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

Novel features of Drosophila hematopoiesis uncovered by long-term live imaging

Stem cells are subject to continuous regulation to ensure that the correct balance between stem cell differentiation and self-renewal is maintained. The dynamic and ongoing nature of stem cell regulation, as well as the complex signaling microenvironment in which stem cells are typically found, means that studying them in their endogenous environment in real time has multiple advantages over static fixed-sample approaches. We recently described a method for long-term, ex-vivo, live imaging of the blood progenitors in the Drosophila larval hematopoietic organ, the Lymph Gland (LG). This methodology has allowed us to analyze multiple aspects of fly hematopoiesis, in real time, in a manner that could not be carried out previously. Here, we describe novel insights derived from our quantitative live imaging approach. These insights include: the identification of extensive filopodia in the progenitors and description of their morphology and dynamics; visualization and quantitative analysis of JAK/STAT signaling in progenitors by the simultaneous tracking of thousands of vesicles containing internalized Domeless receptors; quantitative analysis of the location, morphology, and dynamics of mitochondria in blood progenitors; long-term tracking of patterns of cell division and migration of mature blood cell in the LG; long-term tracking of multiple cell behaviors in the distal committed progenitors; analysis of Ca2+ signaling of blood progenitors in the secondary lobes of the LG. Together, these observations illustrate the power of imaging fly hematopoiesis in real time and identify many previously undescribed processes and behaviors in the LG that are likely to play important roles in the regulation of progenitor differentiation and self-renewal.

Beyond static snapshots: Mitochondria in action

Mitochondria are dynamic organelles essential for cellular homeostasis, undergoing continuous fission and fusion processes that regulate their morphology, distribution, and function. Disruptions in these dynamics are linked to numerous diseases, including neurodegenerative disorders and cancer. Understanding these processes is vital for developing therapeutic strategies aimed at mitigating mitochondrial dysfunction. This review provides an overview of recent perspectives on mitochondrial dynamics, focusing on the need for live video microscopy imaging in order to fully understand mitochondrial phenotypes and pathology. Advanced imaging tools, such as machine learning-based segmentation and label-free microscopy approaches, have the potential to transform our ability to study mitochondrial dynamics in live cells.

Intelligent sensing for the autonomous manipulation of microrobots toward minimally invasive cell surgery

The physiology and pathogenesis of biological cells have drawn enormous research interest. Benefiting from the rapid development of microfabrication and microelectronics, miniaturized robots with a tool size below micrometers have widely been studied for manipulating biological cells in vitro and in vivo. Traditionally, the complex physiological environment and biological fragility require human labor interference to fulfill these tasks, resulting in high risks of irreversible structural or functional damage and even clinical risk. Intelligent sensing devices and approaches have been recently integrated within robotic systems for environment visualization and interaction force control. As a consequence, microrobots can be autonomously manipulated with visual and interaction force feedback, greatly improving accuracy, efficiency, and damage regulation for minimally invasive cell surgery. This review first explores advanced tactile sensing in the aspects of sensing principles, design methodologies, and underlying physics. It also comprehensively discusses recent progress on visual sensing, where the imaging instruments and processing methods are summarized and analyzed. It then introduces autonomous micromanipulation practices utilizing visual and tactile sensing feedback and their corresponding applications in minimally invasive surgery. Finally, this work highlights and discusses the remaining challenges of current robotic micromanipulation and their future directions in clinical trials, providing valuable references about this field.

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.

