Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices

Early neurodevelopmental disturbances during sensitive periods of stem cell differentiation

The high need for appropriate in vitro tests for regulatory and industrial purposes as well as the focused governmental and EU support leads to a constant increase in assays addressing developmental neurotoxicity (DNT). The UKN1 assay is such a test method that has been set up already 10 years ago. By using human embryonic stem cells/induced pluripotent stem cells differentiating towards neuroectodermal precursor cells (NEP), the early neurodevelopment is modeled in vitro. The NEP state corresponds to the neural plate or the early neural tube in vivo. The readout for disturbances in the differentiation is based on the measurement of gene expression of several marker genes (PAX6, OTX2, NANOG, OCT4), it has also been assessed by transcriptome analysis using oligonucleotide microarrays. The UKN1 test has served to study processes of neurodevelopment such as epigenetic mechanisms. Moreover, the test method was used to develop framework conditions for setting up DNT methods in general, for example, strategies have been developed for compound concentration range finding and for quantifying transcriptomics datasets as a DNT endpoint. Especially, for the setup of transcrip-tome conditions for DNT testing, the UKN1-based studies have pioneered the field. In this thesis project the UKN1 assay was further developed. Firstly, the effect of acutely toxic compound concentrations on the transcriptome have been investigated. The aim was to un-derstand the mechanisms of transcriptomic regulation in a developing system in unperturbed and perturbed development. Additionally, it aimed to distinguish between an acute and a de-velopmental effect. Since the test should only identify specific DNT compounds, this represents an important basis. By using genetic markers as a toxicological fingerprint for acute cytotoxi-city, rules were established to exclude compounds tested in the cytotoxic range. In the second part of the project, the NEPs were further differentiated until the cells formed neural rosettes. These represent the in vitro equivalent of the in vivo neural tube. The rosette formation was established as a new endpoint for the UKN1 assay. The new phenotypic readout provides a robust tool to anchor transcriptomics changes to an endophenotype and to investi-gate the relevance of transcriptome changes at early neurodevelopmental stages. A reliable prediction model was setup based on the acquired information. Moreover, this approach served to define a narrow time window of toxicity of a HDAC inhibitor (trichostatin A). HDAC inhibitors are used as anticonvulsants and can induce congenital malformations such as spina bifida in the developing embryo. In the third part of the project, a computational model was developed to predict the influence of different valproic acid (VPA) concentrations on the expression of single genes at every time point during differentiation. The model was based on experimental datasets over time and on data from cells exposed to various VPA concentrations. The computational model delivered Abstract 12 information (predicted) about the pathways that were activated by VPA at intermediate (not measured) time points. The VPA-induced disturbance of rosette formation (endophenotype) was successfully rescued by blocking the wnt pathway. The three parts of this work have to-gether provided a new scientific basis for the UKN1 assay and they are expected to lead to a broader application for regulatory and for industrial purposes.publishe

High Accuracy Classification of Developmental Toxicants by In Vitro Tests of Human Neuroepithelial and Cardiomyoblast Differentiation

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.publishe

Grouping of histone deacetylase inhibitors and other toxicants disturbing neural crest migration by transcriptional profiling

Functional assays, such as the "migration inhibition of neural crest cells" (MINC) developmental toxicity test, can identify toxicants without requiring knowledge on their mode of action (MoA). Here, we were interested, whether (i) inhibition of migration by structurally diverse toxicants resulted in a unified signature of transcriptional changes; (ii) whether statistically-identified transcript patterns would inform on compound grouping even though individual genes were little regulated, and (iii) whether analysis of a small group of biologically-relevant transcripts would allow the grouping of compounds according to their MoA. We analyzed transcripts of 35 'migration genes' after treatment with 16 migration-inhibiting toxicants. Clustering, principal component analysis and correlation analyses of the data showed that mechanistically related compounds (e.g. histone deacetylase inhibitors (HDACi), PCBs) triggered similar transcriptional changes, but groups of structurally diverse toxicants largely differed in their transcriptional effects. Linear discriminant analysis (LDA) confirmed the specific clustering of HDACi across multiple separate experiments. Similarity of the signatures of the HDACi trichostatin A and suberoylanilide hydroxamic acid to the one of valproic acid (VPA), suggested that the latter compound acts as HDACi when impairing neural crest migration. In conclusion, the data suggest that (i) a given functional effect (e.g. inhibition of migration) can be associated with highly diverse signatures of transcript changes; (ii) statistically significant grouping of mechanistically-related compounds can be achieved on the basis of few genes with small regulations. Thus, incorporation of mechanistic markers in functional in vitro tests may support read-across procedures, also for structurally un-related compounds.publishe

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances

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 into neuroepithelial cells for 6 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 (1) the concentration-dependence, (2) the time dependence, and (3) 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.publishe

Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as tolerance (minor transcriptome changes), functional adaptation (moderate/strong transcriptome responses, but no cytotoxicity), and degeneration. The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate biomarkers of cytotoxicity. These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied

Classification of Developmental Toxicants in a Human iPSC Transcriptomics-Based Test

Despite the progress made in developmental toxicology, 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 established the hiPSC-based UKK2 in vitro test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. Compounds were analyzed at the maximal plasma concentration (C-max) and at 20-fold C-max for a 24 h incubation period in three independent experiments. Based on the 1000 probe sets with the highest variance and including information on cytotoxicity, penalized logistic regression with leave-one-out cross-validation was used to classify the compounds as test-positive or test-negative, reaching an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96, and 0.88, respectively. Omitting the cytotoxicity information reduced the test performance to an AUC of 0.94, an accuracy of 0.79, and a sensitivity of 0.74. A second method, which used the number of significantly deregulated probe sets to classify the compounds, resulted in a specificity of 1; however, the AUC (0.90), accuracy (0.90), and sensitivity (0.83) were inferior compared to those of the logistic regression-based procedure. Finally, no increased performance was achieved when the high test concentrations (20-fold C-max) were used, in comparison to testing within the realistic clinical range (1-fold C-max). In conclusion, although further optimization is required, for example, by including additional readouts and cell systems that model different developmental processes, the UKK2-test in its present form can support the early discovery-phase detection of human developmental toxicants

High Accuracy Classification of Developmental Toxicants by In Vitro Tests of Human Neuroepithelial and Cardiomyoblast Differentiation

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

Development of a neural rosette formation assay (RoFA) to identify neurodevelopmental toxicants and to characterize their transcriptome disturbances

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 into neuroepithelial cells for 6 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 (1) the concentration-dependence, (2) the time dependence, and (3) 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