1
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Ponce Dawson S. Biological physics to uncover cell signaling. Biophys Rev 2025; 17:271-283. [PMID: 40376425 PMCID: PMC12075082 DOI: 10.1007/s12551-025-01308-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2024] [Accepted: 03/21/2025] [Indexed: 05/18/2025] Open
Abstract
In this report, I describe some of the subjects and problems that we have addressed over the last 25 years in the area of cell signaling using the tools of biological physics. The report covers part of our work on intracellular Ca2 + signals, pattern formation, transport of messengers in the interior of cells, quantification of biophysical parameters from experiments, and information transmission. The description includes both our modeling and experimental work highlighting how the tools of physics can give useful insights into the workings of biological systems.
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Affiliation(s)
- Silvina Ponce Dawson
- Physics Department, UBA-FCEN, Ciudad Universitaria, Pab I, Buenos Aires, 1428 Argentina
- IFIBA, CONICET-UBA, Ciudad Universitaria, Pab I, Buenos Aires, 1428 Argentina
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2
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Givré A, Colman-Lerner A, Ponce Dawson S. Amplitude and frequency encoding result in qualitatively distinct informational landscapes in cell signaling. Sci Rep 2025; 15:8075. [PMID: 40057610 PMCID: PMC11890874 DOI: 10.1038/s41598-025-92424-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Accepted: 02/27/2025] [Indexed: 05/13/2025] Open
Abstract
Cells continuously sense their surroundings to detect modifications and generate responses. Very often changes in extracellular concentrations initiate signaling cascades that eventually result in changes in gene expression. Increasing stimulus strengths can be encoded in increasing concentration amplitudes or increasing activation frequencies of intermediaries of the pathway. In this paper we show that the different way in which amplitude and frequency encoding map environmental changes endow cells with qualitatively different information transmission capabilities. While amplitude encoding is optimal for a limited range of stimuli strengths, frequency encoding can transmit information with equal reliability over much broader ranges. The qualitative difference between the two strategies stems from the scale invariant discriminating power of the first transducing step in frequency codification. The apparently redundant combination of both strategies in some cell types may then serve the purpose of expanding the span over which stimulus strengths can be reliably discriminated. In this paper we discuss a possible example of this mechanism in yeast.
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Affiliation(s)
- Alan Givré
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, UBA, Buenos Aires, Argentina
- Instituto de Física de Buenos Aires (IFIBA), CONICET-UBA, Buenos Aires, Argentina
| | - Alejandro Colman-Lerner
- Departamento de Fisiología, Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), CONICET-UBA, Buenos Aires, Argentina
| | - Silvina Ponce Dawson
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, UBA, Buenos Aires, Argentina.
- Instituto de Física de Buenos Aires (IFIBA), CONICET-UBA, Buenos Aires, Argentina.
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3
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Lyons AC, Mehta S, Zhang J. Fluorescent biosensors illuminate the spatial regulation of cell signaling across scales. Biochem J 2023; 480:1693-1717. [PMID: 37903110 PMCID: PMC10657186 DOI: 10.1042/bcj20220223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023]
Abstract
As cell signaling research has advanced, it has become clearer that signal transduction has complex spatiotemporal regulation that goes beyond foundational linear transduction models. Several technologies have enabled these discoveries, including fluorescent biosensors designed to report live biochemical signaling events. As genetically encoded and live-cell compatible tools, fluorescent biosensors are well suited to address diverse cell signaling questions across different spatial scales of regulation. In this review, methods of examining spatial signaling regulation and the design of fluorescent biosensors are introduced. Then, recent biosensor developments that illuminate the importance of spatial regulation in cell signaling are highlighted at several scales, including membranes and organelles, molecular assemblies, and cell/tissue heterogeneity. In closing, perspectives on how fluorescent biosensors will continue enhancing cell signaling research are discussed.
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Affiliation(s)
- Anne C. Lyons
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, U.S.A
- Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, U.S.A
| | - Sohum Mehta
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, U.S.A
| | - Jin Zhang
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, U.S.A
- Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, U.S.A
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093, U.S.A
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, U.S.A
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4
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Ho KKY, Srivastava S, Kinnunen PC, Garikipati K, Luker GD, Luker KE. Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways. Bioengineering (Basel) 2023; 10:bioengineering10020269. [PMID: 36829763 PMCID: PMC9952091 DOI: 10.3390/bioengineering10020269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/24/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023] Open
Abstract
Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.
