Blue Intensity Matters for Cell Cycle Profiling in Fluorescence DAPI
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DNA Damage Checkpoint Dynamics Drive Cell Cycle Phase Transitions
bioRxiv preprint doi: https://doi.org/10.1101/137307; this version posted August 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. DNA damage checkpoint dynamics drive cell cycle phase transitions Hui Xiao Chao1,2, Cere E. Poovey1, Ashley A. Privette1, Gavin D. Grant3,4, Hui Yan Chao1, Jeanette G. Cook3,4, and Jeremy E. Purvis1,2,4,† 1Department of Genetics 2Curriculum for Bioinformatics and Computational Biology 3Department of Biochemistry and Biophysics 4Lineberger Comprehensive Cancer Center University of North Carolina, Chapel Hill 120 Mason Farm Road Chapel Hill, NC 27599-7264 †Corresponding Author: Jeremy Purvis Genetic Medicine Building 5061, CB#7264 120 Mason Farm Road Chapel Hill, NC 27599-7264 [email protected] ABSTRACT DNA damage checkpoints are cellular mechanisms that protect the integrity of the genome during cell cycle progression. In response to genotoxic stress, these checkpoints halt cell cycle progression until the damage is repaired, allowing cells enough time to recover from damage before resuming normal proliferation. Here, we investigate the temporal dynamics of DNA damage checkpoints in individual proliferating cells by observing cell cycle phase transitions following acute DNA damage. We find that in gap phases (G1 and G2), DNA damage triggers an abrupt halt to cell cycle progression in which the duration of arrest correlates with the severity of damage. However, cells that have already progressed beyond a proposed “commitment point” within a given cell cycle phase readily transition to the next phase, revealing a relaxation of checkpoint stringency during later stages of certain cell cycle phases. -
Neurogenic Decisions Require a Cell Cycle Independent Function of The
RESEARCH ARTICLE Neurogenic decisions require a cell cycle independent function of the CDC25B phosphatase Fre´ de´ ric Bonnet1†, Angie Molina1†, Me´ lanie Roussat1, Manon Azais2, Sophie Bel-Vialar1, Jacques Gautrais2, Fabienne Pituello1*, Eric Agius1* 1Centre de Biologie du De´veloppement, Centre de Biologie Inte´grative, Universite´ de Toulouse, CNRS, UPS, Toulouse, France; 2Centre de Recherches sur la Cognition Animale, Centre de Biologie Inte´grative., Universite´ de Toulouse, CNRS, UPS, Toulouse, France Abstract A fundamental issue in developmental biology and in organ homeostasis is understanding the molecular mechanisms governing the balance between stem cell maintenance and differentiation into a specific lineage. Accumulating data suggest that cell cycle dynamics play a major role in the regulation of this balance. Here we show that the G2/M cell cycle regulator CDC25B phosphatase is required in mammals to finely tune neuronal production in the neural tube. We show that in chick neural progenitors, CDC25B activity favors fast nuclei departure from the apical surface in early G1, stimulates neurogenic divisions and promotes neuronal differentiation. We design a mathematical model showing that within a limited period of time, cell cycle length modifications cannot account for changes in the ratio of the mode of division. Using a CDC25B point mutation that cannot interact with CDK, we show that part of CDC25B activity is independent *For correspondence: of its action on the cell cycle. [email protected] (FP); [email protected] (EA) †These authors contributed equally to this work Introduction In multicellular organisms, managing the development, homeostasis and regeneration of tissues Competing interests: The requires the tight control of self-renewal and differentiation of stem/progenitor cells. -
DAPI (4',6-Diamidino-2-Phenylindole, Dihydrochloride) for Nucleic Acid Staining
