Single-Cell Information Analysis Reveals That Skeletal Muscles Incorporate Cell-to-Cell Variability as Information Not Noise

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.

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.

Controlled Noise: Evidence of epigenetic regulation of Single-Cell expression variability

MOTIVATION

Understanding single-cell expression variability (scEV) or gene expression noise among cells of the same type and state is crucial for delineating population-level cellular function. While epigenetic mechanisms are widely implicated in gene expression regulation, a definitive link between chromatin accessibility and scEV remains elusive. Recent advances in single-cell techniques enable the study of single-cell multiomics data that include the simultaneous measurement of scATAC-seq and scRNA-seq within individual cells, presenting an unprecedented opportunity to address this gap.

RESULTS

This paper introduces an innovative testing pipeline to investigate the association between chromatin accessibility and scEV. With single-cell multiomics data of scATAC-seq and scRNA-seq, the pipeline hinges on comparing the prediction performance of scATAC-seq data on gene expression levels between highly variable genes (HVGs) and non-highly variable genes (non-HVGs). Applying this pipeline to paired scATAC-seq and scRNA-seq data from human hematopoietic stem and progenitor cells, we observed a significantly superior prediction performance of scATAC-seq data for HVGs compared to non-HVGs. Notably, there was substantial overlap between well-predicted genes and HVGs. The gene pathways enriched from well-predicted genes are highly pertinent to cell type-specific functions. Our findings support the notion that scEV largely stems from cell-to-cell variability in chromatin accessibility, providing compelling evidence for the epigenetic regulation of scEV and offering promising avenues for investigating gene regulation mechanisms at the single-cell level.

AVAILABILITY

The source code and data used in this paper can be found at https://github.com/SiweiCui/EpigeneticControlOfSingle-CellExpressionVariability.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

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.

An optimized reporter of the transcription factor hypoxia-inducible factor 1α reveals complex HIF-1α activation dynamics in single cells

Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting pathogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the transcription factor hypoxia-inducible factor 1α (HIF-1α). Single-cell dynamics have been shown to be an important determinant of cell behavior; however, despite the importance of HIF-1α, little is known about its single-cell dynamics or their effect on metabolism. To address this knowledge gap, here we optimized a HIF-1α fluorescent reporter and applied it to study single-cell dynamics. First, we showed that single cells are likely able to differentiate multiple levels of prolyl hydroxylase inhibition, a marker of metabolic change, via HIF-1α activity. We then applied a physiological stimulus known to trigger metabolic change, interferon-γ, and observed heterogeneous, oscillatory HIF-1α responses in single cells. Finally, we input these dynamics into a mathematical model of HIF-1α-regulated metabolism and discovered a profound difference between cells exhibiting high versus low HIF-1α activation. Specifically, we found cells with high HIF-1α activation are able to meaningfully reduce flux through the tricarboxylic acid cycle and show a notable increase in the NAD+/NADH ratio compared with cells displaying low HIF-1α activation. Altogether, this work demonstrates an optimized reporter for studying HIF-1α in single cells and reveals previously unknown principles of HIF-1α activation.

Efficient information coding and degeneracy in the nervous system

Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environment occurs at all biological levels and timescales. The context dependence of environmental stimuli and the need for stable adaptations make EIC a daunting task. We argue that biological complexity is the principal architect that subserves deft execution of stable EIC. Complexity in a system is characterized by several functionally segregated subsystems that show a high degree of functional integration when they interact with each other. Complex biological systems manifest heterogeneities and degeneracy, wherein structurally different subsystems could interact to yield the same functional outcome. We argue that complex systems offer several choices that effectively implement EIC and homeostasis for each of the different contexts encountered by the system.

