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Ozier-Lafontaine A, Fourneaux C, Durif G, Arsenteva P, Vallot C, Gandrillon O, Gonin-Giraud S, Michel B, Picard F. Kernel-based testing for single-cell differential analysis. Genome Biol 2024; 25:114. [PMID: 38702740 PMCID: PMC11069218 DOI: 10.1186/s13059-024-03255-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 04/22/2024] [Indexed: 05/06/2024] Open
Abstract
Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.
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Affiliation(s)
- A Ozier-Lafontaine
- Nantes Université, Centrale Nantes, Laboratoire de Mathématiques Jean Leray, CNRS UMR 6629, F-44000, Nantes, France.
| | - C Fourneaux
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - G Durif
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - P Arsenteva
- Nantes Université, Centrale Nantes, Laboratoire de Mathématiques Jean Leray, CNRS UMR 6629, F-44000, Nantes, France
| | - C Vallot
- CNRS UMR3244, Institut Curie, PSL University, Paris, France
- Translational Research Department, Institut Curie, PSL University, Paris, France
| | - O Gandrillon
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - S Gonin-Giraud
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - B Michel
- Nantes Université, Centrale Nantes, Laboratoire de Mathématiques Jean Leray, CNRS UMR 6629, F-44000, Nantes, France.
| | - F Picard
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France.
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2
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Fourneaux C, Racine L, Koering C, Dussurgey S, Vallin E, Moussy A, Parmentier R, Brunard F, Stockholm D, Modolo L, Picard F, Gandrillon O, Paldi A, Gonin-Giraud S. Differentiation is accompanied by a progressive loss in transcriptional memory. BMC Biol 2024; 22:58. [PMID: 38468285 DOI: 10.1186/s12915-024-01846-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 02/13/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. RESULTS In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq). CONCLUSIONS We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.
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Affiliation(s)
- Camille Fourneaux
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - Laëtitia Racine
- Ecole Pratique des Hautes Etudes, PSL Research University, Sorbonne Université, INSERM, CRSA, Paris, 75012, France
| | - Catherine Koering
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - Sébastien Dussurgey
- Plateforme AniRA-Cytométrie, Université Claude Bernard Lyon 1, CNRS UAR3444, Inserm US8, ENS de Lyon, SFR Biosciences, Lyon, F-69007, France
| | - Elodie Vallin
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - Alice Moussy
- Ecole Pratique des Hautes Etudes, PSL Research University, Sorbonne Université, INSERM, CRSA, Paris, 75012, France
| | - Romuald Parmentier
- Ecole Pratique des Hautes Etudes, PSL Research University, Sorbonne Université, INSERM, CRSA, Paris, 75012, France
| | - Fanny Brunard
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - Daniel Stockholm
- Ecole Pratique des Hautes Etudes, PSL Research University, Sorbonne Université, INSERM, CRSA, Paris, 75012, France
| | - Laurent Modolo
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - Franck Picard
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
| | - Olivier Gandrillon
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France
- Inria Center, Grenoble Rhone-Alpes, Equipe Dracula, Villeurbanne, F69100, France
| | - Andras Paldi
- Ecole Pratique des Hautes Etudes, PSL Research University, Sorbonne Université, INSERM, CRSA, Paris, 75012, France
| | - Sandrine Gonin-Giraud
- Laboratoire de Biologie et Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard Lyon 1, Lyon, France.
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3
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Petrone-Mendoza E, Benítez M, Lárraga ME, Olson ME. The evolution of ontogenetic "decision-making" in the wood of a clade of tropical plants. Evolution 2024; 78:480-496. [PMID: 38150399 DOI: 10.1093/evolut/qpad232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023]
Abstract
Greater diversity in functional morphology should be associated with the evolution of greater ontogenetic diversity, an expectation difficult to test in most long-lived wild organisms. In the cells derived from the wood meristem (vascular cambium), plants provide extraordinary systems for reconstructing ontogenies in often long-lived organisms. The vascular cambium produces files of cells from the stem center to the periphery, with each cambial derivative "deciding" which of four cell types it differentiates into. Wood cell files remain in place, allowing tracing of the ontogenetic "decisions" taken throughout the life of a stem. We compared cell files from the Pedilanthus clade (genus Euphorbia), which span a range of growth forms from small trees and shrubs of tropical habitats to desert succulents. Using language theory, we represented wood cell types as "letters" and combinations of cell types in cell files as "words," allowing us to measure the diversity of decisions based on word frequency matrices. We also used information content metrics to compare levels of predictability in "decision-making." Our analyses identified a wider array of developmental decisions in woody trees as compared to succulent shrubs, illustrating ways that woody plants provide unparalleled systems for studying the evolution of ontogeny in long-lived, non-model species.
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Affiliation(s)
- Emilio Petrone-Mendoza
- Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México
- Unidad de Posgrado, Posgrado en Ciencias Biológicas, Ciudad de México, México
| | - Mariana Benítez
- Laboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - María E Lárraga
- Instituto de Ingeniería, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Mark E Olson
- Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México
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4
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Racine L, Paldi A. Understanding Cell Differentiation Through Single-Cell Approaches: Conceptual Challenges of the Systemic Approach. Methods Mol Biol 2024; 2745:163-176. [PMID: 38060185 DOI: 10.1007/978-1-0716-3577-3_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
The cells of a multicellular organism are derived from a single zygote and genetically almost identical. Yet, they are phenotypically very different. This difference is the result of a process commonly called cell differentiation. How the phenotypic diversity emerges during ontogenesis or regeneration is a central and intensely studied but still unresolved issue in biology. Cell biology is facing conceptual challenges that are frequently confused with methodological difficulties. How to define a cell type? What stability or change means in the context of cell differentiation and how to deal with the ubiquitous molecular variations seen in the living cells? What are the driving forces of the change? We propose to reframe the problem of cell differentiation in a systemic way by incorporating different theoretical approaches. The new conceptual framework is able to capture the insights made at different levels of cellular organization and considered previously as contradictory. It also provides a formal strategy for further experimental studies.
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Affiliation(s)
- Laëtitia Racine
- Ecole Pratique des Hautes Etudes, PSL Research University, St-Antoine Research Center, INSERM U938, Paris, France
| | - Andras Paldi
- Ecole Pratique des Hautes Etudes, PSL Research University, St-Antoine Research Center, INSERM U938, Paris, France.
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5
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Croydon-Veleslavov IA, Stumpf MPH. Repeated Decision Stumping Distils Simple Rules from Single-Cell Data. J Comput Biol 2024; 31:21-40. [PMID: 38170180 DOI: 10.1089/cmb.2021.0613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approaches focuses on either describing features of these data, or making predictions and classifying unlabeled samples. In this study, we introduce repeated decision stumping (ReDX) as a method to distill simple models from single-cell data. We develop decision trees of depth one-hence "stumps"-to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key players involved in these processes in an unbiased manner without prior knowledge. Our algorithm is deliberately targeting the simplest possible candidate hypotheses that can be extracted from complex high-dimensional data. There are three reasons for this: (1) the predictions become straightforwardly testable hypotheses; (2) the identified candidates form the basis for further mechanistic model development, for example, for engineering and synthetic biology interventions; and (3) this approach complements existing descriptive modeling approaches and frameworks. The approach is computationally efficient, has remarkable predictive power, including in simulation studies where the ground truth is known, and yields robust and statistically stable predictors; the same set of candidates is generated by applying the algorithm to different subsamples of experimental data.
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Affiliation(s)
- Ivan A Croydon-Veleslavov
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
| | - Michael P H Stumpf
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
- School of BioSciences, University of Melbourne, Parkville, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Australia
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6
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Nakamura YT, Himeoka Y, Saito N, Furusawa C. Evolution of hierarchy and irreversibility in theoretical cell differentiation model. PNAS NEXUS 2024; 3:pgad454. [PMID: 38205032 PMCID: PMC10776358 DOI: 10.1093/pnasnexus/pgad454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
The process of cell differentiation in multicellular organisms is characterized by hierarchy and irreversibility in many cases. However, the conditions and selection pressures that give rise to these characteristics remain poorly understood. By using a mathematical model, here we show that the network of differentiation potency (differentiation diagram) becomes necessarily hierarchical and irreversible by increasing the number of terminally differentiated states under certain conditions. The mechanisms generating these characteristics are clarified using geometry in the cell state space. The results demonstrate that the hierarchical organization and irreversibility can manifest independently of direct selection pressures associated with these characteristics, instead they appear to evolve as byproducts of selective forces favoring a diversity of differentiated cell types. The study also provides a new perspective on the structure of gene regulatory networks that produce hierarchical and irreversible differentiation diagrams. These results indicate some constraints on cell differentiation, which are expected to provide a starting point for theoretical discussion of the implicit limits and directions of evolution in multicellular organisms.
