MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology

A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images

Mitochondrial morphology is an important parameter of cellular fitness. Although many approaches are available for assessing mitochondrial morphology in mammalian cells, only a few technically demanding and laborious methods are available for yeast cells. A robust, fully automated and user-friendly approach that would allow (1) segmentation of tubular and spherical mitochondria in the yeast Saccharomyces cerevisiae from conventional wide-field fluorescence images and (2) quantitative assessment of mitochondrial morphology is lacking. To address this, we compared Global thresholding segmentation with deep learning MitoSegNet segmentation, which we retrained on yeast cells. The deep learning model outperformed the Global thresholding segmentation. We applied it to segment mitochondria in strain lacking the MMI1/TMA19 gene encoding an ortholog of the human TCTP protein. Next, we performed a quantitative evaluation of segmented mitochondria by analyses available in ImageJ/Fiji and by MitoA analysis available in the MitoSegNet toolbox. By monitoring a wide range of morphological parameters, we described a novel mitochondrial phenotype of the mmi1Δ strain after its exposure to oxidative stress compared to that of the wild-type strain. The retrained deep learning model, all macros applied to run the analyses, as well as the detailed procedure are now available at https://github.com/LMCF-IMG/Morphology_Yeast_Mitochondria .

Impact of photobleaching on quantitative, spatio-temporal, super-resolution imaging of mitochondria in live C. elegans larvae

Super-resolution (SR) 3D rendering allows superior quantitative analysis of intracellular structures but has largely been limited to fixed or ex vivo samples. Here we developed a method to perform SR live imaging of mitochondria during post-embryonic development of C. elegans larvae. Our workflow includes the drug-free mechanical immobilisation of animals using polystyrene nanobeads, which has previously not been used for in vivo SR imaging. Based on the alignment of moving objects and global threshold-based image segmentation, our method enables an efficient 3D reconstruction of individual mitochondria. We demonstrate for the first time that the frequency distribution of fluorescence intensities is not affected by photobleaching, and that global thresholding alone enables the quantitative comparison of mitochondria along timeseries. Our composite approach significantly improves the study of biological structures and processes in SR during C. elegans post-embryonic development. Furthermore, the discovery that image segmentation does not require any prior correction against photobleaching, a fundamental problem in fluorescence microscopy, will impact experimental strategies aimed at quantitatively studying the dynamics of organelles and other intracellular compartments in any biological system.

Opportunities and challenges for deep learning in cell dynamics research

The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes but has also started to support advances in drug development, precision medicine, and genome-phenome mapping. We survey existing AI-based techniques and tools, as well as open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research.

Dynamic death decisions: How mitochondrial dynamics shape cellular commitment to apoptosis and ferroptosis

The incorporation of mitochondria into early eukaryotes established organelle-based biochemistry and enabled metazoan development. Diverse mitochondrial biochemistry is essential for life, and its homeostatic control via mitochondrial dynamics supports organelle quality and function. Mitochondrial crosstalk with numerous regulated cell death (RCD) pathways controls the decision to die. In this review, we will focus on apoptosis and ferroptosis, two distinct forms of RCD that utilize divergent signaling to kill a targeted cell. We will highlight how proteins and processes involved in mitochondrial dynamics maintain biochemically diverse subcellular compartments to support apoptosis and ferroptosis machinery, as well as unite disparate RCD pathways through dual control of organelle biochemistry and the decision to die.

Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity

MOTIVATION

Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density.

RESULTS

In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (Lhf) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and Lhf, both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and Lhf with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties.

AVAILABILITY AND IMPLEMENTATION

Code and data are openly accessible at https://github.com/cbmi-group/MRE.

Mitochondrial Function and Resistance to Oxidative Stress in the Kidney during Pregnancy

We demonstrated that the serum of pregnant rats increases viability of kidney epithelial cells and promotes their proliferation. The intensity of oxidative stress in the kidneys was also reduced during pregnancy, but only in rats that were not exposed to acute ischemic kidney injury. This decrease in oxidative stress was not associated with changes in transmembrane mitochondrial potential, the size of mitochondria, time of opening of mitochondrial permeability transition pore (mPTP), mitochondrial respiration rate, antioxidant activity, or nitric oxide level.

