Information-theoretic analysis of multivariate single-cell signaling responses

Information Processing in Biochemical Networks

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.

Macrophage memory emerges from coordinated transcription factor and chromatin dynamics

Cells of the immune system operate in dynamic microenvironments where the timing, concentration, and order of signaling molecules constantly change. Despite this complexity, immune cells manage to communicate accurately and control inflammation and infection. It is unclear how these dynamic signals are encoded and decoded and if individual cells retain the memory of past exposure to inflammatory molecules. Here, we use live-cell analysis, ATAC sequencing, and an in vivo model of sepsis to show that sequential inflammatory signals induce memory in individual macrophages through reprogramming the nuclear factor κB (NF-κB) network and the chromatin accessibility landscape. We use transcriptomic profiling and deep learning to show that transcription factor and chromatin dynamics coordinate fine-tuned macrophage responses to new inflammatory signals. This work demonstrates how macrophages retain the memory of previous signals despite single-cell variability and elucidates the mechanisms of signal-induced memory in dynamic inflammatory conditions like sepsis.

Oncogenic PIK3CA corrupts growth factor signaling specificity

Technical limitations have prevented understanding of how growth factor signals are encoded in distinct activity patterns of the phosphoinositide 3-kinase (PI3K)/AKT pathway, and how this is altered by oncogenic pathway mutations. We introduce a kinetic, single-cell framework for precise calculations of PI3K-specific information transfer for different growth factors. This features live-cell imaging of PI3K/AKT activity reporters and multiplexed CyTOF measurements of PI3K/AKT and RAS/ERK signaling markers over time. Using this framework, we found that the PIK3CAH1047R oncogene was not a simple, constitutive activator of the pathway as often presented. Dose-dependent expression of PIK3CAH1047R in human cervical cancer and induced pluripotent stem cells corrupted the fidelity of growth factor-induced information transfer, with preferential amplification of epidermal growth factor receptor (EGFR) signaling responses compared to insulin-like growth factor 1 (IGF1) and insulin receptor signaling. PIK3CAH1047R did not only shift these responses to a higher mean but also enhanced signaling heterogeneity. We conclude that oncogenic PIK3CAH1047R corrupts information transfer in a growth factor-dependent manner and suggest new opportunities for tuning of receptor-specific PI3K pathway outputs for therapeutic benefit.

Dynamics of positional information in the vertebrate neural tube

In developing embryos, cells acquire distinct identities depending on their position in a tissue. Secreted signalling molecules, known as morphogens, act as long-range cues to provide the spatial information that controls these cell fate decisions. In several tissues, both the level and the duration of morphogen signalling appear to be important for determining cell fates. This is the case in the forming vertebrate nervous system where antiparallel morphogen gradients pattern the dorsal-ventral axis by partitioning the tissue into sharply delineated domains of molecularly distinct neural progenitors. How information in the gradients is decoded to generate precisely positioned boundaries of gene expression remains an open question. Here, we adopt tools from information theory to quantify the positional information in the neural tube and investigate how temporal changes in signalling could influence positional precision. The results reveal that the use of signalling dynamics, as well as the signalling level, substantially increases the precision possible for the estimation of position from morphogen gradients. This analysis links the dynamics of opposing morphogen gradients with precise pattern formation and provides an explanation for why time is used to impart positional information.

Single-cell stimulus-response gene expression trajectories reveal the stimulus specificities of dynamic responses by single macrophages

Macrophages induce the expression of hundreds of genes in response to immune threats. However, current technology limits our ability to capture single-cell inducible gene expression dynamics. Here, we generated high-resolution time series single-cell RNA sequencing (scRNA-seq) data from mouse macrophages responding to six stimuli, and imputed ensembles of real-time single-cell gene expression trajectories (scGETs). We found that dynamic information contained in scGETs substantially contributes to macrophage stimulus-response specificity (SRS). Dynamic information also identified correlations between immune response genes, indicating biological coordination. Furthermore, we showed that the microenvironmental context of polarizing cytokines profoundly affects scGETs, such that measuring response dynamics offered clearer discrimination of the polarization state of individual macrophage cells than single time-point measurements. Our findings highlight the important contribution of dynamic information contained in the single-cell expression responses of immune genes in characterizing the SRS and functional states of macrophages.

