Stress cardiomyopathy, also referred to as Takotsubo cardiomyopathy, is an increasingly recognized clinical syndrome characterized by acute reversible apical ventricular dysfunction. We hypothesize ...that stress cardiomyopathy is a form of myocardial stunning, but with different cellular mechanisms to those seen during transient episodes of ischemia secondary to coronary stenoses. In this syndrome, we believe that high levels of circulating epinephrine trigger a switch in intracellular signal trafficking in ventricular cardiomyocytes, from G(s) protein to G(i) protein signaling via the beta(2)-adrenoceptor. Although this switch to beta(2)-adrenoceptor-G(i) protein signaling protects against the proapoptotic effects of intense activation of beta(1)-adrenoceptors, it is also negatively inotropic. This effect is greatest at the apical myocardium, in which the beta-adrenoceptor density is greatest. Our hypothesis has implications for the use of drugs or devices in the treatment of patients with stress cardiomyopathy.
We have carried out a systems-level analysis of the spatial and temporal dynamics of cell cycle regulators in the fission yeast
Schizosaccharomyces pombe
. In a comprehensive single-cell analysis, we ...have precisely quantified the levels of 38 proteins previously identified as regulators of the G2 to mitosis transition and of 7 proteins acting at the G1- to S-phase transition. Only 2 of the 38 mitotic regulators exhibit changes in concentration at the whole-cell level: the mitotic B-type cyclin Cdc13, which accumulates continually throughout the cell cycle, and the regulatory phosphatase Cdc25, which exhibits a complex cell cycle pattern. Both proteins show similar patterns of change within the nucleus as in the whole cell but at higher concentrations. In addition, the concentrations of the major fission yeast cyclin-dependent kinase (CDK) Cdc2, the CDK regulator Suc1, and the inhibitory kinase Wee1 also increase in the nucleus, peaking at mitotic onset, but are constant in the whole cell. The significant increase in concentration with size for Cdc13 supports the view that mitotic B-type cyclin accumulation could act as a cell size sensor. We propose a two-step process for the control of mitosis. First, Cdc13 accumulates in a size-dependent manner, which drives increasing CDK activity. Second, from mid-G2, the increasing nuclear accumulation of Cdc25 and the counteracting Wee1 introduce a bistability switch that results in a rapid rise of CDK activity at the end of G2 and thus, brings about an orderly progression into mitosis.
How cells correct deviations from a mean cell size at mitosis remains uncertain. Classical cell-size homeostasis models are the sizer, timer, and adder 1. Sizers postulate that cells divide at some ...threshold size; timers, that cells grow for a set time; and adders, that cells add a constant volume before division. Here, we show that a size-based probabilistic model of cell-size control at the G2/M transition (P(Div)) can generate realistic cell-size homeostasis in silico. In fission yeast cells, Cyclin BCdc13 scales with size, and we propose that this increases the likelihood of mitotic entry, while molecular noise in its expression adds a probabilistic component to the model. Varying Cdc13 expression levels exogenously using a newly developed tetracycline inducible promoter shows that both the level and variability of its expression influence cell size at division. Our results demonstrate that as cells grow larger, their probability of dividing increases, and this is sufficient to generate cell-size homeostasis. Size-correlated Cdc13 expression forms part of the molecular circuitry of this system.
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•A size-correlated division probability can generate cell-size homeostasis•Cyclin B concentration scales noisily with size in fission yeast•Cells with stochastically suprathreshold cyclin B are the ones that divide•A new tetracycline inducible promoter with linear dose response is developed
Patterson et al. show that a probabilistic model of cell-size control can generate cell-size homeostasis in fission yeast. Cyclin B levels scale noisily with cell size, providing a molecular correlate for the phenomenological model. Using a new inducible promoter, they show that cyclin B and cell-size variability scale proportionally.
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing ...the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.
Understanding nanoparticle uptake by biological cells is fundamentally important to wide-ranging fields from nanotoxicology to drug delivery. It is now accepted that the arrival of nanoparticles at ...the cell is an extremely complicated process, shaped by many factors including unique nanoparticle physico-chemical characteristics, protein-particle interactions and subsequent agglomeration, diffusion and sedimentation. Sequentially, the nanoparticle internalisation process itself is also complex, and controlled by multiple aspects of a cell's state. Despite this multitude of factors, here we demonstrate that the statistical distribution of the nanoparticle dose per endosome is independent of the initial administered dose and exposure duration. Rather, it is the number of nanoparticle containing endosomes that are dependent on these initial dosing conditions. These observations explain the heterogeneity of nanoparticle delivery at the cellular level and allow the derivation of simple, yet powerful probabilistic distributions that accurately predict the nanoparticle dose delivered to individual cells across a population.