Machine learning-based 3D segmentation of mitochondria in polarized epithelial cells

Mitochondria are dynamic organelles that alter their morphological characteristics in response to functional needs. Therefore, mitochondrial morphology is an important indicator of mitochondrial function and cellular health. Reliable segmentation of mitochondrial networks in microscopy images is a crucial initial step for further quantitative evaluation of their morphology. However, 3D mitochondrial segmentation, especially in cells with complex network morphology, such as in highly polarized cells, remains challenging. To improve the quality of 3D segmentation of mitochondria in super-resolution microscopy images, we took a machine learning approach, using 3D Trainable Weka, an ImageJ plugin. We demonstrated that, compared with other commonly used methods, our approach segmented mitochondrial networks effectively, with improved accuracy in different polarized epithelial cell models, including differentiated human retinal pigment epithelial (RPE) cells. Furthermore, using several tools for quantitative analysis following segmentation, we revealed mitochondrial fragmentation in bafilomycin-treated RPE cells.

HeLa cell segmentation using digital image processing

Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.

Quantifying nanoscopic alterations associated with mitochondrial dysfunction using three-dimensional single-molecule localization microscopy

Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.

A preliminary study into the emergence of tendon microstructure during postnatal development

Tendons maintain mechanical function throughout postnatal development whilst undergoing significant microstructural changes. We present a study of postnatal tendon growth and characterise the major changes in collagen fibril architecture in mouse tail tendon from birth to eight weeks by analysing the geometries of cross-sectional transmission electron microscopy images. This study finds that a bimodal distribution of fibril diameters emerges from a unimodal distribution of narrow fibrils as early as the eighth day postnatal, and three distinct fibril populations are visible at around 14 days. The tendons in this study do not show evidence of precise hexagonal packing, even at birth, and the spaces between the fibrils remain constant throughout development. The fibril number in the tissue stabilises around day 28, and the fibril area fraction stabilises around day 26. This study gives coarse-grained insight into the transition periods in early tendon development.

Characterizing the metabolic response of the zebrafish kidney to overfeeding

Obesity is a global epidemic and risk factor for the development of chronic kidney disease. Obesity induces systemic changes in metabolism, but how it affects kidney metabolism specifically is not known. Zebrafish have previously been shown to develop obesity-related kidney pathology and dysfunction when fed hypercaloric diets. To understand the direct effects of obesity on kidney metabolic function, we treated zebrafish for 8 wk with a control and an overfeeding diet. At the end of treatment, we assessed changes in kidney and fish weights and used electron microscopy to evaluate cell ultrastructure. We then performed an untargeted metabolomic analysis on the kidney tissue of fish using ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry and used mummichog and gene set enrichment analysis to uncover differentially affected metabolic pathways. Kidney metabolomes differed significantly and consistently between the control and overfed diets. Among 9,593 features, we identified 235 that were significantly different (P < 0.05) between groups (125 upregulated in overfed diet, 110 downregulated). Pathway analysis demonstrated perturbations in glycolysis and fatty acid synthesis pathways, and analysis of specific metabolites points to perturbations in tryptophan metabolism. Our key findings show that diet-induced obesity leads to metabolic changes in the kidney tissue itself and implicates specific metabolic pathways, including glycolysis and tryptophan metabolism in the pathogenesis of obesity-related kidney disease, demonstrating the power of untargeted metabolomics to identify pathways of interest by directly interrogating kidney tissue.NEW & NOTEWORTHY Obesity causes systemic metabolic dysfunction, but how this affects kidney metabolism is less understood. This study used ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry to analyze the kidneys of overfed zebrafish. Metabolites in the kidneys of obese zebrafish revealed perturbations in metabolic pathways including glycolysis and tryptophan metabolism. These data suggest obesity alters metabolism within the kidney, which may play an important role in obesity-related kidney dysfunction.