Metabolic regulation of mitochondrial morphologies in pancreatic beta cells: coupling of bioenergetics and mitochondrial dynamics

Cellular bioenergetics and mitochondrial dynamics are crucial for the secretion of insulin by pancreatic beta cells in response to elevated levels of blood glucose. To elucidate the interactions between energy production and mitochondrial fission/fusion dynamics, we combine live-cell mitochondria imaging with biophysical-based modeling and graph-based network analysis. The aim is to determine the mechanism that regulates mitochondrial morphology and balances metabolic demands in pancreatic beta cells. A minimalistic differential equation-based model for beta cells is constructed that includes glycolysis, oxidative phosphorylation, calcium dynamics, and fission/fusion dynamics, with ATP synthase flux and proton leak flux as main regulators of mitochondrial dynamics. The model shows that mitochondrial fission occurs in response to hyperglycemia, starvation, ATP synthase inhibition, uncoupling, and diabetic conditions, in which the rate of proton leakage exceeds the rate of mitochondrial ATP synthesis. Under these metabolic challenges, the propensities of tip-to-tip fusion events simulated from the microscopy images of the mitochondrial networks are lower than those in the control group and prevent the formation of mitochondrial networks. The study provides a quantitative framework that couples bioenergetic regulation with mitochondrial dynamics, offering insights into how mitochondria adapt to metabolic challenges.

AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification

High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.

Detailed analysis of Mdivi-1 effects on complex I and respiratory supercomplex assembly

Several human diseases, including cancer and neurodegeneration, are associated with excessive mitochondrial fragmentation. In this context, mitochondrial division inhibitor (Mdivi-1) has been tested as a therapeutic to block the fission-related protein dynamin-like protein-1 (Drp1). Recent studies suggest that Mdivi-1 interferes with mitochondrial bioenergetics and complex I function. Here we show that the molecular mechanism of Mdivi-1 is based on inhibition of complex I at the IQ site. This leads to the destabilization of complex I, impairs the assembly of N- and Q-respirasomes, and is associated with increased ROS production and reduced efficiency of ATP generation. Second, the calcium homeostasis of cells is impaired, which for example affects the electrical activity of neurons. Given the results presented here, a potential therapeutic application of Mdivi-1 is challenging because of its potential impact on synaptic activity. Similar to the Complex I inhibitor rotenone, Mdivi-1 may lead to neurodegenerative effects in the long term.

The actin binding protein profilin 1 localizes inside mitochondria and is critical for their function

The monomer-binding protein profilin 1 (PFN1) plays a crucial role in actin polymerization. However, mutations in PFN1 are also linked to hereditary amyotrophic lateral sclerosis, resulting in a broad range of cellular pathologies which cannot be explained by its primary function as a cytosolic actin assembly factor. This implies that there are important, undiscovered roles for PFN1 in cellular physiology. Here we screened knockout cells for novel phenotypes associated with PFN1 loss of function and discovered that mitophagy was significantly upregulated. Indeed, despite successful autophagosome formation, fusion with the lysosome, and activation of additional mitochondrial quality control pathways, PFN1 knockout cells accumulate depolarized, dysmorphic mitochondria with altered metabolic properties. Surprisingly, we also discovered that PFN1 is present inside mitochondria and provide evidence that mitochondrial defects associated with PFN1 loss are not caused by reduced actin polymerization in the cytosol. These findings suggest a previously unrecognized role for PFN1 in maintaining mitochondrial integrity and highlight new pathogenic mechanisms that can result from PFN1 dysregulation.

A systematic evaluation of computational methods for cell segmentation

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.

Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling

Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifications of organelle cross-regulation that are triggered by infection and syncytia formation. Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides an Artificial Intelligence-enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.

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.

Nano-flow cytometry unveils mitochondrial permeability transition process and multi-pathway cell death induction for cancer therapy

