Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity status.

Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.

High-content image analysis to study phenotypic heterogeneity in endothelial cell monolayers.

Endothelial cells (ECs) are heterogeneous across and within tissues, reflecting distinct, specialised functions. EC heterogeneity has been proposed to underpin EC plasticity independently from vessel microenvironments. However, heterogeneity driven by contact-dependent or short-range cell-cell crosstalk cannot be evaluated with single cell transcriptomic approaches, as spatial and contextual information is lost. Nonetheless, quantification of EC heterogeneity and understanding of its molecular drivers is key to developing novel therapeutics for cancer, cardiovascular diseases and for revascularisation in regenerative medicine. Here, we developed an EC profiling tool (ECPT) to examine individual cells within intact monolayers. We used ECPT to characterise different phenotypes in arterial, venous and microvascular EC populations. In line with other studies, we measured heterogeneity in terms of cell cycle, proliferation, and junction organisation. ECPT uncovered a previously under-appreciated single-cell heterogeneity in NOTCH activation. We correlated cell proliferation with different NOTCH activation states at the single-cell and population levels. The positional and relational information extracted with our novel approach is key to elucidating the molecular mechanisms underpinning EC heterogeneity.

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line.

BACKGROUND: The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. METHODS: To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. RESULTS: By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. CONCLUSIONS: In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

Sensitive Detection of Genotoxic Substances in Complex Food Matrices by Multiparametric High-Content Analysis.

Genotoxic substances widely exist in the environment and the food supply, posing serious health risks due to their potential to induce DNA damage and cancer. Traditional genotoxicity assays, while valuable, are limited by insufficient sensitivity, specificity, and efficiency, particularly when applied to complex food matrices. This study introduces a multiparametric high-content analysis (HCA) for the detection of genotoxic substances in complex food matrices. The developed assay measures three genotoxic biomarkers, including γ-H2AX, p-H3, and RAD51, which enhances the sensitivity and accuracy of genotoxicity screening. Moreover, the assay effectively distinguishes genotoxic compounds with different modes of action, which not only offers a more comprehensive assessment of DNA damage and the cellular response to genotoxic stress but also provides new insights into the exploration of genotoxicity mechanisms. Notably, the five tested food matrices, including coffee, tea, pak choi, spinach, and tomato, were found not to interfere with the detection of these biomarkers under proper dilution ratios, validating the robustness and reliability of the assay for the screening of genotoxic compounds in the food industry. The integration of multiple biomarkers with HCA provides an efficient method for detecting and assessing genotoxic substances in the food supply, with potential applications in toxicology research and food safety.

High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure.

Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.

Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.

We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for example due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilize a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/-100 µm. We characterize the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localization microscopy.

Many microscopy experiments involve extended imaging of samples over timescales from minutes to days, during which the microscope can 'drift' out of focus. When imaging at high magnification, the depth of field is of the order of one micron and so the imaging system should keep the sample in the focal plane of the microscope objective lens to this precision. Unfortunately, temperature changes in the laboratory can cause thermal expansion of microscope components that can move the focal plane by more than a micron and such changes can occur on a timescale of minutes. This is a particular issue for super-resolved microscopy experiments using single molecule localization microscopy (SMLM) techniques, for which 1000s of images are acquired, and for automated imaging of multiple samples in multiwell plates. It is possible to maintain the sample in the focal plane focus position by either automatically moving the sample or adjusting the imaging system, for example by moving the objective lens. This is called 'autofocus' and is frequently achieved by reflecting a light beam from the microscope coverslip and measuring its position of beam profile as a function of defocus of the microscope. The correcting adjustment is then usually calculated analytically but there is recent interest in using machine learning techniques to determine the required focussing adjustment. Here, we present a system that uses a neural network to determine the required defocus correcting adjustment from camera images of a laser beam that is reflected from the coverslip. Unfortunately, this approach will only work when the microscope is in the same condition as it was when the neural network was trained - and this can be compromised by the same drift of the optical system that causes the defocus needing to be corrected. We show, however, that by training a neural network over an extended period, for example 10 days, this approach can 'learn' about the optical system drifts and provide the required autofocus function. We also show that an optical system utilizing a rectangular slit can make two measurements of the defocus simultaneously, with one measurement being optimized for high accuracy over a limited range (±10 µm) near focus and the other providing lower accuracy but over a much longer range (±100 µm). This robust autofocus system is suitable for automated super-resolved microscopy of arrays of samples in a multiwell plate using SMLM, for which an experiment routinely lasts more than 5 h.

