High accuracy classification of developmental toxicants by in vitro tests of human neuroepithelial and cardiomyoblast differentiation
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA863236 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Stem cell-based tests for developmental toxicity are currently intensively studied. Here, we used a protocol that differentiates human induced pluripotent stem cells (hiPSCs) to neural ectodermal progenitor cells (UKN1) and exposed them to 23 teratogens and 16 non-teratogens at human relevant concentrations. Substance-induced, genome-wide expression profiles were analyzed by two procedures that classified the compounds either based on the number of significantly deregulated genes or on a classification technique with cross-validation. Both procedures demonstrated a high performance with accuracy, sensitivity and specificity of 0.90, 0.83 and 1.0, as well as 0.87, 0.92 and 0.80, respectively. Comparing the present findings with the UKN1 test to those from our recently published UKK2 test that instead recapitulates cardiomyogenic differentiation showed a high congruence in compound classification. This may be explained by the fact that the same pathways, such as PI3K-Akt, P53, TGF-beta, MAPK, EGFR and Hippo were overrepresented among the differentially expressed genes for both tests. However, for some teratogens discrepant results were obtained, such as atorvastatin and thalidomide. In conclusion, UKN1 identifies teratogens with high sensitivity and specificity, and combined interpretation analysis with other tests that differentiate hiPSCs into otherdifferent cell types may further improve the results in the future.
2022-11-03 | GSE209962 | GEO
Project description:There is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we used the hiPSC-based UKK-24h-test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. All compounds were analyzed at the maximal plasma concentration (Cmax) and the 20-fold Cmax for a 24 h incubation period in three independent experiments. Using the 1,000 probe sets with the highest variability and including information on cytotoxicity, a penalized logistic regression based classifier (top-1,000 classifier) reached an AUC, accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96 and 0.88, respectively to correctly classify the compounds as teratogens or non-teratogens. This top-1,000 classifier reached a higher AUC, accuracy and sensitivity than a second classifier (SPS-classifier), which used the number of significant probe sets to classify the compounds. At least, the SPS-classifier had a specificity of 1. Classification at 20-fold Cmax did not lead to better performance metrics than testing at 1-fold Cmax. Finally, inclusion of cytotoxicity information into the classifier clearly improved the performance metrics. In conclusion, although further optimization by additional readouts and inclusion of cell systems that model different developmental processes is required, the UKK-24h-test will support the early discovery-phase detection of human developmental toxicants already in its present form.
2022-06-24 | GSE187001 | GEO
Project description:Human-relevant tests to predict developmental toxicity are urgently needed. A currently intensively studied approach makes use of differentiating human stem cells to measure chemically-induced deviations of the normal developmental program, as in a recent study based on cardiac differentiation (UKK2). Here, we (i) tested the performance of an assay modeling neuroepithelial differentiation (UKN1), and (ii) explored the benefit of combining assays (UKN1 and UKK2) that model different germ layers. Substance-induced cytotoxicity and genome-wide expression profiles of 23 teratogens and 16 non-teratogens at human-relevant concentrations were generated and used for statistical classification, resulting in accuracies of the UKN1 assay of 87-90%. A comparison to the UKK2 assay (accuracies of 90-92%) showed, in general, a high congruence in compound classification that may be explained by the fact that there was a high overlap of signaling pathways. Finally, the combination of both assays improved the prediction compared to each test alone, and reached accuracies of 92-95%. Although some compounds were misclassified by the individual tests, we conclude that UKN1 and UKK2 can be used for a reliable detection of teratogens in vitro, and that a combined analysis of tests that differentiate hiPSCs into different germ layers and cell types can even further improve the prediction of developmental toxicants.
| S-EPMC9653768 | biostudies-literature
Project description:Classification of developmental toxicants in a human iPSC transcriptomics-based test
| PRJNA777253 | ENA
Project description:The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox(UKN). For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate to neuroepithelial cells for six days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox(UKN)), and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (i) the concentration-dependence, (ii) the time-dependence, and (iii) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This ‘RoFA predictor gene set’ may be used for a simplified and less costly setup of the STOP-tox(UKN) assay.
2019-12-03 | GSE141253 | GEO
Project description:Plasmodium falciparum enrichment tests
| PRJEB2080 | ENA
Project description:Identification of cardiac developmental toxicants in differentiated human induced pluripotent stem cells [Toxicants]
| PRJNA978589 | ENA
Project description:The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output’s accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.
2022-11-09 | GSE214540 | GEO
Project description:Genome-wide DNA methylation profiling of 8 low-grade neuroepithelial tumors (LGNET) with FGFR2 fusions with various histologic diagnoses including ganglioglioma, multinodular and vacuolating neuronal tumor (MVNT), low-grade glioneuronal tumor NOS, and polymorphous low-grade neuroepithelial tumor of the young (PLNTY). The Illumina Infinium EPIC 850k Human DNA Methylation Beadchip was used to obtain DNA methylation profiles across approximately 850,000 CpG sites of genomic DNA extracted from formalin-fixed, paraffin-embedded tumor tissue of 8 low-grade neuroepithelial tumors with FGFR2 fusions.
2021-07-27 | GSE172081 | GEO
Project description:WDR12 is a ribosome biogenesis factor known to be expressed in Optic tectum Neuroepithelial cells, we define the transcriptome profile of the optic tectum neuroepithelial cells, by cell-sorting followed by RNA-seq (Illumina technology).
2019-04-20 | GSE80497 | GEO