Mitochondrial heterogeneity in diseases

As key organelles involved in cellular metabolism, mitochondria frequently undergo adaptive changes in morphology, components and functions in response to various environmental stresses and cellular demands. Previous studies of mitochondria research have gradually evolved, from focusing on morphological change analysis to systematic multiomics, thereby revealing the mitochondrial variation between cells or within the mitochondrial population within a single cell. The phenomenon of mitochondrial variation features is defined as mitochondrial heterogeneity. Moreover, mitochondrial heterogeneity has been reported to influence a variety of physiological processes, including tissue homeostasis, tissue repair, immunoregulation, and tumor progression. Here, we comprehensively review the mitochondrial heterogeneity in different tissues under pathological states, involving variant features of mitochondrial DNA, RNA, protein and lipid components. Then, the mechanisms that contribute to mitochondrial heterogeneity are also summarized, such as the mutation of the mitochondrial genome and the import of mitochondrial proteins that result in the heterogeneity of mitochondrial DNA and protein components. Additionally, multiple perspectives are investigated to better comprehend the mysteries of mitochondrial heterogeneity between cells. Finally, we summarize the prospective mitochondrial heterogeneity-targeting therapies in terms of alleviating mitochondrial oxidative damage, reducing mitochondrial carbon stress and enhancing mitochondrial biogenesis to relieve various pathological conditions. The possibility of recent technological advances in targeted mitochondrial gene editing is also discussed.

Structural mechanism of mitochondrial membrane remodelling by human OPA1

Distinct morphologies of the mitochondrial network support divergent metabolic and regulatory processes that determine cell function and fate1-3. The mechanochemical GTPase optic atrophy 1 (OPA1) influences the architecture of cristae and catalyses the fusion of the mitochondrial inner membrane4,5. Despite its fundamental importance, the molecular mechanisms by which OPA1 modulates mitochondrial morphology are unclear. Here, using a combination of cellular and structural analyses, we illuminate the molecular mechanisms that are key to OPA1-dependent membrane remodelling and fusion. Human OPA1 embeds itself into cardiolipin-containing membranes through a lipid-binding paddle domain. A conserved loop within the paddle domain inserts deeply into the bilayer, further stabilizing the interactions with cardiolipin-enriched membranes. OPA1 dimerization through the paddle domain promotes the helical assembly of a flexible OPA1 lattice on the membrane, which drives mitochondrial fusion in cells. Moreover, the membrane-bending OPA1 oligomer undergoes conformational changes that pull the membrane-inserting loop out of the outer leaflet and contribute to the mechanics of membrane remodelling. Our findings provide a structural framework for understanding how human OPA1 shapes mitochondrial morphology and show us how human disease mutations compromise OPA1 functions.

Mitochondrial networks through the lens of mathematics

Mitochondria serve a wide range of functions within cells, most notably via their production of ATP. Although their morphology is commonly described as bean-like, mitochondria often form interconnected networks within cells that exhibit dynamic restructuring through a variety of physical changes. Further, though relationships between form and function in biology are well established, the extant toolkit for understanding mitochondrial morphology is limited. Here, we emphasize new and established methods for quantitatively describing mitochondrial networks, ranging from unweighted graph-theoretic representations to multi-scale approaches from applied topology, in particular persistent homology. We also show fundamental relationships between mitochondrial networks, mathematics, and physics, using ideas of graph planarity and statistical mechanics to better understand the full possible morphological space of mitochondrial network structures. Lastly, we provide suggestions for how examination of mitochondrial network form through the language of mathematics can inform biological understanding, and vice versa.

Unbiased single-cell morphology with self-supervised vision transformers

Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINO, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We evaluate DINO on a wide variety of tasks across three publicly available imaging datasets of diverse specifications and biological focus. We find that DINO encodes meaningful features of cellular morphology at multiple scales, from subcellular and single-cell resolution, to multi-cellular and aggregated experimental groups. Importantly, DINO successfully uncovers a hierarchy of biological and technical factors of variation in imaging datasets. The results show that DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between samples, making it an excellent tool for image-based biological discovery.