Small-Molecule Screening for Genetic Diseases.

The genetic determinants of many diseases, including monogenic diseases and cancers, have been identified; nevertheless, targeted therapy remains elusive for most. High-throughput screening (HTS) of small molecules, including high-content analysis (HCA), has been an important technology for the discovery of molecular tools and new therapeutics. HTS can be based on modulation of a known disease target (called reverse chemical genetics) or modulation of a disease-associated mechanism or phenotype (forward chemical genetics). Prominent target-based successes include modulators of transthyretin, used to treat transthyretin amyloidoses, and the BCR-ABL kinase inhibitor Gleevec, used to treat chronic myelogenous leukemia. Phenotypic screening successes include modulators of cystic fibrosis transmembrane conductance regulator, splicing correctors for spinal muscular atrophy, and histone deacetylase inhibitors for cancer. Synthetic lethal screening, in which chemotherapeutics are screened for efficacy against specific genetic backgrounds, is a promising approach that merges phenotype and target. In this article, we introduce HTS technology and highlight its contributions to the discovery of drugs and probes for monogenic diseases and cancer.

Quercetin and hydroxytyrosol as modulators of hepatic steatosis: A NAFLD-on-a-chip study.

Organs-on-chip (OoCs) are catching on as a promising and valuable alternative to animal models, in line with the 3Rs initiative. OoCs enable the creation of three-dimensional (3D) tissue microenvironments with physiological and pathological relevance at unparalleled precision and complexity, offering new opportunities to model human diseases and to test the potential therapeutic effect of drugs, while overcoming the limited predictive accuracy of conventional 2D culture systems. Here, we present a liver-on-a-chip model to investigate the effects of two naturally occurring polyphenols, namely quercetin and hydroxytyrosol, on nonalcoholic fatty liver disease (NAFLD) using a high-content analysis readout methodology. NAFLD is currently the most common form of chronic liver disease; however, its complex pathogenesis is still far from being elucidated, and no definitive treatment has been established so far. In our experiments, we observed that both polyphenols seem to restrain the progression of the free fatty acid-induced hepatocellular steatosis, showing a cytoprotective effect due to their antioxidant and lipid-lowering properties. In conclusion, the findings of the present work could guide novel strategies to contrast the onset and progression of NAFLD.

N6-Furfuryladenine is protective in Huntington's disease models by signaling huntingtin phosphorylation.

The huntingtin N17 domain is a modulator of mutant huntingtin toxicity and is hypophosphorylated in Huntington's disease (HD). We conducted high-content analysis to find compounds that could restore N17 phosphorylation. One lead compound from this screen was N6-furfuryladenine (N6FFA). N6FFA was protective in HD model neurons, and N6FFA treatment of an HD mouse model corrects HD phenotypes and eliminates cortical mutant huntingtin inclusions. We show that N6FFA restores N17 phosphorylation levels by being salvaged to a triphosphate form by adenine phosphoribosyltransferase (APRT) and used as a phosphate donor by casein kinase 2 (CK2). N6FFA is a naturally occurring product of oxidative DNA damage. Phosphorylated huntingtin functionally redistributes and colocalizes with CK2, APRT, and N6FFA DNA adducts at sites of induced DNA damage. We present a model in which this natural product compound is salvaged to provide a triphosphate substrate to signal huntingtin phosphorylation via CK2 during low-ATP stress under conditions of DNA damage, with protective effects in HD model systems.

A high-content, in vitro cardiac fibrosis assay for high-throughput, phenotypic identification of compounds with anti-fibrotic activity.