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Affiliation(s)
- Yoshiyuki T Nakamura
- Department of Physics, The University of Tokyo, Bunkyo-ku 113-0033, Japan
- Universal Biology Institute, The University of Tokyo, Bunkyo-ku 113-0033, Japan
- Center for Biosystems Dynamics Research, RIKEN, Suita 565-0874, Japan
| | - Yusuke Himeoka
- Universal Biology Institute, The University of Tokyo, Bunkyo-ku 113-0033, Japan
| | - Nen Saito
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima 739-8526, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
| | - Chikara Furusawa
- Department of Physics, The University of Tokyo, Bunkyo-ku 113-0033, Japan
- Universal Biology Institute, The University of Tokyo, Bunkyo-ku 113-0033, Japan
- Center for Biosystems Dynamics Research, RIKEN, Suita 565-0874, Japan
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7
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Chen CC, Tran W, Song K, Sugimoto T, Obusan MB, Wang L, Sheu KM, Cheng D, Ta L, Varuzhanyan G, Huang A, Xu R, Zeng Y, Borujerdpur A, Bayley NA, Noguchi M, Mao Z, Morrissey C, Corey E, Nelson PS, Zhao Y, Huang J, Park JW, Witte ON, Graeber TG. Temporal evolution reveals bifurcated lineages in aggressive neuroendocrine small cell prostate cancer trans-differentiation. Cancer Cell 2023; 41:2066-2082.e9. [PMID: 37995683 PMCID: PMC10878415 DOI: 10.1016/j.ccell.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/25/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
Trans-differentiation from an adenocarcinoma to a small cell neuroendocrine state is associated with therapy resistance in multiple cancer types. To gain insight into the underlying molecular events of the trans-differentiation, we perform a multi-omics time course analysis of a pan-small cell neuroendocrine cancer model (termed PARCB), a forward genetic transformation using human prostate basal cells and identify a shared developmental, arc-like, and entropy-high trajectory among all transformation model replicates. Further mapping with single cell resolution reveals two distinct lineages defined by mutually exclusive expression of ASCL1 or ASCL2. Temporal regulation by groups of transcription factors across developmental stages reveals that cellular reprogramming precedes the induction of neuronal programs. TFAP4 and ASCL1/2 feedback are identified as potential regulators of ASCL1 and ASCL2 expression. Our study provides temporal transcriptional patterns and uncovers pan-tissue parallels between prostate and lung cancers, as well as connections to normal neuroendocrine cell states.
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Affiliation(s)
- Chia-Chun Chen
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Wendy Tran
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Kai Song
- Department of Bioengineering, UCLA, Los Angeles, CA, USA
| | - Tyler Sugimoto
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Matthew B Obusan
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Liang Wang
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Katherine M Sheu
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Donghui Cheng
- Eli and Edythe Broad Stem Cell Research Center, UCLA, Los Angeles, CA, USA
| | - Lisa Ta
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Grigor Varuzhanyan
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Arthur Huang
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Runzhe Xu
- Department of Biological Chemistry, UCLA, Los Angeles, CA, USA
| | - Yuanhong Zeng
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Amirreza Borujerdpur
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Nicholas A Bayley
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Miyako Noguchi
- Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA
| | - Zhiyuan Mao
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Colm Morrissey
- Department of Urology, University of Washington School of Medicine, Seattle, WA, USA
| | - Eva Corey
- Department of Urology, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter S Nelson
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA; Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Yue Zhao
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA; Department of Pathology, College of Basic Medical Sciences and the First Hospital, China Medical University, Shenyang, China
| | - Jiaoti Huang
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Jung Wook Park
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Owen N Witte
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA; Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA; Eli and Edythe Broad Stem Cell Research Center, UCLA, Los Angeles, CA, USA; Molecular Biology Institute, UCLA, Los Angeles, CA, USA; Parker Institute for Cancer Immunotherapy, UCLA, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA.
| | - Thomas G Graeber
- Department of Molecular and Medical Pharmacology, University of California Los Angeles (UCLA), Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA; Crump Institute for Molecular Imaging, UCLA, Los Angeles, CA, USA; California NanoSystems Institute, UCLA, Los Angeles, CA, USA; Metabolomics Center, UCLA, Los Angeles, CA, USA.
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8
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Liu J, Kreimer A, Li WV. Differential variability analysis of single-cell gene expression data. Brief Bioinform 2023; 24:bbad294. [PMID: 37598422 PMCID: PMC10516347 DOI: 10.1093/bib/bbad294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/18/2023] [Accepted: 07/29/2023] [Indexed: 08/22/2023] Open
Abstract
The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.
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Affiliation(s)
- Jiayi Liu
- Graduate Programs in Molecular Biosciences, Rutgers, The State University of New Jersey, 604 Allison Rd, Piscataway, 08854, NJ, USA
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, 08854, NJ, USA
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, Piscataway, 08854, NJ, USA
| | - Anat Kreimer
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, 08854, NJ, USA
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, Piscataway, 08854, NJ, USA
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, 900 University Ave, Riverside, 92521, CA, USA
- Previous affiliation where part of the work was completed: Department of Biostatistics and Epidemiology, Rutgers, The State University of New Jersey, 683 Hoes Lane West, Piscataway, 08854, NJ, USA
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9
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Biondo M, Singh A, Caselle M, Osella M. Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise. Phys Biol 2023; 20:10.1088/1478-3975/acea4e. [PMID: 37489881 PMCID: PMC10680095 DOI: 10.1088/1478-3975/acea4e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/25/2023] [Indexed: 07/26/2023]
Abstract
Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments.
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Affiliation(s)
- Marta Biondo
- Department of Physics, University of Turin and INFN, via P. Giuria 1, I-10125 Turin, Italy
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Department of Mathematical Sciences, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19716, United States of America
| | - Michele Caselle
- Department of Physics, University of Turin and INFN, via P. Giuria 1, I-10125 Turin, Italy
| | - Matteo Osella
- Department of Physics, University of Turin and INFN, via P. Giuria 1, I-10125 Turin, Italy
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10
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Aslan Kamil M, Fourneaux C, Yilmaz A, Stavros S, Parmentier R, Paldi A, Gonin-Giraud S, deMello AJ, Gandrillon O. An image-guided microfluidic system for single-cell lineage tracking. PLoS One 2023; 18:e0288655. [PMID: 37527253 PMCID: PMC10393162 DOI: 10.1371/journal.pone.0288655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis.
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Affiliation(s)
- Mahmut Aslan Kamil
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Camille Fourneaux
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France
| | | | - Stavrakis Stavros
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Romuald Parmentier
- Ecole Pratique des Hautes Etudes, St-Antoine Research Center, Inserm U938, PSL Research University, Paris, France
| | - Andras Paldi
- Ecole Pratique des Hautes Etudes, St-Antoine Research Center, Inserm U938, PSL Research University, Paris, France
| | - Sandrine Gonin-Giraud
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France
| | - Andrew J deMello
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland
| | - Olivier Gandrillon
- Laboratory of Biology and Modelling of the Cell, Université de Lyon, Ecole Normale Supérieure de Lyon, CNRS, UMR5239, Université Claude Bernard, Lyon, France
- Inria, France
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11
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Gao NP, Gandrillon O, Páldi A, Herbach U, Gunawan R. Single-cell transcriptional uncertainty landscape of cell differentiation. F1000Res 2023; 12:426. [PMID: 37545651 PMCID: PMC10400935 DOI: 10.12688/f1000research.131861.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/18/2023] [Indexed: 08/08/2023] Open
Abstract
Background: Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. Methods: In this study, we quantified and followed single-cell transcriptional uncertainty - a measure of gene transcriptional stochasticity in single cells - in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model. Results: By visualizing the transcriptional uncertainty as a landscape over a two-dimensional representation of the single-cell gene expression data, we observed universal features in the cell differentiation trajectories that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceding the increase in the cell transcriptional uncertainty. Conclusions: Our findings suggest a possible universal mechanism during the cell differentiation process, in which stem cells engage stochastic exploratory dynamics of gene expression at the start of the cell differentiation by increasing gene transcriptional bursts, and disengage such dynamics once cells have decided on a particular terminal cell identity. Notably, the peak of single-cell transcriptional uncertainty signifies the decision-making point in the cell differentiation process.