DAPI (4',6-Diamidino-2-Phenylindole, Dihydrochloride) for nucleic acid staining Catalog number: AR1176-10 Boster’s DAPI solution is a fluorescent dye with higher efficiency for immunofluorescence microscopy. This package insert must be read in its entirety before using this product. For research use only. Not for use in diagnostic procedures. BOSTER BIOLOGICAL TECHNOLOGY 3942 B Valley Ave, Pleasanton, CA 94566 Phone: 888-466-3604 Fax: 925-215-2184 Email:[email protected] Web: www.bosterbio.com DAPI (4',6-Diamidino-2-Phenylindole, Dihydrochloride) for nucleic acid staining Catalog Number: AR1176-10 Product Overview Material DAPI dihydrochloride (MW = 350.3) Form Liquid Size 10 mL(100 assys) Concentration 1μg/ml 8 mM sodium phosphate, 2 mM potassium phosphate, 140 mM sodium chloride, 10 Buffer mM potassium chloride; pH 7.4. Storage Upon receipt store at -20°C, protect from light. Stability When stored as directed, product should be stable for one year. Molecular formula C16H15N5 • 2 HCL Excitation: DAPI 340nm Emission: DAPI 488nm Excitation: DAPI-DNA complexes 360nm Emission: DAPI-DNA complexes 460nm Thermofisher (Product No. 62247); Thermofisher (Product No. 62248); Millipore Equivalent Sigma (Product No. D9542) BOSTER BIOLOGICAL TECHNOLOGY 3942 B Valley Ave, Pleasanton, CA 94566 Phone: 888-466-3604 Fax: 925-215-2184 Email:[email protected] Web: www.bosterbio.com Notes: Type of DAPI Molecular formula Molecular weight Catalog Number DAPI dihydrochloride C16H15N5 • 2 HCL 350.25 AR1176-10 DAPI dilactate C16H15N5 • 2 C3H6O3 457.48 N/A Introduction DAPI (4',6-diamidino-2-phenylindole) is a fluorescent dye which can bind DNA strands robustly, the fluorescence being detected by fluorescence microscope. -
On Cellular Automaton Approaches to Modeling Biological Cells
ON CELLULAR AUTOMATON APPROACHES TO MODELING BIOLOGICAL CELLS MARK S. ALBER∗, MARIA A. KISKOWSKIy , JAMES A. GLAZIERz , AND YI JIANGx Abstract. We discuss two different types of Cellular Automata (CA): lattice-gas- based cellular automata (LGCA) and the cellular Potts model (CPM), and describe their applications in biological modeling. LGCA were originally developed for modeling ideal gases and fluids. We describe several extensions of the classical LGCA model to self-driven biological cells. In partic- ular, we review recent models for rippling in myxobacteria, cell aggregation, swarming, and limb bud formation. These LGCA-based models show the versatility of CA in modeling and their utility in addressing basic biological questions. The CPM is a more sophisticated CA, which describes individual cells as extended objects of variable shape. We review various extensions to the original Potts model and describe their application to morphogenesis; the development of a complex spatial struc- ture by a collection of cells. We focus on three phenomena: cell sorting in aggregates of embryonic chicken cells, morphological development of the slime mold Dictyostelium discoideum and avascular tumor growth. These models include intercellular and extra- cellular interactions, as well as cell growth and death. 1. Introduction. Cellular automata (CA) consist of discrete agents or particles, which occupy some or all sites of a regular lattice. These par- ticles have one or more internal state variables (which may be discrete or continuous) and a set of rules describing the evolution of their state and position (in older models, particles usually occupied all lattice sites, one particle per node, and did not move). -
Epithelial Cell Death Analysis of Cell Cycle by Flow Cytometry White Paper
Epithelial Cell Death Analysis of Cell Cycle by Flow Cytometry White Paper Authors: Savithri Balasubramanian, John Tigges, Vasilis Toxavidis, Heidi Mariani. Affiliation: Beth Israel Deaconess Medical Center Harvard Stem Cell Institute a Beckman Coulter Life Sciences: Epithelial Cell Death Analysis of Cell Cycle by Flow Cytometry PRINCIPAL OF TECHNIQUE Background: Cell cycle, or cell-division cycle, is the series of events that takes place in a cell leading to its division and duplication (replication). In cells without a nucleus (prokaryotic), cell cycle occurs via a process termed binary fission. In cells with a nucleus (eukaryotes), cell cycle can be divided in two brief periods: interphase—during which the cell grows, accumulating nutrients needed for mitosis and duplicating its DNA—and the mitosis (M) phase, during which the cell splits itself into two distinct cells, often called «daughter cells». Cell-division cycle is a vital process by which a single-celled fertilized egg develops into a mature organism, as well as the process by which hair, skin, blood cells, and some internal organs are renewed. Cell cycle consists of four distinct phases: G1 phase, S phase (synthesis), G2 phase (collectively known as interphase) and M phase (mitosis). M phase is itself composed of two tightly coupled processes: mitosis, in which the cell’s chromosomes are divided between the two daughter cells, and cytokinesis, in which the cell’s cytoplasm divides in half forming distinct cells. Activation of each phase is dependent on the proper progression and completion of the previous one. Cells that have temporarily or reversibly stopped dividing are said to have entered a state of quiescence called G0 phase. -
