Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis

Individual yeast cells signal at different levels but each with good precision

Different isogenic cells exhibit different responses to the same extracellular signals. Several authors assumed that this variation arose from stochastic signalling noise with the implication that single eukaryotic cells could not detect their surroundings accurately, but work by us and others has shown that the variation is dominated instead by persistent cell-to-cell differences. Here, we analysed previously published data to quantify the sources of variation in pheromone-induced gene expression in Saccharomyces cerevisiae. We found that 91% of response variation was due to stable cell-to-cell differences, 8% from experimental measurement error, and 1% from signalling noise and expression noise. Low noise enabled precise signalling; individual cells could transmit over 3 bits of information through the pheromone response system and so respond differently to eight different pheromone concentrations. Additionally, if individual cells could reference their responses against constitutively expressed proteins, then cells could determine absolute pheromone concentrations with 2 bits of accuracy. These results help explain how individual yeast cells can accurately sense and respond to different extracellular pheromone concentrations.

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.

Information transmission in a cell monolayer: A numerical study

Motivated by the spatiotemporal waves of MAPK/ERK activity, crucial for long-range communication in regenerating tissues, we investigated stochastic homoclinic fronts propagating through channels formed by directly interacting cells. We evaluated the efficiency of long-range communication in these channels by examining the rate of information transmission. Our study identified the stochastic phenomena that reduce this rate: front propagation failure, new front spawning, and variability in the front velocity. We found that a trade-off between the frequencies of propagation failures and new front spawning determines the optimal channel width (which geometrically determines the front length). The optimal frequency of initiating new waves is determined by a trade-off between the input information rate (higher with more frequent initiation) and the fidelity of information transmission (lower with more frequent initiation). Our analysis provides insight into the relative timescales of intra- and intercellular processes necessary for successful wave propagation.

Monitoring Cellular Energy Balance in Single Cells Using Fluorescent Biosensors for AMPK

5'-Adenosine monophosphate-activated protein kinase (AMPK) senses cellular metabolic status and reflects the balance between ATP production and ATP usage. This balance varies from cell to cell and changes over time, creating a need for methods that can capture cellular heterogeneity and temporal dynamics. Fluorescent biosensors for AMPK activity offer a unique approach to measure metabolic status nondestructively in single cells in real time. In this chapter, we provide a brief rationale for using live-cell biosensors to measure AMPK activity, survey the current AMPK biosensors, and discuss considerations for using this approach. We provide methodology for introducing AMPK biosensors into a cell line of choice, setting up experiments for live-cell fluorescent microscopy of AMPK activity, and calibrating the biosensors using immunoblot data.

Rapid, potent, and persistent covalent chemical probes to deconvolute PI3Kα signaling

Chemical probes have gained importance in the elucidation of signal transduction in biology. Insufficient selectivity and potency, lack of cellular activity and inappropriate use of chemical probes has major consequences on interpretation of biological results. The catalytic subunit of phosphoinositide 3-kinase α (PI3Kα) is one of the most frequently mutated genes in cancer, but fast-acting, high-quality probes to define PI3Kα's specific function to clearly separate it from other class I PI3K isoforms, are not available. Here, we present a series of novel covalent PI3Kα-targeting probes with optimized intracellular target access and kinetic parameters. On-target TR-FRET and off-target assays provided relevant kinetic parameters (k chem, k inact and K i) to validate our chemical probes. Additional intracellular nanoBRET tracer displacement measurements showed rapid diffusion across the cell membrane and extremely fast target engagement, while investigations of signaling downstream of PI3Kα via protein kinase B (PKB/Akt) and forkhead box O (FOXO) revealed blunted pathway activity in cancer cell lines with constitutively activated PI3Kα lasting for several days. In contrast, persistent PI3Kα inhibition was rapidly bypassed by other class I PI3K isoforms in cells lacking functional phosphatase and tensin homolog (PTEN). Comparing the rapidly-diffusing, fast target-engaging chemical probe 9 to clinical reversible PI3Kα-selective inhibitors alpelisib, inavolisib and 9r, a reversible analogue of 9, revealed 9's superior potency to inhibit growth (up to 600-fold) associated with sustained suppression of PI3Kα signaling in breast cancer cell lines. Finally, using a simple washout protocol, the utility of the highly-selective covalent PI3Kα probe 9 was demonstrated by the quantification of the coupling of insulin, EGF and CXCL12 receptors to distinct PI3K isoforms for signal transduction in response to ligand-dependent activation. Collectively, these findings along with the novel covalent chemical probes against PI3Kα provide insights into isoform-specific functions in cancer cells and highlight opportunities to achieve improved selectivity and long-lasting efficacy.

Mitogen signaling strength and duration can control cell cycle decisions

Decades ago, mitogen-promoted signaling duration and strength were observed to be sensed by the cell and to be critical for its decisions: to proliferate or differentiate. Landmark publications established the importance of mitogen signaling not only in the G1 cell cycle phase but also through the S and the G2/M transition. Despite these early milestones, how mitogen signal duration and strength, short and strong or weaker and sustained, control cell fate has been largely unheeded. Here, we center on cardinal signaling-related questions, including (i) how fluctuating mitogenic signals are converted into cell proliferation-differentiation decisions and (ii) why extended duration of weak signaling is associated with differentiation, while bursts of strong and short induce proliferation but, if too strong and long, induce irreversible senescence. Our innovative broad outlook harnesses cell biology and protein conformational ensembles, helping us to define signaling strength, clarify cell cycle decisions, and thus cell fate.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays

Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over ten-fold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity.

The ability to sense the environment is heterogeneously distributed in cell populations

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.