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Affiliation(s)
- Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Zurich, 8093, Switzerland
| | - Olivier Gandrillon
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon 1, F69364, France
- Équipe Dracula, Inria Center Lyon, Villeurbanne, F69100, France
| | - András Páldi
- St-Antoine Research Center, Ecole Pratique des Hautes Etudes PSL, Paris, F-75012, France
| | - Ulysse Herbach
- CNRS, Inria, IECL, Université de Lorraine, Nancy, F-54000, France
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Zurich, 8093, Switzerland
- Department of Chemical and Biological Engineering, University at Buffalo - SUNY, Buffalo, NY, 14260, USA
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12
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Resztak JA, Wei J, Zilioli S, Sendler E, Alazizi A, Mair-Meijers HE, Wu P, Wen X, Slatcher RB, Zhou X, Luca F, Pique-Regi R. Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution. Genome Res 2023; 33:839-856. [PMID: 37442575 PMCID: PMC10519413 DOI: 10.1101/gr.276765.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/08/2023] [Indexed: 07/15/2023]
Abstract
Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.
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Affiliation(s)
- Justyna A Resztak
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Samuele Zilioli
- Department of Psychology, Wayne State University, Detroit, Michigan 48201, USA
- Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan 48201, USA
| | - Edward Sendler
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Henriette E Mair-Meijers
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Peijun Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Richard B Slatcher
- Department of Psychology, University of Georgia, Athens, Georgia 30602, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA
- Department of Biology, University of Rome "Tor Vergata," 00133 Rome, Italy
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA
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13
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Lee J, Møller AF, Chae S, Bussek A, Park TJ, Kim Y, Lee HS, Pers TH, Kwon T, Sedzinski J, Natarajan KN. A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals nonhierarchical model of development. SCIENCE ADVANCES 2023; 9:eadd5745. [PMID: 37027470 PMCID: PMC10081853 DOI: 10.1126/sciadv.add5745] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
The specialized cell types of the mucociliary epithelium (MCE) lining the respiratory tract enable continuous airway clearing, with its defects leading to chronic respiratory diseases. The molecular mechanisms driving cell fate acquisition and temporal specialization during mucociliary epithelial development remain largely unknown. Here, we profile the developing Xenopus MCE from pluripotent to mature stages by single-cell transcriptomics, identifying multipotent early epithelial progenitors that execute multilineage cues before specializing into late-stage ionocytes and goblet and basal cells. Combining in silico lineage inference, in situ hybridization, and single-cell multiplexed RNA imaging, we capture the initial bifurcation into early epithelial and multiciliated progenitors and chart cell type emergence and fate progression into specialized cell types. Comparative analysis of nine airway atlases reveals an evolutionary conserved transcriptional module in ciliated cells, whereas secretory and basal types execute distinct function-specific programs across vertebrates. We uncover a continuous nonhierarchical model of MCE development alongside a data resource for understanding respiratory biology.
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Affiliation(s)
- Julie Lee
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), University of Copenhagen, Copenhagen, Denmark
| | - Andreas Fønss Møller
- Danish Institute of Advanced Study (DIAS) and Functional Genomics and Metabolism Research Unit, University of Southern Denmark, Odense, Denmark
- Sino-Danish College (SDC), University of Chinese Academy of Sciences, Beijing, China
| | - Shinhyeok Chae
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Alexandra Bussek
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), University of Copenhagen, Copenhagen, Denmark
| | - Tae Joo Park
- Department of Biological Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
| | - Youni Kim
- KNU-Center for Nonlinear Dynamics, School of Life Sciences, BK21 FOUR KNU Creative BioResearch Group, College of Natural Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hyun-Shik Lee
- KNU-Center for Nonlinear Dynamics, School of Life Sciences, BK21 FOUR KNU Creative BioResearch Group, College of Natural Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Tune H. Pers
- The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Taejoon Kwon
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
- Center for Genomic Integrity, Institute for Basic Science (IBS), Ulsan 44919, Republic of Korea
| | - Jakub Sedzinski
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), University of Copenhagen, Copenhagen, Denmark
| | - Kedar Nath Natarajan
- Danish Institute of Advanced Study (DIAS) and Functional Genomics and Metabolism Research Unit, University of Southern Denmark, Odense, Denmark
- DTU Bioengineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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14
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Ventre E, Herbach U, Espinasse T, Benoit G, Gandrillon O. One model fits all: Combining inference and simulation of gene regulatory networks. PLoS Comput Biol 2023; 19:e1010962. [PMID: 36972296 PMCID: PMC10079230 DOI: 10.1371/journal.pcbi.1010962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 04/06/2023] [Accepted: 02/17/2023] [Indexed: 03/29/2023] Open
Abstract
The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation.
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Affiliation(s)
- Elias Ventre
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Lyon, France
- Inria Center Grenoble Rhône-Alpes, Équipe Dracula, Villeurbanne, France
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Ulysse Herbach
- Université de Lorraine, CNRS, Inria, IECL, Nancy, France
| | - Thibault Espinasse
- Inria Center Grenoble Rhône-Alpes, Équipe Dracula, Villeurbanne, France
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Gérard Benoit
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Lyon, France
| | - Olivier Gandrillon
- Laboratoire de Biologie et Modélisation de la Cellule, École Normale Supérieure de Lyon, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Lyon, France
- Inria Center Grenoble Rhône-Alpes, Équipe Dracula, Villeurbanne, France
- * E-mail:
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15
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Le Priol C, Azencott CA, Gidrol X. Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression. PLoS Comput Biol 2023; 19:e1010342. [PMID: 36893104 PMCID: PMC9997931 DOI: 10.1371/journal.pcbi.1010342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 02/09/2023] [Indexed: 03/10/2023] Open
Abstract
The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.
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Affiliation(s)
- Christophe Le Priol
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, Biomics, Grenoble, France
- * E-mail: (CLP); (XG)
| | - Chloé-Agathe Azencott
- Center for Computational Biology, Mines ParisTech, PSL Research University, Paris, France
- Institut Curie, Paris, France
- INSERM U900, Paris, France
| | - Xavier Gidrol
- Univ. Grenoble Alpes, INSERM, CEA-IRIG, Biomics, Grenoble, France
- * E-mail: (CLP); (XG)
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16
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Yan J, Li P, Li Y, Gao R, Bi C, Chen L. Disease Prediction by Network Information Gain on a Single Sample Basis. FUNDAMENTAL RESEARCH 2023. [DOI: 10.1016/j.fmre.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
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17
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Huang X, Han C, Zhong J, Hu J, Jin Y, Zhang Q, Luo W, Liu R, Ling F. Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma. J Transl Med 2023; 21:45. [PMID: 36698183 PMCID: PMC9875500 DOI: 10.1186/s12967-023-03890-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear. METHODS Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed. RESULTS The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway. CONCLUSIONS We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.