Inhibition of the mitochondria-shaping protein Opa1 restores sensitivity to Gefitinib in a lung adenocarcinomaresistant cell line

Drug resistance limits the efficacy of chemotherapy and targeted cancer treatments, calling for the identification of druggable targets to overcome it. Here we show that the mitochondria-shaping protein Opa1 participates in resistance against the tyrosine kinase inhibitor gefitinib in a lung adenocarcinoma cell line. Respiratory profiling revealed that oxidative metabolism was increased in this gefitinib-resistant lung cancer cell line. Accordingly, resistant cells depended on mitochondrial ATP generation, and their mitochondria were elongated with narrower cristae. In the resistant cells, levels of Opa1 were increased and its genetic or pharmacological inhibition reverted the mitochondrial morphology changes and sensitized them to gefitinib-induced cytochrome c release and apoptosis. In vivo, the size of gefitinib-resistant lung orthotopic tumors was reduced when gefitinib was combined with the specific Opa1 inhibitor MYLS22. The combo gefitinib-MYLS22 treatment increased tumor apoptosis and reduced its proliferation. Thus, the mitochondrial protein Opa1 participates in gefitinib resistance and can be targeted to overcome it.

MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. Dysregulation of this four-dimensional (4D: x,y,z,time) network is implicated in numerous diseases ranging from cancer to neurodegeneration. While lattice light-sheet microscopy has recently made it possible to image mitochondria in 4D, quantitative analysis methods for the resulting datasets have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking assignment. To validate the accuracy of tracking, we created a reaction-diffusion simulation to model mitochondrial network motion and remodeling events. We found that our tracking is >90% accurate for ground-truth simulations and agrees well with published motility results for experimental data. We used MitoTNT to quantify 4D mitochondrial networks from human induced pluripotent stem cells. First, we characterized sub-fragment motility and analyzed network branch motion patterns. We revealed that the skeleton node motion is correlated along branch nodes and is uncorrelated in time. Second, we identified fission and fusion events with high spatiotemporal resolution. We found that mitochondrial skeleton nodes near the fission/fusion sites move nearly twice as fast as random skeleton nodes and that microtubules play a role in mediating selective fission/fusion. Finally, we developed graph-based transport simulations that model how material would distribute on experimentally measured mitochondrial temporal networks. We showed that pharmacological perturbations increase network reachability but decrease network resilience through a combination of altered mitochondrial fission/fusion dynamics and motility. MitoTNT's easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analyses aim at making temporal network tracking accessible to the wider mitochondria research community.

ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology

The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.

Dolutegravir-containing HIV therapy reversibly alters mitochondrial health and morphology in cultured human fibroblasts and peripheral blood mononuclear cells

OBJECTIVES

Given the success of combination antiretroviral therapy (cART) in treating HIV viremia, drug toxicity remains an area of interest in HIV research. Despite newer integrase strand transfer inhibitors (InSTIs), such as dolutegravir (DTG) and raltegravir (RAL), having excellent clinical tolerance, there is emerging evidence of off-target effects and toxicities. Although limited in number, recent reports have highlighted the vulnerability of mitochondria to these toxicities. The aim of the present study is to quantify changes in cellular and mitochondrial health following exposure to current cART regimens at pharmacological concentrations. A secondary objective is to determine whether any cART-associated toxicities would be modulated by human telomerase reverse transcriptase (hTERT).

METHODS

We longitudinally evaluated markers of cellular (cell count, apoptosis), and mitochondrial health [mitochondrial reactive oxygen species (mtROS), membrane potential (MMP) and mass (mtMass)] by flow cytometry in WI-38 human fibroblast with differing hTERT expression/localization and peripheral blood mononuclear cells. This was done after 9 days of exposure to, and 6 days following the removal of, seven current cART regimens, including three that contained DTG. Mitochondrial morphology was assessed by florescence microscopy and quantified using a recently developed deep learning-based pipeline.

RESULTS

Exposure to DTG-containing regimens increased apoptosis, mtROS, mtMass, induced fragmented mitochondrial networks compared with non-DTG-containing regimens, including a RAL-based combination. These effects were unmodulated by telomerase expression. All effects were fully reversible following removal of drug pressure.

