1
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Pelayo MA, Yamaguchi N. Old school, new rules: floral meristem development revealed by 3D gene expression atlases and high-resolution transcription factor-chromatin dynamics. FRONTIERS IN PLANT SCIENCE 2023; 14:1323507. [PMID: 38155851 PMCID: PMC10753784 DOI: 10.3389/fpls.2023.1323507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/23/2023] [Indexed: 12/30/2023]
Abstract
The intricate morphology of the flower is primarily established within floral meristems in which floral organs will be defined and from where the developing flower will emerge. Floral meristem development involves multiscale-level regulation, including lineage and positional mechanisms for establishing cell-type identity, and transcriptional regulation mediated by changes in the chromatin environment. However, many key aspects of floral meristem development remain to be determined, such as: 1) the exact role of cellular location in connecting transcriptional inputs to morphological outcomes, and 2) the precise interactions between transcription factors and chromatin regulators underlying the transcriptional networks that regulate the transition from cell proliferation to differentiation during floral meristem development. Here, we highlight recent studies addressing these points through newly developed spatial reconstruction techniques and high-resolution transcription factor-chromatin environment interactions in the model plant Arabidopsis thaliana. Specifically, we feature studies that reconstructed 3D gene expression atlases of the floral meristem. We also discuss how the precise timing of floral meristem specification, floral organ patterning, and floral meristem termination is determined through temporally defined epigenetic dynamics for fine-tuning of gene expression. These studies offer fresh insights into the well-established principles of floral meristem development and outline the potential for further advances in this field in an age of integrated, powerful, multiscale resolution approaches.
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Affiliation(s)
| | - Nobutoshi Yamaguchi
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan
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2
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Negri A, Ward C, Bucci A, D'Angelo G, Cauchy P, Radesco A, Ventura AB, Walton DS, Clarke M, Mandriani B, Pappagallo SA, Mondelli P, Liao K, Gargano G, Zaccaria GM, Viggiano L, Lasorsa FM, Ahmed A, Di Molfetta D, Fiermonte G, Cives M, Guarini A, Vegliante MC, Ciavarella S, Frampton J, Volpe G. Reversal of MYB-dependent suppression of MAFB expression overrides leukaemia phenotype in MLL-rearranged AML. Cell Death Dis 2023; 14:763. [PMID: 37996430 PMCID: PMC10667525 DOI: 10.1038/s41419-023-06276-z] [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: 07/27/2023] [Revised: 10/27/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023]
Abstract
The transcription factor MYB plays a pivotal role in haematopoietic homoeostasis and its aberrant expression is involved in the genesis and maintenance of acute myeloid leukaemia (AML). We have previously demonstrated that not all AML subtypes display the same dependency on MYB expression and that such variability is dictated by the nature of the driver mutation. However, whether this difference in MYB dependency is a general trend in AML remains to be further elucidated. Here, we investigate the role of MYB in human leukaemia by performing siRNA-mediated knock-down in cell line models of AML with different driver lesions. We show that the characteristic reduction in proliferation and the concomitant induction of myeloid differentiation that is observed in MLL-rearranged and t(8;21) leukaemias upon MYB suppression is not seen in AML cells with a complex karyotype. Transcriptome analyses revealed that MYB ablation produces consensual increase of MAFB expression in MYB-dependent cells and, interestingly, the ectopic expression of MAFB could phenocopy the effect of MYB suppression. Accordingly, in silico stratification analyses of molecular data from AML patients revealed a reciprocal relationship between MYB and MAFB expression, highlighting a novel biological interconnection between these two factors in AML and supporting new rationales of MAFB targeting in MLL-rearranged leukaemias.
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Affiliation(s)
- A Negri
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - C Ward
- Edge Impulse Inc., San Jose, CA, USA
| | - A Bucci
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - G D'Angelo
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - P Cauchy
- Max Planck Institute of Immunobiology and Epigenetics, 79108, Freiburg, Germany
| | - A Radesco
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - A B Ventura
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - D S Walton
- Clent Life Sciences, DY84HD, Stourbridge, UK
| | - M Clarke
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, B152TT, Birmingham, UK
| | - B Mandriani
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", 70125, Bari, Italy
| | - S A Pappagallo
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - P Mondelli
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - K Liao
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - G Gargano
- Department of Mathematics, University of Bari "Aldo Moro", Bari, Italy
| | - G M Zaccaria
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
| | - L Viggiano
- Department of Biology, University of Bari "Aldo Moro", Bari, Italy
| | - F M Lasorsa
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", 70125, Bari, Italy
| | - A Ahmed
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", 70125, Bari, Italy
| | - D Di Molfetta
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", 70125, Bari, Italy
| | - G Fiermonte
- Department of Bioscience, Biotechnology and Environment, University of Bari "Aldo Moro", 70125, Bari, Italy
| | - M Cives
- Department of Interdisciplinary Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - A Guarini
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - M C Vegliante
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - S Ciavarella
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - J Frampton
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, B152TT, Birmingham, UK.
| | - G Volpe
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy.
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3
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Zeng Y, He Y, Zheng R, Li M. Inferring single-cell gene regulatory network by non-redundant mutual information. Brief Bioinform 2023; 24:bbad326. [PMID: 37715282 DOI: 10.1093/bib/bbad326] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/12/2023] [Accepted: 08/08/2023] [Indexed: 09/17/2023] Open
Abstract
Gene regulatory network plays a crucial role in controlling the biological processes of living creatures. Deciphering the complex gene regulatory networks from experimental data remains a major challenge in system biology. Recent advances in single-cell RNA sequencing technology bring massive high-resolution data, enabling computational inference of cell-specific gene regulatory networks (GRNs). Many relevant algorithms have been developed to achieve this goal in the past years. However, GRN inference is still less ideal due to the extra noises involved in pseudo-time information and large amounts of dropouts in datasets. Here, we present a novel GRN inference method named Normi, which is based on non-redundant mutual information. Normi manipulates these problems by employing a sliding size-fixed window approach on the entire trajectory and conducts average smoothing strategy on the gene expression of the cells in each window to obtain representative cells. To further alleviate the impact of dropouts, we utilize the mixed KSG estimator to quantify the high-order time-delayed mutual information among genes, then filter out the redundant edges by adopting Max-Relevance and Min Redundancy algorithm. Moreover, we determined the optimal time delay for each gene pair by distance correlation. Normi outperforms other state-of-the-art GRN inference methods on both simulated data and single-cell RNA sequencing (scRNA-seq) datasets, demonstrating its superiority in robustness. The performance of Normi in real scRNA-seq data further reveals its ability to identify the key regulators and crucial biological processes.