Intrinsic entropy model for feature selection of scRNA-seq data

Recent advances of single-cell RNA sequencing (scRNA-seq) technologies have led to extensive study of cellular heterogeneity and cell-to-cell variation. However, the high frequency of dropout events and noise in scRNA-seq data confounds the accuracy of the downstream analysis, i.e. clustering analysis, whose accuracy depends heavily on the selected feature genes. Here, by deriving an entropy decomposition formula, we propose a feature selection method, i.e. an intrinsic entropy (IE) model, to identify the informative genes for accurately clustering analysis. Specifically, by eliminating the 'noisy' fluctuation or extrinsic entropy (EE), we extract the IE of each gene from the total entropy (TE), i.e. TE = IE + EE. We show that the IE of each gene actually reflects the regulatory fluctuation of this gene in a cellular process, and thus high-IE genes provide rich information on cell type or state analysis. To validate the performance of the high-IE genes, we conduct computational analysis on both simulated datasets and real single-cell datasets by comparing with other representative methods. The results show that our IE model is not only broadly applicable and robust for different clustering and classification methods, but also sensitive for novel cell types. Our results also demonstrate that the intrinsic entropy/fluctuation of a gene serves as information rather than noise in contrast to its total entropy/fluctuation.

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.

Biomimetic cell-actuated artificial muscle with nanofibrous bundles

Biohybrid artificial muscle produced by integrating living muscle cells and their scaffolds with free movement in vivo is promising for advanced biomedical applications, including cell-based microrobotic systems and therapeutic drug delivery systems. Herein, we provide a biohybrid artificial muscle constructed by integrating living muscle cells and their scaffolds, inspired by bundled myofilaments in skeletal muscle. First, a bundled biohybrid artificial muscle was fabricated by the integration of skeletal muscle cells and hydrophilic polyurethane (HPU)/carbon nanotube (CNT) nanofibers into a fiber shape similar to that of natural skeletal muscle. The HPU/CNT nanofibers provided a stretchable basic backbone of the 3-dimensional fiber structure, which is similar to actin-myosin scaffolds. The incorporated skeletal muscle fibers contribute to the actuation of biohybrid artificial muscle. In fact, electrical field stimulation reversibly leads to the contraction of biohybrid artificial muscle. Therefore, the current development of cell-actuated artificial muscle provides great potential for energy delivery systems as actuators for implantable medibot movement and drug delivery systems. Moreover, the innervation of the biohybrid artificial muscle with motor neurons is of great interest for human-machine interfaces.

Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells.

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Insulin signaling is heterogeneous between cells in the same population

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The temporal response of signaling components within a cell is highly reproducible

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Upstream responses (Akt) can only partially predict downstream response (GLUT4)

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Protein expression variance is a driver of intercellular signaling heterogeneity

Trans-omic Analysis Reveals ROS-Dependent Pentose Phosphate Pathway Activation after High-Frequency Electrical Stimulation in C2C12 Myotubes

Skeletal muscle adaptation is mediated by cooperative regulation of metabolism, signal transduction, and gene expression. However, the global regulatory mechanism remains unclear. To address this issue, we performed electrical pulse stimulation (EPS) in differentiated C2C12 myotubes at low and high frequency, carried out metabolome and transcriptome analyses, and investigated phosphorylation status of signaling molecules. EPS triggered extensive and specific changes in metabolites, signaling phosphorylation, and gene expression during and after EPS in a frequency-dependent manner. We constructed trans-omic network by integrating these data and found selective activation of the pentose phosphate pathway including metabolites, upstream signaling molecules, and gene expression of metabolic enzymes after high-frequency EPS. We experimentally validated that activation of these molecules after high-frequency EPS was dependent on reactive oxygen species (ROS). Thus, the trans-omic analysis revealed ROS-dependent activation in signal transduction, metabolome, and transcriptome after high-frequency EPS in C2C12 myotubes, shedding light on possible mechanisms of muscle adaptation.

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We performed electrical pulse stimulation in differentiated C2C12 myotubes

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We constructed trans-omic network after high-frequency electrical pulse stimulation

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Trans-omic network integrates metabolome, transcriptome, and signaling molecules

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We identified ROS-dependent pentose phosphate pathway activation