A key feature in the pathogenesis of heart failure is cardiac fibrosis, but effective treatments that specifically target cardiac fibrosis are currently not available. A major impediment to progress has been the lack of reliable in vitro models with sufficient throughput to screen for activity against cardiac fibrosis. Here, we established cell culture conditions in micro-well format that support extracellular deposition of mature collagen from primary human cardiac fibroblasts - a hallmark of cardiac fibrosis. Based on robust biochemical characterization we developed a high-content phenotypic screening platform, that allows for high-throughput identification of compounds with activity against cardiac fibrosis. Our platform correctly identifies compounds acting on known cardiac fibrosis pathways. Moreover, it can detect anti-fibrotic activity for compounds acting on targets that have not previously been reported in in vitro cardiac fibrosis assays. Taken together, our experimental approach provides a powerful platform for high-throughput screening of anti-fibrotic compounds as well as discovery of novel targets to develop new therapeutic strategies for heart failure.

High-content tripartite split-GFP cell-based assays to screen for modulators of small GTPase activation.

The human Ras superfamily of small GTPases controls essential cellular processes such as gene expression and cell proliferation. As their deregulation is widely associated with human cancer, small GTPases and their regulatory proteins have become increasingly attractive for the development of novel therapeutics. Classical methods to monitor GTPase activation include pulldown assays that limit the analysis of GTP-bound form of proteins from cell lysates. Alternatively, live-cell FRET biosensors may be used to study GTPase activation dynamics in response to stimuli, but these sensors often require further optimization for high-throughput applications. Here, we describe a cell-based approach that is suitable to monitor the modulation of small GTPase activity in a high-content analysis. The assay relies on a genetically encoded tripartite split-GFP (triSFP) system that we integrated in an optimized cellular model to monitor modulation of RhoA and RhoB GTPases. Our results indicate the robust response of the reporter, allowing the interrogation of inhibition and stimulation of Rho activity, and highlight potential applications of this method to discover novel modulators and regulators of small GTPases and related protein-binding domains.

Construction and application of a high-content analysis for identifying human carboxylesterase 2 inhibitors in living cell system.

Human carboxylesterase 2 (hCE2), one of the most principal drug-metabolizing enzymes, catalyzes the hydrolysis of a variety of endogenous esters, anticancer agents, and environmental toxicants. The significant roles of hCE2 in both endobiotic and xenobiotic metabolism sparked great interest in the discovery and development of efficacious and selective inhibitors. However, the safe and effective inhibitors of hCE2 are scarce, due to the lack of efficient screening and evaluation systems for complex biological systems. To offer a solution to this problem, a high-content analysis (HCA)-based cell imaging and multiparametric assay method was constructed for evaluating the inhibitory effect and safety of hCE2 inhibitors in living cell system. In this study, we first established a cell imaging-based method for identifying hCE2 inhibitors at the living cell level with hCE2 fluorescent probe NCEN. Meanwhile, two nuclear probes, Hoechst 33342 and PI, were integrated to evaluate the potential cytotoxicity of compounds simultaneously. Then, the accuracy of the HCA-based method was verified by the LC-FD-based method with a positive inhibitor BNPP, and the results showed that the HCA-based method exhibited excellent precision, robustness, and reliability. Finally, the newly established HCA-based multiparametric assay panel was successfully applied to re-evaluate a series of reported hCE2 inhibitors in living cells. In summary, the HCA-based multiparametric method could serve as an efficient tool for the accuracy measurement inhibitory effect and cytotoxicity of compounds against hCE2 in living cell system. Graphical abstract.

Label-Free Identification of White Blood Cells Using Machine Learning.

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Cell-based two-dimensional morphological assessment system to predict cancer drug-induced cardiotoxicity using human induced pluripotent stem cell-derived cardiomyocytes.