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Affiliation(s)
- Xiaoqi Huang
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Chongyin Han
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Jiayuan Zhong
- grid.79703.3a0000 0004 1764 3838School of Mathematics, South China University of Technology, Guangzhou, 510641 China
| | - Jiaqi Hu
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Yabin Jin
- grid.452881.20000 0004 0604 5998Institute of Clinical Research, The First People’s Hospital of Foshan, Foshan, 528000 China
| | - Qinqin Zhang
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Wei Luo
- grid.452881.20000 0004 0604 5998Institute of Clinical Research, The First People’s Hospital of Foshan, Foshan, 528000 China
| | - Rui Liu
- grid.79703.3a0000 0004 1764 3838School of Mathematics, South China University of Technology, Guangzhou, 510641 China ,grid.513189.7Pazhou Lab, Guangzhou, 510330 China
| | - Fei Ling
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
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18
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Dandekar T, Kunz M. How to Better Understand Signal Cascades and Measure the Encoded Information. Bioinformatics 2023. [DOI: 10.1007/978-3-662-65036-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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19
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Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation. Innovation (N Y) 2022; 4:100364. [PMID: 36632190 PMCID: PMC9827382 DOI: 10.1016/j.xinn.2022.100364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The generation of ectoderm, mesoderm, and endoderm layers is the most critical biological process during the gastrulation of embryo development. Such a differentiation process in human embryonic stem cells (hESCs) is an inherently nonlinear multi-stage dynamical process which contain multiple tipping points playing crucial roles in the cell-fate decision. However, the tipping points of the process are largely unknown, letting alone the understanding of the molecular regulation on these critical events. Here by designing a module-based dynamic network biomarker (M-DNB) model, we quantitatively pinpointed two tipping points of the differentiation of hESCs toward definitive endoderm, which leads to the identification of M-DNB factors (FOS, HSF1, MYCN, TP53, and MYC) of this process. We demonstrate that before the tipping points, M-DNB factors are able to maintain the cell states and orchestrate cell-fate determination during hESC (ES)-to-ME and ME-to-DE differentiation processes, which not only leads to better understanding of endodermal specification of hESCs but also reveals the power of the M-DNB model to identify critical transition points with their key factors in diverse biological processes, including cell differentiation and transdifferentiation dynamics.
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20
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Faure L, Techameena P, Hadjab S. Emergence of neuron types. Curr Opin Cell Biol 2022; 79:102133. [PMID: 36347131 DOI: 10.1016/j.ceb.2022.102133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 01/31/2023]
Abstract
Neuron types are the building blocks of the nervous system, and therefore, of functional circuits. Understanding the origin of neuronal diversity has always been an essential question in neuroscience and developmental biology. While knowledge on the molecular control of their diversification has largely increased during the last decades, it is now possible to reveal the dynamic mechanisms and the actual stepwise molecular changes occurring at single-cell level with the advent of single-cell omics technologies and analysis with high temporal resolution. Here, we focus on recent advances in the field and in technical and analytical tools that enable detailed insights into the emergence of neuron types in the central and peripheral nervous systems.
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Affiliation(s)
- Louis Faure
- Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, 1090, Vienna, Austria
| | - Prach Techameena
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Saida Hadjab
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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21
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Luo Q, Maity AK, Teschendorff AE. Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data. iScience 2022; 25:105709. [PMID: 36578319 PMCID: PMC9791356 DOI: 10.1016/j.isci.2022.105709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/08/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022] Open
Abstract
Cell-fate transitions are fundamental to development and differentiation. Studying them with single-cell omic data is important to advance our understanding of the cell-fate commitment process, yet this remains challenging. Here we present a computational method called DICE, which analyzes the entropy of expression covariation patterns and which is applicable to static and dynamically changing cell populations. Using only single-cell RNA-Seq data, DICE is able to predict multipotent primed states and their regulatory factors, which we subsequently validate with single-cell epigenomic data. DICE reveals that primed states are often defined by epigenetic regulators or pioneer factors alongside lineage-specific transcription factors. In developmental time course single-cell RNA-Seq datasets, DICE can pinpoint the timing of bifurcations more precisely than lineage-trajectory inference algorithms or competing variance-based methods. In summary, by studying the dynamic changes of expression covariation entropy, DICE can help elucidate primed states and bifurcation dynamics without the need for single-cell epigenomic data.
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Affiliation(s)
- Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Alok K. Maity
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Andrew E. Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China,Corresponding author
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22
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Parmentier R, Racine L, Moussy A, Chantalat S, Sudharshan R, Papili Gao N, Stockholm D, Corre G, Fourel G, Deleuze JF, Gunawan R, Paldi A. Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells. PLoS Biol 2022; 20:e3001849. [PMID: 36288293 PMCID: PMC9604949 DOI: 10.1371/journal.pbio.3001849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 09/23/2022] [Indexed: 11/07/2022] Open
Abstract
When human cord blood-derived CD34+ cells are induced to differentiate, they undergo rapid and dynamic morphological and molecular transformations that are critical for fate commitment. In particular, the cells pass through a transitory phase known as "multilineage-primed" state. These cells are characterized by a mixed gene expression profile, different in each cell, with the coexpression of many genes characteristic for concurrent cell lineages. The aim of our study is to understand the mechanisms of the establishment and the exit from this transitory state. We investigated this issue using single-cell RNA sequencing and ATAC-seq. Two phases were detected. The first phase is a rapid and global chromatin decompaction that makes most of the gene promoters in the genome accessible for transcription. It results 24 h later in enhanced and pervasive transcription of the genome leading to the concomitant increase in the cell-to-cell variability of transcriptional profiles. The second phase is the exit from the multilineage-primed phase marked by a slow chromatin closure and a subsequent overall down-regulation of gene transcription. This process is selective and results in the emergence of coherent expression profiles corresponding to distinct cell subpopulations. The typical time scale of these events spans 48 to 72 h. These observations suggest that the nonspecificity of genome decompaction is the condition for the generation of a highly variable multilineage expression profile. The nonspecific phase is followed by specific regulatory actions that stabilize and maintain the activity of key genes, while the rest of the genome becomes repressed again by the chromatin recompaction. Thus, the initiation of differentiation is reminiscent of a constrained optimization process that associates the spontaneous generation of gene expression diversity to subsequent regulatory actions that maintain the activity of some genes, while the rest of the genome sinks back to the repressive closed chromatin state.
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Affiliation(s)
- Romuald Parmentier
- École Pratique des Hautes Études, PSL Research University, St-Antoine Research Center, Inserm U938, AP-HP, SIRIC CURAMUS, Paris, France
| | - Laëtitia Racine
- École Pratique des Hautes Études, PSL Research University, St-Antoine Research Center, Inserm U938, AP-HP, SIRIC CURAMUS, Paris, France
| | - Alice Moussy
- École Pratique des Hautes Études, PSL Research University, St-Antoine Research Center, Inserm U938, AP-HP, SIRIC CURAMUS, Paris, France
| | | | - Ravi Sudharshan
- Department of Chemical and Biological Engineering, University, Buffalo, New York, United States of America
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Daniel Stockholm
- École Pratique des Hautes Études, PSL Research University, St-Antoine Research Center, Inserm U938, AP-HP, SIRIC CURAMUS, Paris, France
| | | | - Geneviève Fourel
- Laboratory of Biology and Modelling of the Cell, University of Lyon, ENS de Lyon, University of Claude Bernard, CNRS UMR 5239, Inserm U1210, Lyon, France
- Centre Blaise Pascal, ENS de Lyon, Lyon, France
| | | | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University, Buffalo, New York, United States of America
| | - Andras Paldi
- École Pratique des Hautes Études, PSL Research University, St-Antoine Research Center, Inserm U938, AP-HP, SIRIC CURAMUS, Paris, France
- * E-mail:
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23
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Toh K, Saunders D, Verd B, Steventon B. Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo. iScience 2022; 25:105216. [PMID: 36274939 PMCID: PMC9579027 DOI: 10.1016/j.isci.2022.105216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/05/2022] [Accepted: 09/22/2022] [Indexed: 11/30/2022] Open
Abstract
The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.
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Affiliation(s)
- Kane Toh
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Dillan Saunders
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Berta Verd
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
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24
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Gill GS, Schultz MC. Multienzyme activity profiling for evaluation of cell‐to‐cell variability of metabolic state. FASEB Bioadv 2022; 4:709-723. [PMID: 36349298 PMCID: PMC9635011 DOI: 10.1096/fba.2022-00073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/29/2022] Open
Abstract
In solid organs, cells of the same “type” can vary in their molecular phenotype. The basis of this state variation is being revealed by characterizing cell features including the expression pattern of mRNAs and the internal distribution of proteins. Here, the variability of metabolic state between cells is probed by enzyme activity profiling. We study individual cells of types that can be identified during the post‐mitotic phase of oogenesis in Xenopus laevis. Whole‐cell homogenates of isolated oocytes are used for kinetic analysis of enzymes, with a focus on the initial reaction rate. For each oocyte type studied, the activity signatures of glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) and malate dehydrogenase 1 (MDH1) vary more between the homogenates of single oocytes than between repeat samplings of control homogenates. Unexpectedly, the activity signatures of GAPDH and MDH1 strongly co‐vary between oocytes of each type and change in strength of correlation during oogenesis. Therefore, variability of the kinetic behavior of these housekeeping enzymes between “identical” cells is physiologically programmed. Based on these findings, we propose that single‐cell profiling of enzyme kinetics will improve understanding of how metabolic state heterogeneity is related to heterogeneity revealed by omics methods including proteomics, epigenomics, and metabolomics.