Mitochondrial permeability transition (mPT)-mediated mitochondrial dysfunction plays a pivotal role in various human diseases. However, the intricate details of its mechanisms and the sequence of events remain elusive, primarily due to the interference caused by Bax/Bak-induced mitochondrial outer membrane permeabilization (MOMP). To address these, we have developed a methodology that utilizes nano-flow cytometry (nFCM) to quantitatively analyze the opening of mitochondrial permeability transition pore (mPTP), dissipation of mitochondrial membrane potential ( Δ Ψm), release of cytochrome c (Cyt c), and other molecular alternations of isolated mitochondria in response to mPT induction at the single-mitochondrion level. It was identified that betulinic acid (BetA) and antimycin A can directly induce mitochondrial dysfunction through mPT-mediated mechanisms, while cisplatin and staurosporine cannot. In addition, the nFCM analysis also revealed that BetA primarily induces mPTP opening through a reduction in Bcl-2 and Bcl-xL protein levels, along with an elevation in ROS content. Employing dose and time-dependent strategies of BetA, for the first time, we experimentally verified the sequential occurrence of mPTP opening and Δ Ψm depolarization prior to the release of Cyt c during mPT-mediated mitochondrial dysfunction. Notably, our study uncovers a simultaneous release of cell-death-associated factors, including Cyt c, AIF, PNPT1, and mtDNA during mPT, implying the initiation of multiple cell death pathways. Intriguingly, BetA induces caspase-independent cell death, even in the absence of Bax/Bak, thereby overcoming drug resistance. The presented findings offer new insights into mPT-mediated mitochondrial dysfunction using nFCM, emphasizing the potential for targeting such dysfunction in innovative cancer therapies and interventions.

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.

Elevated SLC7A2 expression is associated with an abnormal neuroinflammatory response and nitrosative stress in Huntington's disease

We previously identified solute carrier family 7 member 2 (SLC7A2) as one of the top upregulated genes when normal Huntingtin was deleted. SLC7A2 has a high affinity for L-arginine. Arginine is implicated in inflammatory responses, and SLC7A2 is an important regulator of innate and adaptive immunity in macrophages. Although neuroinflammation is clearly demonstrated in animal models and patients with Huntington's disease (HD), the question of whether neuroinflammation actively participates in HD pathogenesis is a topic of ongoing research and debate. Here, we studied the role of SLC7A2 in mediating the neuroinflammatory stress response in HD cells. RNA sequencing (RNA-seq), quantitative RT-PCR and data mining of publicly available RNA-seq datasets of human patients were performed to assess the levels of SLC7A2 mRNA in different HD cellular models and patients. Biochemical studies were then conducted on cell lines and primary mouse astrocytes to investigate arginine metabolism and nitrosative stress in response to neuroinflammation. The CRISPR-Cas9 system was used to knock out SLC7A2 in STHdhQ7 and Q111 cells to investigate its role in mediating the neuroinflammatory response. Live-cell imaging was used to measure mitochondrial dynamics. Finally, exploratory studies were performed using the Enroll-HD periodic human patient dataset to analyze the effect of arginine supplements on HD progression. We found that SLC7A2 is selectively upregulated in HD cellular models and patients. HD cells exhibit an overactive response to neuroinflammatory challenges, as demonstrated by abnormally high iNOS induction and NO production, leading to increased protein nitrosylation. Depleting extracellular Arg or knocking out SLC7A2 blocked iNOS induction and NO production in STHdhQ111 cells. We further examined the functional impact of protein nitrosylation on a well-documented protein target, DRP-1, and found that more mitochondria were fragmented in challenged STHdhQ111 cells. Last, analysis of Enroll-HD datasets suggested that HD patients taking arginine supplements progressed more rapidly than others. Our data suggest a novel pathway that links arginine uptake to nitrosative stress via upregulation of SLC7A2 in the pathogenesis and progression of HD. This further implies that arginine supplements may potentially pose a greater risk to HD patients.

Organelle-specific phase contrast microscopy (OS-PCM) enables facile correlation study of organelles and proteins

Current methods for studying organelle and protein interactions and correlations depend on multiplex fluorescent labeling, which is experimentally complex and harmful to cells. Here we propose to solve this challenge via OS-PCM, where organelles are imaged and segmented without labels, and combined with standard fluorescence microscopy of protein distributions. In this work, we develop new neural networks to obtain unlabeled organelle, nucleus and membrane predictions from a single 2D image. Automated analysis is also implemented to obtain quantitative information regarding the spatial distribution and co-localization of both protein and organelle, as well as their relationship to the landmark structures of nucleus and membrane. Using mitochondria and DRP1 protein as a proof-of-concept, we conducted a correlation study where only DRP1 is labeled, with results consistent with prior reports utilizing multiplex labeling. Thus our work demonstrates that OS-PCM simplifies the correlation study of organelles and proteins.