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Affiliation(s)
- Kenneth K. Y. Ho
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Siddhartha Srivastava
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Patrick C. Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Krishna Garikipati
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Computational Discovery & Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gary D. Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (G.D.L.); (K.E.L.)
| | - Kathryn E. Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (G.D.L.); (K.E.L.)
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5
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Pizzoni A, Zhang X, Naim N, Altschuler DL. Soluble cyclase-mediated nuclear cAMP synthesis is sufficient for cell proliferation. Proc Natl Acad Sci U S A 2023; 120:e2208749120. [PMID: 36656863 PMCID: PMC9942871 DOI: 10.1073/pnas.2208749120] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/09/2022] [Indexed: 01/20/2023] Open
Abstract
cAMP, a key player in many physiological processes, was classically considered to originate solely from the plasma membrane (PM). This view was recently challenged by observations showing that upon internalization GsPCRs can sustain signaling from endosomes and/or the trans-Golgi network (TGN). In this new view, after the first PM-generated cAMP wave, the internalization of GsPCRs and ACs generates a second wave that was strictly associated with nuclear transcriptional events responsible for triggering specific biological responses. Here, we report that the endogenously expressed TSHR, a canonical GsPCR, triggers an internalization-dependent, calcium-mediated nuclear sAC activation that drives PKA activation and CREB phosphorylation. Both pharmacological and genetic sAC inhibition, which did not affect the cytosolic cAMP levels, blunted nuclear cAMP accumulation, PKA activation, and cell proliferation, while an increase in nuclear sAC expression significantly enhanced cell proliferation. Furthermore, using novel nuclear-targeted optogenetic actuators, we show that light-stimulated nuclear cAMP synthesis can mimic the proliferative action of TSH by activating PKA and CREB. Therefore, based on our results, we propose a novel three-wave model in which the "third" wave of cAMP is generated by nuclear sAC. Despite being downstream of events occurring at the PM (first wave) and endosomes/TGN (second wave), the nuclear sAC-generated cAMP (third wave) is sufficient and rate-limiting for thyroid cell proliferation.
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Affiliation(s)
- Alejandro Pizzoni
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA15261
| | - Xuefeng Zhang
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA15261
| | - Nyla Naim
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA15261
| | - Daniel L. Altschuler
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA15261
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6
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A closed-loop multi-scale model for intrinsic frequency-dependent regulation of axonal growth. Math Biosci 2021; 344:108768. [PMID: 34952037 DOI: 10.1016/j.mbs.2021.108768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/10/2021] [Accepted: 12/15/2021] [Indexed: 11/22/2022]
Abstract
This article develops a closed-loop multi-scale model for axon length regulation based on a frequency-dependent negative feedback mechanism. It builds on earlier models by linking molecular motor dynamics to signaling delays that then determine signal oscillation period. The signal oscillation is treated as a front end for a signaling pathway that modulates axonal length. This model is used to demonstrate the feasibility of such a mechanism and is tested against two previously published reports in which experimental manipulations were performed that resulted in axon growth. The model captures these observations and yields an expression for equilibrium axonal length. One major prediction of the model is that increasing motor density in the body of an axon results in axonal growth-this idea has not yet been explored experimentally.
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7
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Guo L, Zhu K, Pargett M, Contreras A, Tsai P, Qing Q, Losert W, Albeck J, Zhao M. Electrically synchronizing and modulating the dynamics of ERK activation to regulate cell fate. iScience 2021; 24:103240. [PMID: 34746704 PMCID: PMC8554532 DOI: 10.1016/j.isci.2021.103240] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/18/2021] [Accepted: 10/05/2021] [Indexed: 11/18/2022] Open
Abstract
Intracellular signaling dynamics play fundamental roles in cell biology. Precise modulation of the amplitude, duration, and frequency of signaling activation will be a powerful approach to investigate molecular mechanisms as well as to engineer signaling to control cell behaviors. Here, we showed a practical approach to achieve precise amplitude modulation (AM), frequency modulation (FM), and duration modulation (DM) of MAP kinase activation. Alternating current (AC) electrical stimulation induced synchronized ERK activation. Amplitude and duration of ERK activation were controlled by varying stimulation strength and duration. ERK activation frequencies were arbitrarily modulated with trains of short AC applications with accurately defined intervals. Significantly, ERK dynamics coded by well-designed AC can rewire PC12 cell fate independent of growth factors. This technique can be used to synchronize and modulate ERK activation dynamics, thus would offer a practical way to control cell behaviors in vivo without the use of biochemical agents or genetic manipulation. Alternating-current (AC) electric field activates ERK independently of growth factors AC stimulation length modulates the amplitude and duration of ERK activation On-off time interval of AC modulates the frequency of ERK activation peaks Electrical modulation of ERK dynamics promotes neuronal differentiation of PC12 cells