Information-Theoretic Analysis of a Model of CAR-4-1BB-Mediated NFκB Activation

Systems biology utilizes computational approaches to examine an array of biological processes, such as cell signaling, metabolomics and pharmacology. This includes mathematical modeling of CAR T cells, a modality of cancer therapy by which genetically engineered immune cells recognize and combat a cancerous target. While successful against hematologic malignancies, CAR T cells have shown limited success against other cancer types. Thus, more research is needed to understand their mechanisms of action and leverage their full potential. In our work, we set out to apply information theory on a mathematical model of NFκB signaling initiated by the CAR following antigen encounter. First, we estimated channel capacity for CAR-4-1BB-mediated NFκB signal transduction. Next, we evaluated the pathway's ability to distinguish contrasting "low" and "high" antigen concentration levels, depending on the amount of variability in protein concentrations. Finally, we assessed the fidelity by which NFκB activation reflects the encountered antigen concentration, depending on the prevalence of antigen-positive targets in tumor population. We found that in most scenarios, fold change in the nuclear concentration of NFκB carries a higher channel capacity for the pathway than NFκB's absolute response. Additionally, we found that most errors in transducing the antigen signal through the pathway skew towards underestimating the concentration of encountered antigen. Finally, we found that disabling IKKβ deactivation could increase signaling fidelity against targets with antigen-negative cells. Our information-theoretic analysis of signal transduction can provide novel perspectives on biological signaling, as well as enable a more informed path to cell engineering.Kindly check and confirm whether the corresponding affiliation is correctly identified.this is correct.

A Role for Bottom-Up Synthetic Cells in the Internet of Bio-Nano Things?

The potential role of bottom-up Synthetic Cells (SCs) in the Internet of Bio-Nano Things (IoBNT) is discussed. In particular, this perspective paper focuses on the growing interest in networks of biological and/or artificial objects at the micro- and nanoscale (cells and subcellular parts, microelectrodes, microvessels, etc.), whereby communication takes place in an unconventional manner, i.e., via chemical signaling. The resulting "molecular communication" (MC) scenario paves the way to the development of innovative technologies that have the potential to impact biotechnology, nanomedicine, and related fields. The scenario that relies on the interconnection of natural and artificial entities is briefly introduced, highlighting how Synthetic Biology (SB) plays a central role. SB allows the construction of various types of SCs that can be designed, tailored, and programmed according to specific predefined requirements. In particular, "bottom-up" SCs are briefly described by commenting on the principles of their design and fabrication and their features (in particular, the capacity to exchange chemicals with other SCs or with natural biological cells). Although bottom-up SCs still have low complexity and thus basic functionalities, here, we introduce their potential role in the IoBNT. This perspective paper aims to stimulate interest in and discussion on the presented topics. The article also includes commentaries on MC, semantic information, minimal cognition, wetware neuromorphic engineering, and chemical social robotics, with the specific potential they can bring to the IoBNT.

Information-theoretic analysis of a model of CAR-4-1BB-mediated NFκB activation

Systems biology utilizes computational approaches to examine an array of biological processes, such as cell signaling, metabolomics and pharmacology. This includes mathematical modeling of CAR T cells, a modality of cancer therapy by which genetically engineered immune cells recognize and combat a cancerous target. While successful against hematologic malignancies, CAR T cells have shown limited success against other cancer types. Thus, more research is needed to understand their mechanisms of action and leverage their full potential. In our work, we set out to apply information theory on a mathematical model of cell signaling of CAR-mediated activation following antigen encounter. First, we estimated channel capacity for CAR-4-1BB-mediated NFκB signal transduction. Next, we evaluated the pathway's ability to distinguish contrasting "low" and "high" antigen concentration levels, depending on the amount of intrinsic noise. Finally, we assessed the fidelity by which NFκB activation reflects the encountered antigen concentration, depending on the prevalence of antigen-positive targets in tumor population. We found that in most scenarios, fold change in the nuclear concentration of NFκB carries a higher channel capacity for the pathway than NFκB's absolute response. Additionally, we found that most errors in transducing the antigen signal through the pathway skew towards underestimating the concentration of encountered antigen. Finally, we found that disabling IKKβ deactivation could increase signaling fidelity against targets with antigen-negative cells. Our information-theoretic analysis of signal transduction can provide novel perspectives on biological signaling, as well as enable a more informed path to cell engineering.

