Learning time-varying information flow from single-cell epithelial to mesenchymal transition data

Signal amplification by cyclic extension enables high-sensitivity single-cell mass cytometry

Mass cytometry uses metal-isotope-tagged antibodies to label targets of interest, which enables simultaneous measurements of ~50 proteins or protein modifications in millions of single cells, but its sensitivity is limited. Here, we present a signal amplification technology, termed Amplification by Cyclic Extension (ACE), implementing thermal-cycling-based DNA in situ concatenation in combination with 3-cyanovinylcarbazole phosphoramidite-based DNA crosslinking to enable signal amplification simultaneously on >30 protein epitopes. We demonstrate the utility of ACE in low-abundance protein quantification with suspension mass cytometry to characterize molecular reprogramming during the epithelial-to-mesenchymal transition as well as the mesenchymal-to-epithelial transition. We show the capability of ACE to quantify the dynamics of signaling network responses in human T lymphocytes. We further present the application of ACE in imaging mass cytometry-based multiparametric tissue imaging to identify tissue compartments and profile spatial aspects related to pathological states in polycystic kidney tissues.

Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

Unravelling the Mysteries of the Sonic Hedgehog Pathway in Cancer Stem Cells: Activity, Crosstalk and Regulation

The Sonic Hedgehog (Shh) signalling pathway plays a critical role in normal development and tissue homeostasis, guiding cell differentiation, proliferation, and survival. Aberrant activation of this pathway, however, has been implicated in the pathogenesis of various cancers, largely due to its role in regulating cancer stem cells (CSCs). CSCs are a subpopulation of cancer cells with the ability to self-renew, differentiate, and initiate tumour growth, contributing significantly to tumorigenesis, recurrence, and resistance to therapy. This review focuses on the intricate activity of the Shh pathway within the context of CSCs, detailing the molecular mechanisms through which Shh signalling influences CSC properties, including self-renewal, differentiation, and survival. It further explores the regulatory crosstalk between the Shh pathway and other signalling pathways in CSCs, highlighting the complexity of this regulatory network. Here, we delve into the upstream regulators and downstream effectors that modulate Shh pathway activity in CSCs. This review aims to cast a specific focus on the role of the Shh pathway in CSCs, provide a detailed exploration of molecular mechanisms and regulatory crosstalk, and discuss current and developing inhibitors. By summarising key findings and insights gained, we wish to emphasise the importance of further elucidating the interplay between the Shh pathway and CSCs to develop more effective cancer therapies.

The actin cytoskeleton: Morphological changes in pre- and fully developed lung cancer

Actin, a primary component of the cell cytoskeleton can have multiple isoforms, each of which can have specific properties uniquely suited for their purpose. These monomers are then bound together to form polymeric filaments utilizing adenosine triphosphate hydrolysis as a source of energy. Proteins, such as Arp2/3, VASP, formin, profilin, and cofilin, serve important roles in the polymerization process. These filaments can further be linked to form stress fibers by proteins called actin-binding proteins, such as α-actinin, myosin, fascin, filamin, zyxin, and epsin. These stress fibers are responsible for mechanotransduction, maintaining cell shape, cell motility, and intracellular cargo transport. Cancer metastasis, specifically epithelial mesenchymal transition (EMT), which is one of the key steps of the process, is accompanied by the formation of thick stress fibers through the Rho-associated protein kinase, MAPK/ERK, and Wnt pathways. Recently, with the advent of "field cancerization," pre-malignant cells have also been demonstrated to possess stress fibers and related cytoskeletal features. Analytical methods ranging from western blot and RNA-sequencing to cryo-EM and fluorescent imaging have been employed to understand the structure and dynamics of actin and related proteins including polymerization/depolymerization. More recent methods involve quantifying properties of the actin cytoskeleton from fluorescent images and utilizing them to study biological processes, such as EMT. These image analysis approaches exploit the fact that filaments have a unique structure (curvilinear) compared to the noise or other artifacts to separate them. Line segments are extracted from these filament images that have assigned lengths and orientations. Coupling such methods with statistical analysis has resulted in development of a new reporter for EMT in lung cancer cells as well as their drug responses.

