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Andersson E, Rothenberg EV, Peterson C, Olariu V. T-cell commitment inheritance-an agent-based multi-scale model. NPJ Syst Biol Appl 2024; 10:40. [PMID: 38632273 PMCID: PMC11024127 DOI: 10.1038/s41540-024-00368-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
T-cell development provides an excellent model system for studying lineage commitment from a multipotent progenitor. The intrathymic development process has been thoroughly studied. The molecular circuitry controlling it has been dissected and the necessary steps like programmed shut off of progenitor genes and T-cell genes upregulation have been revealed. However, the exact timing between decision-making and commitment stage remains unexplored. To this end, we implemented an agent-based multi-scale model to investigate inheritance in early T-cell development. Treating each cell as an agent provides a powerful tool as it tracks each individual cell of a simulated T-cell colony, enabling the construction of lineage trees. Based on the lineage trees, we introduce the concept of the last common ancestors (LCA) of committed cells and analyse their relations, both at single-cell level and population level. In addition to simulating wild-type development, we also conduct knockdown analysis. Our simulations predicted that the commitment is a three-step process that occurs on average over several cell generations once a cell is first prepared by a transcriptional switch. This is followed by the loss of the Bcl11b-opposing function approximately two to three generations later. This is when our LCA analysis indicates that the decision to commit is taken even though in general another one to two generations elapse before the cell actually becomes committed by transitioning to the DN2b state. Our results showed that there is decision inheritance in the commitment mechanism.
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Affiliation(s)
- Emil Andersson
- Computational Science for Health and Environment, Centre for Environmental and Climate Science, Lund University, Lund, Sweden
| | - Ellen V Rothenberg
- Division of Biology and Biological Engineering, 156-29, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Carsten Peterson
- Computational Science for Health and Environment, Centre for Environmental and Climate Science, Lund University, Lund, Sweden
| | - Victor Olariu
- Computational Science for Health and Environment, Centre for Environmental and Climate Science, Lund University, Lund, Sweden.
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Andersson E, Rothenberg EV, Peterson C, Olariu V. T-cell commitment inheritance - an agent-based multi-scale model. bioRxiv 2023:2023.10.18.562905. [PMID: 37905091 PMCID: PMC10614897 DOI: 10.1101/2023.10.18.562905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
T-cell development provides an excellent model system for studying lineage commitment from a multipotent progenitor. The intrathymic development process has been thoroughly studied. The molecular circuitry controlling it has been dissected and the necessary steps like programmed shut off of progenitor genes and T-cell genes upregulation have been revealed. However, the exact timing between decision-making and commitment stage remains unexplored. To this end, we implemented an agent-based multi-scale model to investigate inheritance in early T-cell development. Treating each cell as an agent provides a powerful tool as it tracks each individual cell of a simulated T-cell colony, enabling the construction of lineage trees. Based on the lineage trees, we introduce the concept of the last common ancestors (LCA) of committed cells and analyse their relations, both at single-cell level and population level. In addition to simulating wild-type development, we also conduct knockdown analysis. Our simulations showed that the commitment is a three-step process over several cell generations where a cell is first prepared by a transcriptional switch. This is followed by the loss of the Bcl11b-opposing function two to three generations later which is when the decision to commit is taken. Finally, after another one to two generations, the cell becomes committed by transitioning to the DN2b state. Our results showed that there is inheritance in the commitment mechanism.
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Affiliation(s)
- Emil Andersson
- Computational Biology and Biological Physics, Centre for Environmental and Climate Science, Lund University, Lund, Sweden
| | - Ellen V. Rothenberg
- Division of Biology and Biological Engineering, 156-29, California Institute of Technology, Pasadena, CA 91125, USA
| | - Carsten Peterson
- Computational Biology and Biological Physics, Centre for Environmental and Climate Science, Lund University, Lund, Sweden
| | - Victor Olariu
- Computational Biology and Biological Physics, Centre for Environmental and Climate Science, Lund University, Lund, Sweden
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Ogata N, Konishi S, Yokoyama T. In vivo-like Culture of Monophagous Animal Organ using Dietary Components. J Biotechnol Biomed 2023; 6:42-48. [PMID: 36874218 PMCID: PMC9983661 DOI: 10.26502/jbb.2642-91280070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Animals depend on other species to live, with monophagy being an extreme mode. Monophagous animals depend on their diet not only for nutritients but also for developmental and reproductive controls. Thus, dietary components may be useful in culturing tissues from monophagous animals. We hypothesized that a dedifferentiated tissue from the monophagous silkworm, Bombyx mori, would re-differentiate when cultured in a medium containing an extract of mulberry (Morus alba) leaves, the only food of B. mori. Over 40 fat-body transcriptomes were sequenced, and we concluded that it is possible to establish in vivo-like silkworm tissue cultures using their diet.