Proteomic and Bioinformatic Investigation of Altered Pathways in Neuroglobin-Deficient Breast Cancer Cells
molecules Article Proteomic and Bioinformatic Investigation of Altered Pathways in Neuroglobin-Deficient Breast Cancer Cells Michele Costanzo 1,2 , Marco Fiocchetti 3 , Paolo Ascenzi 3, Maria Marino 3 , Marianna Caterino 1,2,* and Margherita Ruoppolo 1,2,* 1 Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy; [email protected] 2 CEINGE—Biotecnologie Avanzate S.C.Ar.L., 80145 Naples, Italy 3 Department of Science, University Roma Tre, 00146 Rome, Italy; marco.fi[email protected] (M.F.); [email protected] (P.A.); [email protected] (M.M.) * Correspondence: [email protected] (M.C.); [email protected] (M.R.) Abstract: Neuroglobin (NGB) is a myoglobin-like monomeric globin that is involved in several processes, displaying a pivotal redox-dependent protective role in neuronal and extra-neuronal cells. NGB remarkably exerts its function upon upregulation by NGB inducers, such as 17β-estradiol (E2) and H2O2. However, the molecular bases of NGB’s functions remain undefined, mainly in non- neuronal cancer cells. Human MCF-7 breast cancer cells with a knocked-out (KO) NGB gene obtained using CRISPR/Cas9 technology were analyzed using shotgun label-free quantitative proteomics in comparison with control cells. The differential proteomics experiments were also performed after treatment with E2, H2O2, and E2 + H2O2. All the runs acquired using liquid chromatography–tandem mass spectrometry were elaborated within the same MaxQuant analysis, leading to the quantification Citation: Costanzo, M.; Fiocchetti, of 1872 proteins in the global proteomic dataset. Then, a differentially regulated protein dataset was M.; Ascenzi, P.; Marino, M.; Caterino, M.; Ruoppolo, M. -
Cell Cycle Profile Using the Cellometer Vision Image Cytometer
Measuring Drug Effect on Cell Cycle Profile Using the Cellometer Vision Image Cytometer Nexcelom Bioscience LLC. | 360 Merrimack Street, Building 9 | Lawrence, MA 01843 T: 978.327.5340 | F: 978.327.5341 | E: [email protected] | www.nexcelom.com 1001276 Rev. A Measuring Drug Effect on Cell Cycle Profile Using the Cellometer Vision Image Cytometer Introduction Cell cycle analysis is a commonly used assay in both clinical diagnosis and biomedical research. This analysis distinguishes cells in different phases of cell cycle and is often used to determine cellular response to drugs and biological stimulations [1, 2]. Because this assay is based on measuring the DNA content in a cell population, it can also be used to analyze DNA fragmentation during apoptosis, requiring multicolor fluorescent staining of biomarkers and DNA [3]. Recently, a small desktop imaging cytometry system (Cellometer Vision) has been developed by Nexcelom Bioscience LLC for automated cell concentration and viability measurement using bright-field (BR) and fluorescent (FL) imaging methods [4]. The system can perform rapid cell enumeration using disposable counting slides. The software utilizes a novel counting algorithm for accurate and consistent measurement of cell concentration and viability on a variety of cell types [5]. By developing fluorescent-based cell cycle assays, the Cellometer imaging cytometry can provide a quick, simple, and inexpensive alternative for biomedical research, which may be beneficial for smaller research laboratories and clinics. In this work, we demonstrate new applications of the Cellometer Vision for fluorescence- based cell population analysis as an alternative for flow cytometry. Cell cycle analysis was performed by inducing specific arrest in G0/G1, S, and G2/M phase of Jurkat cell population with aphidicolin, etoposide, and nocodazole, respectively [6-8]. -