Imaging flow cytometry combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. Here we demonstrate label-free prediction of DNA content and quantification ...of the mitotic cell cycle phases by applying supervised machine learning to morphological features extracted from brightfield and the typically ignored darkfield images of cells from an imaging flow cytometer. This method facilitates non-destructive monitoring of cells avoiding potentially confounding effects of fluorescent stains while maximizing available fluorescence channels. The method is effective in cell cycle analysis for mammalian cells, both fixed and live, and accurately assesses the impact of a cell cycle mitotic phase blocking agent. As the same method is effective in predicting the DNA content of fission yeast, it is likely to have a broad application to other cell types.
•Imaging flow cytometry enables potentially powerful, multiplexed single-cell analysis.•Data analysis techniques for imaging flow cytometry are largely manual and subjective.•Our machine learning ...workflow identifies phenotypes in imaging flow cytometry.•The workflow uses open-source software and does not require computational expertise.
Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.
Summary Background A detailed description of viral kinetics, duration of virus shedding, and intraviral evolution in different body sites is warranted to understand Ebola virus pathogenesis. Patients ...with Ebola virus infections admitted to university hospitals provide a unique opportunity to do such in-depth virological investigations. We describe the clinical, biological, and virological follow-up of a case of Ebola virus disease. Methods A 43-year-old medical doctor who contracted an Ebola virus infection in Sierra Leone on Nov 16, 2014 (day 1), was airlifted to Geneva University Hospitals, Geneva, Switzerland, on day 5 after disease onset. The patient received an experimental antiviral treatment of monoclonal antibodies (ZMAb) and favipiravir. We monitored daily viral load kinetics, estimated viral clearance, calculated the half-life of the virus in plasma, and analysed the viral genome via high-throughput sequencing, in addition to clinical and biological signs. Findings The patient recovered rapidly, despite an initial high viral load (about 1 × 107 RNA copies per mL 24 h after onset of fever). We noted a two-phase viral decay. The virus half-life decreased from about 26 h to 9·5 h after the experimental antiviral treatment. Compared with a consensus sequence of June 18, 2014, the isolate that infected this patient displayed only five synonymous nucleotide substitutions on the full genome (4901A→C, 7837C→T, 8712A→G, 9947T→C, 16201T→C) despite 5 months of human-to-human transmission. Interpretation This study emphasises the importance of virological investigations to fully understand the course of Ebola virus disease and adaptation of the virus. Whether the viral decay was caused by the effects of the immune response alone, an additional benefit from the antiviral treatment, or a combination of both is unclear. In-depth virological analysis and randomised controlled trials are needed before any conclusion on the potential effect of antiviral treatment can be drawn. Funding Geneva University Hospitals, Swiss Office of Public Health, Swiss Agency for Development and Cooperation, and Swiss National Science Foundation.
Maintenance of cell size homeostasis is a property that is conserved throughout eukaryotes. Cell size homeostasis is brought about by the co-ordination of cell division with cell growth and requires ...restriction of smaller cells from undergoing mitosis and cell division, whilst allowing larger cells to do so. Cyclin-CDK is the fundamental driver of mitosis and therefore ultimately ensures size homeostasis. Here we dissect determinants of CDK activity in vivo to investigate how cell size information is processed by the cell cycle network in fission yeast. We develop a high-throughput single-cell assay system of CDK activity in vivo and show that inhibitory tyrosine phosphorylation of CDK encodes cell size information, with the phosphatase PP2A aiding to set a size threshold for division. CDK inhibitory phosphorylation works synergistically with PP2A to prevent mitosis in smaller cells. Finally, we find that diploid cells of equivalent size to haploid cells exhibit lower CDK activity in response to equal cyclin-CDK enzyme concentrations, suggesting that CDK activity is reduced by increased DNA levels. Therefore, scaling of cyclin-CDK levels with cell size, CDK inhibitory phosphorylation, PP2A, and DNA-dependent inhibition of CDK activity, all inform the cell cycle network of cell size, thus contributing to cell size homeostasis.
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