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Affiliation(s)
- Norichika Ogata
- Nihon BioData Corporation, 3-2-1 Sakado, Takatsu-ku, Kawasaki, Kanagawa 213-0012, Japan
| | - Shogo Konishi
- Nihon BioData Corporation, 3-2-1 Sakado, Takatsu-ku, Kawasaki, Kanagawa 213-0012, Japan
| | - Takeshi Yokoyama
- Laboratory of Sericultural Science, Faculty of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8, Saiwai-cho, Fuchu, Tokyo, 183-8501, Japan
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Abstract
Tissue culture environment liberate cells from ordinary laws of multi-cellular organisms. This liberation enables cells several behaviors, such as growth, dedifferentiation, acquisition of pluripotency, immortalization and reprogramming. Each phenomenon is relating to each other and hardly to determine. Recently, dedifferentiation of animal cell was quantified as increasing liberality which is information entropy of transcriptome. The increasing liberality induced by tissue culture may reappear in plant cells too. Here we corroborated it. Measuring liberality during reprogramming of plant cells suggested that reprogramming is a combined phenomenon of dedifferentiation and re-differentiation.
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Senra D, Guisoni N, Diambra L. ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data. MethodsX 2022; 9:101778. [PMID: 35855951 PMCID: PMC9287638 DOI: 10.1016/j.mex.2022.101778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/28/2022] [Indexed: 11/27/2022] Open
Abstract
Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set.ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package. ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.
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Qi G, Xu C, Wang J, Tian Y, Wang B, Zhang Y, Ma K, Diao X, Jin Y. Optoplasmonic Modulation of Cell Metabolic State Promotes Rapid Cell Differentiation. Anal Chem 2022; 94:8354-8364. [PMID: 35622722 DOI: 10.1021/acs.analchem.2c00837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cell differentiation plays a vital role in mediating organ formation and tissue repair and regeneration. Although rapid and effective methods to stimulate cell differentiation for clinical purposes are highly desired, it remains a great challenge in the medical fields. Herein, a highly effective and conceptual optical method was developed based on a plasmonic chip platform (made of 2D AuNPs nanomembranes). through effective light-augmented plasmonic regulation of cellular bioenergetics (CBE) and an entropy effect at bionano interfaces, to promote rapid cell differentiation. Compared with traditional methods, the developed optoplasmonic method greatly shortens cell differentiation time from usually more than 10 days to only about 3 days. Upon the optoplasmonic treatment of cells, the conformational and vibration entropy changes of cell membranes were clearly revealed through theoretical simulation and fingerprint spectra of cell membranes. Meanwhile, during the treatment process, bioenergetics levels of cells were elevated with increasing mitochondrial membrane potential (Δψm), which accelerates cell differentiation and proliferation. The developed optoplasmonic method is highly efficient and easy to implement, provides a new perspective and avenue for cell differentiation and proliferation, and has potential application prospects in accelerating tissue repair and regeneration.
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Affiliation(s)
- Guohua Qi
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China
| | - Chen Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Jiafeng Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,Department of Endodontics, School and Hospital of Stomatology, Jilin University, Changchun 130021, Jilin, P.R. China
| | - Yu Tian
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China
| | - Bo Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China
| | - Ying Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Kongshuo Ma
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Xingkang Diao
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
| | - Yongdong Jin
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, Jilin, P. R. China.,University of Science and Technology of China, Hefei 230026, P. R. China
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Dussiau C, Boussaroque A, Gaillard M, Bravetti C, Zaroili L, Knosp C, Friedrich C, Asquier P, Willems L, Quint L, Bouscary D, Fontenay M, Espinasse T, Plesa A, Sujobert P, Gandrillon O, Kosmider O. Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level. BMC Biol 2022; 20:60. [PMID: 35260165 DOI: 10.1186/s12915-022-01264-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/22/2022] [Indexed: 12/11/2022] Open
Abstract
Background Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made. Results Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. Conclusions These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01264-9.
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Ghazanfar S, Lin Y, Su X, Lin DM, Patrick E, Han ZG, Marioni JC, Yang JYH. Investigating higher-order interactions in single-cell data with scHOT. Nat Methods 2020; 17:799-806. [PMID: 32661426 PMCID: PMC7610653 DOI: 10.1038/s41592-020-0885-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 06/03/2020] [Indexed: 12/12/2022]
Abstract
Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered 'pseudotime' offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT-single-cell higher-order testing-which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene-gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.
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Affiliation(s)
- Shila Ghazanfar
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Yingxin Lin
- School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Xianbin Su
- Key Laboratory of Systems Biomedicine (Ministry of Education) and Collaborative Innovation Center of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - David Ming Lin
- Department of Biomedical Sciences, Cornell University, Ithaca, NY, USA
| | - Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia
- Westmead Institute for Medical Research, Westmead, New South Wales, Australia
| | - Ze-Guang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education) and Collaborative Innovation Center of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
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Abstract
Computation is a useful concept far beyond the disciplinary boundaries of computer science. Perhaps the most important class of natural computers can be found in biological systems that perform computation on multiple levels. From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets, we so far lack principles to understand rigorously how computation is done in living, or active, matter. What is the ultimate nature of natural computation that has evolved, and how can we use these principles to engineer intelligent technologies and biological tissues?
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Affiliation(s)
- Dominique Chu
- School of Computing, University of Kent, Canterbury CT2 7NF, UK
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, New South Wales 2006, Australia
| | - J. Christian J. Ray
- Center for Computational Biology, Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045, USA
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