Significance of quantitative analyses of the impact of heterogeneity in mitochondrial content and shape on cell differentiation

Mitochondria, classically known as the powerhouse of cells, are unique double membrane-bound multifaceted organelles carrying a genome. Mitochondrial content varies between cell types and precisely doubles within cells during each proliferating cycle. Mitochondrial content also increases to a variable degree during cell differentiation triggered after exit from the proliferating cycle. The mitochondrial content is primarily maintained by the regulation of mitochondrial biogenesis, while damaged mitochondria are eliminated from the cells by mitophagy. In any cell with a given mitochondrial content, the steady-state mitochondrial number and shape are determined by a balance between mitochondrial fission and fusion processes. The increase in mitochondrial content and alteration in mitochondrial fission and fusion are causatively linked with the process of differentiation. Here, we critically review the quantitative aspects in the detection methods of mitochondrial content and shape. Thereafter, we quantitatively link these mitochondrial properties in differentiating cells and highlight the implications of such quantitative link on stem cell functionality. Finally, we discuss an example of cell size regulation predicted from quantitative analysis of mitochondrial shape and content. To highlight the significance of quantitative analyses of these mitochondrial properties, we propose three independent rationale based hypotheses and the relevant experimental designs to test them.

Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulation of cells and organelles. However, stochastic noise inherently restricts optical interrogation quality and exacerbates observation fidelity when balancing the joint demands of high frame rate, long-term recording and low phototoxicity. Here we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing signal-to-noise ratio by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi effectively denoises images with no loss of spatiotemporal resolution. In combination with confocal microscopy, DeepSeMi allows for recording organelle interactions in four colors at high frame rates across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames. Through comprehensive validations across various samples and instruments, we prove DeepSeMi to be a versatile and biocompatible tool for breaking the shot-noise limit.

Live-cell imaging in the deep learning era

Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.

DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning

Single-molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques require long acquisition times, typically a few minutes, to yield a single super-resolved image, because they depend on accumulation of many localizations over thousands of recorded frames. Hence, the capability of SMLM to observe dynamics at high temporal resolution has always been limited. In this work, we present DBlink, a deep-learning-based method for super spatiotemporal resolution reconstruction from SMLM data. The input to DBlink is a recorded video of SMLM data and the output is a super spatiotemporal resolution video reconstruction. We use a convolutional neural network combined with a bidirectional long short-term memory network architecture, designed for capturing long-term dependencies between different input frames. We demonstrate DBlink performance on simulated filaments and mitochondria-like structures, on experimental SMLM data under controlled motion conditions and on live-cell dynamic SMLM. DBlink's spatiotemporal interpolation constitutes an important advance in super-resolution imaging of dynamic processes in live cells.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

Microenvironmental stiffness induces metabolic reprogramming in glioblastoma

The mechanical properties of solid tumors influence tumor cell phenotype and the ability to invade surrounding tissues. Using bioengineered scaffolds to provide a matrix microenvironment for patient-derived glioblastoma (GBM) spheroids, this study demonstrates that a soft, brain-like matrix induces GBM cells to shift to a glycolysis-weighted metabolic state, which supports invasive behavior. We first show that orthotopic murine GBM tumors are stiffer than peritumoral brain tissues, but tumor stiffness is heterogeneous where tumor edges are softer than the tumor core. We then developed 3D scaffolds with μ-compressive moduli resembling either stiffer tumor core or softer peritumoral brain tissue. We demonstrate that the softer matrix microenvironment induces a shift in GBM cell metabolism toward glycolysis, which manifests in lower proliferation rate and increased migration activities. Finally, we show that these mechanical cues are transduced from the matrix via CD44 and integrin receptors to induce metabolic and phenotypic changes in cancer cells.

