Determining biological noise via single cell analysis

Microfluidic single-cell measurements of oxidative stress as a function of cell cycle position

Single-cell measurements routinely demonstrate high levels of variation between cells, but fewer studies provide insight into the analytical and biological sources of this variation. This is particularly true of chemical cytometry, in which individual cells are lysed and their contents separated, compared to more established single-cell measurements of the genome and transcriptome. To characterize population-level variation and its sources, we analyzed oxidative stress levels in 1278 individual Dictyostelium discoideum cells as a function of exogenous stress level and cell cycle position. Cells were exposed to varying levels of oxidative stress via singlet oxygen generation using the photosensitizer Rose Bengal. Single-cell data reproduced the dose-response observed in ensemble measurements by CE-LIF, superimposed with high levels of heterogeneity. Through experiments and data analysis, we explored possible biological sources of this heterogeneity. No trend was observed between population variation and oxidative stress level, but cell cycle position was a major contributor to heterogeneity in oxidative stress. Cells synchronized to the same stage of cell division were less heterogeneous than unsynchronized cells (RSD of 37-51% vs 93%), and mitotic cells had higher levels of reactive oxygen species than interphase cells. While past research has proposed changes in cell size during the cell cycle as a source of biological noise, the measurements presented here use an internal standard to normalize for effects of cell volume, suggesting a more complex contribution of cell cycle to heterogeneity of oxidative stress.

Photoactivatable Reporter to Perform Multiplexed and Temporally Controlled Measurements of Kinase and Protease Activity in Single Cells

Peptide bioreporters were developed to perform multiplexed measurements of the activation of epidermal growth factor receptor kinase (EGFR), Akt kinase (Akt/protein kinase B), and proteases/peptidases in single cells. The performance characteristics of the three reporters were assessed by measuring the reporter's proteolytic stability, kinetic constants for EGFR and Akt, and dephosphorylation rate. The reporter displaying optimal performance was composed of 6-carboxyfluorescein (6-FAM) on the peptide N-terminus, an Akt substrate sequence employing a threonine phosphorylation site for Akt, followed by a tri-D arginine linker, and finally an EGFR substrate sequence bearing a phosphatase-resistant 7-(S)-hydroxy-1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid (L-htc) residue as the EGFR phosphorylation site. Importantly, use of a single electrophoretic condition separated the mono- and diphosphorylated products as well as proteolytic forms permitting the quantitation of multiple enzyme activities simultaneously using a single reporter. Because the Akt and EGFR substrates were linked, a known ratio (EGFR/Akt) of the reporter was loaded into cells. A photoactivatable version of the reporter was synthesized by adding two 4,5-dimethoxy-2-nitrobenzyl (DMNB) moieties to mask the EGFR and Akt phosphorylation sites. The DMNB moieties were readily photocleaved following exposure to 360 nm light, unmasking the phosphorylation sites on the reporter. The new photoactivatable reporter permitted multiplexed measurements of kinase signaling and proteolytic degradation in single cells in a temporally controlled manner. This work will facilitate the development of a new generation of multiplexed activity-based reporters capable of light-initiated measurement of enzymatic activity in single cells.

Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops

Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures.

Cellular behavior can be affected by noise in molecular interactions. Signaling pathways should process noisy input signals and support cellular decision making by properly transducing the signals, while removing noise from them. Three component networks of feed-forward loops (FFLs) have been proposed to serve as ideal noise reducers, while linear pathways were shown to be good signal transducers. These signaling units do not work in isolation, so there is a possibility that a combination of various feed-forward loops can provide good noise reduction, while maintaining good signal transduction. To test this hypothesis, we have systematically tested the noise reducing and signal transducing capabilities of all possible combinations of feed-forward loops and compared them with the performance of individual FFLs. We built mathematical models of all these systems and compared their capabilities at reducing noise in the input signal while maintaining responses to meaningful changes in the incoming signal. We found that a combination of two copies of a special type of fully positive signaling FFLs is the best noise reducer, while a combination of two incoherent (one positive, one negative signal) FFLs can provide the best signal transduction. The combination of these two FFLs could provide good signal processing where both noise reduction and signal transduction are achieved.