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Affiliation(s)
- Yanping Zeng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yongxin He
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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4
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Van Bortle K, Marciano DP, Liu Q, Chou T, Lipchik AM, Gollapudi S, Geller BS, Monte E, Kamakaka RT, Snyder MP. A cancer-associated RNA polymerase III identity drives robust transcription and expression of snaR-A noncoding RNA. Nat Commun 2022; 13:3007. [PMID: 35637192 PMCID: PMC9151912 DOI: 10.1038/s41467-022-30323-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 04/13/2022] [Indexed: 11/09/2022] Open
Abstract
RNA polymerase III (Pol III) includes two alternate isoforms, defined by mutually exclusive incorporation of subunit POLR3G (RPC7α) or POLR3GL (RPC7β), in mammals. The contributions of POLR3G and POLR3GL to transcription potential has remained poorly defined. Here, we discover that loss of subunit POLR3G is accompanied by a restricted repertoire of genes transcribed by Pol III. Particularly sensitive is snaR-A, a small noncoding RNA implicated in cancer proliferation and metastasis. Analysis of Pol III isoform biases and downstream chromatin features identifies loss of POLR3G and snaR-A during differentiation, and conversely, re-establishment of POLR3G gene expression and SNAR-A gene features in cancer contexts. Our results support a model in which Pol III identity functions as an important transcriptional regulatory mechanism. Upregulation of POLR3G, which is driven by MYC, identifies a subgroup of patients with unfavorable survival outcomes in specific cancers, further implicating the POLR3G-enhanced transcription repertoire as a potential disease factor. RNA polymerase III changes its subunit composition during mammalian development. Here the authors report that loss of subunit POLR3G, which re-emerges in cancer, promotes expression of small NF90-associated RNA (snaR-A), a noncoding RNA implicated in cell proliferation and metastasis.
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5
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Gao Y, Yang Y. A joint estimation for the high-dimensional regression modeling on stratified data. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.2008435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Yimiao Gao
- School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
| | - Yuehan Yang
- School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
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6
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Raharinirina NA, Peppert F, von Kleist M, Schütte C, Sunkara V. Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments. PATTERNS 2021; 2:100332. [PMID: 34553172 PMCID: PMC8441581 DOI: 10.1016/j.patter.2021.100332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 02/23/2021] [Accepted: 07/22/2021] [Indexed: 11/30/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealized due to technical and computational challenges. In this work we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: data pre-processing, feature extraction, and inference. We observed that the regulatory signature is captured in the statistical moments of scRNA-seq data and requires computationally intensive minimization solvers to extract it. Furthermore, current data pre-processing might not conserve these statistical moments. Although our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking—finding computationally feasible multi-dimensional statistics of data—is imperative for designing GRN inference methods. Single-cell RNA-seq temporal snapshot data for detecting regulation Challenges in data pre-processing, feature extraction, and network inference for GRNs Encoding of regulatory information in higher-order raw moments Non-linear least-squares inference for temporal scRNA-seq snapshot data
Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. A recent benchmark of 12 GRN methods demonstrated that the algorithms struggled to predict the ground-truth GRNs and speculated that the low performance was due to the insufficient resolution in the scRNA-seq data. Rather than proposing another method, this paper focuses on how to decompose a GRN problem into three subproblems (pre-processing, feature extraction, and inference), so that the gene regulatory information is preserved in each step. Subsequently, we discuss how to best approach each of the three subproblems.
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Affiliation(s)
| | - Felix Peppert
- Explainable A.I. for Biology, Zuse Institute Berlin, 14195 Berlin, Germany
| | - Max von Kleist
- MF1 Bioinformatics, Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
| | - Christof Schütte
- Mathematics of Complex Systems, Zuse Institute Berlin, 14195 Berlin, Germany.,Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany
| | - Vikram Sunkara
- Mathematics of Complex Systems, Zuse Institute Berlin, 14195 Berlin, Germany.,Explainable A.I. for Biology, Zuse Institute Berlin, 14195 Berlin, Germany
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7
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Dou Y, Kalmykova S, Pashkova M, Oghbaie M, Jiang H, Molloy KR, Chait BT, Rout MP, Fenyö D, Jensen TH, Altukhov I, LaCava J. Affinity proteomic dissection of the human nuclear cap-binding complex interactome. Nucleic Acids Res 2020; 48:10456-10469. [PMID: 32960270 PMCID: PMC7544204 DOI: 10.1093/nar/gkaa743] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/22/2020] [Accepted: 08/25/2020] [Indexed: 12/14/2022] Open
Abstract
A 5′,7-methylguanosine cap is a quintessential feature of RNA polymerase II-transcribed RNAs, and a textbook aspect of co-transcriptional RNA processing. The cap is bound by the cap-binding complex (CBC), canonically consisting of nuclear cap-binding proteins 1 and 2 (NCBP1/2). Interest in the CBC has recently renewed due to its participation in RNA-fate decisions via interactions with RNA productive factors as well as with adapters of the degradative RNA exosome. A novel cap-binding protein, NCBP3, was recently proposed to form an alternative CBC together with NCBP1, and to interact with the canonical CBC along with the protein SRRT. The theme of post-transcriptional RNA fate, and how it relates to co-transcriptional ribonucleoprotein assembly, is abundant with complicated, ambiguous, and likely incomplete models. In an effort to clarify the compositions of NCBP1-, 2- and 3-related macromolecular assemblies, we have applied an affinity capture-based interactome screen where the experimental design and data processing have been modified to quantitatively identify interactome differences between targets under a range of experimental conditions. This study generated a comprehensive view of NCBP-protein interactions in the ribonucleoprotein context and demonstrates the potential of our approach to benefit the interpretation of complex biological pathways.