Recent developments of novel targeted therapies are contributing to the increased long-term survival of cancer patients; however, drug-induced cardiotoxicity induced by cancer drugs remains a serious problem in clinical settings. Nevertheless, there are few in vitro cell-based assays available to predict this toxicity, especially from the aspect of morphology. Here, we developed a simple two-dimensional (2D) morphological assessment system, 2DMA, to predict drug-induced cardiotoxicity in cancer patients using human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with image-based high-content analysis in a high-throughput manner. To assess the effects of drugs on cardiomyocytes, we treated iPSC-CMs with 28 marketed pharmaceuticals and measured two key parameters: number of cell nuclei and sarcomere morphology. Drugs that significantly perturbed these two parameters at concentrations ≤30 times the human Cmax value were regarded as positive in the test. Based on these criteria, the sensitivity and specificity of the 2DMA system were 81% and 100%, respectively. Moreover, the translational predictability of 2DMA was comparable with that of a three-dimensional cardiotoxicity assay. RNA sequencing further revealed that the expression levels of several genes related to sarcomere components decreased following treatment with sunitinib, suggesting that inhibition of the synthesis of proteins that comprise the sarcomere contributes to drug-induced sarcomere disruption. Based on these features, the 2DMA system provides mechanistic insight with high predictability of cancer drug-induced cardiotoxicity in humans, and could thus contribute to the reduction of drug attrition rates at early stages of drug development.

High-content analysis boosts identification of the initial cause of triptolide-induced hepatotoxicity.

Triptolide (TP) has been widely used in China for more than 40 years as an immunosuppressive agent. Recently, serious concerns have been raised over TP-induced liver injury, though the real hepatotoxic mechanism is still unclear, particularly in terms of the initial cause. To our knowledge, this study is the first to screen systematically the mechanism of TP-induced toxicity through a global cytotoxicity profile high-content analysis using three independent cytotoxic assay panels with multiple endpoints of cytotoxicity, including cell loss, mitochondrial membrane potential, nuclear membrane permeability, manganese superoxide dismutase, phosphorylated gamma-H2AX, light chain 3B, lysosome, reactive oxygen species and glutathione. We assessed nine parameters and four stress response pathway models by labeling nuclear factor erythroid 2-related factor 2, activating transcription factor 6, hypoxia inducible factor 1α and nuclear factor κB and found that all testing parameters except glutathione and manganese superoxide dismutase showed concentration- and time-dependent changes, as well as increased cell loss after TP treatment. Considering that RNA polymerase II is the molecular target of TP, we quantified transcription from inducible genes, bromodeoxyuridine incorporation, and expression from transiently transfected green fluorescence protein plasmids in HepG2 cells. The results show that inhibition of global transcription by TP took place at earlier times and at lower concentrations than those observed for cell death. Therefore, global transcriptional suppression and the cell dysfunction it drives play a central role in TP-induced hepatotoxicity. This provides valuable information for the safe use of TP in the clinic.

High-content analysis for mitophagy response to nanoparticles: A potential sensitive biomarker for nanosafety assessment.

Mitophagy, a selective autophagy of mitochondria, clears up damaged mitochondria to maintain cell homeostasis. We performed high-content analysis (HCA) to detect the increase of PINK1, an essential protein controlling mitophagy, in hepatic cells treated with several nanoparticles (NPs). PINK1 immunofluorescence-based HCA was more sensitive than assays and detections for cell viability and mitochondrial functions. Of which, superparamagnetic iron oxide (SPIO)-NPs or graphene oxide-quantum dots (GO-QDs) was selected as representatives for positive or negative inducer of mitophagy. SPIO-NPs, but not GO-QDs, activated PINK1-dependent mitophagy as demonstrated by recruitment of PARKIN to mitochondria and degradation of injured mitochondria. SPIO-NPs caused the loss of mitochondrial membrane potential, decrease in ATP, and increase in mitochondrial reactive oxide species and Ca2+. Blocking mitophagy with PARKIN siRNA aggravated the cytotoxicity of SPIO-NPs. Taken together, PINK1 immunofluorescence-based HCA is considered to be an early, sensitive, and reliable approach to evaluate the bioimpacts of NPs.

Novel Insights on the Toxicity of Phycotoxins on the Gut through the Targeting of Enteric Glial Cells.

