The nuclear factor κB (NF-κB) transcription factor regulates cellular stress responses and the immune response to infection. NF-κB activation results in oscillations in nuclear NF-κB abundance. To define the function of these oscillations, we treated cells with repeated short pulses of tumor necrosis factor–α at various intervals to mimic pulsatile inflammatory signals. At all pulse intervals that were analyzed, we observed synchronous cycles of NF-κB nuclear translocation. Lower frequency stimulations gave repeated full-amplitude translocations, whereas higher frequency pulses gave reduced translocation, indicating a failure to reset. Deterministic and stochastic mathematical models predicted how negative feedback loops regulate both the resetting of the system and cellular heterogeneity. Altering the stimulation intervals gave different patterns of NF-κB–dependent gene expression, which supports the idea that oscillation frequency has a functional role.

In individual mammalian cells the expression of some genes such as prolactin is highly variable over time and has been suggested to occur in stochastic pulses. To investigate the origins of this behavior and to understand its functional relevance, we quantitatively analyzed this variability using new mathematical tools that allowed us to reconstruct dynamic transcription rates of different reporter genes controlled by identical promoters in the same living cell. Quantitative microscopic analysis of two reporter genes, firefly luciferase and destabilized EGFP, was used to analyze the dynamics of prolactin promoter-directed gene expression in living individual clonal and primary pituitary cells over periods of up to 25 h. We quantified the time-dependence and cyclicity of the transcription pulses and estimated the length and variation of active and inactive transcription phases. We showed an average cycle period of approximately 11 h and demonstrated that while the measured time distribution of active phases agreed with commonly accepted models of transcription, the inactive phases were differently distributed and showed strong memory, with a refractory period of transcriptional inactivation close to 3 h. Cycles in transcription occurred at two distinct prolactin-promoter controlled reporter genes in the same individual clonal or primary cells. However, the timing of the cycles was independent and out-of-phase. For the first time, we have analyzed transcription dynamics from two equivalent loci in real-time in single cells. In unstimulated conditions, cells showed independent transcription dynamics at each locus. A key result from these analyses was the evidence for a minimum refractory period in the inactive-phase of transcription. The response to acute signals and the result of manipulation of histone acetylation was consistent with the hypothesis that this refractory period corresponded to a phase of chromatin remodeling which significantly increased the cyclicity. Stochastically timed bursts of transcription in an apparently random subset of cells in a tissue may thus produce an overall coordinated but heterogeneous phenotype capable of acute responses to stimuli. Timing of biological processes such as gene transcription is crucial to ensure that cells and tissues respond appropriately to their environment. Until recently it was assumed that most cells in a tissue responded in a similar way, and that changes in cellular activity were relatively stable. However, studies of messenger RNA and protein levels in single cells have shown the presence of considerable heterogeneity. This suggested that transcription in single cells may be highly dynamic over time. Using a combined experimental and theoretical approach, with time-lapse imaging of reporter gene expression over 25 h periods, we measured the rate of prolactin gene transcription in single pituitary cells and detected clear cycles of transcriptional activity. Mathematical analysis, using a binary model that assumed transcription was on or off, showed that these cycles were characterized by a minimum refractory period that involved chromatin remodeling. The timing of transcription from two different reporter constructs driven by identical promoters in the same cell was out-of-phase, suggesting that the pulses of gene expression are due to processes intrinsic to expression of a particular gene and not to environmental effects. We further show that the pulses of transcription are independent chromatin cycles at each gene locus. Therefore, heterogeneous patterns of gene expression may facilitate flexible transcriptional responses in cells within intact tissue, while maintaining a well-regulated average level of gene expression in the resting state.

Heterogeneity between individual cells is a common feature of dynamic cellular processes, including signaling, transcription, and cell fate; yet the overall tissue level physiological phenotype needs to be carefully controlled to avoid fluctuations. Here we show that in the NF-κB signaling system, the precise timing of a dual-delayed negative feedback motif [involving stochastic transcription of inhibitor κB (IκB)-α and -ε] is optimized to induce heterogeneous timing of NF-κB oscillations between individual cells. We suggest that this dual-delayed negative feedback motif enables NF-κB signaling to generate robust single cell oscillations by reducing sensitivity to key parameter perturbations. Simultaneously, enhanced cell heterogeneity may represent a mechanism that controls the overall coordination and stability of cell population responses by decreasing temporal fluctuations of paracrine signaling. It has often been thought that dynamic biological systems may have evolved to maximize robustness through cell-to-cell coordination and homogeneity. Our analyses suggest in contrast, that this cellular variation might be advantageous and subject to evolutionary selection. Alternative types of therapy could perhaps be designed to modulate this cellular heterogeneity.

Progesterone is present at micromolar concentrations in the cumulus matrix, which surrounds mammalian oocytes. Exposure of human spermatozoa to a concentration gradient of progesterone (0–3 μm) to simulate approach to the oocyte induced a slowly developing increase in [Ca²⁺]_i upon which, in many cells, slow oscillations were superimposed. [Ca²⁺]_i oscillations often started at very low progesterone (<10 nm), and their frequency did not change during the subsequent rise in concentration. Oscillations also occurred, but in a much smaller proportion of cells, in response to stepped application of progesterone (3 μm). When progesterone was removed, [Ca²⁺]_i oscillations often persisted or quickly resumed. Superfusion with low-Ca²⁺ bathing medium (no added Ca²⁺) did not prevent [Ca²⁺]_i oscillations, but they could be abolished by addition of EGTA or La³⁺. Inhibitors of sarcoplasmic/endoplasmic reticulum Ca²⁺-ATPases or inositol trisphosphate signaling had no effect on [Ca²⁺]_i oscillations, but pharmacological manipulation of ryanodine receptors affected both their frequency and amplitude. Staining of live spermatozoa with BODIPY FL-X ryanodine showed localization of ryanodine binding primarily to the caudal part of the head and mid-piece. [Ca²⁺]_i oscillations did not induce acrosome reaction, but in cells generating oscillations, the flagellar beat mode alternated in synchrony with the oscillation cycle. Flagellar bending and lateral movement of the sperm head during [Ca²⁺]_i peaks were markedly increased compared with during [Ca²⁺]_i troughs. This alternating pattern of activity is likely to facilitate zona penetration. These observations show that progesterone initiates unusual and complex store-mediated [Ca²⁺]_i signaling in human spermatozoa and identify a previously unrecognized effect of progesterone in regulating sperm “behavior” during fertilization.

Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data.