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Affiliation(s)
- Liang Guo
- Department of Ophthalmology & Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Davis, Sacramento, CA 95817, USA.,College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China
| | - Kan Zhu
- Department of Ophthalmology & Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Davis, Sacramento, CA 95817, USA
| | - Michael Pargett
- Department of Molecular and Cellular Biology, University of California Davis, Davis, CA 95616, USA
| | - Adam Contreras
- Department of Ophthalmology & Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Davis, Sacramento, CA 95817, USA
| | - Patrick Tsai
- Department of Ophthalmology & Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Davis, Sacramento, CA 95817, USA
| | - Quan Qing
- Department of Physics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA
| | - Wolfgang Losert
- Department of Physics, Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
| | - John Albeck
- Department of Molecular and Cellular Biology, University of California Davis, Davis, CA 95616, USA
| | - Min Zhao
- Department of Ophthalmology & Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Davis, Sacramento, CA 95817, USA
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8
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Maity A, Wollman R. Information transmission from NFkB signaling dynamics to gene expression. PLoS Comput Biol 2020; 16:e1008011. [PMID: 32797040 PMCID: PMC7478807 DOI: 10.1371/journal.pcbi.1008011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 09/08/2020] [Accepted: 06/02/2020] [Indexed: 02/06/2023] Open
Abstract
The dynamic signal encoding paradigm suggests that information flows from the extracellular environment into specific signaling patterns (encoding) that are then read by downstream effectors to control cellular behavior. Previous work empirically quantified the information content of dynamic signaling patterns. However, whether this information can be faithfully transmitted to the gene expression level is unclear. Here we used NFkB signaling as a model to understand the accuracy of information transmission from signaling dynamics into gene expression. Using a detailed mathematical model, we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allows us to determine information loss in transmission between receptors to dynamic signaling patterns and between signaling dynamics to gene expression. Information loss could occur due to biochemical noise or due to a lack of specificity. We found that noise-free gene expression has very little information loss suggesting that gene expression can preserve specificity in NFkB patterns. As expected, the addition of noise to the gene expression model results in information loss. Interestingly, this effect can be mitigated by a specific choice of parameters that can substantially reduce information loss due to biochemical noise during gene expression. Overall our results show that the cellular capacity for information transmission from dynamic signaling patterns to gene expression can be high enough to preserve ligand specificity and thereby the accuracy of cellular response to environmental cues.
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Affiliation(s)
- Alok Maity
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
| | - Roy Wollman
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America
- Departments of Integrative Biology and Physiology and Chemistry and Biochemistry, University of California UCLA, California, United States of America
- * E-mail:
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9
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Wang H, Liu P, Li Q, Zhou T. Entangled signal pathways can both control expression stability and induce stochastic focusing. FEBS Lett 2018; 592:1135-1149. [DOI: 10.1002/1873-3468.13012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 01/28/2018] [Accepted: 02/08/2018] [Indexed: 11/08/2022]
Affiliation(s)
- Haohua Wang
- Department of Mathematics College of Information Science and Technology Hainan University Haikou China
| | - Peijiang Liu
- School of Statistics and Mathematics Guangdong University of Finance & Economics Guangzhou China
| | - Qingqing Li
- Guangdong Province Key Laboratory of Computational Science School of Mathematics Sun Yat‐Sen University Guangzhou China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science School of Mathematics Sun Yat‐Sen University Guangzhou China
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10
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Network Motifs Capable of Decoding Transcription Factor Dynamics. Sci Rep 2018; 8:3594. [PMID: 29483553 PMCID: PMC5827039 DOI: 10.1038/s41598-018-21945-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 02/13/2018] [Indexed: 11/08/2022] Open
Abstract
Transcription factors (TFs) can encode the information of upstream signal in terms of its temporal activation dynamics. However, it remains unclear how different types of TF dynamics are decoded by downstream signalling networks. In this work, we studied all three-node transcriptional networks for their ability to distinguish two types of TF dynamics: amplitude modulation (AM), where the TF is activated with a constant amplitude, and frequency modulation (FM), where the TF activity displays an oscillatory behavior. We found two sets of network topologies: one set can differentially respond to AM TF signal but not to FM; the other set to FM signal but not to AM. Interestingly, there is little overlap between the two sets. We identified the prevalent topological features in each set and gave a mechanistic explanation as to why they can differentially respond to only one type of TF signal. We also found that some network topologies have a weak (not robust) ability to differentially respond to both AM and FM input signals by using different values of parameters for AM and FM cases. Our results provide a novel network mechanism for decoding different TF dynamics.
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11
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Mestre ALG, Inácio PMC, Elamine Y, Asgarifar S, Lourenço AS, Cristiano MLS, Aguiar P, Medeiros MCR, Araújo IM, Ventura J, Gomes HL. Extracellular Electrophysiological Measurements of Cooperative Signals in Astrocytes Populations. Front Neural Circuits 2017; 11:80. [PMID: 29109679 PMCID: PMC5660104 DOI: 10.3389/fncir.2017.00080] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 10/06/2017] [Indexed: 01/28/2023] Open
Abstract
Astrocytes are neuroglial cells that exhibit functional electrical properties sensitive to neuronal activity and capable of modulating neurotransmission. Thus, electrophysiological recordings of astroglial activity are very attractive to study the dynamics of glial signaling. This contribution reports on the use of ultra-sensitive planar electrodes combined with low noise and low frequency amplifiers that enable the detection of extracellular signals produced by primary cultures of astrocytes isolated from mouse cerebral cortex. Recorded activity is characterized by spontaneous bursts comprised of discrete signals with pronounced changes on the signal rate and amplitude. Weak and sporadic signals become synchronized and evolve with time to higher amplitude signals with a quasi-periodic behavior, revealing a cooperative signaling process. The methodology presented herewith enables the study of ionic fluctuations of population of cells, complementing the single cells observation by calcium imaging as well as by patch-clamp techniques.
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Affiliation(s)
- Ana L G Mestre
- Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal.,Instituto de Telecomunicações, Lisboa, Portugal
| | - Pedro M C Inácio
- Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal.,Instituto de Telecomunicações, Lisboa, Portugal
| | - Youssef Elamine
- Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal.,Instituto de Telecomunicações, Lisboa, Portugal
| | - Sanaz Asgarifar
- Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal.,Instituto de Telecomunicações, Lisboa, Portugal
| | - Ana S Lourenço
- Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, Faro, Portugal.,Centro de Investigação em Biomedicina, Universidade do Algarve, Faro, Portugal
| | - Maria L S Cristiano
- Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal.,Centro de Ciências do Mar, Universidade do Algarve, Faro, Portugal
| | - Paulo Aguiar
- Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Maria C R Medeiros
- Departamento de Engenharia Electrotécnica e de Computadores, Instituto de Telecomunicações, Universidade de Coimbra, Coimbra, Portugal
| | - Inês M Araújo
- Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, Faro, Portugal.,Centro de Investigação em Biomedicina, Universidade do Algarve, Faro, Portugal
| | - João Ventura
- Departamento de Física e Astronomia, Instituto de Física dos Materiais da Universidade do Porto, Instituto de Nanociências e Nanotecnologia, Universidade do Porto, Porto, Portugal
| | - Henrique L Gomes
- Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal.,Instituto de Telecomunicações, Lisboa, Portugal
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12
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Dessauges C, Pertz O. Developmental ERK Signaling Goes Digital. Dev Cell 2017; 42:443-444. [PMID: 28898676 DOI: 10.1016/j.devcel.2017.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Reporting in Developmental Cell, de la Cova et al. (2017) present a biosensor to measure ERK activity dynamics in C. elegans larvae. They find that fate decision signaling involves frequency-modulated, digital ERK activity pulses. These findings may explain how graded morphogen signals are converted into precise and robust cell fate patterns.
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Affiliation(s)
- Coralie Dessauges
- Institute of Cell Biology, University of Bern, Baltzerstrasse 4, 3012 Bern, Switzerland
| | - Olivier Pertz
- Institute of Cell Biology, University of Bern, Baltzerstrasse 4, 3012 Bern, Switzerland.
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13
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Phillips NE, Manning C, Papalopulu N, Rattray M. Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes. PLoS Comput Biol 2017; 13:e1005479. [PMID: 28493880 PMCID: PMC5444866 DOI: 10.1371/journal.pcbi.1005479] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 05/25/2017] [Accepted: 03/24/2017] [Indexed: 12/05/2022] Open
Abstract
Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5’ LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package. Technological advances now allow us to observe gene expression in real-time at a single-cell level. In a wide variety of biological contexts this new data has revealed that gene expression is highly dynamic and possibly oscillatory. It is thought that periodic gene expression may be useful for keeping track of time and space, as well as transmitting information about signalling cues. Classifying a time series as periodic from single cell data is difficult because it is necessary to distinguish whether peaks and troughs are generated from an underlying oscillator or whether they are aperiodic fluctuations. To this end, we present a novel tool to classify live-cell data as oscillatory or non-oscillatory that accounts for inherent biological noise. We first demonstrate that the method outperforms a competing scheme in classifying computationally simulated single-cell data, and we subsequently analyse live-cell imaging time series. Our method is able to successfully detect oscillations in a known genetic oscillator, but it classifies data from a constitutively expressed gene as aperiodic. The method forms a basis for discovering new gene expression oscillators and quantifying how oscillatory activity alters in response to changes in cell fate and environmental or genetic perturbations.
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Affiliation(s)
- Nick E. Phillips
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Cerys Manning
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Nancy Papalopulu
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- * E-mail: (NP); (MR)
| | - Magnus Rattray
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- * E-mail: (NP); (MR)
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14
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Liu P, Wang H, Huang L, Zhou T. The dynamic mechanism of noisy signal decoding in gene regulation. Sci Rep 2017; 7:42128. [PMID: 28176840 PMCID: PMC5296728 DOI: 10.1038/srep42128] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 01/06/2017] [Indexed: 11/08/2022] Open
Abstract
Experimental evidence supports that signaling pathways can induce different dynamics of transcription factor (TF) activation, but how an input signal is encoded by such a dynamic, noisy TF and further decoded by downstream genes remains largely unclear. Here, using a system of stochastic transcription with signal regulation, we show that (1) keeping the intensity of the signal noise invariant but prolonging the signal duration can both enhance the mutual information (MI) and reduce the energetic cost (EC); (2) if the signal duration is fixed, the larger MI needs the larger EC, but if the signal period is fixed, there is an optimal time that the signal spends at one lower branch, such that MI reaches the maximum; (3) if both the period and the duration are simultaneously fixed, increasing the input noise can always enhance MI in the case of transcription regulation rather than in the case of degradation regulation. In addition, we find that the input noise can induce stochastic focusing in a regulation-dependent manner. These results reveal not only the dynamic mechanism of noisy signal decoding in gene regulation but also the essential role of external noise in controlling gene expression levels.
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Affiliation(s)
- Peijiang Liu
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
| | - Haohua Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
- Department of Mathematics College of Information Science and Technology Hainan University, Haikou 570228, People’s Republic of China
| | - Lifang Huang
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, People’s Republic of China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
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15
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Mackay L, Mikolajewicz N, Komarova SV, Khadra A. Systematic Characterization of Dynamic Parameters of Intracellular Calcium Signals. Front Physiol 2016; 7:525. [PMID: 27891096 PMCID: PMC5102910 DOI: 10.3389/fphys.2016.00525] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 10/24/2016] [Indexed: 12/14/2022] Open
Abstract
Dynamic processes, such as intracellular calcium signaling, are hallmark of cellular biology. As real-time imaging modalities become widespread, a need for analytical tools to reliably characterize time-series data without prior knowledge of the nature of the recordings becomes more pressing. The goal of this study is to develop a signal-processing algorithm for MATLAB that autonomously computes the parameters characterizing prominent single transient responses (TR) and/or multi-peaks responses (MPR). The algorithm corrects for signal contamination and decomposes experimental recordings into contributions from drift, TRs, and MPRs. It subsequently provides numerical estimates for the following parameters: time of onset after stimulus application, activation time (time for signal to increase from 10 to 90% of peak), and amplitude of response. It also provides characterization of the (i) TRs by quantifying their area under the curve (AUC), response duration (time between 1/2 amplitude on ascent and descent of the transient), and decay constant of the exponential decay region of the deactivation phase of the response, and (ii) MPRs by quantifying the number of peaks, mean peak magnitude, mean periodicity, standard deviation of periodicity, oscillatory persistence (time between first and last discernable peak), and duty cycle (fraction of period during which system is active) for all the peaks in the signal, as well as coherent oscillations (i.e., deterministic spikes). We demonstrate that the signal detection performance of this algorithm is in agreement with user-mediated detection and that parameter estimates obtained manually and algorithmically are correlated. We then apply this algorithm to study how metabolic acidosis affects purinergic (P2) receptor-mediated calcium signaling in osteoclast precursor cells. Our results reveal that acidosis significantly attenuates the amplitude and AUC calcium responses at high ATP concentrations. Collectively, our data validated this algorithm as a general framework for comprehensively analyzing dynamic time-series.
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Affiliation(s)
- Laurent Mackay
- Department of Physiology, McGill University Montreal, QC, Canada
| | - Nicholas Mikolajewicz
- Faculty of Dentistry, McGill UniversityMontreal, QC, Canada; Shriners Hospital for Children-CanadaMontreal, QC, Canada
| | - Svetlana V Komarova
- Faculty of Dentistry, McGill UniversityMontreal, QC, Canada; Shriners Hospital for Children-CanadaMontreal, QC, Canada
| | - Anmar Khadra
- Department of Physiology, McGill University Montreal, QC, Canada
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16
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Zhang C, Tsoi R, Wu F, You L. Processing Oscillatory Signals by Incoherent Feedforward Loops. PLoS Comput Biol 2016; 12:e1005101. [PMID: 27623175 PMCID: PMC5021367 DOI: 10.1371/journal.pcbi.1005101] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 08/04/2016] [Indexed: 11/19/2022] Open
Abstract
From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose. From circadian clocks to ultradian rhythms, oscillatory signals are found ubiquitously in nature. These oscillations are crucial in the regulation of cellular processes. While the fundamental design principles underlying the generation of these oscillations are extensively studied, the mechanisms for decoding these signals are underappreciated. With implications in both the basic understanding of how cells process temporal signals and the design of synthetic systems, we use quantitative modeling to probe one mechanism, the counting of pulses. We demonstrate the capability of an Incoherent Feedforward Loop motif for the differentiation between sustained and oscillatory input signals.
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Affiliation(s)
- Carolyn Zhang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Ryan Tsoi
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Feilun Wu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, United States of America
- * E-mail:
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17
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Wang H, Yuan Z, Liu P, Zhou T. Mechanisms of information decoding in a cascade system of gene expression. Phys Rev E 2016; 93:052411. [PMID: 27300928 DOI: 10.1103/physreve.93.052411] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Indexed: 06/06/2023]
Abstract
Biotechnology advances have allowed investigation of heterogeneity of cellular responses to stimuli on the single-cell level. Functionally, this heterogeneity can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. However, the mechanism of how this response heterogeneity is generated remains elusive. Here, by systematically analyzing a representative cellular signaling system, we show that (1) the upstream activator always amplifies the downstream burst frequency (BF) but the noiseless activator performs better than the noisy one, remarkably for small or moderate input signal strengths, and the repressor always reduces the downstream BF but the difference in the reducing effect between noiseless and noise repressors is very small; (2) both the downstream burst size and mRNA mean are a monotonically increasing function of the activator strength but a monotonically decreasing function of the repressor strength; (3) for repressor-type input, there is a noisy signal strength such that the downstream mRNA noise arrives at an optimal level, but for activator-type input, the output noise intensity is fundamentally a monotonically decreasing function of the input strength. Our results reveal the essential mechanisms of both signal information decoding and cellular response heterogeneity, whereas our analysis provides a paradigm for analyzing dynamics of noisy biochemical signaling systems.
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Affiliation(s)
- Haohua Wang
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
- Department of Mathematics, College of Information Science and Technology, Hainan University, Haikou 570228, People's Republic of China
| | - Zhanjiang Yuan
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Peijiang Liu
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Tianshou Zhou
- School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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18
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Aquino G, Wingreen NS, Endres RG. Know the Single-Receptor Sensing Limit? Think Again. JOURNAL OF STATISTICAL PHYSICS 2015; 162:1353-1364. [PMID: 26941467 PMCID: PMC4761375 DOI: 10.1007/s10955-015-1412-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 10/29/2015] [Indexed: 05/28/2023]
Abstract
How cells reliably infer information about their environment is a fundamentally important question. While sensing and signaling generally start with cell-surface receptors, the degree of accuracy with which a cell can measure external ligand concentration with even the simplest device-a single receptor-is surprisingly hard to pin down. Recent studies provide conflicting results for the fundamental physical limits. Comparison is made difficult as different studies either suggest different readout mechanisms of the ligand-receptor occupancy, or differ on how ligand diffusion is implemented. Here we critically analyse these studies and present a unifying perspective on the limits of sensing, with wide-ranging biological implications.
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Affiliation(s)
- Gerardo Aquino
- />Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, London, United Kingdom
| | - Ned S. Wingreen
- />Department of Molecular Biology, Princeton University, Princeton, NJ 08544 USA
| | - Robert G. Endres
- />Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, London, United Kingdom
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