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Affiliation(s)
- Govind S. Gill
- Department of Biochemistry University of Alberta Edmonton Alberta Canada
- Department of Pediatrics & Group on the Molecular and Cell Biology of Lipids University of Alberta Edmonton Alberta Canada
| | - Michael C. Schultz
- Department of Biochemistry University of Alberta Edmonton Alberta Canada
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25
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Chen P, Zhong J, Yang K, Zhang X, Chen Y, Liu R. TPD: a web tool for tipping-point detection based on dynamic network biomarker. Brief Bioinform 2022; 23:6693599. [DOI: 10.1093/bib/bbac399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/04/2022] [Accepted: 08/16/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Tipping points or critical transitions widely exist during the progression of many biological processes. It is of great importance to detect the tipping point with the measured omics data, which may be a key to achieving predictive or preventive medicine. We present the tipping point detector (TPD), a web tool for the detection of the tipping point during the dynamic process of biological systems, and further its leading molecules or network, based on the input high-dimensional time series or stage course data. With the solid theoretical background of dynamic network biomarker (DNB) and a series of computational methods for DNB detection, TPD detects the potential tipping point/critical state from the input omics data and outputs multifarious visualized results, including a suggested tipping point with a statistically significant P value, the identified key genes and their functional biological information, the dynamic change in the DNB/leading network that may drive the critical transition and the survival analysis based on DNB scores that may help to identify ‘dark’ genes (nondifferential in terms of expression but differential in terms of DNB scores). TPD fits all current browsers, such as Chrome, Firefox, Edge, Opera, Safari and Internet Explorer. TPD is freely accessible at http://www.rpcomputationalbiology.cn/TPD.
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Affiliation(s)
- Pei Chen
- School of Mathematics, South China University of Technology , Guangzhou 510640, China
| | - Jiayuan Zhong
- School of Mathematics and Big Data, Foshan University , Foshan 528000, China
| | - Kun Yang
- School of Computer Science and Engineering, South China University of Technology , Guangzhou 510640, China
| | - Xuhang Zhang
- School of Computer Science and Engineering, South China University of Technology , Guangzhou 510640, China
| | - Yingqi Chen
- School of Computer Science and Engineering, South China University of Technology , Guangzhou 510640, China
| | - Rui Liu
- School of Mathematics, South China University of Technology , Guangzhou 510640, China
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26
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Zhong J, Liu H, Chen P. The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression. J Mol Cell Biol 2022; 14:6693713. [PMID: 36069893 PMCID: PMC9923387 DOI: 10.1093/jmcb/mjac052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/27/2022] [Accepted: 09/02/2022] [Indexed: 11/12/2022] Open
Abstract
The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at which a drastic and qualitative shift occurs. The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration, which allows the timely implementation of appropriate measures to prevent a catastrophic transition. However, identifying the pre-deterioration stage is a challenging task in clinical medicine, especially when only a single sample is available for most patients, which is responsible for the failure of most statistical methods. In this study, a novel computational method, called single-sample network module biomarkers (sNMB), is presented to predict the pre-deterioration stage or critical point using only a single sample. Specifically, the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples. Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets, including acute lung injury, stomach adenocarcinoma, esophageal carcinoma, and rectum adenocarcinoma. In addition, it provides signaling biomarkers for further practical application, which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.
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Affiliation(s)
- Jiayuan Zhong
- School of Mathematics and Big Data, Foshan University, Foshan 528000, China,School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Huisheng Liu
- School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Pei Chen
- Correspondence to: Pei Chen, E-mail:
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27
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Nagaharu K, Kojima Y, Hirose H, Minoura K, Hinohara K, Minami H, Kageyama Y, Sugimoto Y, Masuya M, Nii S, Seki M, Suzuki Y, Tawara I, Shimamura T, Katayama N, Nishikawa H, Ohishi K. A bifurcation concept for B-lymphoid/plasmacytoid dendritic cells with largely fluctuating transcriptome dynamics. Cell Rep 2022; 40:111260. [PMID: 36044861 DOI: 10.1016/j.celrep.2022.111260] [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: 10/19/2021] [Revised: 06/02/2022] [Accepted: 08/04/2022] [Indexed: 11/24/2022] Open
Abstract
Hematopoiesis was considered a hierarchical stepwise process but was revised to a continuous process following single-cell RNA sequencing. However, the uncertainty or fluctuation of single-cell transcriptome dynamics during differentiation was not considered, and the dendritic cell (DC) pathway in the lymphoid context remains unclear. Here, we identify human B-plasmacytoid DC (pDC) bifurcation as large fluctuating transcriptome dynamics in the putative B/NK progenitor region by dry and wet methods. By converting splicing kinetics into diffusion dynamics in a deep generative model, our original computational methodology reveals strong fluctuation at B/pDC bifurcation in IL-7Rα+ regions, and LFA-1 fluctuates positively in the pDC direction at the bifurcation. These expectancies are validated by the presence of B/pDC progenitors in the IL-7Rα+ fraction and preferential expression of LFA-1 in pDC-biased progenitors with a niche-like culture system. We provide a model of fluctuation-based differentiation, which reconciles continuous and discrete models and is applicable to other developmental systems.
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Affiliation(s)
- Keiki Nagaharu
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Yasuhiro Kojima
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
| | - Haruka Hirose
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
| | - Kodai Minoura
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
| | - Kunihiko Hinohara
- Department of Immunology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan; Institute for Advanced Research, Nagoya University, Nagoya, Japan
| | - Hirohito Minami
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Yuki Kageyama
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Yuka Sugimoto
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Masahiro Masuya
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Shigeru Nii
- Shiroko Women's Hospital, Suzuka 510-0235, Japan
| | - Masahide Seki
- Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa 277-8561, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa 277-8561, Japan
| | - Isao Tawara
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Teppei Shimamura
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan; Institute for Advanced Research, Nagoya University, Nagoya, Japan
| | - Naoyuki Katayama
- Department of Hematology and Oncology, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
| | - Hiroyoshi Nishikawa
- Department of Immunology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan; Institute for Advanced Research, Nagoya University, Nagoya, Japan; Division of Cancer Immunology, Research Institute, National Cancer Center, Tokyo 104-0045, Japan; Division of Cancer Immunology, Exploratory Oncology Research and Clinical Trial Center (EPOC), National Cancer Center, Chiba 277-8577, Japan.
| | - Kohshi Ohishi
- Department of Transfusion Medicine and Cell Therapy, Mie University Hospital, Tsu 514-8507, Japan.
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28
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Capp JP, Thomas F. From developmental to atavistic bet-hedging: How cancer cells pervert the exploitation of random single-cell phenotypic fluctuations. Bioessays 2022; 44:e2200048. [PMID: 35839471 DOI: 10.1002/bies.202200048] [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: 02/28/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/08/2022]
Abstract
Stochastic gene expression plays a leading developmental role through its contribution to cell differentiation. It is also proposed to promote phenotypic diversification in malignant cells. However, it remains unclear if these two forms of cellular bet-hedging are identical or rather display distinct features. Here we argue that bet-hedging phenomena in cancer cells are more similar to those occurring in unicellular organisms than to those of normal metazoan cells. We further propose that the atavistic bet-hedging strategies in cancer originate from a hijacking of the normal developmental bet-hedging of metazoans. Finally, we discuss the constraints that may shape the atavistic bet-hedging strategies of cancer cells.
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Affiliation(s)
- Jean-Pascal Capp
- Toulouse Biotechnology Institute, INSA / University of Toulouse, CNRS, INRAE, Toulouse, France
| | - Frédéric Thomas
- CREEC, UMR IRD 224-CNRS 5290-University of Montpellier, Montpellier, France
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29
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Zreika S, Fourneaux C, Vallin E, Modolo L, Seraphin R, Moussy A, Ventre E, Bouvier M, Ozier-Lafontaine A, Bonnaffoux A, Picard F, Gandrillon O, Gonin-Giraud S. Evidence for close molecular proximity between reverting and undifferentiated cells. BMC Biol 2022; 20:155. [PMID: 35794592 PMCID: PMC9258043 DOI: 10.1186/s12915-022-01363-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/27/2022] [Indexed: 11/28/2022] Open
Abstract
Background According to Waddington’s epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or “attractor states” to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 h the ability to self-renew when transferred back in self-renewal conditions. Despite those intriguing and promising results, the underlying molecular state of those “reverting” cells remains unexplored. The aim of the present study was therefore to molecularly characterize the T2EC reversion process by combining advanced statistical tools to make the most of single-cell transcriptomic data. For this purpose, T2EC, initially maintained in a self-renewal medium (0H), were induced to differentiate for 24H (24H differentiating cells); then, a part of these cells was transferred back to the self-renewal medium (48H reverting cells) and the other part was maintained in the differentiation medium for another 24H (48H differentiating cells). For each time point, cell transcriptomes were generated using scRT-qPCR and scRNAseq. Results Our results showed a strong overlap between 0H and 48H reverting cells when applying dimensional reduction. Moreover, the statistical comparison of cell distributions and differential expression analysis indicated no significant differences between these two cell groups. Interestingly, gene pattern distributions highlighted that, while 48H reverting cells have gene expression pattern more similar to 0H cells, they are not completely identical, which suggest that for some genes a longer delay may be required for the cells to fully recover. Finally, sparse PLS (sparse partial least square) analysis showed that only the expression of 3 genes discriminates 48H reverting and 0H cells. Conclusions Altogether, we show that reverting cells return to an earlier molecular state almost identical to undifferentiated cells and demonstrate a previously undocumented physiological and molecular plasticity during the differentiation process, which most likely results from the dynamic behavior of the underlying molecular network. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01363-7.
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30
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Making a head: Neural crest and ectodermal placodes in cranial sensory development. Semin Cell Dev Biol 2022; 138:15-27. [PMID: 35760729 DOI: 10.1016/j.semcdb.2022.06.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 04/11/2022] [Accepted: 06/19/2022] [Indexed: 01/04/2023]
Abstract
During development of the vertebrate sensory system, many important components like the sense organs and cranial sensory ganglia arise within the head and neck. Two progenitor populations, the neural crest, and cranial ectodermal placodes, contribute to these developing vertebrate peripheral sensory structures. The interactions and contributions of these cell populations to the development of the lens, olfactory, otic, pituitary gland, and cranial ganglia are vital for appropriate peripheral nervous system development. Here, we review the origins of both neural crest and placode cells at the neural plate border of the early vertebrate embryo and investigate the molecular and environmental signals that influence specification of different sensory regions. Finally, we discuss the underlying molecular pathways contributing to the complex vertebrate sensory system from an evolutionary perspective, from basal vertebrates to amniotes.
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31
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Allègre N, Chauveau S, Dennis C, Renaud Y, Meistermann D, Estrella LV, Pouchin P, Cohen-Tannoudji M, David L, Chazaud C. NANOG initiates epiblast fate through the coordination of pluripotency genes expression. Nat Commun 2022; 13:3550. [PMID: 35729116 PMCID: PMC9213552 DOI: 10.1038/s41467-022-30858-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 05/24/2022] [Indexed: 12/20/2022] Open
Abstract
The epiblast is the source of all mammalian embryonic tissues and of pluripotent embryonic stem cells. It differentiates alongside the primitive endoderm in a “salt and pepper” pattern from inner cell mass (ICM) progenitors during the preimplantation stages through the activity of NANOG, GATA6 and the FGF pathway. When and how epiblast lineage specification is initiated is still unclear. Here, we show that the coordinated expression of pluripotency markers defines epiblast identity. Conversely, ICM progenitor cells display random cell-to-cell variability in expression of various pluripotency markers, remarkably dissimilar from the epiblast signature and independently from NANOG, GATA6 and FGF activities. Coordination of pluripotency markers expression fails in Nanog and Gata6 double KO (DKO) embryos. Collectively, our data suggest that NANOG triggers epiblast specification by ensuring the coordinated expression of pluripotency markers in a subset of cells, implying a stochastic mechanism. These features are likely conserved, as suggested by analysis of human embryos. Pluripotent epiblast cells segregate from primitive endoderm in the blastocyst inner cell mass (ICM). Here the authors show that mosaic epiblast differentiation during mouse and human preimplantation development initiates stochastically in ICM progenitors, independently of the FGF pathway, and requires NANOG activity
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Affiliation(s)
- Nicolas Allègre
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France
| | - Sabine Chauveau
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France
| | - Cynthia Dennis
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France
| | - Yoan Renaud
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France.,Byonet, 19 rue du courait, F-63200, Riom, France
| | - Dimitri Meistermann
- Université de Nantes, CHU Nantes, INSERM, CR2TI, UMR 1064, ITUN, F-44000, Nantes, France.,Université de Nantes, CNRS, LS2N, CNRS UMR 6004, F-44000, Nantes, France
| | - Lorena Valverde Estrella
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France
| | - Pierre Pouchin
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France
| | - Michel Cohen-Tannoudji
- Institut Pasteur, Université Paris Cité, CNRS UMR3738, Epigenomics, Proliferation, and the Identity of Cells, Department of Developmental and Stem Cell Biology, F-75015, Paris, France
| | - Laurent David
- Université de Nantes, CHU Nantes, INSERM, CR2TI, UMR 1064, ITUN, F-44000, Nantes, France.,Université de Nantes, CHU Nantes, INSERM, CNRS, UMS Biocore, INSERM UMS 016, CNRS UMS 3556, F-44000, Nantes, France
| | - Claire Chazaud
- Université Clermont Auvergne, CNRS, INSERM, GReD Institute, Faculté de Médecine, F-63000, Clermont-Ferrand, France.
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32
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Sáez M, Briscoe J, Rand DA. Dynamical landscapes of cell fate decisions. Interface Focus 2022; 12:20220002. [PMID: 35860004 PMCID: PMC9184965 DOI: 10.1098/rsfs.2022.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/25/2022] [Indexed: 12/11/2022] Open
Abstract
The generation of cellular diversity during development involves differentiating cells transitioning between discrete cell states. In the 1940s, the developmental biologist Conrad Waddington introduced a landscape metaphor to describe this process. The developmental path of a cell was pictured as a ball rolling through a terrain of branching valleys with cell fate decisions represented by the branch points at which the ball decides between one of two available valleys. Here we discuss progress in constructing quantitative dynamical models inspired by this view of cellular differentiation. We describe a framework based on catastrophe theory and dynamical systems methods that provides the foundations for quantitative geometric models of cellular differentiation. These models can be fit to experimental data and used to make quantitative predictions about cellular differentiation. The theory indicates that cell fate decisions can be described by a small number of decision structures, such that there are only two distinct ways in which cells make a binary choice between one of two fates. We discuss the biological relevance of these mechanisms and suggest the approach is broadly applicable for the quantitative analysis of differentiation dynamics and for determining principles of developmental decisions.
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Affiliation(s)
- M. Sáez
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- IQS, Universitat Ramon Llull, Via Augusta 390, Barcelona 08017, Spain
| | - J. Briscoe
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - D. A. Rand
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
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33
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Yang XH, Goldstein A, Sun Y, Wang Z, Wei M, Moskowitz IP, Cunningham JM. Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors. Nucleic Acids Res 2022; 50:e91. [PMID: 35640613 PMCID: PMC9458468 DOI: 10.1093/nar/gkac452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 04/02/2022] [Accepted: 05/13/2022] [Indexed: 12/29/2022] Open
Abstract
Analyzing single-cell transcriptomes promises to decipher the plasticity, heterogeneity, and rapid switches in developmental cellular state transitions. Such analyses require the identification of gene markers for semi-stable transition states. However, there are nontrivial challenges such as unexplainable stochasticity, variable population sizes, and alternative trajectory constructions. By advancing current tipping-point theory-based models with feature selection, network decomposition, accurate estimation of correlations, and optimization, we developed BioTIP to overcome these challenges. BioTIP identifies a small group of genes, called critical transition signal (CTS), to characterize regulated stochasticity during semi-stable transitions. Although methods rooted in different theories converged at the same transition events in two benchmark datasets, BioTIP is unique in inferring lineage-determining transcription factors governing critical transition. Applying BioTIP to mouse gastrulation data, we identify multiple CTSs from one dataset and validated their significance in another independent dataset. We detect the established regulator Etv2 whose expression change drives the haemato-endothelial bifurcation, and its targets together in CTS across three datasets. After comparing to three current methods using six datasets, we show that BioTIP is accurate, user-friendly, independent of pseudo-temporal trajectory, and captures significantly interconnected and reproducible CTSs. We expect BioTIP to provide great insight into dynamic regulations of lineage-determining factors.
