS can accurately, and efficiently, segment 3D mitochondria in previously difficult situations. Furthermore, we will release a new, manually annotated, 3D mitochondria segmentation dataset. Finally, we show this approach can be extended to segment objects in 3D light microscopy datasets. These results bridge the gap between existing segmentation approaches and increases the accessibility for three-dimensional biomedical image analysis.

MitoNet: A generalizable model for segmentation of individual mitochondria within electron microscopy datasets

Volume electron microscopy provides a powerful approach to investigating physical connectivity within biological systems. In an article in this issue of Cell Systems, Conrad and Narayan overcome a major hurdle in volume electron microscopy by developing "MitoNet," a broadly applicable model for labeling individual mitochondria across volume electron microscopy datasets.