The impact of non-genetic heterogeneity on cancer cell death

The goal of cancer chemotherapy is to induce homogeneous cell death within the population of targeted cancer cells. However, no two cells are exactly alike at the molecular level, and sensitivity to drug-induced cell death, therefore, varies within a population. Genetic alterations can contribute to this variability and lead to selection for drug resistant clones. However, there is a growing appreciation for the role of non-genetic variation in producing drug-tolerant cellular states that exhibit reduced sensitivity to cell death for extended periods of time, from hours to weeks. These cellular states may result from individual variation in epigenetics, gene expression, metabolism, and other processes that impact drug mechanism of action or the execution of cell death. Such population-level non-genetic heterogeneity may contribute to treatment failure and provide a cellular "substrate" for the emergence of genetic alterations that confer frank drug resistance.

Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017

Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.

Analysis of single-cell cytokine secretion reveals a role for paracrine signaling in coordinating macrophage responses to TLR4 stimulation

Macrophages not only produce multiple cytokines but also respond to multiple cytokines, which likely shapes the ultimate response of the population. To determine the role of paracrine signaling in shaping the profile of inflammatory cytokines secreted by macrophages in response to stimulation of Toll-like receptor 4 (TLR4) with lipopolysaccharide (LPS), we combined multiplexed, microwell-based measurements of cytokine secretion by single cells with analysis of cytokine secretion by cell populations. Loss of paracrine signaling as a result of cell isolation reduced the secretion by macrophage-like U937 cells and human monocyte-derived macrophages (MDMs) of a subset of LPS-stimulated cytokines, including interleukin-6 (IL-6) and IL-10. Graphical Gaussian modeling (GGM) of the single-cell data defined a regulatory network of paracrine signals, which was validated experimentally in the population through antibody-mediated neutralization of individual cytokines. Tumor necrosis factor-α (TNF-α) was the most influential cytokine in the GGM network. Paracrine signaling by TNF-α secreted from a small subpopulation of "high-secreting" cells was necessary, but not sufficient, for the secretion of large amounts of IL-6 and IL-10 by the cell population. Decreased relative IL-10 secretion by isolated MDMs was linked to increased TNF-α secretion, suggesting that inhibition of the inflammatory response also depends on paracrine signaling. Our results reveal a previously uncharacterized role for cell-to-cell communication within a population in coordinating a rapid innate immune response despite underlying cell-to-cell heterogeneity.

The function of chromatin modifiers in lineage commitment and cell fate specification

Proteins that modify the structure of chromatin are known to be important for various aspects of metazoan biology including development, disease and possibly ageing. Yet functional details of why these proteins are important, i.e. how their action influences a given biological process, are lacking. While it is now possible to describe the biochemistry of how these proteins remodel chromatin, their chromatin binding profiles in cell lines, or gene expression changes upon loss of a given protein, in very few cases has this easily translated into an understanding of how the function of that protein actually influences a developmental process. Given that many chromatin modifying proteins will largely exert their influence through control of gene expression, it is useful to consider developmental processes as changes in the gene regulatory network (GRN), with each cell type exhibiting a unique gene expression profile. In this essay we consider the impact of two abundant and highly conserved chromatin modifying complexes, namely the nucleosome remodelling and deacetylation (NuRD) complex and the polycomb repressive complex 2 (PRC2), on the change in GRNs associated with lineage commitment during early mammalian development. We propose that while the NuRD complex limits the stability of cell states and defines the developmental trajectory between two stable states, PRC2 activity is important for stabilizing a new GRN once established. Although these two complexes display different biochemical activities, chromatin binding profiles and mutant phenotypes, we propose a model to explain how they cooperate to facilitate the transition through cell states that is development.

Modeling heterogeneous responsiveness of intrinsic apoptosis pathway

BACKGROUND

Apoptosis is a cell suicide mechanism that enables multicellular organisms to maintain homeostasis and to eliminate individual cells that threaten the organism's survival. Dependent on the type of stimulus, apoptosis can be propagated by extrinsic pathway or intrinsic pathway. The comprehensive understanding of the molecular mechanism of apoptotic signaling allows for development of mathematical models, aiming to elucidate dynamical and systems properties of apoptotic signaling networks. There have been extensive efforts in modeling deterministic apoptosis network accounting for average behavior of a population of cells. Cellular networks, however, are inherently stochastic and significant cell-to-cell variability in apoptosis response has been observed at single cell level.

