Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction

Motivation: For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. Results: In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development. Availability and implementation: The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics online. Contact: [email protected] or [email protected] Supplementary information: Supplementary Materials are available at Bioinfomatics onlin

Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples

Progenitor identification and SARS-CoV-2 infection in human distal lung organoids

The distal lung contains terminal bronchioles and alveoli that facilitate gas exchange. Three-dimensional in vitro human distal lung culture systems would strongly facilitate investigation of pathologies including interstitial lung disease, cancer, and SARS-CoV-2-associated COVID-19 pneumonia. We generated long-term feeder-free, chemically defined culture of distal lung progenitors as organoids derived from single adult human alveolar epithelial type II (AT2) or KRT5+ basal cells. AT2 organoids exhibited AT1 transdifferentiation potential while basal cell organoids developed lumens lined by differentiated club and ciliated cells. Single cell analysis of basal organoid KRT5+ cells revealed a distinct ITGA6+ITGB4+ mitotic population whose proliferation further segregated to a TNFRSF12Ahi subfraction comprising ~10% of KRT5+ basal cells, residing in clusters within terminal bronchioles and exhibiting enriched clonogenic organoid growth activity. Distal lung organoids were created with apical-out polarity to display ACE2 on the exposed external surface, facilitating SARS-CoV-2 infection of AT2 and basal cultures and identifying club cells as a novel target population. This long-term, feeder-free organoid culture of human distal lung, coupled with single cell analysis, identifies unsuspected basal cell functional heterogeneity and establishes a facile in vitro organoid model for human distal lung infections including COVID-19-associated pneumonia

Modeling molecular signaling and gene expression using Dynamic Nested Effects Models

Cellular decision making in differentiation, proliferation or apoptosis is mediated by molecular signaling processes, which control the regulation and expression of genes. Vice versa, the expression of genes can trigger the activity of signaling pathways. I summarize methodology by Markowetz et al. known as the Nested Effects Models (NEMs) to reconstruct static non-transcriptional networks using subset relationships from perturbation data and bring out its limitation to model slow-going biological processes like cell differentiation. I introduce and describe new statistical methodologies called Dynamic Nested Effects Models (DNEMs) and Cyclic Dynamic Nested Effects Models (CDNEMs) for analyzing the temporal interplay of cell signaling and gene expression. DNEMs and CDNEMs are Bayesian models of signal propagation in a network. They decompose observed time delays of multiple step signaling processes into single steps. Time delays are assumed to be exponentially distributed. Rate constants of signal propagation are model parameters, whose joint posterior distribution is assessed via Gibbs sampling. They hold information on the interplay of different forms of biological signal propagation: Molecular signaling in the cytoplasm acts at high rates, direct signal propagation via transcription and translation at intermediate rates, while secondary effects operate at low rates. I evaluate my methods in simulation experiments and demonstrate their practical applications to embryonic stem cell development in mice. The results from these models explain how stem cells could succeed to carry out differentiation to specialized cells of the body such as muscle cells or neurons, a process that goes in one direction. The inferred molecular communication underlying such a process proposes how organisms protect themselves against the reversal of cell differentiation and thereby against cancer

Exact likelihood computation in Boolean networks with probabilistic time delays, and its application in signal network reconstruction

Motivation: For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. Results: In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development

CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

<div><p>A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.</p></div

Visualization of 13 markers across heterogeneous population of T-cells.

<p>These 13×13 scatter plots show pair-wise distribution of 13 markers (unlabeled) per cell from pooled single cell data of 4 T-cell subtypes. Primary data was made publicly available by Bendall <i>et al. </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi.1003664-Bendall1" target="_blank">[9]</a>.</p

Signaling behavior in B-cell subtypes for CCAST vs manual gating strategy.

<p><b>A</b> Heatmap of BCR, IFNa, FTL3, IL3, IL7, and SCF induced intracellular signaling responses in 5 B-cell CCAST-derived subtypes, compared with those of an unstimulated control. <b>B</b> Heatmap of BCR, IFNa, FTL3, IL3, IL7, and SCF induced intracellular signaling responses in the five B-cell subtypes obtained from the manual gates in Bendall <i>et al. </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi.1003664-Bendall1" target="_blank">[9]</a>, compared with those of an unstimulated control. The higher difference implies a stronger signal in the CCAST-derived cell type compared to the manually gated cell type.</p