Proofreading does not result in more reliable ligand discrimination in receptor signaling due to its inherent stochasticity

Kinetic proofreading (KPR) has been used as a paradigmatic explanation for the high specificity of ligand discrimination by cellular receptors. KPR enhances the difference in the mean receptor occupancy between different ligands compared to a nonproofread receptor, thus potentially enabling better discrimination. On the other hand, proofreading also attenuates the signal and introduces additional stochastic receptor transitions relative to a nonproofreading receptor. This increases the relative magnitude of noise in the downstream signal, which can interfere with reliable ligand discrimination. To understand the effect of noise on ligand discrimination beyond the comparison of the mean signals, we formulate the task of ligand discrimination as a problem of statistical estimation of the receptor affinity of ligands based on the molecular signaling output. Our analysis reveals that proofreading typically worsens ligand resolution compared to a nonproofread receptor. Furthermore, the resolution decreases further with more proofreading steps under most commonly biologically considered conditions. This contrasts with the usual notion that KPR universally improves ligand discrimination with additional proofreading steps. Our results are consistent across a variety of different proofreading schemes and metrics of performance, suggesting that they are inherent to the KPR mechanism itself rather than any particular model of molecular noise. Based on our results, we suggest alternative roles for KPR schemes such as multiplexing and combinatorial encoding in multi-ligand/multi-output pathways.

Quantifying stimulus-response specificity to probe the functional state of macrophages

Immune sentinel macrophages initiate responses to pathogens via hundreds of immune response genes. Each immune threat demands a tailored response, suggesting that the capacity for stimulus-specific gene expression is a key functional hallmark of healthy macrophages. To quantify this property, termed "stimulus-response specificity" (SRS), we developed a single-cell experimental workflow and analytical approaches based on information theory and machine learning. We found that the response specificity of macrophages is driven by combinations of specific immune genes that show low cell-to-cell heterogeneity and are targets of separate signaling pathways. The "response specificity profile," a systematic comparison of multiple stimulus-response distributions, was distinctly altered by polarizing cytokines, and it enabled an assessment of the functional state of macrophages. Indeed, the response specificity profile of peritoneal macrophages from old and obese mice showed characteristic differences, suggesting that SRS may be a basis for measuring the functional state of innate immune cells. A record of this paper's transparent peer review process is included in the supplemental information.

Single-cell measurement quality in bits

Single-cell measurements have revolutionized our understanding of heterogeneity in cellular response. However, there is no universally comparable way to assess single-cell measurement quality. Here, we show how information theory can be used to assess and compare single-cell measurement quality in bits, which provides a universally comparable metric for information content. We anticipate that the experimental and theoretical approaches we show here will generally enable comparisons of quality between any single-cell measurement methods.

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Introduction to Genomic Network Reconstruction for Cancer Research

High-throughput genomic technologies have revolutionized the study of cancer. Current research in oncology is now limited more for the capacity of analyzing and interpreting data, rather than the availability of data itself. Integrative approaches to obtain functional information from data are at the core of the disciplines gathered under the systems biology banner. In this context, network models have been used to study cancer, from the identification of key molecules involved in the disease to the discovery of functional alterations associated with specific manifestations of the disease.In this chapter, we describe the state of the art of network reconstruction from genomic data, with an emphasis in gene expression experiments. We explore the strengths and limitations of correlation, Bayesian, and information theoretic approaches to network reconstruction. We present tools that leverage the flexibility of network science to gain a deeper understanding of cancer biology.

Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression

Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly understood. Here, we devise networked optogenetic pathways that achieve dynamic signal processing functions that recapitulate cellular information processing. Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling edge pulse detector and show that this circuit can be employed to demultiplex dynamically encoded signals. We combine this demultiplexer with dCas9-based gene networks to construct pulsatile signal filters and decoders. Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state. Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway. Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.

Representing Stimulus Information in an Energy Metabolism Pathway

We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.

Phenotypic variability, not noise, accounts for most of the cell-to-cell heterogeneity in IFN-γ and oncostatin M signaling responses

Cellular signaling responses show substantial cell-to-cell heterogeneity, which is often ascribed to the inherent randomness of biochemical reactions, termed molecular noise, wherein high noise implies low signaling fidelity. Alternatively, heterogeneity could arise from differences in molecular content between cells, termed molecular phenotypic variability, which does not necessarily imply imprecise signaling. The contribution of these two processes to signaling heterogeneity is unclear. Here, we fused fibroblasts to produce binuclear syncytia to distinguish noise from phenotypic variability in the analysis of cytokine signaling. We reasoned that the responses of the two nuclei within one syncytium could approximate the signaling outcomes of two cells with the same molecular content, thereby disclosing noise contribution, whereas comparison of different syncytia should reveal contribution of phenotypic variability. We found that ~90% of the variance in the primary response (which was the abundance of phosphorylated, nuclear STAT) to stimulation with the cytokines interferon-γ and oncostatin M resulted from differences in the molecular content of individual cells. Thus, our data reveal that cytokine signaling in the system used here operates in a reproducible, high-fidelity manner.