In vitro and in vivo studies have shown that phycotoxins can impact intestinal epithelial cells and can cross the intestinal barrier to some extent. Therefore, phycotoxins can reach cells underlying the epithelium, such as enteric glial cells (EGCs), which are involved in gut homeostasis, motility, and barrier integrity. This study compared the toxicological effects of pectenotoxin-2 (PTX2), yessotoxin (YTX), okadaic acid (OA), azaspiracid-1 (AZA1), 13-desmethyl-spirolide C (SPX), and palytoxin (PlTX) on the rat EGC cell line CRL2690. Cell viability, morphology, oxidative stress, inflammation, cell cycle, and specific glial markers were evaluated using RT-qPCR and high content analysis (HCA) approaches. PTX2, YTX, OA, AZA1, and PlTX induced neurite alterations, oxidative stress, cell cycle disturbance, and increase of specific EGC markers. An inflammatory response for YTX, OA, and AZA1 was suggested by the nuclear translocation of NF-κB. Caspase-3-dependent apoptosis and induction of DNA double strand breaks (γH2AX) were also observed with PTX2, YTX, OA, and AZA1. These findings suggest that PTX2, YTX, OA, AZA1, and PlTX may affect intestinal barrier integrity through alterations of the human enteric glial system. Our results provide novel insight into the toxicological effects of phycotoxins on the gut.

After In Vitro Digestion, Jackfruit Flake Affords Protection against Acrylamide-Induced Oxidative Damage.

Some studies have demonstrated that acrylamide (AA) was correlated with oxidative stress, resulting in physical damage. The jackfruit flake was an immature pulp that contained a high level of antioxidant activity. This study aimed to assess the defensive efficacy of jackfruit flake in AA-induced oxidative stress before and after simulated gastrointestinal digestion. Our results indicate that the total polyphenol content of Jackfruit flake digest (Digestive products of jackfruit flake after gastrointestinal, JFG) was diminished; however, JFG had raised the relative antioxidant capacity compared to Jackfruit flake extract (JFE). Additionally, the results of High Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) implied that a proportion of compounds were degraded/converted into other unknown and/or undetected metabolites. Further, by high content analysis (HCA) techniques, JFG markedly reduced cytotoxicity and excessive production of reactive oxygen species (ROS) in cells, thereby alleviating mitochondrial disorders. In this study, it may be converted active compounds after digestion that had preferable protective effects against AA-induced oxidative damage.

Visualizing quantitative microscopy data: History and challenges.

Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

Generation of Fabry cardiomyopathy model for drug screening using induced pluripotent stem cell-derived cardiomyocytes from a female Fabry patient.

BACKGROUND: Fabry disease is an X-linked disease caused by mutations in α-galactosidase A (GLA); these mutations result in the accumulation of its substrates, mainly globotriaosylceramide (Gb3). The accumulation of glycosphingolipids induces pathogenic changes in various organs, including the heart, and Fabry cardiomyopathy is the most frequent cause of death in patients with Fabry disease. Existing therapies to treat Fabry disease have limited efficacy, and new approaches to improve the prognosis of patients with Fabry cardiomyopathy are required. METHODS AND RESULTS: We generated induced pluripotent stem cell (iPSC) lines from a female patient and her son. Each iPSC clone from the female patient showed either deficient or normal GLA activity, which could be used as a Fabry disease model or its isogenic control, respectively. Erosion of the inactivated X chromosome developed heterogeneously among clones, and mono-allelic expression of the GLA gene was maintained for a substantial period in a subset of iPSC clones. Gb3 accumulation was observed in iPSC-derived cardiomyocytes (iPS-CMs) from GLA activity-deficient iPSCs by mass-spectrometry and immunofluorescent staining. The expression of ANP was increased, but the cell surface area was decreased in iPS-CMs from the Fabry model, suggesting that cardiomyopathic change is ongoing at the molecular level in Fabry iPS-CMs. We also established an algorithm for selecting proper Gb3 staining that could be used for high-content analysis-based drug screening. CONCLUSIONS: We generated a Fabry cardiomyopathy model and a drug screening system by using iPS-CMs from a female Fabry patient. Drug screening using our system may help discover new drugs that would improve the prognosis of patients with Fabry cardiomyopathy.