Mitochondrial modulation with leriglitazone as a potential treatment for Rett syndrome

BACKGROUND

Rett syndrome is a neuropediatric disease occurring due to mutations in MECP2 and characterized by a regression in the neuronal development following a normal postnatal growth, which results in the loss of acquired capabilities such as speech or purposeful usage of hands. While altered neurotransmission and brain development are the center of its pathophysiology, alterations in mitochondrial performance have been previously outlined, shaping it as an attractive target for the disease treatment.

METHODS

We have thoroughly described mitochondrial performance in two Rett models, patients' primary fibroblasts and female Mecp2tm1.1Bird-/+ mice brain, discriminating between different brain areas. The characterization was made according to their bioenergetics function, oxidative stress, network dynamics or ultrastructure. Building on that, we have studied the effect of leriglitazone, a PPARγ agonist, in the modulation of mitochondrial performance. For that, we treated Rett female mice with 75 mg/kg/day leriglitazone from weaning until sacrifice at 7 months, studying both the mitochondrial performance changes and their consequences on the mice phenotype. Finally, we studied its effect on neuroinflammation based on the presence of reactive glia by immunohistochemistry and through a cytokine panel.

RESULTS

We have described mitochondrial alterations in Rett fibroblasts regarding both shape and bioenergetic functions, as they displayed less interconnected and shorter mitochondria and reduced ATP production along with increased oxidative stress. The bioenergetic alterations were recalled in Rett mice models, being especially significant in cerebellum, already detectable in pre-symptomatic stages. Treatment with leriglitazone recovered the bioenergetic alterations both in Rett fibroblasts and female mice and exerted an anti-inflammatory effect in the latest, resulting in the amelioration of the mice phenotype both in general condition and exploratory activity.

CONCLUSIONS

Our studies confirm the mitochondrial dysfunction in Rett syndrome, setting the differences through brain areas and disease stages. Its modulation through leriglitazone is a potential treatment for this disorder, along with other diseases with mitochondrial involvement. This work constitutes the preclinical necessary evidence to lead to a clinical trial.

NME3 binds to phosphatidic acid and mediates PLD6-induced mitochondrial tethering

Mitochondria are dynamic organelles regulated by fission and fusion processes. The fusion of membranes requires elaborative coordination of proteins and lipids and is particularly crucial for the function and quality control of mitochondria. Phosphatidic acid (PA) on the mitochondrial outer membrane generated by PLD6 facilitates the fusion of mitochondria. However, how PA promotes mitochondrial fusion remains unclear. Here, we show that a mitochondrial outer membrane protein, NME3, is required for PLD6-induced mitochondrial tethering or clustering. NME3 is enriched at the contact interface of two closely positioned mitochondria depending on PLD6, and NME3 binds directly to PA-exposed lipid packing defects via its N-terminal amphipathic helix. The PA binding function and hexamerization confer NME3 mitochondrial tethering activity. Importantly, nutrient starvation enhances the enrichment efficiency of NME3 at the mitochondrial contact interface, and the tethering ability of NME3 contributes to fusion efficiency. Together, our findings demonstrate NME3 as a tethering protein promoting selective fusion between PLD6-remodeled mitochondria for quality control.

Polysiloxane-Based Molecular Logic Gate for Dual-Channel Visualizing Mitochondrial pH and Sulphite Changes during Cuproptosis