Increased Processing of SINE B2 Ncrnas Unveils a Novel Type Of
RESEARCH ARTICLE Increased processing of SINE B2 ncRNAs unveils a novel type of transcriptome deregulation in amyloid beta neuropathology Yubo Cheng1,2,3,4†, Luke Saville1,2,3,4†, Babita Gollen1,2,3,4†, Christopher Isaac1,2,3,4†, Abel Belay1,2,3,4, Jogender Mehla3, Kush Patel1,2,3, Nehal Thakor1,2,3, Majid H Mohajerani3, Athanasios Zovoilis1,2,3,4* 1Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Canada; 2Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, Canada; 3Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Canada; 4Alberta RNA Research and Training Institute, University of Lethbridge, Lethbridge, Canada Abstract The functional importance of many non-coding RNAs (ncRNAs) generated by repetitive elements and their connection with pathologic processes remains elusive. B2 RNAs, a class of ncRNAs of the B2 family of SINE repeats, mediate through their processing the transcriptional activation of various genes in response to stress. Here, we show that this response is dysfunctional during amyloid beta toxicity and pathology in the mouse hippocampus due to increased levels of B2 RNA processing, leading to constitutively elevated B2 RNA target gene expression and high Trp53 levels. Evidence indicates that Hsf1, a master regulator of stress response, mediates B2 RNA *For correspondence: processing in hippocampal cells and is activated during amyloid toxicity, accelerating the [email protected] processing of SINE RNAs and gene hyper-activation. Our study reveals that in mouse, SINE RNAs † constitute a novel pathway deregulated in amyloid beta pathology, with potential implications for These authors contributed equally to this work similar cases in the human brain, such as Alzheimer’s disease (AD). -
Genetic Algorithms and Their Application to In-Silico Evolution of Genetic Regulatory Networks
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Hertfordshire Research Archive Genetic Algorithms and their Application to in-silico Evolution of Genetic Regulatory Networks Johannes F. Knabe‡†, Katja Wegner†, Chrystopher L. Nehaniv‡†, and Maria J. Schilstra†* †Biological and Neural Computation Laboratory, and ‡Adaptive Systems Research Group, STRI, University of Hertfordshire, Hatfield, AL10 9AB United Kingdom *To whom correspondence should be addressed ([email protected]) i. Abstract A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through “mutation” and “recombination” in a “population” of possible solutions to a problem, encoded as strings of characters in “genomes”, and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the “fitter” individuals. GA’s are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought. In nature, genetic regulatory networks (GRN’s) form the basic control layer in the regulation of gene expression levels. GRN’s are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GA’s may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. -
Self-Reproduction Based on Turing Machines
MEASURES OF ORGANIZATION IN A MODEL OF CELLULAR SELF-REPRODUCTION BASED ON TURING MACHINES WALTER R. STAHL OREGON REGIONAL PRIMATE RESEARCH CENTER BEAVERTON, OREGON 1. Introductory summary The late John von Neumann first realized that self-reproduction, as observed in biological systems, was a legitimate problem of mathematics, specifically of automata and algorithm theory. He founded the study of self-reproduction in systems of "natural automata." This report deals with the self-organizing prop- erties of a new type of cellular self-reproduction model. The action of enzymes is simulated by Turing machines acting as molecular automata or computers, with their highly standardized coding corresponding to genes of the cell. Com- puter experiments have been done to test the stability and logical homeostatic properties of a 36 gene cell model. It is shown that the system will continue to organize itself and reproduce in spite of a variety of environmental deficiencies and disturbances, and also to repair itself after certain kinds of injury. The most pertinent organizational criteria for the described model are amounts of "energy" and "time cycles" needed to reach maturity and reproduce. Comparisons are drawn between these organizational measures and thermodynamic informa- tional parameters such as Gibbsean chemical potential and entropy. The total amount of genetic and cellular information can be quantified in the cell model and this helps clarify some confusing aspects of genetic information measures. The model is highly idealized. It bears somewhat the same relationship to real cells as computer circuits do to the brain, but shows that automata theory can be applied to molecular biology in a meaningful way. -