CONCLUSION

Taken together, our observations indicate that DTG-containing regimens negatively impact cellular and mitochondrial health and may be more toxic to mitochondria, even among the generally well tolerated InSTI-based cART.

Mitochondrial dynamics involves molecular and mechanical events in motility, fusion and fission

Mitochondria are cell organelles that play pivotal roles in maintaining cell survival, cellular metabolic homeostasis, and cell death. Mitochondria are highly dynamic entities which undergo fusion and fission, and have been shown to be very motile in vivo in neurons and in vitro in multiple cell lines. Fusion and fission are essential for maintaining mitochondrial homeostasis through control of morphology, content exchange, inheritance of mitochondria, maintenance of mitochondrial DNA, and removal of damaged mitochondria by autophagy. Mitochondrial motility occurs through mechanical and molecular mechanisms which translocate mitochondria to sites of high energy demand. Motility also plays an important role in intracellular signaling. Here, we review key features that mediate mitochondrial dynamics and explore methods to advance the study of mitochondrial motility as well as mitochondrial dynamics-related diseases and mitochondrial-targeted therapeutics.

Event-driven acquisition for content-enriched microscopy

A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.

Weighted average ensemble-based semantic segmentation in biological electron microscopy images

Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications.

An aco-2::gfp knock-in enables the monitoring of mitochondrial morphology throughout C. elegans lifespan

We used CRISPR/Cas9 gene editing in C. elegans in order to fluorescently tag endogenous aconitase-2 (ACO-2). ACO-2 is a mitochondrially localized protein, and the aco-2::gfp strain enabled the examination of native mitochondrial morphology in live animals. Here we validate that the aco-2::gfp strain displays the prototypic changes in mitochondrial morphology known to occur during aging and upon paraquat (PQ) induced mitochondrial stress. We also provide evidence that the ACO-2::GFP reporter can serve as a superior means for tracking mitochondrial morphology than conventional MitoTracker dyes-especially in aged-worms.

ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamics

The ability of plants to sense and orient their root growth towards gravity is studied in many laboratories. It is known that manual analysis of image data is subjected to human bias. Several semi-automated tools are available for analysing images from flatbed scanners, but there is no solution to automatically measure root bending angle over time for vertical-stage microscopy images. To address these problems, we developed ACORBA, which is an automated software that can measure root bending angle over time from vertical-stage microscope and flatbed scanner images. ACORBA also has a semi-automated mode for camera or stereomicroscope images. It represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. As the software is automated, it limits human interactions and is reproducible. ACORBA will support the plant biologist community by reducing labour and increasing reproducibility of image analysis of root gravitropism.

Automated segmentation and tracking of mitochondria in live-cell time-lapse images

Mitochondria display complex morphology and movements, which complicates their segmentation and tracking in time-lapse images. Here, we introduce Mitometer, an algorithm for fast, unbiased, and automated segmentation and tracking of mitochondria in live-cell two-dimensional and three-dimensional time-lapse images. Mitometer requires only the pixel size and the time between frames to identify mitochondrial motion and morphology, including fusion and fission events. The segmentation algorithm isolates individual mitochondria via a shape- and size-preserving background removal process. The tracking algorithm links mitochondria via differences in morphological features and displacement, followed by a gap-closing scheme. Using Mitometer, we show that mitochondria of triple-negative breast cancer cells are faster, more directional, and more elongated than those in their receptor-positive counterparts. Furthermore, we show that mitochondrial motility and morphology in breast cancer, but not in normal breast epithelia, correlate with metabolic activity. Mitometer is an unbiased and user-friendly tool that will help resolve fundamental questions regarding mitochondrial form and function.