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Affiliation(s)
- Xinan H Yang
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - Andrew Goldstein
- Department of Statistics, The University of Chicago, Chicago IL, USA
| | - Yuxi Sun
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - Zhezhen Wang
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - Megan Wei
- Johns Hopkins University, Baltimore, MD, USA
| | - Ivan P Moskowitz
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - John M Cunningham
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
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34
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Peng H, Zhong J, Chen P, Liu R. Identifying the critical states of complex diseases by the dynamic change of multivariate distribution. Brief Bioinform 2022; 23:6590435. [DOI: 10.1093/bib/bbac177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/10/2022] [Accepted: 04/18/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.
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Affiliation(s)
- Hao Peng
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Jiayuan Zhong
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
- School of mathematics and big data, Foshan University, Foshan 528225, China
| | - Pei Chen
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
- Pazhou Lab, Guangzhou 510330, China
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35
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Chen S, Li D, Yu D, Li M, Ye L, Jiang Y, Tang S, Zhang R, Xu C, Jiang S, Wang Z, Aschner M, Zheng Y, Chen L, Chen W. Determination of tipping point in course of PM 2.5 organic extracts-induced malignant transformation by dynamic network biomarkers. JOURNAL OF HAZARDOUS MATERIALS 2022; 426:128089. [PMID: 34933256 DOI: 10.1016/j.jhazmat.2021.128089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/11/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
The dynamic network biomarkers (DNBs) are designed to identify the tipping point and specific molecules in initiation of PM2.5-induced lung cancers. To discover early-warning signals, we analyzed time-series gene expression datasets over a course of PM2.5 organic extraction-induced human bronchial epithelial (HBE) cell transformation (0th~16th week). A composition index of DNB (CIDNB) was calculated to determine correlations and fluctuations in molecule clusters at each timepoint. We identified a group of genes with the highest CIDNB at the 10th week, implicating a tipping point and corresponding DNBs. Functional experiments revealed that manipulating respective DNB genes at the tipping point led to remarkable changes in malignant phenotypes, including four promoters (GAB2, NCF1, MMP25, LAPTM5) and three suppressors (BATF2, DOK3, DAP3). Notably, co-altered expression of seven core DNB genes resulted in an enhanced activity of malignant transformation compared to effects of single-gene manipulation. Perturbation of pathways (EMT, HMGB1, STAT3, NF-κB, PTEN) appeared in HBE cells at the tipping point. The core DNB genes were involved in regulating lung cancer cell growth and associated with poor survival, indicating their synergistic effects in initiation and development of lung cancers. These findings provided novel insights into the mechanism of dynamic networks attributable to PM2.5-induced cell transformation.
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Affiliation(s)
- Shen Chen
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Daochuan Li
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Dianke Yu
- Department of Toxicology, School of Public Health, Qingdao University, Qingdao 266021, China
| | - Miao Li
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Lizhu Ye
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Yue Jiang
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Shijie Tang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Rui Zhang
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Chi Xu
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Shuyun Jiang
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Ziwei Wang
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Michael Aschner
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Forchheimer 209, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Yuxin Zheng
- Department of Toxicology, School of Public Health, Qingdao University, Qingdao 266021, China
| | - Liping Chen
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China.
| | - Wen Chen
- Department of Toxicology, School of Public Health, Sun Yat-sen University, 74 Zhongshan Road 2, Guangzhou 510080, China.
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Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level. BMC Biol 2022; 20:60. [PMID: 35260165 PMCID: PMC8905725 DOI: 10.1186/s12915-022-01264-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/22/2022] [Indexed: 12/11/2022] Open
Abstract
Background Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made. Results Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. Conclusions These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01264-9.
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Han C, Zhong J, Zhang Q, Hu J, Liu R, Liu H, Mo Z, Chen P, Ling F. Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development. Comput Struct Biotechnol J 2022; 20:1189-1197. [PMID: 35317238 PMCID: PMC8907966 DOI: 10.1016/j.csbj.2022.02.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 01/13/2023] Open
Abstract
The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.
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Affiliation(s)
- Chongyin Han
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiayuan Zhong
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Qinqin Zhang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiaqi Hu
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Rui Liu
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Huisheng Liu
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Zongchao Mo
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Pei Chen
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Fei Ling
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
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Huo Y, Zhao G, Ruan L, Xu P, Fang G, Zhang F, Bao Z, Li X. Detect the early-warning signals of diseases based on signaling pathway perturbations on a single sample. BMC Bioinformatics 2022; 22:367. [PMID: 35045824 PMCID: PMC8772045 DOI: 10.1186/s12859-021-04286-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. RESULTS Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. CONCLUSIONS These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.
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Affiliation(s)
- Yanhao Huo
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Geng Zhao
- Netease Youdao Information Technology (Hangzhou) Co., Ltd., Hangzhou, 310000, Zhejiang, China
| | - Luoshan Ruan
- Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430000, Hubei, China
| | - Peng Xu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China
| | - Gang Fang
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China
| | - Fengyue Zhang
- Department of Biomedical Engineering, School of Life Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Zhenshen Bao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
| | - Xin Li
- Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430000, Hubei, China.
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Abstract
This paper reviews theory of DNB (Dynamical Network Biomarkers) and its applications including both modern medicine and traditional medicine. We show that omics data such as gene/protein expression profiles can be effectively used to detect pre-disease states before critical transitions from healthy states to disease states by using the DNB theory. The DNB theory with big biological data is expected to lead to ultra-early precision and preventive medicine.
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Abstract
The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.
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Affiliation(s)
- Mariano Bizzarri
- Istituto Superiore di Sanità AND Sapienza University, Environment and Health Department AND Department of Experimental Medicine, Rome, Italy
| | - Alessandro Giuliani
- Istituto Superiore di Sanità AND Sapienza University, Environment and Health Department AND Department of Experimental Medicine, Rome, Italy.
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41
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Ventre E, Espinasse T, Bréhier CE, Calvez V, Lepoutre T, Gandrillon O. Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty. J Math Biol 2021; 83:59. [PMID: 34739605 DOI: 10.1007/s00285-021-01684-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 09/02/2021] [Accepted: 10/13/2021] [Indexed: 12/16/2022]
Abstract
Differentiation is the process whereby a cell acquires a specific phenotype, by differential gene expression as a function of time. This is thought to result from the dynamical functioning of an underlying Gene Regulatory Network (GRN). The precise path from the stochastic GRN behavior to the resulting cell state is still an open question. In this work we propose to reduce a stochastic model of gene expression, where a cell is represented by a vector in a continuous space of gene expression, to a discrete coarse-grained model on a limited number of cell types. We develop analytical results and numerical tools to perform this reduction for a specific model characterizing the evolution of a cell by a system of piecewise deterministic Markov processes (PDMP). Solving a spectral problem, we find the explicit variational form of the rate function associated to a large deviations principle, for any number of genes. The resulting Lagrangian dynamics allows us to define a deterministic limit of which the basins of attraction can be identified to cellular types. In this context the quasipotential, describing the transitions between these basins in the weak noise limit, can be defined as the unique solution of an Hamilton-Jacobi equation under a particular constraint. We develop a numerical method for approximating the coarse-grained model parameters, and show its accuracy for a symmetric toggle-switch network. We deduce from the reduced model an approximation of the stationary distribution of the PDMP system, which appears as a Beta mixture. Altogether those results establish a rigorous frame for connecting GRN behavior to the resulting cellular behavior, including the calculation of the probability of jumps between cell types.
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Affiliation(s)
- Elias Ventre
- ENS de Lyon, CNRS UMR 5239, Laboratory of Biology and Modelling of the Cell, Lyon, France. .,Inria Center Grenoble Rhone-Alpes, Team Dracula, Villeurbanne, France. .,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France.
| | - Thibault Espinasse
- Inria Center Grenoble Rhone-Alpes, Team Dracula, Villeurbanne, France.,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Charles-Edouard Bréhier
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Vincent Calvez
- Inria Center Grenoble Rhone-Alpes, Team Dracula, Villeurbanne, France.,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Thomas Lepoutre
- Inria Center Grenoble Rhone-Alpes, Team Dracula, Villeurbanne, France.,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Olivier Gandrillon
- ENS de Lyon, CNRS UMR 5239, Laboratory of Biology and Modelling of the Cell, Lyon, France.,Inria Center Grenoble Rhone-Alpes, Team Dracula, Villeurbanne, France
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42
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Noise distorts the epigenetic landscape and shapes cell-fate decisions. Cell Syst 2021; 13:83-102.e6. [PMID: 34626539 DOI: 10.1016/j.cels.2021.09.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/21/2021] [Accepted: 09/02/2021] [Indexed: 12/24/2022]
Abstract
The Waddington epigenetic landscape has become an iconic representation of the cellular differentiation process. Recent single-cell transcriptomic data provide new opportunities for quantifying this originally conceptual tool, offering insight into the gene regulatory networks underlying cellular development. While many methods for constructing the landscape have been proposed, by far the most commonly employed approach is based on computing the landscape as the negative logarithm of the steady-state probability distribution. Here, we use simple models to highlight the complexities and limitations that arise when reconstructing the potential landscape in the presence of stochastic fluctuations. We consider how the landscape changes in accordance with different stochastic systems and show that it is the subtle interplay between the deterministic and stochastic components of the system that ultimately shapes the landscape. We further discuss how the presence of noise has important implications for the identifiability of the regulatory dynamics from experimental data. A record of this paper's transparent peer review process is included in the supplemental information.
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Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis. BMC Bioinformatics 2021; 22:478. [PMID: 34607573 PMCID: PMC8489053 DOI: 10.1186/s12859-021-04373-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/28/2021] [Indexed: 12/02/2022] Open
Abstract
Background Nonlinear mixed effects models provide a way to mathematically describe experimental data involving a lot of inter-individual heterogeneity. In order to assess their practical identifiability and estimate confidence intervals for their parameters, most mixed effects modelling programs use the Fisher Information Matrix. However, in complex nonlinear models, this approach can mask practical unidentifiabilities. Results Herein we rather propose a multistart approach, and use it to simplify our model by reducing the number of its parameters, in order to make it identifiable. Our model describes several cell populations involved in the in vitro differentiation of chicken erythroid progenitors grown in the same environment. Inter-individual variability observed in cell population counts is explained by variations of the differentiation and proliferation rates between replicates of the experiment. Alternatively, we test a model with varying initial condition. Conclusions We conclude by relating experimental variability to precise and identifiable variations between the replicates of the experiment of some model parameters.
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Affiliation(s)
- Ronan Duchesne
- Laboratory of Biology and Modeling of the Cell, CNRS UMR 5239, INSERM U1210, Université de Lyon, ENS de Lyon, Université Claude Bernard Lyon 1, 46 allée d'Italie, 69007, Lyon, France. .,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France.
| | - Anissa Guillemin
- Laboratory of Biology and Modeling of the Cell, CNRS UMR 5239, INSERM U1210, Université de Lyon, ENS de Lyon, Université Claude Bernard Lyon 1, 46 allée d'Italie, 69007, Lyon, France
| | - Olivier Gandrillon
- Laboratory of Biology and Modeling of the Cell, CNRS UMR 5239, INSERM U1210, Université de Lyon, ENS de Lyon, Université Claude Bernard Lyon 1, 46 allée d'Italie, 69007, Lyon, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France
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Resztak JA, Wei J, Zilioli S, Sendler E, Alazizi A, Mair-meijers HE, Wu P, Slatcher RB, Zhou X, Luca F, Pique-regi R. Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution.. [PMID: 35313584 PMCID: PMC8936121 DOI: 10.1101/2021.09.30.462672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Synthetic glucocorticoids, such as dexamethasone, have been used as treatment for many immune conditions, such as asthma and more recently severe COVID-19. Single cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.
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45
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Dong M, Zhang X, Yang K, Liu R, Chen P. Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network. PeerJ 2021; 9:e11603. [PMID: 34249495 PMCID: PMC8253113 DOI: 10.7717/peerj.11603] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 05/21/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. METHODS By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study. RESULTS The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.
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Affiliation(s)
- Min Dong
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xuhang Zhang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Kun Yang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, Guangdong, China
| | - Pei Chen
- School of Mathematics, South China University of Technology, Guangzhou, China
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46
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Park JH, de Lomana ALG, Marzese DM, Juarez T, Feroze A, Hothi P, Cobbs C, Patel AP, Kesari S, Huang S, Baliga NS. A Systems Approach to Brain Tumor Treatment. Cancers (Basel) 2021; 13:3152. [PMID: 34202449 PMCID: PMC8269017 DOI: 10.3390/cancers13133152] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/11/2021] [Accepted: 06/17/2021] [Indexed: 12/12/2022] Open
Abstract
Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.
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Affiliation(s)
- James H. Park
- Institute for Systems Biology, Seattle, WA 98109, USA; (J.H.P.); (S.H.)
| | | | - Diego M. Marzese
- Balearic Islands Health Research Institute (IdISBa), 07010 Palma, Spain;
| | - Tiffany Juarez
- St. John’s Cancer Institute, Santa Monica, CA 90401, USA; (T.J.); (S.K.)
| | - Abdullah Feroze
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA; (A.F.); (A.P.P.)
| | - Parvinder Hothi
- Swedish Neuroscience Institute, Seattle, WA 98122, USA; (P.H.); (C.C.)
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Seattle, WA 98122, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle, WA 98122, USA; (P.H.); (C.C.)
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Seattle, WA 98122, USA
| | - Anoop P. Patel
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA; (A.F.); (A.P.P.)
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Brotman-Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA
| | - Santosh Kesari
- St. John’s Cancer Institute, Santa Monica, CA 90401, USA; (T.J.); (S.K.)
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA 98109, USA; (J.H.P.); (S.H.)
| | - Nitin S. Baliga
- Institute for Systems Biology, Seattle, WA 98109, USA; (J.H.P.); (S.H.)
- Departments of Microbiology, Biology, and Molecular Engineering Sciences, University of Washington, Seattle, WA 98105, USA
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47
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Stein-O'Brien GL, Ainsile MC, Fertig EJ. Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:24-32. [PMID: 34660940 PMCID: PMC8516130 DOI: 10.1016/j.coisb.2021.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
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Affiliation(s)
- Genevieve L Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
| | - Michaela C Ainsile
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Elana J Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD
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Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021; 22:459-476. [PMID: 33875884 PMCID: PMC10152720 DOI: 10.1038/s41576-021-00341-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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49
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Collective fluctuation implies imminent state transition: Comment on "Dynamic and thermodynamic models of adaptation" by A.N. Gorban et al. Phys Life Rev 2021; 37:103-107. [PMID: 33887574 DOI: 10.1016/j.plrev.2021.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 04/12/2021] [Indexed: 12/16/2022]
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50
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Zhang X, Xie R, Liu Z, Pan Y, Liu R, Chen P. Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker. BMC Infect Dis 2021; 21:6. [PMID: 33446118 PMCID: PMC7809731 DOI: 10.1186/s12879-020-05709-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. Results By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the outbreak scale. Specifically, we collected the historical information of clinic visits caused by HFMD infection between years 2009 and 2018 respectively from public records of Tokyo, Hokkaido, and Osaka, Japan. When applied to the city networks we modelled, our method successfully identified pre-outbreak signals in an average 5 weeks ahead of the HFMD outbreak. Moreover, from the performance comparisons with other methods, it is seen that the L-DNM based system performs better when given only the records of clinic visits. Conclusions The study on the dynamical changes of clinic visits in local district networks reveals the dynamic or landscapes of HFMD spread at the network level. Moreover, the results of this study can be used as quantitative references for disease control during the HFMD outbreak seasons. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-020-05709-w.
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Affiliation(s)
- Xuhang Zhang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Rong Xie
- School of Information, Guangdong University of Finance and Economics, Guangzhou, 510320, China
| | - Zhengrong Liu
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China
| | - Yucong Pan
- Guangdong Science and Technology Infrastructure Center, Guangzhou, 510033, China
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
| | - Pei Chen
- School of Mathematics, South China University of Technology, Guangzhou, 510640, China.
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