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Affiliation(s)
- Yuhui Dou
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | | | - Maria Pashkova
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Mehrnoosh Oghbaie
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, USA.,European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hua Jiang
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, USA
| | - Kelly R Molloy
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, USA
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, USA
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, USA
| | - David Fenyö
- Department of Biochemistry and Molecular Pharmacology, Institute for Systems Genetics, NYU Langone Health, New York, USA
| | - Torben Heick Jensen
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Ilya Altukhov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - John LaCava
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, USA.,European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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8
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Blanquero R, Carrizosa E, Ramírez-Cobo P, Sillero-Denamiel MR. A cost-sensitive constrained Lasso. ADV DATA ANAL CLASSI 2020. [DOI: 10.1007/s11634-020-00389-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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9
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Wang H, Lian Y, Li C, Ma Y, Yan Z, Dong C. SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data. J Bioinform Comput Biol 2020; 17:1950035. [PMID: 32019417 DOI: 10.1142/s0219720019500355] [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: 11/18/2022]
Abstract
As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yuanyuan Lian
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Chun Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yue Ma
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Zhiliang Yan
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Chunlin Dong
- Dryland Agriculture Research Center, Shanxi Academy of Agricultural Sciences, Taiyuan, Shanxi, China
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10
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SCOUT: A new algorithm for the inference of pseudo-time trajectory using single-cell data. Comput Biol Chem 2019; 80:111-120. [PMID: 30947069 DOI: 10.1016/j.compbiolchem.2019.03.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 03/23/2019] [Indexed: 11/21/2022]
Abstract
Single cell technology is a powerful tool to reveal intercellular heterogeneity and discover cellular developmental processes. When analyzing the complexity of cellular dynamics and variability, it is important to construct a pseudo-time trajectory using single-cell expression data to reflect the process of cellular development. Although a number of computational and statistical methods have been developed recently for single-cell analysis, more effective and efficient methods are still strongly needed. In this work we propose a new method named SCOUT for the inference of single-cell pseudo-time ordering with bifurcation trajectories. We first propose to use the fixed-radius near neighbors algorithms based on cell densities to find landmarks to represent the cell states, and employ the minimum spanning tree (MST) to determine the developmental branches. We then propose to use the projection of Apollonian circle or a weighted distance to determine the pseudo-time trajectories of single cells. The proposed algorithm is applied to one synthetic and two realistic single-cell datasets (including single-branching and multi-branching trajectories) and the cellular developmental dynamics is recovered successfully. Compared with other popular methods, numerical results show that our proposed method is able to generate more robust and accurate pseudo-time trajectories. The code of the method is implemented in Python and available at https://github.com/statway/SCOUT.
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11
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Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol 2019; 37:547-554. [DOI: 10.1038/s41587-019-0071-9] [Citation(s) in RCA: 666] [Impact Index Per Article: 133.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 02/13/2019] [Indexed: 11/09/2022]
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12
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Wang Y, Ni T, Wang W, Liu F. Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity. Biol Rev Camb Philos Soc 2019; 94:248-258. [PMID: 30024089 PMCID: PMC7379551 DOI: 10.1111/brv.12452] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 06/13/2018] [Accepted: 06/27/2018] [Indexed: 01/24/2023]
Abstract
There is accumulating evidence that, from bacteria to mammalian cells, messenger RNAs (mRNAs) are produced in intermittent bursts - a much 'noisier' process than traditionally thought. Based on quantitative measurements at individual promoters, diverse phenomenological models have been proposed for transcriptional bursting. Nevertheless, the underlying molecular mechanisms and significance for cellular signalling remain elusive. Here, we review recent progress, address the above issues and illuminate our viewpoints with simulation results. Despite being widely used in modelling and in interpreting experimental data, the traditional two-state model is far from adequate to describe or infer the molecular basis and stochastic principles of transcription. In bacteria, DNA supercoiling contributes to the bursting of those genes that express at high levels and are topologically constrained in short loops; moreover, low-affinity cis-regulatory elements and unstable protein complexes can play a key role in transcriptional regulation. Integrating data on the architecture, kinetics, and transcriptional input-output function is a promising approach to uncovering the underlying dynamic mechanism. For eukaryotes, distinct bursting features described by the multi-scale and continuum models coincide with those predicted by four theoretically derived principles that govern how the transcription apparatus operates dynamically. This consistency suggests a unified framework for comprehending bursting dynamics at the level of the structural and kinetic basis of transcription. Moreover, the existing models can be unified by a generic model. Remarkably, transcriptional bursting enables regulatory information to be transmitted in a digital manner, with the burst frequency representing the strength of regulatory signals. Such a mode guarantees high fidelity for precise transcriptional regulation and also provides sufficient randomness for realizing cellular heterogeneity.
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Affiliation(s)
- Yaolai Wang
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
- School of ScienceJiangnan UniversityWuxi214122China
| | - Tengfei Ni
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
| | - Wei Wang
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
| | - Feng Liu
- National Laboratory of Solid State Microstructures, Department of Physics, and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing210093China
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13
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Zhao P, Bhowmick S, Yu J, Wang J. Highly Multiplexed Single-Cell Protein Profiling with Large-Scale Convertible DNA-Antibody Barcoded Arrays. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2018; 5:1800672. [PMID: 30250804 PMCID: PMC6145231 DOI: 10.1002/advs.201800672] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 06/05/2018] [Indexed: 05/11/2023]
Abstract
Highly multiplexed detection of proteins secreted by single cells is always challenging. Herein, a multiplexed in situ tagging technique based on single-stranded DNA encoded microbead arrays and multicolor successive imaging for assaying single-cell secreted proteins with high throughput and high sensitivity is presented. This technology is demonstrated to be capable of increasing the multiplexity exponentially. Upon integration with polydimethylsiloxane microwells, this platform is applied to detect ten immune effector proteins from differentiated single macrophages stimulated with lipopolysaccharide. Significant heterogeneity is observed when the derived human primary macrophages are analyzed. This versatile technology is expected to open new opportunities in systems biology, immune regulation studies, signaling analysis, and molecular diagnostics.
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Affiliation(s)
- Peng Zhao
- Multiplex Biotechnology Laboratory Department of Chemistry University at Albany State University of New York Albany NY 12222 USA
| | - Sirsendu Bhowmick
- Multiplex Biotechnology Laboratory Department of Chemistry University at Albany State University of New York Albany NY 12222 USA
| | - Jianchao Yu
- Multiplex Biotechnology Laboratory Department of Chemistry University at Albany State University of New York Albany NY 12222 USA
| | - Jun Wang
- Multiplex Biotechnology Laboratory Department of Chemistry University at Albany State University of New York Albany NY 12222 USA
- Cancer Research Center University at Albany State University of New York Rensselaer NY 12144 USA
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14
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The early signaling pathway of live yeast cell derivative in THP-1 monocytes. Cell Calcium 2018; 73:112-120. [PMID: 29734114 DOI: 10.1016/j.ceca.2018.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 04/05/2018] [Accepted: 04/18/2018] [Indexed: 11/24/2022]
Abstract
Live yeast cell derivative (LYCD) is a medicinal yeast extract that has been used in the treatment of burns, wounds and hemorrhoids for over 70 years. It has been shown to enhance the closure of skin wounds in diabetic mice by increasing inflammation, angiogenesis, formation of granulation tissue and epithelial migration. An active fraction of LYCD has been identified as a mixture of peptides ranging in size from 5 kDA to 17 kDA. Despite its widespread use over many years, understanding of the mechanism by which LYCD acts to effect tissue repair responses is very limited. In this study, we have used a human monocyte-derived cell line, THP-1, as a representative of the inflammatory component of the wound healing process. We have identified two of the earliest responses to LYCD as an increase in cytoplasmic free calcium ([Ca2+]i) and the transcripts for c-fos. We have found that the increase in [Ca2+]i is both necessary and sufficient to account for the LYCD-induced elevation of c-fos. Furthermore, we have shown that the signaling pathway by which LYCD elevates [Ca2+]i involves both mobilization of Ca2+ from intracellular stores and influx of Ca2+ from the extracellular medium. Mobilization of store Ca2+ occurs first via activation of phospholipase C and this is followed by influx through activation of store operated calcium channels. These results constitute the first delineation of the early steps of the LYCD signaling pathway.
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Zhang H, Lee CAA, Li Z, Garbe JR, Eide CR, Petegrosso R, Kuang R, Tolar J. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa. PLoS Comput Biol 2018; 14:e1006053. [PMID: 29630593 PMCID: PMC5908193 DOI: 10.1371/journal.pcbi.1006053] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 04/19/2018] [Accepted: 02/21/2018] [Indexed: 12/31/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC. scRNA-seq enables detailed profiling of heterogeneous cell populations and can be used to reveal lineage relationships or discover new cell types. In the literature, there has been little effort directed towards developing computational methods for cross-population transcriptome analysis of multiple single-cell populations. The cross-cell-population clustering problem is different from the traditional clustering problem because single-cell populations can be collected from different patients, different samples of a tissue, or different experimental replicates. The accompanying biological and technical variation tends to dominate the signals for clustering the pooled single cells from the multiple populations. In this work, we have developed a multitask clustering method to address the cross-population clustering problem. The method simultaneously clusters each individual cell population and controls variance among the cell-type cluster centers within each cell population and across the cell populations. We demonstrate that our multitask clustering method significantly improves clustering accuracy and marker discovery in three public scRNA-seq datasets and also apply the method to an in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) dataset. Our results make it evident that multitask clustering is a promising new approach for cross-population analysis of scRNA-seq data.
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Affiliation(s)
- Huanan Zhang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Catherine A. A. Lee
- Department of Genetics, Cell Biology and Development, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Zhuliu Li
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - John R. Garbe
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Cindy R. Eide
- Department of Pediatrics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Raphael Petegrosso
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Rui Kuang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
- * E-mail: (RK); (JT)
| | - Jakub Tolar
- Department of Pediatrics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
- * E-mail: (RK); (JT)
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16
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Rossetti S, Ren M, Visconti N, Corlazzoli F, Gagliostro V, Somenzi G, Yao J, Sun Y, Sacchi N. Tracing anti-cancer and cancer-promoting actions of all-trans retinoic acid in breast cancer to a RARα epigenetic mechanism of mammary epithelial cell fate. Oncotarget 2018; 7:87064-87080. [PMID: 27894085 PMCID: PMC5349971 DOI: 10.18632/oncotarget.13500] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 10/28/2016] [Indexed: 01/16/2023] Open
Abstract
A hallmark of cancer cells is the ability to evade the growth inhibitory/pro-apoptotic action of physiological all-trans retinoic acid (RA) signal, the bioactive derivative of Vitamin A. However, as we and others reported, RA can also promote cancer cell growth and invasion. Here we show that anticancer and cancer-promoting RA actions in breast cancer have roots in a mechanism of mammary epithelial cell morphogenesis that involves both transcriptional (epigenetic) and non-transcriptional RARα (RARA) functions. We found that the mammary epithelial cell-context specific degree of functionality of the RARA transcriptional (epigenetic) component of this mechanism, by tuning the effects of the non-transcriptional RARA component, determines different cell fate decisions during mammary morphogenesis. Indeed, factors that hamper the RARA epigenetic function make physiological RA drive aberrant morphogenesis via non-transcriptional RARA, thus leading to cell transformation. Remarkably, also the cell context-specific degree of functionality of the RARA epigenetic component retained by breast cancer cells is critical to determine cell fate decisions in response to physiological as well as supraphysiological RA variation. Overall this study supports the proof of principle that the epigenetic functional plasticity of the mammary epithelial cell RARA mechanism, which is essential for normal morphogenetic processes, is necessary to deter breast cancer onset/progression consequent to the insidious action of physiological RA.
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Affiliation(s)
- Stefano Rossetti
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - MingQiang Ren
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Nicolo Visconti
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Francesca Corlazzoli
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Vincenzo Gagliostro
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Giulia Somenzi
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Jin Yao
- The State University of New York at Buffalo, Center of Excellence in Bioinformatics and Life Sciences, Buffalo, NY 14203, USA
| | - Yijun Sun
- The State University of New York at Buffalo, Center of Excellence in Bioinformatics and Life Sciences, Buffalo, NY 14203, USA
| | - Nicoletta Sacchi
- Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
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17
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Chlis NK, Wolf FA, Theis FJ. Model-based branching point detection in single-cell data by K-branches clustering. Bioinformatics 2017; 33:3211-3219. [PMID: 28582478 PMCID: PMC5860029 DOI: 10.1093/bioinformatics/btx325] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 04/11/2017] [Accepted: 05/31/2017] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The identification of heterogeneities in cell populations by utilizing single-cell technologies such as single-cell RNA-Seq, enables inference of cellular development and lineage trees. Several methods have been proposed for such inference from high-dimensional single-cell data. They typically assign each cell to a branch in a differentiation trajectory. However, they commonly assume specific geometries such as tree-like developmental hierarchies and lack statistically sound methods to decide on the number of branching events. RESULTS We present K-Branches, a solution to the above problem by locally fitting half-lines to single-cell data, introducing a clustering algorithm similar to K-Means. These halflines are proxies for branches in the differentiation trajectory of cells. We propose a modified version of the GAP statistic for model selection, in order to decide on the number of lines that best describe the data locally. In this manner, we identify the location and number of subgroups of cells that are associated with branching events and full differentiation, respectively. We evaluate the performance of our method on single-cell RNA-Seq data describing the differentiation of myeloid progenitors during hematopoiesis, single-cell qPCR data of mouse blastocyst development, single-cell qPCR data of human myeloid monocytic leukemia and artificial data. AVAILABILITY AND IMPLEMENTATION An R implementation of K-Branches is freely available at https://github.com/theislab/kbranches. CONTACT fabian.theis@helmholtz-muenchen.de. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nikolaos K Chlis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - F Alexander Wolf
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Department of Mathematics, Technical University of Munich, Garching, Germany
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18
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Van Bortle K, Phanstiel DH, Snyder MP. Topological organization and dynamic regulation of human tRNA genes during macrophage differentiation. Genome Biol 2017; 18:180. [PMID: 28931413 PMCID: PMC5607496 DOI: 10.1186/s13059-017-1310-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 08/25/2017] [Indexed: 12/11/2022] Open
Abstract
Background The human genome is hierarchically organized into local and long-range structures that help shape cell-type-specific transcription patterns. Transfer RNA (tRNA) genes (tDNAs), which are transcribed by RNA polymerase III (RNAPIII) and encode RNA molecules responsible for translation, are dispersed throughout the genome and, in many cases, linearly organized into genomic clusters with other tDNAs. Whether the location and three-dimensional organization of tDNAs contribute to the activity of these genes has remained difficult to address, due in part to unique challenges related to tRNA sequencing. We therefore devised integrated tDNA expression profiling, a method that combines RNAPIII mapping with biotin-capture of nascent tRNAs. We apply this method to the study of dynamic tRNA gene regulation during macrophage development and further integrate these data with high-resolution maps of 3D chromatin structure. Results Integrated tDNA expression profiling reveals domain-level and loop-based organization of tRNA gene transcription during cellular differentiation. tRNA genes connected by DNA loops, which are proximal to CTCF binding sites and expressed at elevated levels compared to non-loop tDNAs, change coordinately with tDNAs and protein-coding genes at distal ends of interactions mapped by in situ Hi-C. We find that downregulated tRNA genes are specifically marked by enhanced promoter-proximal binding of MAF1, a transcriptional repressor of RNAPIII activity, altogether revealing multiple levels of tDNA regulation during cellular differentiation. Conclusions We present evidence of both local and coordinated long-range regulation of human tDNA expression, suggesting the location and organization of tRNA genes contribute to dynamic tDNA activity during macrophage development. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1310-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kevin Van Bortle
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Douglas H Phanstiel
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, 27599, USA.,Thurston Arthritis Research Center and Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA.
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19
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Papili Gao N, Ud-Dean SMM, Gandrillon O, Gunawan R. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics 2017; 34:258-266. [PMID: 28968704 PMCID: PMC5860204 DOI: 10.1093/bioinformatics/btx575] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 06/12/2017] [Accepted: 09/13/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. Results We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. Availability and implementation MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - S M Minhaz Ud-Dean
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Olivier Gandrillon
- Laboratory of Biology and Modelling of the Cell, Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR, INSERM Lyon, France.,Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Rhône-Alpes, France
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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20
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Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development. Mol Cell 2017; 67:1037-1048.e6. [PMID: 28890333 DOI: 10.1016/j.molcel.2017.08.006] [Citation(s) in RCA: 187] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 07/21/2017] [Accepted: 08/11/2017] [Indexed: 01/25/2023]
Abstract
The three-dimensional arrangement of the human genome comprises a complex network of structural and regulatory chromatin loops important for coordinating changes in transcription during human development. To better understand the mechanisms underlying context-specific 3D chromatin structure and transcription during cellular differentiation, we generated comprehensive in situ Hi-C maps of DNA loops in human monocytes and differentiated macrophages. We demonstrate that dynamic looping events are regulatory rather than structural in nature and uncover widespread coordination of dynamic enhancer activity at preformed and acquired DNA loops. Enhancer-bound loop formation and enhancer activation of preformed loops together form multi-loop activation hubs at key macrophage genes. Activation hubs connect 3.4 enhancers per promoter and exhibit a strong enrichment for activator protein 1 (AP-1)-binding events, suggesting that multi-loop activation hubs involving cell-type-specific transcription factors represent an important class of regulatory chromatin structures for the spatiotemporal control of transcription.
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22
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Richard A, Boullu L, Herbach U, Bonnafoux A, Morin V, Vallin E, Guillemin A, Papili Gao N, Gunawan R, Cosette J, Arnaud O, Kupiec JJ, Espinasse T, Gonin-Giraud S, Gandrillon O. Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process. PLoS Biol 2016; 14:e1002585. [PMID: 28027290 PMCID: PMC5191835 DOI: 10.1371/journal.pbio.1002585] [Citation(s) in RCA: 128] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/22/2016] [Indexed: 12/31/2022] Open
Abstract
In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network. A single-cell transcriptomics analysis offers a new dynamical view of the differentiation process, involving an increase in between-cell variability prior to commitment. The differentiation process has classically been seen as a stereotyped program leading from one progenitor toward a functional cell. This vision was based upon cell population-based analyses averaged over millions of cells. However, new methods have recently emerged that allow interrogation of the molecular content at the single-cell level, challenging this view with a new model suggesting that cell-to-cell gene expression stochasticity could play a key role in differentiation. We took advantage of a physiologically relevant avian cellular model to analyze the expression level of 92 genes in individual cells collected at several time-points during differentiation. We first observed that the process analyzed at the single-cell level is very different and much less well ordered than the population-based average view. Furthermore, we showed that cell-to-cell variability in gene expression peaks transiently before strongly decreasing. This rise in variability precedes two key events: an irreversible commitment to differentiation, followed by a significant increase in cell size variability. Altogether, our results support the idea that differentiation is not a “simple” series of well-ordered molecular events executed identically by all cells in a population but likely results from dynamical behavior of the underlying molecular network.
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Affiliation(s)
- Angélique Richard
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Loïs Boullu
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France
- Département de Mathématiques et de statistiques de l’Université de Montréal, Pavillon André-Aisenstadt, 2920, chemin de la Tour, Montréal (Québec) H3T 1J4 Canada
| | - Ulysse Herbach
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France
| | - Arnaud Bonnafoux
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- The CoSMo company. 5 passage du Vercors – 69007 LYON – France
| | - Valérie Morin
- Univ Lyon, Univ Claude Bernard, CNRS UMR 5310 - INSERM U1217, Institut NeuroMyoGène, F-69622 Villeurbanne-Cedex, France
| | - Elodie Vallin
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Anissa Guillemin
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Nan Papili Gao
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne Switzerland
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne Switzerland
| | - Jérémie Cosette
- Genethon – Institut National de la Santé et de la Recherche Médicale – INSERM, Université d’Evry-Val-d’Essone – 1 rue de l’internationale 91000 Evry, France
| | - Ophélie Arnaud
- RIKEN - Center for Life Science Technologies (Division of Genomic Technologies)—CLST (DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | | | - Thibault Espinasse
- Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France
| | - Sandrine Gonin-Giraud
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
| | - Olivier Gandrillon
- Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d’Italie Site Jacques Monod, F-69007, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France
- * E-mail:
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Cannoodt R, Saelens W, Saeys Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur J Immunol 2016; 46:2496-2506. [DOI: 10.1002/eji.201646347] [Citation(s) in RCA: 112] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Revised: 08/30/2016] [Accepted: 09/26/2016] [Indexed: 12/22/2022]
Affiliation(s)
- Robrecht Cannoodt
- Data Mining and Modelling for Biomedicine group; VIB Inflammation Research Center; Ghent Belgium
- Department of Internal Medicine; Ghent University; Ghent Belgium
- Center for Medical Genetics; Ghent University; Ghent Belgium
- Cancer Research Institute Ghent (CRIG); Ghent Belgium
| | - Wouter Saelens
- Data Mining and Modelling for Biomedicine group; VIB Inflammation Research Center; Ghent Belgium
- Department of Internal Medicine; Ghent University; Ghent Belgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine group; VIB Inflammation Research Center; Ghent Belgium
- Department of Internal Medicine; Ghent University; Ghent Belgium
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24
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Reid JE, Wernisch L. Pseudotime estimation: deconfounding single cell time series. Bioinformatics 2016; 32:2973-80. [PMID: 27318198 PMCID: PMC5039927 DOI: 10.1093/bioinformatics/btw372] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 05/20/2016] [Accepted: 06/07/2016] [Indexed: 11/21/2022] Open
Abstract
MOTIVATION Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression. RESULTS We present a principled probabilistic model with a Bayesian inference scheme to analyse such data. We demonstrate our method's utility on public microarray, nCounter and RNA-seq datasets from three organisms. Our method almost perfectly recovers withheld capture times in an Arabidopsis dataset, it accurately estimates cell cycle peak times in a human prostate cancer cell line and it correctly identifies two precocious cells in a study of paracrine signalling in mouse dendritic cells. Furthermore, our method compares favourably with Monocle, a state-of-the-art technique. We also show using held-out data that uncertainty in the temporal dimension is a common confounder and should be accounted for in analyses of repeated cross-sectional time series. AVAILABILITY AND IMPLEMENTATION Our method is available on CRAN in the DeLorean package. CONTACT john.reid@mrc-bsu.cam.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- John E Reid
- MRC Biostatistics Unit, Cambridge CB2 0SR, UK
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25
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duVerle DA, Yotsukura S, Nomura S, Aburatani H, Tsuda K. CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data. BMC Bioinformatics 2016; 17:363. [PMID: 27620863 PMCID: PMC5020541 DOI: 10.1186/s12859-016-1175-6] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 08/11/2016] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths. RESULTS Here we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. CONCLUSIONS With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .
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Affiliation(s)
- David A duVerle
- Graduate School of Frontier Sciences at the University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Japan. .,Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, Japan.
| | - Sohiya Yotsukura
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan
| | - Seitaro Nomura
- Genome Science Division, Laboratory of Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan
| | - Hiroyuki Aburatani
- Genome Science Division, Laboratory of Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan
| | - Koji Tsuda
- Graduate School of Frontier Sciences at the University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Japan. .,Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Japan. .,Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, Japan.
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26
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Wei Y, Schober A. MicroRNA regulation of macrophages in human pathologies. Cell Mol Life Sci 2016; 73:3473-95. [PMID: 27137182 PMCID: PMC11108364 DOI: 10.1007/s00018-016-2254-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 04/15/2016] [Accepted: 04/26/2016] [Indexed: 12/19/2022]
Abstract
Macrophages play a crucial role in the innate immune system and contribute to a broad spectrum of pathologies, like in the defence against infectious agents, in inflammation resolution, and wound repair. In the past several years, microRNAs (miRNAs) have been demonstrated to play important roles in immune diseases by regulating macrophage functions. In this review, we will summarize the role of miRNAs in the differentiation of monocytes into macrophages, in the classical and alternative activation of macrophages, and in the regulation of phagocytosis and apoptosis. Notably, miRNAs preferentially target genes related to the cellular cholesterol metabolism, which is of key importance for the inflammatory activation and phagocytic activity of macrophages. miRNAs functionally link various mechanisms involved in macrophage activation and contribute to initiation and resolution of inflammation. miRNAs represent promising diagnostic and therapeutic targets in different conditions, such as infectious diseases, atherosclerosis, and cancer.
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Affiliation(s)
- Yuanyuan Wei
- Experimental Vascular Medicine, Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Pettenkoferstrasse 9, 80336, Munich, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802, Munich, Germany
| | - Andreas Schober
- Experimental Vascular Medicine, Institute for Cardiovascular Prevention, Ludwig-Maximilians-University Munich, Pettenkoferstrasse 9, 80336, Munich, Germany.
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802, Munich, Germany.
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Espinosa Angarica V, del Sol A. Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation. Bioessays 2016; 38:758-68. [PMID: 27321053 PMCID: PMC5094535 DOI: 10.1002/bies.201600103] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self-renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation-specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.
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Affiliation(s)
- Vladimir Espinosa Angarica
- Luxembourg Center for Systems Biomedicine (LCSB)University of Luxembourg, Campus BelvalBelvauxLuxembourg
| | - Antonio del Sol
- Luxembourg Center for Systems Biomedicine (LCSB)University of Luxembourg, Campus BelvalBelvauxLuxembourg
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28
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Coskun AF, Eser U, Islam S. Cellular identity at the single-cell level. MOLECULAR BIOSYSTEMS 2016; 12:2965-79. [PMID: 27460751 DOI: 10.1039/c6mb00388e] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A single cell creates surprising heterogeneity in a multicellular organism. While every organismal cell shares almost an identical genome, molecular interactions in cells alter the use of DNA sequences to modulate the gene of interest for specialization of cellular functions. Each cell gains a unique identity through molecular coding across the DNA, RNA, and protein conversions. On the other hand, loss of cellular identity leads to critical diseases such as cancer. Most cell identity dissection studies are based on bulk molecular assays that mask differences in individual cells. To probe cell-to-cell variability in a population, we discuss single cell approaches to decode the genetic, epigenetic, transcriptional, and translational mechanisms for cell identity formation. In combination with molecular instructions, the physical principles behind cell identity determination are examined. Deciphering and reprogramming cellular types impact biology and medicine.
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Affiliation(s)
- Ahmet F Coskun
- Division of Chemistry and Chemical Engineering, California Institute of Technology, California, USA.
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29
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Matsumoto H, Kiryu H. SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation. BMC Bioinformatics 2016; 17:232. [PMID: 27277014 PMCID: PMC4898467 DOI: 10.1186/s12859-016-1109-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Accepted: 06/02/2016] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Single-cell technologies make it possible to quantify the comprehensive states of individual cells, and have the power to shed light on cellular differentiation in particular. Although several methods have been developed to fully analyze the single-cell expression data, there is still room for improvement in the analysis of differentiation. RESULTS In this paper, we propose a novel method SCOUP to elucidate differentiation process. Unlike previous dimension reduction-based approaches, SCOUP describes the dynamics of gene expression throughout differentiation directly, including the degree of differentiation of a cell (in pseudo-time) and cell fate. SCOUP is superior to previous methods with respect to pseudo-time estimation, especially for single-cell RNA-seq. SCOUP also successfully estimates cell lineage more accurately than previous method, especially for cells at an early stage of bifurcation. In addition, SCOUP can be applied to various downstream analyses. As an example, we propose a novel correlation calculation method for elucidating regulatory relationships among genes. We apply this method to a single-cell RNA-seq data and detect a candidate of key regulator for differentiation and clusters in a correlation network which are not detected with conventional correlation analysis. CONCLUSIONS We develop a stochastic process-based method SCOUP to analyze single-cell expression data throughout differentiation. SCOUP can estimate pseudo-time and cell lineage more accurately than previous methods. We also propose a novel correlation calculation method based on SCOUP. SCOUP is a promising approach for further single-cell analysis and available at https://github.com/hmatsu1226/SCOUP.
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Affiliation(s)
- Hirotaka Matsumoto
- Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan. .,Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan
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Saadatpour A, Lai S, Guo G, Yuan GC. Single-Cell Analysis in Cancer Genomics. Trends Genet 2016; 31:576-586. [PMID: 26450340 DOI: 10.1016/j.tig.2015.07.003] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 06/26/2015] [Accepted: 07/20/2015] [Indexed: 02/04/2023]
Abstract
Genetic changes and environmental differences result in cellular heterogeneity among cancer cells within the same tumor, thereby complicating treatment outcomes. Recent advances in single-cell technologies have opened new avenues to characterize the intra-tumor cellular heterogeneity, identify rare cell types, measure mutation rates, and, ultimately, guide diagnosis and treatment. In this paper we review the recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels, and discuss their applications in cancer research.
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Affiliation(s)
- Assieh Saadatpour
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Shujing Lai
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Guoji Guo
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China.
| | - Guo-Cheng Yuan
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA.
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31
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Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks. NPJ Syst Biol Appl 2016; 2:16001. [PMID: 28725466 PMCID: PMC5516853 DOI: 10.1038/npjsba.2016.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 11/05/2015] [Accepted: 12/15/2015] [Indexed: 01/12/2023] Open
Abstract
The epigenetic landscape was introduced by Conrad Waddington as a metaphor of cellular development. Like a ball rolling down a hillside is channelled through a succession of valleys until it reaches the bottom, cells follow specific trajectories from a pluripotent state to a committed state. Transcription factors (TFs) interacting as a network (the gene regulatory network (GRN)) orchestrate this developmental process within each cell. Here, we quantitatively model the epigenetic landscape using a kind of artificial neural network called the Hopfield network (HN). An HN is composed of nodes (genes/TFs) and weighted undirected edges, resulting in a weight matrix (W) that stores interactions among the nodes over the entire network. We used gene co-expression to compute the edge weights. Through W, we then associate an energy score (E) to each input pattern (pattern of co-expression for a specific developmental stage) such that each pattern has a specific E. We propose that, based on the co-expression values stored in W, HN associates lower E values to stable phenotypic states and higher E to transient states. We validate our model using time course gene-expression data sets representing stages of development across 12 biological processes including differentiation of human embryonic stem cells into specialized cells, differentiation of THP1 monocytes to macrophages during immune response and trans-differentiation of epithelial to mesenchymal cells in cancer. We observe that transient states have higher energy than the stable phenotypic states, yielding an arc-shaped trajectory. This relationship was confirmed by perturbation analysis. HNs offer an attractive framework for quantitative modelling of cell differentiation (as a landscape) from empirical data. Using HNs, we identify genes and TFs that drive cell-fate transitions, and gain insight into the global dynamics of GRNs.
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32
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Gene Regulatory Network Inference Using Time-Stamped Cross-Sectional Single Cell Expression Data. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.ifacol.2016.12.117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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33
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Herderschee J, Fenwick C, Pantaleo G, Roger T, Calandra T. Emerging single-cell technologies in immunology. J Leukoc Biol 2015; 98:23-32. [PMID: 25908734 DOI: 10.1189/jlb.6ru0115-020r] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 03/26/2015] [Indexed: 12/14/2022] Open
Abstract
During evolution, the immune system has diversified to protect the host from the extremely wide array of possible pathogens. Until recently, immune responses were dissected by use of global approaches and bulk tools, averaging responses across samples and potentially missing particular contributions of individual cells. This is a strongly limiting factor, considering that initial immune responses are likely to be triggered by a restricted number of cells at the vanguard of host defenses. The development of novel, single-cell technologies is a major innovation offering great promise for basic and translational immunology with the potential to overcome some of the limitations of traditional research tools, such as polychromatic flow cytometry or microscopy-based methods. At the transcriptional level, much progress has been made in the fields of microfluidics and single-cell RNA sequencing. At the protein level, mass cytometry already allows the analysis of twice as many parameters as flow cytometry. In this review, we explore the basis and outcome of immune-cell diversity, how genetically identical cells become functionally different, and the consequences for the exploration of host-immune defense responses. We will highlight the advantages, trade-offs, and potential pitfalls of emerging, single-cell-based technologies and how they provide unprecedented detail of immune responses.
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Affiliation(s)
- Jacobus Herderschee
- *Infectious Diseases Service and Division of Immunology and Allergy, Department of Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; and Swiss Vaccine Research Institute, Lausanne, Switzerland
| | - Craig Fenwick
- *Infectious Diseases Service and Division of Immunology and Allergy, Department of Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; and Swiss Vaccine Research Institute, Lausanne, Switzerland
| | - Giuseppe Pantaleo
- *Infectious Diseases Service and Division of Immunology and Allergy, Department of Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; and Swiss Vaccine Research Institute, Lausanne, Switzerland
| | - Thierry Roger
- *Infectious Diseases Service and Division of Immunology and Allergy, Department of Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; and Swiss Vaccine Research Institute, Lausanne, Switzerland
| | - Thierry Calandra
- *Infectious Diseases Service and Division of Immunology and Allergy, Department of Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; and Swiss Vaccine Research Institute, Lausanne, Switzerland
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34
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Wilkinson AC, Kawata VKS, Schütte J, Gao X, Antoniou S, Baumann C, Woodhouse S, Hannah R, Tanaka Y, Swiers G, Moignard V, Fisher J, Hidetoshi S, Tijssen MR, de Bruijn MFTR, Liu P, Göttgens B. Single-cell analyses of regulatory network perturbations using enhancer-targeting TALEs suggest novel roles for PU.1 during haematopoietic specification. Development 2014; 141:4018-30. [PMID: 25252941 PMCID: PMC4197694 DOI: 10.1242/dev.115709] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Transcription factors (TFs) act within wider regulatory networks to control cell identity and fate. Numerous TFs, including Scl (Tal1) and PU.1 (Spi1), are known regulators of developmental and adult haematopoiesis, but how they act within wider TF networks is still poorly understood. Transcription activator-like effectors (TALEs) are a novel class of genetic tool based on the modular DNA-binding domains of Xanthomonas TAL proteins, which enable DNA sequence-specific targeting and the manipulation of endogenous gene expression. Here, we report TALEs engineered to target the PU.1-14kb and Scl+40kb transcriptional enhancers as efficient new tools to perturb the expression of these key haematopoietic TFs. We confirmed the efficiency of these TALEs at the single-cell level using high-throughput RT-qPCR, which also allowed us to assess the consequences of both PU.1 activation and repression on wider TF networks during developmental haematopoiesis. Combined with comprehensive cellular assays, these experiments uncovered novel roles for PU.1 during early haematopoietic specification. Finally, transgenic mouse studies confirmed that the PU.1-14kb element is active at sites of definitive haematopoiesis in vivo and PU.1 is detectable in haemogenic endothelium and early committing blood cells. We therefore establish TALEs as powerful new tools to study the functionality of transcriptional networks that control developmental processes such as early haematopoiesis.