Metabolic Network Modeling with Model Organisms
Available online at www.sciencedirect.com ScienceDirect Metabolic network modeling with model organisms L Safak Yilmaz and Albertha JM Walhout Flux balance analysis (FBA) with genome-scale metabolic reconstruct a genome-scale metabolic network model network models (GSMNM) allows systems level predictions of (GSMNM), which is then used to calculate the flux metabolism in a variety of organisms. Different types of distribution over the entire network in any defined con- predictions with different accuracy levels can be made dition for the organism (see below). For model organisms, depending on the applied experimental constraints ranging high-quality genomic annotations allow the reconstruc- from measurement of exchange fluxes to the integration of tion of comprehensive GSMNMs that can be parame- gene expression data. Metabolic network modeling with model terized and validated with publically available experi- organisms has pioneered method development in this field. In mental datasets. In addition, since many properties of addition, model organism GSMNMs are useful for basic metabolic networks are conserved across taxa, model understanding of metabolism, and in the case of animal organism GSMNMs can be used to study human disease models, for the study of metabolic human diseases. Here, we with animal models, while plant models can instruct discuss GSMNMs of most highly used model organisms with agriculture. the emphasis on recent reconstructions. Here, we first summarize the basics of genome-scale meta- Address bolic network modeling, and then explore GSMNMs Program in Systems Biology, Program in Molecular Medicine, University and their applications in common model organisms [3] of Massachusetts Medical School, Worcester, MA, United States (Figure 1). -
Introduction to DNA Cell Cycle Analysis
INTRODUCTION TO CELL CYCLE ANALYSIS Written by Dr. Peter Rabinovitch, M.D., Ph.D. Published by Phoenix Flow Systems, Inc. 6790 Top Gun St. #1 San Diego, CA. USA 92121 858 453-5095 fax 858 453-2117 The Biological Cell Cycle .........................2 DNA Analysis and the Flow Cytometric Cell Cycle ....................3 Diploid and Aneuploid DNA Contents......5 Cell Cycle Analysis of DNA Content Histograms.........................6 Fitting of Background “Debris” and Effects of Nucleus Sectioning..................9 Fitting and Correction for the Effects of Cellular or Nuclear Aggregation.........16 Quantitation of Background, Aggregates And Debris.....................................................31 Analysis of Apoptosis.............................31 Beyond Single Parameter Analysis: DNA vs. Immunofluorescence................32 Basics of DNA Cell Cycle Analysis www.phoenixflow.com Page 1 This chapter is organized into progressively more advanced sections. Feel free to skip ahead to the level appropriate for your background. THE BIOLOGICAL CELL CYCLE Reproduction of cells requires cell division, with production of two daughter cells. The most obvious cellular structure that requires duplication and division into daughter cells is the cell nucleus - the repository of the cell's genetic material, DNA. With few exceptions each cell in an organism contains the same amount of DNA and the same complement of chromosomes. Thus, cells must duplicate their allotment of DNA prior to division so that each daughter will receive the same DNA content as the parent. The cycle of increase in components (growth) and division, followed by growth and division of these daughter cells, etc., is called the cell cycle. The two most obvious features of the cell cycle are the synthesis and duplication of nuclear DNA before division, and the process of cellular division itself - mitosis.