Computational analysis of signaling patterns in single cells

NF-kB in Signaling Patterns and Its Temporal Dynamics Encode/Decode Human Diseases

Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells' survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives.

microRNA-Mediated Encoding and Decoding of Time-Dependent Signals in Tumorigenesis

microRNAs, pivotal post-transcriptional regulators of gene expression, in the past decades have caught the attention of researchers for their involvement in different biological processes, ranging from cell development to cancer. Although lots of effort has been devoted to elucidate the topological features and the equilibrium properties of microRNA-mediated motifs, little is known about how the information encoded in frequency, amplitude, duration, and other features of their regulatory signals can affect the resulting gene expression patterns. Here, we review the current knowledge about microRNA-mediated gene regulatory networks characterized by time-dependent input signals, such as pulses, transient inputs, and oscillations. First, we identify the general characteristic of the main motifs underlying temporal patterns. Then, we analyze their impact on two commonly studied oncogenic networks, showing how their dysfunction can lead to tumorigenesis.

Inferring the structures of signaling motifs from paired dynamic traces of single cells

Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell's upstream signal was randomly paired with another cell's downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells.

Isolation of Highly Purified and Viable Retinal Endothelial Cells

The neonatal mouse retinal vascularization model has been widely used in the vascular biology field to investigate mechanisms of angiogenesis and arterial-venous fate specification during blood vessel formation and maturation. Recent advances in next-generation sequencing can further elucidate mechanisms of blood vessel formation and remodeling in this, as well as other, vascular development models. However, an optimized method for isolating retinal endothelial cells that limits tissue digestion-induced cell damage is required for next-generation sequencing applications. In this study, we established a method for isolating neonatal retinal endothelial cells that optimizes cell viability and purity. The CD31+/CD45- endothelial cell population was fluorescence-activated cell sorting (FACS)-isolated from digested postnatal retinas, found to be highly enriched for endothelial cell gene expression, and exhibited no change in viability for 60 min post-FACS. Thus, this method for retinal endothelial cell isolation is compatible with next-generation sequencing applications. Combining this isolation method with next-generation sequencing will enable further delineation of mechanisms underlying vascular development and maturation.

Accurate delineation of cell cycle phase transitions in living cells with PIP-FUCCI

Cell cycle phase transitions are tightly orchestrated to ensure efficient cell cycle progression and genome stability. Interrogating these transitions is important for understanding both normal and pathological cell proliferation. By quantifying the dynamics of the popular FUCCI reporters relative to the transitions into and out of S phase, we found that their dynamics are substantially and variably offset from true S phase boundaries. To enhance detection of phase transitions, we generated a new reporter whose oscillations are directly coupled to DNA replication and combined it with the FUCCI APC/C reporter to create "PIP-FUCCI". The PIP degron fusion protein precisely marks the G1/S and S/G2 transitions; shows a rapid decrease in signal in response to large doses of DNA damage only during G1; and distinguishes cell type-specific and DNA damage source-dependent arrest phenotypes. We provide guidance to investigators in selecting appropriate fluorescent cell cycle reporters and new analysis strategies for delineating cell cycle transitions.

The Cell Cycle Browser: An Interactive Tool for Visualizing, Simulating, and Perturbing Cell-Cycle Progression

The cell cycle is driven by precise temporal coordination among many molecular activities. To understand and explore this process, we developed the Cell Cycle Browser (CCB), an interactive web interface based on real-time reporter data collected in proliferating human cells. This tool facilitates visualizing, organizing, simulating, and predicting the outcomes of perturbing cell-cycle parameters. Time-series traces from individual cells can be combined to build a multi-layered timeline of molecular activities. Users can simulate the cell cycle using computational models that capture the dynamics of molecular activities and phase transitions. By adjusting individual expression levels and strengths of molecular relationships, users can predict effects on the cell cycle. Virtual assays, such as growth curves and flow cytometry, provide familiar outputs to compare cell-cycle behaviors for data and simulations. The CCB serves to unify our understanding of cell-cycle dynamics and provides a platform for generating hypotheses through virtual experiments.

Integrated Platform for Monitoring Single-cell MAPK Kinetics in Computer-controlled Temporal Stimulations

Extracellular response kinase (ERK) is one of the key regulator of cell fate, such as proliferation, differentiation and cell migration. Here, we propose a novel experimental pipeline to learn ERK kinetics by temporal growth factor (GF) stimulation. High signal-to-noise ratio of genetically encoded Fluorescence resonance energy transfer (FRET) biosensor enables to get a large number of single-cell ERK activity at each time point, while computer-controlled microfluidics fine-tune the temporal stimulation. Using this platform, we observed that static Epidermal growth factor (EGF) stimulation led to transient ERK activation with a significant cell-to-cell variation, while dynamic stimulation of 3′ EGF pulse led to faster adaptation kinetics with no discrepancy. Multiple EGF pulses retriggered ERK activity with respect to frequency of stimulation. We also observed oscillation of ERK activity of each cell at basal state. Introducing of Mitogen-activated protein kinase kinase (MEK) inhibitor, U0126, was not only dropping the average of basal activity for 7.5%, but also diminishing oscillatory behavior. Activity level raised up when inhibitor was removed, followed by transient peak of ERK kinetics. We expect this platform to probe Mitogen-associated protein kinase (MAPK) signaling network for systems biology research at single cellular level.