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Affiliation(s)
- Adam C Wilkinson
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Viviane K S Kawata
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK Division of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, Sendai 980-8575, Japan
| | - Judith Schütte
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Xuefei Gao
- Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Stella Antoniou
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Claudia Baumann
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Steven Woodhouse
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Rebecca Hannah
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Yosuke Tanaka
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Gemma Swiers
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Victoria Moignard
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
| | - Jasmin Fisher
- Microsoft Research Cambridge, 21 Station Road, Cambridge CB1 2FB, UK Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Shimauchi Hidetoshi
- Division of Periodontology and Endodontology, Tohoku University Graduate School of Dentistry, Sendai 980-8575, Japan
| | - Marloes R Tijssen
- Department of Haematology, University of Cambridge and National Health Service Blood and Transplant, Cambridge CB2 0PT, UK
| | - Marella F T R de Bruijn
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Pentao Liu
- Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Berthold Göttgens
- Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Road, Cambridge CB2 0XY, UK
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35
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Tomaru Y, Hasegawa R, Suzuki T, Sato T, Kubosaki A, Suzuki M, Kawaji H, Forrest ARR, Hayashizaki Y, Shin JW, Suzuki H. A transient disruption of fibroblastic transcriptional regulatory network facilitates trans-differentiation. Nucleic Acids Res 2014; 42:8905-13. [PMID: 25013174 PMCID: PMC4132712 DOI: 10.1093/nar/gku567] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 06/11/2014] [Accepted: 06/11/2014] [Indexed: 12/15/2022] Open
Abstract
Transcriptional Regulatory Networks (TRNs) coordinate multiple transcription factors (TFs) in concert to maintain tissue homeostasis and cellular function. The re-establishment of target cell TRNs has been previously implicated in direct trans-differentiation studies where the newly introduced TFs switch on a set of key regulatory factors to induce de novo expression and function. However, the extent to which TRNs in starting cell types, such as dermal fibroblasts, protect cells from undergoing cellular reprogramming remains largely unexplored. In order to identify TFs specific to maintaining the fibroblast state, we performed systematic knockdown of 18 fibroblast-enriched TFs and analyzed differential mRNA expression against the same 18 genes, building a Matrix-RNAi. The resulting expression matrix revealed seven highly interconnected TFs. Interestingly, suppressing four out of seven TFs generated lipid droplets and induced PPARG and CEBPA expression in the presence of adipocyte-inducing medium only, while negative control knockdown cells maintained fibroblastic character in the same induction regime. Global gene expression analyses further revealed that the knockdown-induced adipocytes expressed genes associated with lipid metabolism and significantly suppressed fibroblast genes. Overall, this study reveals the critical role of the TRN in protecting cells against aberrant reprogramming, and demonstrates the vulnerability of donor cell's TRNs, offering a novel strategy to induce transgene-free trans-differentiations.
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Affiliation(s)
- Yasuhiro Tomaru
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan
| | - Ryota Hasegawa
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan Division of Genomic Information Resources, International Graduate School of Arts and Sciences, Yokohama City University, Yokohama 230-0045, Japan
| | - Takahiro Suzuki
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan
| | - Taiji Sato
- Discovery Pharmacology Department 1, Research Division, Chugai Pharmaceutical Co., Ltd, 1-135 Komakado, Gotemba, Shizuoka 412-8513, Japan
| | - Atsutaka Kubosaki
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan
| | - Masanori Suzuki
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan Division of Genomic Information Resources, International Graduate School of Arts and Sciences, Yokohama City University, Yokohama 230-0045, Japan
| | - Hideya Kawaji
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Preventive Medicine and Diagnosis Innovative Program, Wako, Saitama 351-0198, Japan
| | - Alistair R R Forrest
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan
| | - Yoshihide Hayashizaki
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Preventive Medicine and Diagnosis Innovative Program, Wako, Saitama 351-0198, Japan
| | - Jay W Shin
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan
| | - Harukazu Suzuki
- RIKEN Omics Science Center, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama 230-0045, Japan
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36
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Sive JI, Göttgens B. Transcriptional network control of normal and leukaemic haematopoiesis. Exp Cell Res 2014; 329:255-64. [PMID: 25014893 PMCID: PMC4261078 DOI: 10.1016/j.yexcr.2014.06.021] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 06/26/2014] [Accepted: 06/28/2014] [Indexed: 12/23/2022]
Abstract
Transcription factors (TFs) play a key role in determining the gene expression profiles of stem/progenitor cells, and defining their potential to differentiate into mature cell lineages. TF interactions within gene-regulatory networks are vital to these processes, and dysregulation of these networks by TF overexpression, deletion or abnormal gene fusions have been shown to cause malignancy. While investigation of these processes remains a challenge, advances in genome-wide technologies and growing interactions between laboratory and computational science are starting to produce increasingly accurate network models. The haematopoietic system provides an attractive experimental system to elucidate gene regulatory mechanisms, and allows experimental investigation of both normal and dysregulated networks. In this review we examine the principles of TF-controlled gene regulatory networks and the key experimental techniques used to investigate them. We look in detail at examples of how these approaches can be used to dissect out the regulatory mechanisms controlling normal haematopoiesis, as well as the dysregulated networks associated with haematological malignancies.
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Affiliation(s)
- Jonathan I Sive
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
| | - Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
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