MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data.

BACKGROUND: Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable. RESULTS: We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. We demonstrate that MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network, followed by application to previously published single-cell qPCR data from mouse embryonic stem cells. We show that MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations. CONCLUSIONS: MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.

Integrated systems modeling of severe asthma: Exploration of IL-33/ST2 antagonism.

Asthma is a complex, heterogeneous disease with a high unmet medical need, despite therapies targeting a multitude of pathways. The ability to quantitatively integrate preclinical and clinical data on these pathways could aid in the development and testing of novel targets and therapeutics. In this work, we develop a computational model of asthma biology, including key cell types and mediators, and create a virtual population capturing clinical heterogeneity. The simulated responses to therapies targeting IL-13, IL-4Rα, IL-5, IgE, and TSLP demonstrate agreement with clinical endpoints and biomarkers of type 2 (T2) inflammation, including blood eosinophils, FEV1, IgE, and FeNO. We use the model to explore the potential benefit of targeting the IL-33 pathway with anti-IL-33 and anti-ST2. Model predictions are compared with data on blood eosinophils, FeNO, and FEV1 from recent anti-IL-33 and anti-ST2 trials and used to interpret trial results based on pathway biology and pharmacology. Results of sensitivity analyses on the contributions of IL-33 to the predicted biomarker changes suggest that anti-ST2 therapy reduces circulating blood eosinophil levels primarily through its impact on eosinophil progenitor maturation and IL-5-dependent survival, and induces changes in FeNO and FEV1 through its effect on immune cells involved in T2 cytokine production. Finally, we also investigate the impact of ST2 genetics on the conferred benefit of anti-ST2. The model includes representation of a wide array of biologic mechanisms and interventions that will provide mechanistic insight and support clinical program design for a wide range of novel therapies during drug development.

Age at initiation and frequency of screening to detect type 2 diabetes: a cost-effectiveness analysis.

BACKGROUND: No clinical trials have assessed the effects or cost-effectiveness of sequential screening strategies to detect new cases of type 2 diabetes. We used a mathematical model to estimate the cost-effectiveness of several screening strategies. METHODS: We used person-specific data from a representative sample of the US population to create a simulated population of 325,000 people aged 30 years without diabetes. We used the Archimedes model to compare eight simulated screening strategies for type 2 diabetes with a no-screening control strategy. Strategies differed in terms of age at initiation and frequency of screening. Once diagnosed, diabetes treatment was simulated in a standard manner. We calculated the effects of each strategy on the incidence of type 2 diabetes, myocardial infarction, stroke, and microvascular complications in addition to quality of life, costs, and cost per quality-adjusted life-year (QALY). FINDINGS: Compared with no screening, all simulated screening strategies reduced the incidence of myocardial infarction (3-9 events prevented per 1000 people screened) and diabetes-related microvascular complications (3-9 events prevented per 1000 people), and increased the number of QALYs (93-194 undiscounted QALYs) added over 50 years. Most strategies prevented a significant number of simulated deaths (2-5 events per 1000 people). There was little or no effect of screening on incidence of stroke (0-1 event prevented per 1000 people). Five screening strategies had costs per QALY of about US$10,500 or less, whereas costs were much higher for screening started at 45 years of age and repeated every year ($15,509), screening started at 60 years of age and repeated every 3 years ($25,738), or a maximum screening strategy (screening started at 30 years of age and repeated every 6 months; $40,778). Several strategies differed substantially in the number of QALYs gained. Costs per QALY were sensitive to the disutility assigned to the state of having diabetes diagnosed with or without symptoms. INTERPRETATION: In the US population, screening for type 2 diabetes is cost effective when started between the ages of 30 years and 45 years, with screening repeated every 3-5 years. FUNDING: Novo Nordisk, Bayer HealthCare, [corrected] and Pfizer.

gQSPSim: A SimBiology-Based GUI for Standardized QSP Model Development and Application.

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface-based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology-built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation-ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim's capabilities with a standard target-mediated drug disposition model and a published model of anti-proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.

The influence of myocardial substrate on ventricular fibrillation waveform: a swine model of acute and postmyocardial infarction.