Intracellular Cu-induced regulated cell death, characterized by the aggregation of lipidizing mitochondrial enzymes, is called cuproptosis. Mitochondria play a vital role in the metabolic regulation of cell injury and stressful immune responses. The pH levels and sulfur dioxide (SO2) content in mitochondria have important indicative roles in the regulation of cuproptosis. However, fluorescent probes that simultaneously detect changes in pH and SO2 in mitochondria during cuprotosis have not been reported. To fill this blank, in this study, we dexterously used functional polysiloxane as a fluorescent platform to propose a molecular logic gate probe P0-pH-SO2 for detecting changes in intramitochondrial pH and SO2 content through a dual-channel mode. In addition, we defined a new function to reflect the cellular state of the elesclomol-induced cuproptosis process based on the input and output of the relevant logic relationship. This new fluorescent molecular logic gate probe P0-pH-SO2 can be rapidly activated by mitochondrial sulfites to induce green fluorescence, while the red fluorescence is quenched with the proton in the mitochondria. Overall, this study developed a novel logic-gated molecular probe that provided a versatile strategy for monitoring the role played by intramitochondrial sulfites and H+ in cuproptosis. This work will open the way to broaden the applications of molecular logic gates and fluorescent polysiloxanes.

The actin binding protein profilin 1 is critical for mitochondria function

Profilin 1 (PFN1) is an actin binding protein that is vital for the polymerization of monomeric actin into filaments. Here we screened knockout cells for novel functions of PFN1 and discovered that mitophagy, a type of selective autophagy that removes defective or damaged mitochondria from the cell, was significantly upregulated in the absence of PFN1. Despite successful autophagosome formation and fusion with the lysosome, and activation of additional mitochondrial quality control pathways, PFN1 knockout cells still accumulate damaged, dysfunctional mitochondria. Subsequent imaging and functional assays showed that loss of PFN1 significantly affects mitochondria morphology, dynamics, and respiration. Further experiments revealed that PFN1 is located to the mitochondria matrix and is likely regulating mitochondria function from within rather than through polymerizing actin at the mitochondria surface. Finally, PFN1 mutants associated with amyotrophic lateral sclerosis (ALS) fail to rescue PFN1 knockout mitochondrial phenotypes and form aggregates within mitochondria, further perturbing them. Together, these results suggest a novel function for PFN1 in regulating mitochondria and identify a potential pathogenic mechanism of ALS-linked PFN1 variants.

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images

Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.

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.

The microenvironment dictates glycocalyx construction and immune surveillance

Efforts to identify anti-cancer therapeutics and understand tumor-immune interactions are built with in vitro models that do not match the microenvironmental characteristics of human tissues. Using in vitro models which mimic the physical properties of healthy or cancerous tissues and a physiologically relevant culture medium, we demonstrate that the chemical and physical properties of the microenvironment regulate the composition and topology of the glycocalyx. Remarkably, we find that cancer and age-related changes in the physical properties of the microenvironment are sufficient to adjust immune surveillance via the topology of the glycocalyx, a previously unknown phenomenon observable only with a physiologically relevant culture medium.

Spatial topology of organelle is a new breast cancer cell classifier

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

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.

Typhaneoside-Tetrahedral Framework Nucleic Acids System: Mitochondrial Recovery and Antioxidation for Acute Kidney Injury treatment

Acute kidney injury (AKI) is not only a worldwide problem with a cruel hospital mortality rate but also an independent risk factor for chronic kidney disease and a promoting factor for its progression. Despite supportive therapeutic measures, there is no effective treatment for AKI. This study employs tetrahedral framework nucleic acid (tFNA) as a vehicle and combines typhaneoside (Typ) to develop the tFNA-Typ complex (TTC) for treating AKI. With the precise targeting ability on mitochondria and renal tubule, increased antiapoptotic and antioxidative effect, and promoted mitochondria and kidney function restoration, the TTC represents a promising nanomedicine for AKI treatment. Overall, this study has developed a dual-targeted nanoparticle with enhanced therapeutic effects on AKI and could have critical clinical applications in the future.

Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics

Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.

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.

Amyloid-β accumulation in human astrocytes induces mitochondrial disruption and changed energy metabolism

BACKGROUND

Astrocytes play a central role in maintaining brain energy metabolism, but are also tightly connected to the pathogenesis of Alzheimer's disease (AD). Our previous studies demonstrate that inflammatory astrocytes accumulate large amounts of aggregated amyloid-beta (Aβ). However, in which way these Aβ deposits influence their energy production remain unclear.

