Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro.
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ABSTRACT:
SUBMITTER: Guo J
PROVIDER: S-EPMC8185434 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.
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Project description:Nephron progenitors in the embryonic kidney propagate while generating differentiated nephrons. However, in mice, the progenitors terminally differentiate shortly after birth. Here, we report a method for selectively expanding nephron progenitors in vitro in an undifferentiated state. Combinatorial and concentration-dependent stimulation with LIF, FGF2/9, BMP7, and a WNT agonist is critical for expansion. The purified progenitors proliferated beyond the physiological limits observed in vivo, both for cell numbers and lifespan. Neonatal progenitors were maintained for a week, while progenitors from embryonic day 11.5 expanded 1,800-fold for nearly 20 days and still reconstituted 3D nephrons containing glomeruli and renal tubules. Furthermore, progenitors generated from mouse embryonic stem cells and human induced pluripotent cells could be expanded with retained nephron-forming potential. Thus, we have established in vitro conditions for promoting the propagation of nephron progenitors, which will be essential for dissecting the mechanisms of kidney organogenesis and for regenerative medicine.
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Project description:Development of predictive in vitro assays for early toxicity evaluation is extremely important for improving the drug development process and reducing drug attrition rates during clinical development. High-content imaging-based in vitro toxicity assays are emerging as efficient tools for safety and efficacy testing to improve drug development efficiency. In this report we have used an induced pluripotent stem cell (iPSC)-derived hepatocyte cell model having a primary tissue-like phenotype, unlimited availability, and the potential to compare cells from different individuals. We examined a number of assays and phenotypic markers and developed automated screening methods for assessing multiparameter readouts of general and mechanism-specific hepatotoxicity. Endpoints assessed were cell viability, nuclear shape, average and integrated cell area, mitochondrial membrane potential, phospholipid accumulation, cytoskeleton integrity, and apoptosis. We assayed compounds with known mechanisms of toxicity and also evaluated a diverse hepatotoxicity library of 240 compounds. We conclude that high-content automated screening assays using iPSC-derived hepatocytes are feasible, provide information about mechanisms of toxicity, and can facilitate the safety assessment of drugs and chemicals.
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Project description:Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.
2021-07-07 | GSE172181 | GEO
Project description:Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug.
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Project description:Cell-based high-content screening (HCS) assays have become an increasingly attractive alternative to traditional in vitro and in vivo testing in pharmaceutical drug development and toxicological safety assessment. The time- and cost-effectiveness of HCS assays, combined with the organotypic nature of human induced pluripotent stem cell (iPSC)-derived cells, open new opportunities to employ physiologically relevant in vitro model systems to improve screening for potential chemical hazards. In this study, we used two human iPSC types, cardiomyocytes and hepatocytes, to test various high-content and molecular assay combinations for their applicability in a multiparametric screening format. Effects on cardiomyocyte beat frequency were characterized by calcium flux measurements for up to 90?min. Subsequent correlation with intracellular cAMP levels was used to determine if the effects on cardiac physiology were G-protein-coupled receptor dependent. In addition, we utilized high-content cell imaging to simultaneously determine cell viability, mitochondrial integrity, and reactive oxygen species (ROS) formation in both cell types. Kinetic analysis indicated that ROS formation is best detectable 30?min following initial treatment, whereas cytotoxic effects were most stable after 24?h. For hepatocytes, high-content imaging was also used to evaluate cytotoxicity and cytoskeletal integrity, as well as mitochondrial integrity and the potential for lipid accumulation. Lipid accumulation, a marker for hepatic steatosis, was most reliably detected 48?h following treatment with test compounds. Overall, our results demonstrate how a compendium of assays can be utilized for quantitative screening of chemical effects in iPSC cardiomyocytes and hepatocytes and enable rapid and cost-efficient multidimensional biological profiling of toxicity.
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Project description:Hepatotoxicity is one of the most cited reasons for withdrawal of approved drugs from the market. The use of nonclinically relevant in vitro and in vivo testing systems contributes to the high attrition rates. Recent advances in differentiating human induced pluripotent stem cells (hiPSCs) into pure cultures of hepatocyte-like cells expressing functional drug metabolizing enzymes open up possibilities for novel, more relevant human cell based toxicity models. The present study aimed to investigate the use of hiPSC derived hepatocytes for conducting mechanistic toxicity testing by image based high content analysis (HCA). The hiPSC derived hepatocytes were exposed to drugs known to cause hepatotoxicity through steatosis and phospholipidosis, measuring several endpoints representing different mechanisms involved in drug induced hepatotoxicity. The hiPSC derived hepatocytes were benchmarked to the HepG2 cell line and generated robust HCA data with low imprecision between plates and batches. The different parameters measured were detected at subcytotoxic concentrations and the order of which the compounds were categorized (as severe, moderate, mild, or nontoxic) based on the degree of injury at isomolar concentration corresponded to previously published data. Taken together, the present study shows how hiPSC derived hepatocytes can be used as a platform for screening drug induced hepatotoxicity by HCA.
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Project description:Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide an excellent platform for potential clinical and research applications. Identifying abnormal Ca2+ transients is crucial for evaluating cardiomyocyte function that requires labor-intensive manual effort. Therefore, we develop an analytical pipeline for automatic assessment of Ca2+ transient abnormality, by employing advanced machine learning methods together with an Analytical Algorithm. First, we adapt an existing Analytical Algorithm to identify Ca2+ transient peaks and determine peak abnormality based on quantified peak characteristics. Second, we train a peak-level Support Vector Machine (SVM) classifier by using human-expert assessment of peak abnormality as outcome and profiled peak variables as predictive features. Third, we train another cell-level SVM classifier by using human-expert assessment of cell abnormality as outcome and quantified cell-level variables as predictive features. This cell-level SVM classifier can be used to assess additional Ca2+ transient signals. By applying this pipeline to our Ca2+ transient data, we trained a cell-level SVM classifier using 200 cells as training data, then tested its accuracy in an independent dataset of 54 cells. As a result, we obtained 88% training accuracy and 87% test accuracy. Further, we provide a free R package to implement our pipeline for high-throughput CM Ca2+ analysis.
| S-EPMC7550597 | biostudies-literature
Project description:Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) are being investigated as a new source of cardiac cells for drug safety assessment. We developed a novel scalable high content microscopy-based method for the detection of cell death in hPSC-CM that can serve for future predictive in vitro cardio-toxicological screens. Using rat neonatal ventricular cardiomyocytes (RVNC) or hPSC-CM, assays for nuclear remodelling, mitochondrial status, apoptosis and necrosis were designed using a combination of fluorescent dyes and antibodies on an automated microscopy platform. This allowed the observation of a chelerythrine-induced concentration-dependent apoptosis to necrosis switch and time-dependent progression of early apoptotic cells towards a necrotic-like phenotype. Susceptibility of hPSC-CM to chelerythrine-stimulated apoptosis varied with time after differentiation, but at most time points, hPSC-CM were more resistant than RVNC. This simple and scalable humanized high-content assay generates accurate cardiotoxicity profiles that can serve as a base for further assessment of cardioprotective strategies and drug safety.
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Project description:MotivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.ResultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.Availability and implementationThe Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Supplementary informationSupplementary data are available at Bioinformatics online.
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