OBJECTIVE: In cardiac arrest resulting from ventricular fibrillation, the ventricular fibrillation waveform may be a clue to its duration and predict the likelihood of shock success. However, ventricular fibrillation occurs in different myocardial substrates such as ischemia, heart failure, and structurally normal hearts. We hypothesized that ventricular fibrillation is altered by myocardial infarction and varies from the acute to postmyocardial infarction periods. DESIGN: An animal intervention study was conducted with comparison to a control group. SETTING: This study took place in a university animal laboratory. SUBJECTS: Study subjects included 37 swine. INTERVENTIONS: Myocardial infarction was induced by occlusion of the midleft anterior descending artery. Ventricular fibrillation was induced in control swine, acute myocardial infarction swine, and in postmyocardial infarction swine after a 2-wk recovery period. MEASUREMENTS AND MAIN RESULTS: Ventricular fibrillation was recorded in 11 swine with acute myocardial infarction, ten postmyocardial infarction, and 16 controls. Frequency (mean, median, dominant, and bandwidth) and amplitude-related content (slope, slope-amp [slope divided by amplitude], and amplitude-spectrum area) were analyzed. Frequencies at 5 mins of ventricular fibrillation were altered in both acute myocardial infarction (p < .001 for all frequency characteristics) and postmyocardial infarction swine (p = .015 for mean, .002 for median, .002 for dominant frequency, and <.001 for bandwidth). At 5 mins, median frequency was highest in controls, 10.9 +/- .4 Hz; lowest in acute myocardial infarction, 8.4 +/- .5 Hz; and intermediate in postmyocardial infarction, 9.7 +/- .5 Hz (p < .001 for acute myocardial infarction and p = .002 for postmyocardial infarction compared with control). Slope and amplitude-spectrum area were similar among the three groups with a shallow decline after minute 2, whereas slope-amp remained significantly altered for acute myocardial infarction swine at 5 mins (p = .003). CONCLUSIONS: Ventricular fibrillation frequencies depend on myocardial substrate and evolve from the acute through healing phases of myocardial infarction. Amplitude related measures, however, are similar among these groups. It is unknown how defibrillation may be affected by relying on the ventricular fibrillation waveform without considering myocardial substrate.

Combinatorial Histone Acetylation Patterns Are Generated by Motif-Specific Reactions.

Post-translational modifications (PTMs) are pivotal to cellular information processing, but how combinatorial PTM patterns ("motifs") are set remains elusive. We develop a computational framework, which we provide as open source code, to investigate the design principles generating the combinatorial acetylation patterns on histone H4 in Drosophila melanogaster. We find that models assuming purely unspecific or lysine site-specific acetylation rates were insufficient to explain the experimentally determined motif abundances. Rather, these abundances were best described by an ensemble of models with acetylation rates that were specific to motifs. The model ensemble converged upon four acetylation pathways; we validated three of these using independent data from a systematic enzyme depletion study. Our findings suggest that histone acetylation patterns originate through specific pathways involving motif-specific acetylation activity.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells.

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.

Inference of spatiotemporal effects on cellular state transitions from time-lapse microscopy.

BACKGROUND: Time-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context. Combined with single cell tracking and appropriate cell state markers, transition events can be observed within the genealogical relationship of a proliferating population. However, to infer the correlations between the spatiotemporal context and cell state transitions, statistical analysis with an appropriately large number of samples is required. RESULTS: Here, we present a method to infer spatiotemporal features predictive of the state transition events observed in time-lapse microscopy data. We first formulate a generative model, simulate different scenarios, such as time-dependent or local cell density-dependent transitions, and illustrate how to estimate univariate transition rates. Second, we formulate the problem in a machine-learning language using regularized linear models. This allows for a multivariate analysis and to disentangle indirect dependencies via feature selection. We find that our method can accurately recover the relevant features and reconstruct the underlying interaction kernels if a critical number of samples is available. Finally, we explicitly use the tree structure of the data to validate if the estimated model is sufficient to explain correlated transition events of sister cells. CONCLUSIONS: Using synthetic cellular genealogies, we prove that our method is able to correctly identify features predictive of state transitions and we moreover validate the chosen model. Our approach allows to estimate the number of cellular genealogies required for the proposed spatiotemporal statistical analysis, and we thus provide an important tool for the experimental design of challenging single cell time-lapse microscopy assays.

Network plasticity of pluripotency transcription factors in embryonic stem cells.

Transcription factor (TF) networks are thought to regulate embryonic stem cell (ESC) pluripotency. However, TF expression dynamics and regulatory mechanisms are poorly understood. We use reporter mouse ESC lines allowing non-invasive quantification of Nanog or Oct4 protein levels and continuous long-term single-cell tracking and quantification over many generations to reveal diverse TF protein expression dynamics. For cells with low Nanog expression, we identified two distinct colony types: one re-expressed Nanog in a mosaic pattern, and the other did not re-express Nanog over many generations. Although both expressed pluripotency markers, they exhibited differences in their TF protein correlation networks and differentiation propensities. Sister cell analysis revealed that differences in Nanog levels are not necessarily accompanied by differences in the expression of other pluripotency factors. Thus, regulatory interactions of pluripotency TFs are less stringently implemented in individual self-renewing ESCs than assumed at present.