Identification of individual cells from z-stacks of bright-field microscopy images.

Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.

Automatic Control of Gene Expression in Mammalian Cells.

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

Metabolic regulation of the ultradian oscillator Hes1 by reactive oxygen species.

Ultradian oscillators are cyclically expressed genes with a period of less than 24h, found in the major signalling pathways. The Notch effector hairy and enhancer of split Hes genes are ultradian oscillators. The physiological signals that synchronise and entrain Hes oscillators remain poorly understood. We investigated whether cellular metabolism modulates Hes1 cyclic expression. We demonstrated that, in mouse myoblasts (C2C12), Hes1 oscillation depends on reactive oxygen species (ROS), which are generated by the mitochondria electron transport chain and by NADPH oxidases NOXs. In vitro, the regulation of Hes1 by ROS occurs via the calcium-mediated signalling. The modulation of Hes1 by ROS was relevant in vivo, since perturbing ROS homeostasis was sufficient to alter Medaka (Oryzias latipes) somitogenesis, a process that is dependent on Hes1 ultradian oscillation during embryo development. Moreover, in a Medaka model for human microphthalmia with linear skin lesions syndrome, in which mitochondrial ROS homeostasis was impaired, we documented important somitogenesis defects and the deregulation of Hes homologues genes involved in somitogenesis. Notably, both molecular and developmental defects were rescued by antioxidant treatments. Our studies provide the first evidence of a coupling between cellular redox metabolism and an ultradian biological oscillator with important pathophysiological implication for somitogenesis.

MiRNAs confer phenotypic robustness to gene networks by suppressing biological noise.

miRNAs are small non-coding RNAs able to modulate target gene expression. It has been postulated that miRNAs confer robustness to biological processes, but clear experimental evidence is still missing. Here, using a synthetic biological approach, we demonstrate that microRNAs provide phenotypic robustness to transcriptional regulatory networks by buffering fluctuations in protein levels. We construct a network motif in mammalian cells exhibiting a 'toggle-switch' phenotype in which two alternative protein expression levels define its ON and OFF states. The motif consists of an inducible transcription factor that self-regulates its own transcription and that of a miRNA against the transcription factor itself. We confirm, using mathematical modelling and experimental approaches, that the microRNA confers robustness to the toggle-switch by enabling the cell to maintain and transmit its state. When absent, a dramatic increase in protein noise level occurs, causing the cell to randomly switch between the two states.

The non-steroidal anti-inflammatory drug indomethacin activates the eIF2α kinase PKR, causing a translational block in human colorectal cancer cells.

The NSAID (non-steroidal anti-inflammatory drug) indomethacin, a cyclo-oxygenase-1 and -2 inhibitor with anti-inflammatory and analgesic properties, is known to possess anticancer activity against CRC (colorectal cancer) and other malignancies in humans; however, the mechanism underlying the anticancer action remains elusive. In the present study we show that indomethacin selectively activates the dsRNA (double-stranded RNA)-dependent protein kinase PKR in a cyclo-oxygenase-independent manner, causing rapid phosphorylation of eIF2α (the α-subunit of eukaryotic translation initiation factor 2) and inhibiting protein synthesis in colorectal carcinoma and other types of cancer cells. The PKR-mediated translational block was followed by inhibition of CRC cell proliferation and apoptosis induction. Indomethacin did not affect the activity of the eIF2α kinases PERK (PKR-like endoplasmic reticulum-resident kinase), GCN2 (general control non-derepressible-2) and HRI (haem-regulated inhibitor kinase), and induced eIF2α phosphorylation in PERK-knockout and GCN2-knockout cells, but not in PKR-knockout cells or in human PKR-silenced CRC cells, identifying PKR as a selective target for indomethacin-induced translational inhibition. The fact that indomethacin induced PKR activity in vitro, an effect reversed by the PKR inhibitor 2-aminopurine, suggests a direct effect of the drug in kinase activation. The results of the present study identify PKR as a novel target of indomethacin, suggesting new scenarios on the molecular mechanisms underlying the pleiotropic activity of this traditional NSAID.

Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line.

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracycline-controlled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the "switch off" times, as compared to the non-autoregulated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour.