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Farrel A, Li P, Veenbergen S, Patel K, Maris JM, Leonard WJ. ROGUE: an R Shiny app for RNA sequencing analysis and biomarker discovery. BMC Bioinformatics 2023; 24:303. [PMID: 37516886 PMCID: PMC10386769 DOI: 10.1186/s12859-023-05420-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 07/18/2023] [Indexed: 07/31/2023] Open
Abstract
BACKGROUND The growing power and ever decreasing cost of RNA sequencing (RNA-Seq) technologies have resulted in an explosion of RNA-Seq data production. Comparing gene expression values within RNA-Seq datasets is relatively easy for many interdisciplinary biomedical researchers; however, user-friendly software applications increase the ability of biologists to efficiently explore available datasets. RESULTS Here, we describe ROGUE (RNA-Seq Ontology Graphic User Environment, https://marisshiny. RESEARCH chop.edu/ROGUE/ ), a user-friendly R Shiny application that allows a biologist to perform differentially expressed gene analysis, gene ontology and pathway enrichment analysis, potential biomarker identification, and advanced statistical analyses. We use ROGUE to identify potential biomarkers and show unique enriched pathways between various immune cells. CONCLUSIONS User-friendly tools for the analysis of next generation sequencing data, such as ROGUE, will allow biologists to efficiently explore their datasets, discover expression patterns, and advance their research by allowing them to develop and test hypotheses.
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Affiliation(s)
- Alvin Farrel
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
- Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Peng Li
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sharon Veenbergen
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Laboratory of Pediatric Gastroenterology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Laboratory of Medical Immunology, Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Khushbu Patel
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John M Maris
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Warren J Leonard
- Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
- Immunology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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Alencar D, Filho A, Alves T, Alves G, Ferreira R, Lima F. Modified diffusive epidemic process on Apollonian networks. J Biol Phys 2023:10.1007/s10867-023-09634-2. [PMID: 37118345 PMCID: PMC10147538 DOI: 10.1007/s10867-023-09634-2] [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: 09/29/2022] [Accepted: 03/19/2023] [Indexed: 04/30/2023] Open
Abstract
We present an analysis of an epidemic spreading process on an Apollonian network that can describe an epidemic spreading in a non-sedentary population. We studied the modified diffusive epidemic process using the Monte Carlo method by computational analysis. Our model may be helpful for modeling systems closer to reality consisting of two classes of individuals: susceptible (A) and infected (B). The individuals can diffuse in a network according to constant diffusion rates [Formula: see text] and [Formula: see text], for the classes A and B, respectively, and obeying three diffusive regimes, i.e., [Formula: see text], [Formula: see text], and [Formula: see text]. Into the same site i, the reaction occurs according to the dynamical rule based on Gillespie's algorithm. Finite-size scaling analysis has shown that our model exhibits continuous phase transition to an absorbing state with a set of critical exponents given by [Formula: see text], [Formula: see text], and [Formula: see text] familiar to every investigated regime. In summary, the continuous phase transition, characterized by this set of critical exponents, does not have the same exponents of the mean-field universality class in both regular lattices and complex networks.
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Affiliation(s)
- David Alencar
- Departamento de Física, Universidade Federal do Piauí, 57072-970, Teresina, PI, Brazil
| | - Antonio Filho
- Departamento de Física, Universidade Estadual do Piauí, Teresina, PI, 64002-150, Brazil.
| | - Tayroni Alves
- Departamento de Física, Universidade Federal do Piauí, 57072-970, Teresina, PI, Brazil
| | - Gladstone Alves
- Departamento de Física, Universidade Estadual do Piauí, Teresina, PI, 64002-150, Brazil
| | - Ronan Ferreira
- Departamento de Ciências Exatas e Aplicadas, Universidade Federal de Ouro Preto, 35931-008, João Monlevade, MG, Brazil
| | - Francisco Lima
- Departamento de Física, Universidade Federal do Piauí, 57072-970, Teresina, PI, Brazil
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Whitman J, Dhanji A, Hayot F, Sealfon SC, Jayaprakash C. Spatio-temporal dynamics of Host-Virus competition: A model study of influenza A. J Theor Biol 2019; 484:110026. [PMID: 31574283 DOI: 10.1016/j.jtbi.2019.110026] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 09/16/2019] [Accepted: 09/26/2019] [Indexed: 12/31/2022]
Abstract
We present results of a study of the early-time response of the innate immune system to influenza virus infection in an agent-based model (ABM) of epithelial cell layers. We find that the competition between the anti-viral immune response and viral antagonism can lead to viral titers non-monotonic in the initial infection fraction as found in experiments. Our model includes a coarse-grained version of intra-cellular processes and inter-cellular communication via cytokine and virion diffusion. We use ABM to follow the propagation of viral infection in the layer and the increase of the viral load as a function of time for different values of the multiplicity of infection (MOI), the initial number of viruses added per cell. We find that for moderately strong host immune response, the number of infected cells and viral load for a smaller MOI exceeds that for larger MOI, as seen in experiments. We elucidate the mechanism underlying this result as the synergistic action of cytokines secreted by infected cells in controlling viral amplification for larger MOI. We investigate the length and time scales that determine this non-monotonic behavior within the ABM. We study the diffusive spread of virions and cytokines from a single infected cell in an absorbing medium analytically and numerically and deduce the length scale that yields a good estimate of the MOI at which we find non-monotonicity. Detailed computations of the temporal behavior of averaged quantities and spatial measures provide further insights into host-viral interactions and connections to experimental observations.
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Affiliation(s)
- John Whitman
- Department of Physics, Ohio State University, 191 W. Woodruff Avenue, Columbus, OH 43201, United States.
| | - Aleya Dhanji
- Department of Physics, Ohio State University, 191 W. Woodruff Avenue, Columbus, OH 43201, United States; Highline College, 2400 S. 240th St, Des Moines, WA 98198, United States.
| | - Fernand Hayot
- Department of Neurology and Center for Translational Systems Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Stuart C Sealfon
- Department of Neurology and Center for Translational Systems Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
| | - Ciriyam Jayaprakash
- Department of Physics, Ohio State University, 191 W. Woodruff Avenue, Columbus, OH 43201, United States.
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Engblom S. Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach. Bull Math Biol 2018; 81:3010-3023. [PMID: 29926381 PMCID: PMC6677715 DOI: 10.1007/s11538-018-0454-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 06/08/2018] [Indexed: 12/11/2022]
Abstract
We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie's celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single-cell computational modeling, here taken to be spatial stochastic reaction-diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch-Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites and via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.
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Affiliation(s)
- Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden.
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Montagna S, Omicini A, Pianini D. Extending the Gillespie’s Stochastic Simulation Algorithm for Integrating Discrete-Event and Multi-Agent Based Simulation. MULTI-AGENT BASED SIMULATION XVI 2016. [DOI: 10.1007/978-3-319-31447-1_1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
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Satija R, Shalek AK. Heterogeneity in immune responses: from populations to single cells. Trends Immunol 2014; 35:219-29. [PMID: 24746883 DOI: 10.1016/j.it.2014.03.004] [Citation(s) in RCA: 137] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2014] [Revised: 03/05/2014] [Accepted: 03/07/2014] [Indexed: 12/18/2022]
Abstract
The mammalian immune system is tasked with protecting the host against a broad range of threats. Understanding how immune populations leverage cellular diversity to achieve this breadth and flexibility, particularly during dynamic processes such as differentiation and antigenic response, is a core challenge that is well suited for single cell analysis. Recent years have witnessed transformative and intersecting advances in nanofabrication and genomics that enable deep profiling of individual cells, affording exciting opportunities to study heterogeneity in the immune response at an unprecedented scope. In light of these advances, here we review recent work exploring how immune populations generate and leverage cellular heterogeneity at multiple molecular and phenotypic levels. Additionally, we highlight opportunities for single cell technologies to shed light on the causes and consequences of heterogeneity in the immune system.
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Affiliation(s)
- Rahul Satija
- Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.
| | - Alex K Shalek
- Department of Chemistry and Chemical Biology and Department of Physics, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA.
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Tabbaa OP, Nudelman G, Sealfon SC, Hayot F, Jayaprakash C. Noise propagation through extracellular signaling leads to fluctuations in gene expression. BMC SYSTEMS BIOLOGY 2013; 7:94. [PMID: 24067165 PMCID: PMC3906959 DOI: 10.1186/1752-0509-7-94] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Accepted: 09/17/2013] [Indexed: 11/10/2022]
Abstract
BACKGROUND Cell-to-cell variability in mRNA and proteins has been observed in many biological systems, including the human innate immune response to viral infection. Most of these studies have focused on variability that arises from (a) intrinsic stochastic fluctuations in gene expression and (b) extrinsic sources (e.g. fluctuations in transcription factors). The main focus of our study is the effect of extracellular signaling on enhancing intrinsic stochastic fluctuations. As a new source of noise, the communication between cells with fluctuating numbers of components has received little attention. We use agent-based modeling to study this contribution to noise in a system of human dendritic cells responding to viral infection. RESULTS Our results, validated by single-cell experiments, show that in the transient state cell-to-cell variability in an interferon-stimulated gene (DDX58) arises from the interplay between the spatial randomness of the cellular sources of the interferon and the temporal stochasticity of its own production. The numerical simulations give insight into the time scales on which autocrine and paracrine signaling act in a heterogeneous population of dendritic cells upon viral infection. We study the effect of different factors that influence the magnitude of the cell-to-cell-variability of the induced gene, including the cell density, multiplicity of infection, and the time scale over which the cellular sources begin producing the cytokine. CONCLUSIONS We propose a mechanism of noise propagation through extracellular communication and establish conditions under which the mechanism is operative. The cellular stochasticity of gene induction, which we investigate, is not limited to the specific interferon-induced gene we have studied; a broad distribution of copy numbers across cells is to be expected for other interferon-stimulated genes. This can lead to functional consequences for the system-level response to a viral challenge.
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Affiliation(s)
- Omar P Tabbaa
- Department of Physics, Ohio State University, Columbus 43210, USA
| | - German Nudelman
- Department of Neurology, Mount Sinai School of Medicine, New York 10029, USA
| | - Stuart C Sealfon
- Department of Neurology, Mount Sinai School of Medicine, New York 10029, USA
- Center for Translational Systems Biology, Mount Sinai School of Medicine, New York 10029, USA
| | - Fernand Hayot
- Department of Neurology, Mount Sinai School of Medicine, New York 10029, USA
- Center for Translational Systems Biology, Mount Sinai School of Medicine, New York 10029, USA
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