A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging

Fluorescent tools have revolutionized our ability to probe biological dynamics, particularly at the cellular level. Fluorescent sensors have been developed on several platforms, utilizing either small-molecule dyes or fluorescent proteins, to monitor proteins, RNA, DNA, small molecules, and even cellular properties, such as pH and membrane potential. We briefly summarize the impressive history of tool development for these various applications and then discuss the most recent noteworthy developments in more detail. Particular emphasis is placed on tools suitable for single-cell analysis and especially live-cell imaging applications. Finally, we discuss prominent areas of need in future fluorescent tool development-specifically, advancing our capability to analyze and integrate the plethora of high-content data generated by fluorescence imaging.

Processing Oscillatory Signals by Incoherent Feedforward Loops

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose.

From circadian clocks to ultradian rhythms, oscillatory signals are found ubiquitously in nature. These oscillations are crucial in the regulation of cellular processes. While the fundamental design principles underlying the generation of these oscillations are extensively studied, the mechanisms for decoding these signals are underappreciated. With implications in both the basic understanding of how cells process temporal signals and the design of synthetic systems, we use quantitative modeling to probe one mechanism, the counting of pulses. We demonstrate the capability of an Incoherent Feedforward Loop motif for the differentiation between sustained and oscillatory input signals.

Single-cell approaches for molecular classification of endocrine tumors

PURPOSE OF REVIEW

In this review, we summarize recent developments in single-cell technologies that can be employed for the functional and molecular classification of endocrine cells in normal and neoplastic tissue.

RECENT FINDINGS

The emergence of new platforms for the isolation, analysis, and dynamic assessment of individual cell identity and reactive behavior enables experimental deconstruction of intratumoral heterogeneity and other contexts where variability in cell signaling and biochemical responsiveness inform biological function and clinical presentation. These tools are particularly appropriate for examining and classifying endocrine neoplasias, as the clinical sequelae of these tumors are often driven by disrupted hormonal responsiveness secondary to compromised cell signaling. Single-cell methods allow for multidimensional experimental designs incorporating both spatial and temporal parameters with the capacity to probe dynamic cell signaling behaviors and kinetic response patterns dependent upon sequential agonist challenge.

SUMMARY

Intratumoral heterogeneity in the provenance, composition, and biological activity of different forms of endocrine neoplasia presents a significant challenge for prognostic assessment. Single-cell technologies provide an array of powerful new approaches uniquely well suited for dissecting complex endocrine tumors. Studies examining the relationship between clinical behavior and tumor compositional variations in cellular activity are now possible, providing new opportunities to deconstruct the underlying mechanisms of endocrine neoplasia.

Positive Feedback Loops Between Inflammatory, Bone and Cancer Cells During Metastatic Niche Construction

Bone, which includes several cell populations and numerous cytokines and chemokines that provide cell-cell signaling, is a common destination for many cancer metastases. Bone metastasis skews this signaling to develop vicious cycles between immune, bone and cancer populations that lead to abnormal bone remodeling during cancer niche construction. Temporal models utilize positive feedback systems as an integrative tool providing insights into the rate-limiting processes that determine multiple stages of the bone metastasis. We develop a logical-transient-threshold framework by linking temporal responses of the cancer, bone and immune systems through macrophages during ecological niche construction of cancer in host bone.

Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features

Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.

Single cell studies have shown that differential patterns in the dynamics of signaling proteins, transcription factor activity, gene expression, etc. produce distinct downstream outcomes. The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules. Signaling pathways, therefore, serve as signal processing units to inform specific downstream regulation. However, the functional capabilities of the dynamic aspects of signaling are not well understood. To address this issue, we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity. Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations. Moreover, our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses. Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling.

Evodiamine inhibits invasion and metastasis of human hepatocellular carcinoma HepG2 cells

AIM: To assess the effect of evodiamine on the invasion and metastasis of human hepatocellular carcinoma HepG2 cells and to discuss the underlying mechanism.

METHODS: The effect of evodiamine on the expression levels of ERK1/2 and p-ERK1/2 in HepG2 cells was assessed by Western blot. The invasion and metastasis of HepG2 cells were tested by transwell assays. Colony-forming ability was determined by colony formation assay.

RESULTS: After HepG2 cells were treated with 0, 5, 25, or 50 μmol/L evodiamine, the numbers of invasion cells were 97.6 ± 6.3, 32.3 ± 1.3, 22.3 ± 2.5, and 10.6 ± 3.7, respectively, and the numbers of metastatic cells were 106.3 ± 3.2, 57.7 ± 3.2, 26.8 ± 1.6, and 15.4 ± 3.9, respectively (P < 0.05). Evodiamine had a significant inhibitory effect on HepG2 cell colony-forming ability (P < 0.05). Evodiamine treatment down-regulated the expression of p-ERK1/2, but had no significant impact on ERK1/2 expression.

CONCLUSION: Evodiamine can inhibit the invasion, metastasis and colony-forming ability of human hepatocellular carcinoma HepG2 cells, and the possible mechanism may be related to down-regulation of p-ERK 1/2 protein.