Genome-wide RNAi screen for regulators of UPRmt in Caenorhabditis elegans mutants with defects in mitochondrial fusion

Mitochondrial dynamics plays an important role in mitochondrial quality control and the adaptation of metabolic activity in response to environmental changes. The disruption of mitochondrial dynamics has detrimental consequences for mitochondrial and cellular homeostasis and leads to the activation of the mitochondrial unfolded protein response (UPRmt), a quality control mechanism that adjusts cellular metabolism and restores homeostasis. To identify genes involved in the induction of UPRmt in response to a block in mitochondrial fusion, we performed a genome-wide RNAi screen in Caenorhabditis elegans mutants lacking the gene fzo-1, which encodes the ortholog of mammalian Mitofusin, and identified 299 suppressors and 86 enhancers. Approximately 90% of these 385 genes are conserved in humans, and one third of the conserved genes have been implicated in human disease. Furthermore, many have roles in developmental processes, which suggests that mitochondrial function and the response to stress are defined during development and maintained throughout life. Our dataset primarily contains mitochondrial enhancers and non-mitochondrial suppressors of UPRmt, indicating that the maintenance of mitochondrial homeostasis has evolved as a critical cellular function, which, when disrupted, can be compensated for by many different cellular processes. Analysis of the subsets 'non-mitochondrial enhancers' and 'mitochondrial suppressors' suggests that organellar contact sites, especially between the ER and mitochondria, are of importance for mitochondrial homeostasis. In addition, we identified several genes involved in IP3 signaling that modulate UPRmt in fzo-1 mutants and found a potential link between pre-mRNA splicing and UPRmt activation.

ATP13A2-mediated endo-lysosomal polyamine export counters mitochondrial oxidative stress

Mutations in ATP13A2 cause a spectrum of related neurodegenerative disorders. ATP13A2 is a lysosomal exporter of polyamines that contributes to lysosomal health and controls cellular polyamine content. Conversely, loss of ATP13A2 leads to lysosomal dysfunction, a hallmark of neurodegeneration. Here, we show that polyamines transported by ATP13A2 provide cellular protection by lowering reactive oxygen species (ROS), which may relate to the antioxidant properties of polyamines. Consequently, dysfunctional ATP13A2 sensitizes cells to oxidative stress, which impairs mitochondria, and induces toxicity and cell death. ATP13A2-mediated polyamine transport represents a conserved pathway that protects against mitochondrial oxidative stress. The combined protective impact of ATP13A2 on lysosomal health and mitochondrial oxidative stress may explain why ATP13A2 exerts potent neuroprotective effects.

Recessive loss-of-function mutations in ATP13A2 (PARK9) are associated with a spectrum of neurodegenerative disorders, including Parkinson’s disease (PD). We recently revealed that the late endo-lysosomal transporter ATP13A2 pumps polyamines like spermine into the cytosol, whereas ATP13A2 dysfunction causes lysosomal polyamine accumulation and rupture. Here, we investigate how ATP13A2 provides protection against mitochondrial toxins such as rotenone, an environmental PD risk factor. Rotenone promoted mitochondrial-generated superoxide (MitoROS), which was exacerbated by ATP13A2 deficiency in SH-SY5Y cells and patient-derived fibroblasts, disturbing mitochondrial functionality and inducing toxicity and cell death. Moreover, ATP13A2 knockdown induced an ATF4-CHOP-dependent stress response following rotenone exposure. MitoROS and ATF4-CHOP were blocked by MitoTEMPO, a mitochondrial antioxidant, suggesting that the impact of ATP13A2 on MitoROS may relate to the antioxidant properties of spermine. Pharmacological inhibition of intracellular polyamine synthesis with α-difluoromethylornithine (DFMO) also increased MitoROS and ATF4 when ATP13A2 was deficient. The polyamine transport activity of ATP13A2 was required for lowering rotenone/DFMO-induced MitoROS, whereas exogenous spermine quenched rotenone-induced MitoROS via ATP13A2. Interestingly, fluorescently labeled spermine uptake in the mitochondria dropped as a consequence of ATP13A2 transport deficiency. Our cellular observations were recapitulated in vivo, in a Caenorhabditis elegans strain deficient in the ATP13A2 ortholog catp-6. These animals exhibited a basal elevated MitoROS level, mitochondrial dysfunction, and enhanced stress response regulated by atfs-1, the C. elegans ortholog of ATF4, causing hypersensitivity to rotenone, which was reversible with MitoTEMPO. Together, our study reveals a conserved cell protective pathway that counters mitochondrial oxidative stress via ATP13A2-mediated lysosomal spermine export.