METHODS

The aim of the present study was to investigate how Aβ pathology in astrocytes affects their mitochondria functionality and overall energy metabolism. For this purpose, human induced pluripotent cell (hiPSC)-derived astrocytes were exposed to sonicated Aβ₄₂ fibrils for 7 days and analyzed over time using different experimental approaches.

RESULTS

Our results show that to maintain stable energy production, the astrocytes initially increased their mitochondrial fusion, but eventually the Aβ-mediated stress led to abnormal mitochondrial swelling and excessive fission. Moreover, we detected increased levels of phosphorylated DRP-1 in the Aβ-exposed astrocytes, which co-localized with lipid droplets. Analysis of ATP levels, when blocking certain stages of the energy pathways, indicated a metabolic shift to peroxisomal-based fatty acid β-oxidation and glycolysis.

CONCLUSIONS

Taken together, our data conclude that Aβ pathology profoundly affects human astrocytes and changes their entire energy metabolism, which could result in disturbed brain homeostasis and aggravated disease progression.

Small-molecule fluorogenic probes for mitochondrial nanoscale imaging

Mitochondria are inextricably linked to the development of diseases and cell metabolism disorders. Super-resolution imaging (SRI) is crucial in enhancing our understanding of mitochondrial ultrafine structures and functions. In addition to high-precision instruments, super-resolution microscopy relies heavily on fluorescent materials with unique photophysical properties. Small-molecule fluorogenic probes (SMFPs) have excellent properties that make them ideal for mitochondrial SRI. This paper summarizes recent advances in the field of SMFPs, with a focus on the chemical and spectroscopic properties required for mitochondrial SRI. Finally, we discuss future challenges in this field, including the design principles of SMFPs and nanoscopic techniques.

Mitochondrial network expansion and dynamic redistribution during islet morphogenesis in zebrafish larvae

Mitochondria, organelles critical for energy production, modify their shape and location in response to developmental state and metabolic demands. Mitochondria are altered in diabetes, but the mechanistic basis is poorly defined, due to difficulties in assessing mitochondria within an intact organism. Here, we use in vivo imaging in transparent zebrafish larvae to demonstrate filamentous, interconnected mitochondrial networks within islet cells. Mitochondrial movements highly resemble what has been reported for human islet cells in vitro, showing conservation in behaviour across species and cellular context. During islet development, mitochondrial content increases with emergence of cell motility, and mitochondria disperse within fine protrusions. Overall, this work presents quantitative analysis of mitochondria within their native environment and provides insights into mitochondrial behaviour during organogenesis.

Universal dynamics of mitochondrial networks: a finite-size scaling analysis

Evidence from models and experiments suggests that the networked structure observed in mitochondria emerges at the critical point of a phase transition controlled by fission and fusion rates. If mitochondria are poised at criticality, the relevant network quantities should scale with the system's size. However, whether or not the expected finite-size effects take place has not been demonstrated yet. Here, we first provide a theoretical framework to interpret the scaling behavior of mitochondrial network quantities by analyzing two conceptually different models of mitochondrial dynamics. Then, we perform a finite-size scaling analysis of real mitochondrial networks extracted from microscopy images and obtain scaling exponents comparable with critical exponents from models and theory. Overall, we provide a universal description of the structural phase transition in mammalian mitochondria.

Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning

Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope's point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria.

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.