Mapping the dynamic transfer functions of eukaryotic gene regulation

Biological information can be encoded within the dynamics of signaling components, which has been implicated in a broad range of physiological processes including stress response, oncogenesis, and stem cell differentiation. To study the complexity of information transfer across the eukaryotic promoter, we screened 119 dynamic conditions-modulating the pulse frequency, amplitude, and pulse width of light-regulating the binding of an epigenome editor to a fluorescent reporter. This system revealed tunable gene expression and filtering behaviors and provided a quantification of the limit to the amount of information that can be reliably transferred across a single promoter as ∼1.7 bits. Using a library of over 100 orthogonal chromatin regulators, we further determined that chromatin state could be used to tune mutual information and expression levels, as well as completely alter the input-output transfer function of the promoter. This system unlocks the information-rich content of eukaryotic gene regulation.

Multiplexed Single-Cell Plasmonic Immunoassay of Intracellular Signaling Proteins Enables Non-Destructive Monitoring of Cell Fate

It is of significant importance in cancer biology to identify signaling pathways that play key roles in cell fate determination. Dissecting cellular signaling pathways requires the measurement of a large number of signaling proteins. However, tools for simultaneously monitoring multiple signaling pathway components in single living cells remain limited at present. Herein, we describe an approach, termed multiplexed single-cell plasmonic immunosandwich assay (mxscPISA), for simultaneous detection of multiple signaling proteins in individual living cells. This approach enabled simultaneous non-destructive monitoring of multiple (up to five, currently the highest multiplexing capacity in living cells) cytoplasmic and nucleus signaling proteins in individual cells with ultrahigh detection sensitivity. As a proof of principle, the epidermal growth factor receptor (EGFR) pathway, which plays a central role in cell fate determination, was investigated using this approach in this study. We found that there were differential attenuation rate of pro-survival and accumulation rate of pro-death signaling protein of the EGFR pathway in response to EGFR inactivation. These findings implicate that, after EGFR inactivation, a transient imbalance between survival and apoptotic signaling outputs contributed to the final cell fate of death. The mxscPISA approach can be a promising tool to reveal a signaling dynamic pattern at the single-cell level and to identify key components of signaling pathways that contribute to the final cell fate using only a limited number of cells.

Determinants of Ligand Specificity and Functional Plasticity in Type I Interferon Signaling

The Type I Interferon family of cytokines all act through the same cell surface receptor and induce phosphorylation of the same subset of response regulators of the STAT family. Despite their shared receptor, different Type I Interferons have different functions during immune response to infection. In particular, they differ in the potency of their induced anti-viral and anti-proliferative responses in target cells. It remains not fully understood how these functional differences can arise in a ligand-specific manner both at the level of STAT phosphorylation and the downstream function. We use a minimal computational model of Type I Interferon signaling, focusing on Interferon-α and Interferon-β. We validate the model with quantitative experimental data to identify the key determinants of specificity and functional plasticity in Type I Interferon signaling. We investigate different mechanisms of signal discrimination, and how multiple system components such as binding affinity, receptor expression levels and their variability, receptor internalization, short-term negative feedback by SOCS1 protein, and differential receptor expression play together to ensure ligand specificity on the level of STAT phosphorylation. Based on these results, we propose phenomenological functional mappings from STAT activation to downstream anti-viral and anti-proliferative activity to investigate differential signal processing steps downstream of STAT phosphorylation. We find that the negative feedback by the protein USP18, which enhances differences in signaling between Interferons via ligand-dependent refractoriness, can give rise to functional plasticity in Interferon-α and Interferon-β signaling, and explore other factors that control functional plasticity. Beyond Type I Interferon signaling, our results have a broad applicability to questions of signaling specificity and functional plasticity in signaling systems with multiple ligands acting through a bottleneck of a small number of shared receptors.