RESULTS

To address the inevitable randomness in the intrinsic apoptosis mechanism, we develop a theoretical and computational modeling framework of intrinsic apoptosis pathway at single-cell level, accounting for both deterministic and stochastic behavior. Our deterministic model, adapted from the well-accepted Fussenegger model, shows that an additional positive feedback between the executioner caspase and the initiator caspase plays a fundamental role in yielding the desired property of bistability. We then examine the impact of intrinsic fluctuations of biochemical reactions, viewed as intrinsic noise, and natural variation of protein concentrations, viewed as extrinsic noise, on behavior of the intrinsic apoptosis network. Histograms of the steady-state output at varying input levels show that the intrinsic noise could elicit a wider region of bistability over that of the deterministic model. However, the system stochasticity due to intrinsic fluctuations, such as the noise of steady-state response and the randomness of response delay, shows that the intrinsic noise in general is insufficient to produce significant cell-to-cell variations at physiologically relevant level of molecular numbers. Furthermore, the extrinsic noise represented by random variations of two key apoptotic proteins, namely Cytochrome C and inhibitor of apoptosis proteins (IAP), is modeled separately or in combination with intrinsic noise. The resultant stochasticity in the timing of intrinsic apoptosis response shows that the fluctuating protein variations can induce cell-to-cell stochastic variability at a quantitative level agreeing with experiments. Finally, simulations illustrate that the mean abundance of fluctuating IAP protein is positively correlated with the degree of cellular stochasticity of the intrinsic apoptosis pathway.

CONCLUSIONS

Our theoretical and computational study shows that the pronounced non-genetic heterogeneity in intrinsic apoptosis responses among individual cells plausibly arises from extrinsic rather than intrinsic origin of fluctuations. In addition, it predicts that the IAP protein could serve as a potential therapeutic target for suppression of the cell-to-cell variation in the intrinsic apoptosis responsiveness.

The roles of integration in molecular systems biology

A common way to think about scientific practice involves classifying it as hypothesis- or data-driven. We argue that although such distinctions might illuminate scientific practice very generally, they are not sufficient to understand the day-to-day dynamics of scientific activity and the development of programmes of research. One aspect of everyday scientific practice that is beginning to gain more attention is integration. This paper outlines what is meant by this term and how it has been discussed from scientific and philosophical points of view. We focus on methodological, data and explanatory integration, and show how they are connected. Then, using some examples from molecular systems biology, we will show how integration works in a range of inquiries to generate surprising insights and even new fields of research. From these examples we try to gain a broader perspective on integration in relation to the contexts of inquiry in which it is implemented. In today's environment of data-intensive large-scale science, integration has become both a practical and normative requirement with corresponding implications for meta-methodological accounts of scientific practice. We conclude with a discussion of why an understanding of integration and its dynamics is useful for philosophy of science and scientific practice in general.

Measuring enzyme activity in single cells

Seemingly identical cells can differ in their biochemical state, function and fate, and this variability plays an increasingly recognized role in organism-level outcomes. Cellular heterogeneity arises in part from variation in enzyme activity, which results from interplay between biological noise and multiple cellular processes. As a result, single-cell assays of enzyme activity, particularly those that measure product formation directly, are crucial. Recent innovations have yielded a range of techniques to obtain these data, including image-, flow- and separation-based assays. Research to date has focused on easy-to-measure glycosylases and clinically-relevant kinases. Expansion of these techniques to a wider range and larger number of enzymes will answer contemporary questions in proteomics and glycomics, specifically with respect to biological noise and cellular heterogeneity.

The role of physiological heterogeneity in microbial population behavior

As the ability to analyze individual cells in microbial populations expands, it is becoming apparent that isogenic microbial populations contain substantial cell-to-cell differences in physiological parameters such as growth rate, resistance to stress and regulatory circuit output. Subpopulations exist that are manyfold different in these parameters from the population average, and these differences arise by stochastic processes. Such differences can dramatically affect the response of cells to perturbations, especially stress, which in turn dictates overall population response. Defining the role of cell-to-cell heterogeneity in population behavior is important for understanding population-based research problems, including those involving infecting populations, normal flora and bacterial populations in water and soils. Emerging technological breakthroughs are poised to transform single-cell analysis and are critical for the next phase of insights into physiological heterogeneity in the near future. These include technologies for multiparameter analysis of live cells, with downstream processing and analysis.