Rapid and high-throughput testing of antifungal susceptibility using an AIEgen-based analytical system

The increasing resistance among fungi to antimicrobials are posing global threats to health. Early treatment with appropriate antifungal drugs guided by the antifungal susceptibility testing (AFST) can dramatically reduce the mortality of severe fungal infections. However, the long test time (24-48 h) of the standard AFSTs cannot provide timely results due to the slow growth of the pathogen. Herein, we report a new AFST that is independent of growth rate analysis using a luminogen with aggregation-induced emission characteristics (AIEgen) named DMASP. DMASP is a water-soluble small-molecule probe that can readily penetrate the dense fungal cell wall. Based on its mitochondria-targeting ability and AIE characteristics, fungal activity can be dynamically indicated via real-time fluorescence monitoring. This allows fungal susceptibility to various antimicrobials to be assessed within 12 h in a wash-free, one-step manner. This method may serve as a promising tool to rapidly detect possible drug-resistant fungal strain and guide the precise use of antimicrobial against fungal diseases.

Precise control of microtubule disassembly in living cells

Microtubules tightly regulate various cellular activities. Our understanding of microtubules is largely based on experiments using microtubule-targeting agents, which, however, are insufficient to dissect the dynamic mechanisms of specific microtubule populations, due to their slow effects on the entire pool of microtubules. To overcome this technological limitation, we have used chemo and optogenetics to disassemble specific microtubule subtypes, including tyrosinated microtubules, primary cilia, mitotic spindles, and intercellular bridges, by rapidly recruiting engineered microtubule-cleaving enzymes onto target microtubules in a reversible manner. Using this approach, we show that acute microtubule disassembly swiftly halts vesicular trafficking and lysosomal dynamics. It also immediately triggers Golgi and ER reorganization and slows the fusion/fission of mitochondria without affecting mitochondrial membrane potential. In addition, cell rigidity is increased after microtubule disruption owing to increased contractile stress fibers. Microtubule disruption furthermore prevents cell division, but does not cause cell death during interphase. Overall, the reported tools facilitate detailed analysis of how microtubules precisely regulate cellular architecture and functions.

Insights Into Mitochondrial Dynamics in Chlamydial Infection

Mitochondria are intracellular organelles that are instrumental in the creation of energy, metabolism, apoptosis, and intrinsic immunity. Mitochondria exhibit an extraordinarily high degree of flexibility, and are constantly undergoing dynamic fusion and fission changes. Chlamydia is an intracellular bacterium that causes serious health problems in both humans and animals. Due to a deficiency of multiple metabolic enzymes, these pathogenic bacteria are highly dependent on their eukaryotic host cells, resulting in a close link between Chlamydia infection and host cell mitochondria. Indeed, Chlamydia increase mitochondrial fusion by inhibiting the activation of dynein-related protein 1 (DRP1), which can regulate host cell metabolism for extra energy. Additionally, Chlamydia can inhibit mitochondrial fission by blocking DRP1 oligomerization, preventing host cell apoptosis. These mechanisms are critical for maintaining a favorable environment for reproduction and growth of Chlamydia. This review discusses the molecular mechanisms of mitochondrial fusion and fission, as well as the mechanisms by which Chlamydia infection alters the mitochondrial dynamics and the prospects of limiting chlamydial development by altering mitochondrial dynamics.

An Overview of Organs-on-Chips Based on Deep Learning

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of "big data" and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Mitochondrial Fission and Fusion in Tumor Progression to Metastasis

Mitochondria are highly dynamic organelles which can change their shape, via processes termed fission and fusion, in order to adapt to different environmental and developmental contexts. Due to the importance of these processes in maintaining a physiologically healthy pool of mitochondria, aberrant cycles of fission/fusion are often seen in pathological contexts. In this review we will discuss how dysregulated fission and fusion promote tumor progression. We focus on the molecular mechanisms involved in fission and fusion, discussing how altered mitochondrial fission and fusion change tumor cell growth, metabolism, motility, and invasion and, finally how changes to these tumor-cell intrinsic phenotypes directly and indirectly impact tumor progression to metastasis. Although this is an emerging field of investigation, the current consensus is that mitochondrial fission positively influences metastatic potential in a broad variety of tumor types. As mitochondria are now being investigated as vulnerable targets in a variety of cancer types, we underscore the importance of their dynamic nature in potentiating tumor progression.