The tyrosine phosphatase SHP2 increases robustness and information transfer within IL-6-induced JAK/STAT signalling

Background

Cell-to-cell heterogeneity is an inherent feature of multicellular organisms and is central in all physiological and pathophysiological processes including cellular signal transduction. The cytokine IL-6 is an essential mediator of pro- and anti-inflammatory processes. Dysregulated IL-6-induced intracellular JAK/STAT signalling is associated with severe inflammatory and proliferative diseases. Under physiological conditions JAK/STAT signalling is rigorously controlled and timely orchestrated by regulatory mechanisms such as expression of the feedback-inhibitor SOCS3 and activation of the protein-tyrosine phosphatase SHP2 (PTPN11). Interestingly, the function of negative regulators seems not to be restricted to controlling the strength and timely orchestration of IL-6-induced STAT3 activation. Exemplarily, SOCS3 increases robustness of late IL-6-induced STAT3 activation against heterogenous STAT3 expression and reduces the amount of information transferred through JAK/STAT signalling.

Methods

Here we use multiplexed single-cell analyses and information theoretic approaches to clarify whether also SHP2 contributes to robustness of STAT3 activation and whether SHP2 affects the amount of information transferred through IL-6-induced JAK/STAT signalling.

Results

SHP2 increases robustness of both basal, cytokine-independent STAT3 activation and early IL-6-induced STAT3 activation against differential STAT3 expression. However, SHP2 does not affect robustness of late IL-6-induced STAT3 activation. In contrast to SOCS3, SHP2 increases the amount of information transferred through IL-6-induced JAK/STAT signalling, probably by reducing cytokine-independent STAT3 activation and thereby increasing sensitivity of the cells. These effects are independent of SHP2-dependent MAPK activation.

Conclusion

In summary, the results of this study extend our knowledge of the functions of SHP2 in IL-6-induced JAK/STAT signalling. SHP2 is not only a repressor of basal and cytokine-induced STAT3 activity, but also ensures robustness and transmission of information.

Plain English summary Cells within a multicellular organism communicate with each other to exchange information about the environment. Communication between cells is facilitated by soluble molecules that transmit information from one cell to the other. Cytokines such as interleukin-6 are important soluble mediators that are secreted when an organism is faced with infections or inflammation. Secreted cytokines bind to receptors within the membrane of their target cells. This binding induces activation of an intracellular cascade of reactions called signal transduction, which leads to cellular responses. An important example of intracellular signal transduction is JAK/STAT signalling. In healthy organisms signalling is controlled and timed by regulatory mechanisms, whose activation results in a controlled shutdown of signalling pathways. Interestingly, not all cells within an organism are identical. They differ in the amount of proteins involved in signal transduction, such as STAT3. These differences shape cellular communication and responses to intracellular signalling. Here, we show that an important negative regulatory protein called SHP2 (or PTPN11) is not only responsible for shutting down signalling, but also for steering signalling in heterogeneous cell populations. SHP2 increases robustness of STAT3 activation against variable STAT3 amounts in individual cells. Additionally, it increases the amount of information transferred through JAK/STAT signalling by increasing the dynamic range of pathway activation in heterogeneous cell populations. This is an amazing new function of negative regulatory proteins that contributes to communication in heterogeneous multicellular organisms in health and disease.

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose–response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Computational methods for characterizing and learning from heterogeneous cell signaling data

Heterogeneity in cell signaling pathways is increasingly appreciated as a fundamental feature of cell biology and a driver of clinically relevant disease phenotypes. Understanding the causes of heterogeneity, the cellular mechanisms used to control heterogeneity, and the downstream effects of heterogeneity in single cells are all key obstacles for manipulating cellular populations and treating disease. Recent advances in genetic engineering, including multiplexed fluorescent reporters, have provided unprecedented measurements of signaling heterogeneity, but these vast data sets are often difficult to interpret, necessitating the use of computational techniques to extract meaning from the data. Here, we review recent advances in computational methods for extracting meaning from these novel data streams. In particular, we evaluate how machine learning methods related to dimensionality reduction and classification can identify structure in complex, dynamic datasets, simplifying interpretation. We also discuss how mechanistic models can be merged with heterogeneous data to understand the underlying differences between cells in a population. These methods are still being developed, but the work reviewed here offers useful applications of specific analysis techniques that could enable the translation of single-cell signaling data to actionable biological understanding.