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Statistical parametrization of cell cytoskeleton reveals lung cancer cytoskeletal phenotype with partial EMT signature

Epithelial–mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, developing and using an image quantification tool, Statistical Parametrization of Cell Cytoskeleton (SPOCC), we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify their cytoskeletal response to drugs. SPOCC has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and by inference invasiveness) of the intermediate EMT phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for future treatment strategies that combat metastasis by specifically targeting the fiber alignment process.

A computational method for automated quantification of actin stress fiber alignment in fluorescence images of cultured cells is presented, used to detect changes in stress fiber organization during EMT, with pathways regulating actin dynamics manipulated leading to the discovery of a cytoskeletal phenotype.

Cancer progression as a learning process

Drug resistance and metastasis-the major complications in cancer-both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.

Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition

Intracellular signaling pathways are at the core of cellular information processing. The states of these pathways and their inputs determine signaling dynamics and drive cell function. Within a cancerous tumor, many combinations of cell states and microenvironments can lead to dramatic variations in responses to treatment. Network rewiring has been thought to underlie these context-dependent differences in signaling; however, from a biochemical standpoint, rewiring of signaling networks should not be a prerequisite for heterogeneity in responses to stimuli. Here we address this conundrum by analyzing an in vitro model of the epithelial mesenchymal transition (EMT), a biological program implicated in increased tumor invasiveness, heterogeneity, and drug resistance. We used mass cytometry to measure EGF signaling dynamics in the ERK and AKT signaling pathways before and after induction of EMT in Py2T murine breast cancer cells. Analysis of the data with standard network inference methods suggested EMT-dependent network rewiring. In contrast, use of a modeling approach that adequately accounts for single-cell variation demonstrated that a single reaction-based pathway model with constant structure and near-constant parameters is sufficient to represent differences in EGF signaling across EMT. This result indicates that rewiring of the signaling network is not necessary for heterogeneous responses to a signal and that unifying reaction-based models should be employed for characterization of signaling in heterogeneous environments, such as cancer.

Cancer Stem Cells and Epithelial-to-Mesenchymal Transition in Cancer Metastasis

The cancer stem cell (CSC) concept stands for undifferentiated tumor cells with the ability to initiate heterogeneous tumors. It is also relevant in metastasis and can explain how metastatic tumors mirror the heterogeneity of primary tumors. Cellular plasticity, including the epithelial-to-mesenchymal transition (EMT), enables the generation of CSCs at different steps of the metastatic process including metastatic colonization. In this review, we update the concept of CSCs and provide evidence of the existence of metastatic stem cells (MetSCs). In addition, we highlight the nuanced understanding of EMT that has been gained recently and the association of mesenchymal-to-epithelial transition (MET) with the acquisition of CSCs properties during metastasis. We also comment on the computational approaches that have profoundly influenced our understanding of CSCs and EMT; and how these studies and new experimental technologies can yield a deeper understanding of the biological aspects of metastasis.

Profiling Cell Signaling Networks at Single-cell Resolution

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

Fabrication of Hybrid Silver Microstructures from Vermiculite Templates as SERS Substrates

There is great interest in developing complex, 3D plasmonic materials with unusual structural properties. This can be achieved via template-assisted approaches exploiting scaffold elements to engineer unique plasmonic substrates, which would be otherwise impossible to synthesize. Herein, we present a novel, simple, and low-cost template-assisted method for producing interconnected 3-D silver microstructures by utilizing vermiculite, a well-known silicate, as both in-situ reductant and template for silver growth. The silicate network of the vermiculite can be easily removed by dissolution with hydrofluoric acid, which, simultaneously, leads to the formation of a magnesium fluoride skeleton supporting a plasmonically active silver film. Optical, morphological, and chemical properties of the materials were extensively investigated, revealing, for example, that hybrid silver microstructures can be exploited as valuable SERS substrates over a broad spectral range of excitation wavelengths.

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.

Intermediate transitions between epithelial and mesenchymal states are associated with tumor progression. Here using mass cytometry, Plevritis and colleagues develop a computational framework to resolve and map these trajectories in lung cancer cells and clinical specimens.