The ability to sense the environment is heterogeneously distributed in cell populations

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information theoretic framework to quantify the distribution of sensing abilities from single cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an " average cell ". We verify these predictions using live cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information theoretic framework will significantly improve our understanding of how cells sense in their environment.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Improved approaches to channel capacity estimation discover compromised GPCR signaling in diverse cancer cells

Intracellular signaling orchestrates an organism's development and functioning and underlies various pathologies, such as cancer, when aberrant. A universal cell signaling characteristic is channel capacity - the measure of how much information a given transmitting system can reliably transduce. Here, we describe improved approaches to quantify GPCR signaling channel capacity in single cells, averaged across cell population. We assess the channel capacity based on distribution of residuals by the cellular response amplitude. We further develop means to handle irregularly responding cancer cells using the integral values of their response to different agonist concentrations. These approaches enabled us to analyze, for the first time, channel capacity in single cancer cells. A universal feature emerging for different cancer cell types is a decreased channel capacity of their GPCR signaling. These findings provide experimental validation to the hypothesis that cancer is an information disease, bearing importance for basic cancer biology and anticancer drug discovery.

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.

Ras/MAPK signalling intensity defines subclonal fitness in a mouse model of hepatocellular carcinoma

Quantitative differences in signal transduction are to date an understudied feature of tumour heterogeneity. The MAPK Erk pathway, which is activated in a large proportion of human tumours, is a prototypic example of distinct cell fates being driven by signal intensity. We have used primary hepatocyte precursors transformed with different dosages of an oncogenic form of Ras to model subclonal variations in MAPK signalling. Orthotopic allografts of Ras-transformed cells in immunocompromised mice gave rise to fast-growing aggressive tumours, both at the primary location and in the peritoneal cavity. Fluorescent labelling of cells expressing different oncogene levels, and consequently varying levels of MAPK Erk activation, highlighted the selection processes operating at the two sites of tumour growth. Indeed, significantly higher Ras expression was observed in primary as compared to secondary, metastatic sites, despite the apparent evolutionary trade-off of increased apoptotic death in the liver that correlated with high Ras dosage. Analysis of the immune tumour microenvironment at the two locations suggests that fast peritoneal tumour growth in the immunocompromised setting is abrogated in immunocompetent animals due to efficient antigen presentation by peritoneal dendritic cells. Furthermore, our data indicate that, in contrast to the metastatic-like outgrowth, strong MAPK signalling is required in the primary liver tumours to resist elimination by NK (natural killer) cells. Overall, this study describes a quantitative aspect of tumour heterogeneity and points to a potential vulnerability of a subtype of hepatocellular carcinoma as a function of MAPK Erk signalling intensity.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Relating individual cell division events to single-cell ERK and Akt activity time courses

Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~ 8.5-40 h). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Simple metrics of central tendency in this time window are associated with subsequent cell division fates. ERK activity was more strongly associated with division fates than Akt activity, suggesting Akt activity dynamics may contribute less to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics may be more important than Akt activity dynamics for driving cell division in this non-transformed context.

Comparison of bias and resolvability in single-cell and single-transcript methods

Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.

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.

Single-cell resolved imaging reveals intra-tumor heterogeneity in glycolysis, transitions between metabolic states, and their regulatory mechanisms

Inter-cellular heterogeneity in metabolic state has been proposed to influence many cancer phenotypes, including responses to targeted therapy. Here, we track the transitions and heritability of metabolic states in single PIK3CA mutant breast cancer cells, identify non-genetic glycolytic heterogeneity, and build on observations derived from methods reliant on bulk analyses. Using fluorescent biosensors in vitro and in tumors, we have identified distinct subpopulations of cells whose glycolytic and mitochondrial metabolism are regulated by combinations of phosphatidylinositol 3-kinase (PI3K) signaling, bromodomain activity, and cell crowding effects. The actin severing protein cofilin, as well as PI3K, regulates rapid changes in glucose metabolism, whereas treatment with the bromodomain inhibitor slowly abrogates a subpopulation of cells whose glycolytic activity is PI3K independent. We show how bromodomain function and PI3K signaling, along with actin remodeling, independently modulate glycolysis and how targeting these pathways affects distinct subpopulations of cancer cells.

Iron overload reduces adiponectin receptor expression via a ROS/FOXO1-dependent mechanism leading to adiponectin resistance in skeletal muscle cells

Iron overload (IO) is a common yet underappreciated finding in metabolic syndrome (MetS) patients. With the prevalence of MetS continuing to rise, it is imperative to further elucidate cellular mechanisms leading to metabolic dysfunction. Adiponectin has many beneficial effects and is a therapeutic target for the treatment of MetS and cardiovascular diseases. IO positively correlates with reduced circulating adiponectin levels yet the impact of IO on adiponectin action is unknown. Here, we established a model of IO in L6 skeletal muscle cells and found that IO-induced adiponectin resistance. This was shown via reduced p38 mitogen-activated protein kinase phosphorylation in response to the small molecule adiponectin receptor (AdipoR) agonist, AdipoRon, in presence of IO. This correlated with reduced messenger RNA and protein levels of AdipoR1 and its facilitative signaling binding partner, APPL1. IO caused phosphorylation, nuclear extrusion, and thus inhibition of FOXO1, a known transcription factor regulating AdipoR1 expression. The antioxidant N-acetyl cystine attenuated the production of reactive oxygen species (ROS) by IO, and blunted its effect on FOXO1 phosphorylation and removal from the nucleus, as well as subsequent adiponectin resistance. In conclusion, our study identifies a ROS/FOXO1/AdipoR1 axis as a cause of skeletal muscle adiponectin resistance in response to IO. This new knowledge provides insight into a cellular mechanism with potential relevance to disease pathophysiology in MetS patients with IO.