Six distinct NFκB signaling codons convey discrete information to distinguish stimuli and enable appropriate macrophage responses

Macrophages initiate inflammatory responses via the transcription factor NFκB. The temporal pattern of NFκB activity determines which genes are expressed and thus, the type of response that ensues. Here, we examined how information about the stimulus is encoded in the dynamics of NFκB activity. We generated an mVenus-RelA reporter mouse line to enable high-throughput live-cell analysis of primary macrophages responding to host- and pathogen-derived stimuli. An information-theoretic workflow identified six dynamical features-termed signaling codons-that convey stimulus information to the nucleus. In particular, oscillatory trajectories were a hallmark of responses to cytokine but not pathogen-derived stimuli. Single-cell imaging and RNA sequencing of macrophages from a mouse model of Sjögren's syndrome revealed inappropriate responses to stimuli, suggestive of confusion of two NFκB signaling codons. Thus, the dynamics of NFκB signaling classify immune threats through six signaling codons, and signal confusion based on defective codon deployment may underlie the etiology of some inflammatory diseases.

Concepts and Applications of Information Theory to Immuno-Oncology

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

Quantifying information accumulation encoded in the dynamics of biochemical signaling

Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements, and enables understanding how temporal regulatory codes transmit information over time.

Sensitivity minimization, biological homeostasis and information theory

All organisms must be able to adapt to changes in the environment. To this end, they have developed sophisticated regulatory mechanisms to ensure homeostasis. Control engineers, who must design similar regulatory systems, have developed a number of general principles that govern feedback regulation. These lead to constraints which impose trade-offs that arise when developing controllers to minimize the effect of external disturbances on systems. Here, we review some of these trade-offs, particularly Bode's integral formula. We also highlight its connection to information theory, by showing that the constraints in sensitivity minimization can be cast as limitations on the information transmission through a system, and these have their root in causality. Finally, we look at how these constraints arise in two biological systems: glycolytic oscillations and the energy cost of perfect adaptation in a bacterial chemotactic pathway.

An information theoretic approach to insulin sensing by human kidney podocytes

Podocytes are key components of the glomerular filtration barrier (GFB). They are insulin-responsive but can become insulin-resistant, causing features of the leading global cause of kidney failure, diabetic nephropathy. Insulin acts via insulin receptors to control activities fundamental to GFB integrity, but the amount of information transferred is unknown. Here we measure this in human podocytes, using information theory-derived statistics that take into account cell-cell variability. High content imaging was used to measure insulin effects on Akt, FOXO and ERK. Mutual Information (MI) and Channel Capacity (CC) were calculated as measures of information transfer. We find that insulin acts via noisy communication channels with more information flow to Akt than to ERK. Information flow estimates were increased by consideration of joint sensing (ERK and Akt) and response trajectory (live cell imaging of FOXO1-clover translocation). Nevertheless, MI values were always <1Bit as most information was lost through signaling. Constitutive PI3K activity is a predominant feature of the system that restricts the proportion of CC engaged by insulin. Negative feedback from Akt supressed this activity and thereby improved insulin sensing, whereas sensing was robust to manipulation of feedforward signaling by inhibiting PI3K, PTEN or PTP1B. The decisions made by individual podocytes dictate GFB integrity, so we suggest that understanding the information on which the decisions are based will improve understanding of diabetic kidney disease and its treatment.

Liuweidihuang Pill Alleviates Inflammation of the Testis via AMPK/SIRT1/NF-κB Pathway in Aging Rats

Liuweidihuang Pill (LP) is a traditional Chinese herbal formula that is often used in clinical practice to treat kidney deficiency syndrome. The present study investigated the antiaging effects of LP in a D-galactose- (D-Gal-) induced subacute aging rat model. The study also attempted to explore whether anti-inflammatory mechanisms that underpin the antiaging effects are mediated by the AMPK/SIRT1/NF-κB signaling pathway. Rats were subcutaneously injected with D-Gal at a dosage of 100 mg/kg/d for 8 weeks. Upon successful induction of aging in the rats, the animal was administered LP at 0.9 g/kg/d by gavage for 4 weeks. Proteins of the testis were subsequently examined by western blot analysis, and associated locations in the testicular tissue were determined by immunohistochemistry. We observed that LP exerted antiaging effects in aging rats following the activation of AMPK/SIRT1. It was also observed that LP inhibited the expression of NF-κB, thereby further attenuating inflammation of the testis. Therefore, LP can alleviate inflammation of the testis via the AMPK/SIRT1/NF-κB pathway in aging rats.