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Sommer MJ, Cha S, Varabyou A, Rincon N, Park S, Minkin I, Pertea M, Steinegger M, Salzberg SL. Structure-guided isoform identification for the human transcriptome. eLife 2022; 11:e82556. [PMID: 36519529 PMCID: PMC9812405 DOI: 10.7554/elife.82556] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome.
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Affiliation(s)
- Markus J Sommer
- Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of EngineeringBaltimoreUnited States
- Center for Computational Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Sooyoung Cha
- School of Biological Sciences, Seoul National UniversitySeoulRepublic of Korea
- Artificial Intelligence Institute, Seoul National UniversitySeoulRepublic of Korea
| | - Ales Varabyou
- Center for Computational Biology, Johns Hopkins UniversityBaltimoreUnited States
- Department of Computer Science, Johns Hopkins UniversityBaltimoreUnited States
| | - Natalia Rincon
- Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of EngineeringBaltimoreUnited States
- Center for Computational Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Sukhwan Park
- School of Biological Sciences, Seoul National UniversitySeoulRepublic of Korea
- Artificial Intelligence Institute, Seoul National UniversitySeoulRepublic of Korea
| | - Ilia Minkin
- Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of EngineeringBaltimoreUnited States
- Center for Computational Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Mihaela Pertea
- Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of EngineeringBaltimoreUnited States
- Center for Computational Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Martin Steinegger
- School of Biological Sciences, Seoul National UniversitySeoulRepublic of Korea
- Artificial Intelligence Institute, Seoul National UniversitySeoulRepublic of Korea
- Institute of Molecular Biology and Genetics, Seoul National UniversitySeoulRepublic of Korea
| | - Steven L Salzberg
- Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of EngineeringBaltimoreUnited States
- Center for Computational Biology, Johns Hopkins UniversityBaltimoreUnited States
- Department of Computer Science, Johns Hopkins UniversityBaltimoreUnited States
- Department of Biostatistics, Johns Hopkins UniversityBaltimoreUnited States
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Shi X, Zarkan A. Bacterial survivors: evaluating the mechanisms of antibiotic persistence. MICROBIOLOGY (READING, ENGLAND) 2022; 168. [PMID: 36748698 DOI: 10.1099/mic.0.001266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Bacteria withstand antibiotic onslaughts by employing a variety of strategies, one of which is persistence. Persistence occurs in a bacterial population where a subpopulation of cells (persisters) survives antibiotic treatment and can regrow in a drug-free environment. Persisters may cause the recalcitrance of infectious diseases and can be a stepping stone to antibiotic resistance, so understanding persistence mechanisms is critical for therapeutic applications. However, current understanding of persistence is pervaded by paradoxes that stymie research progress, and many aspects of this cellular state remain elusive. In this review, we summarize the putative persister mechanisms, including toxin-antitoxin modules, quorum sensing, indole signalling and epigenetics, as well as the reasons behind the inconsistent body of evidence. We highlight present limitations in the field and underscore a clinical context that is frequently neglected, in the hope of supporting future researchers in examining clinically important persister mechanisms.
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Affiliation(s)
- Xiaoyi Shi
- Cambridge Centre for International Research, Cambridge CB4 0PZ, UK
| | - Ashraf Zarkan
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
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53
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NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures. Cells 2022; 11:cells11213526. [DOI: 10.3390/cells11213526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/29/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.
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54
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Toward predictive engineering of gene circuits. Trends Biotechnol 2022; 41:760-768. [PMID: 36435671 DOI: 10.1016/j.tibtech.2022.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/26/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2022]
Abstract
Many synthetic biology applications rely on programming living cells using gene circuits - the assembly and wiring of genetic elements to control cellular behaviors. Extensive progress has been made in constructing gene circuits with diverse functions and applications. For many circuit functions, however, it remains challenging to ensure that the circuits operate in a predictable manner. Although the notion of predictability may appear intuitive, close inspection suggests that it is not always clear what constitutes predictability. We dissect this concept and how it can be confounded by the complexity of a circuit, the complexity of the context, and the interplay between the two. We discuss circuit engineering strategies, in both computation and experiment, that have been used to improve the predictability of gene circuits.
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55
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An Alternate Approach to Generate Induced Pluripotent Stem Cells with Precise CRISPR/Cas9 Tool. Stem Cells Int 2022; 2022:4537335. [PMID: 36187228 PMCID: PMC9522500 DOI: 10.1155/2022/4537335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022] Open
Abstract
The induced pluripotent stem cells (iPSCs) are considered powerful tools in pharmacology, biomedicine, toxicology, and cell therapy. Multiple approaches have been used to generate iPSCs with the expression of reprogramming factors. Here, we generated iPSCs by integrating the reprogramming cassette into a genomic safe harbor, CASH-1, with the use of a precise genome editing tool, CRISPR/Cas9. The integration of cassette at CASH-1 into target cells did not alter the pattern of proliferation and interleukin-6 secretion as a response to ligands of multiple signaling pathways involving tumor necrosis factor-α receptor, interleukin-1 receptor, and toll-like receptors. Moreover, doxycycline-inducible expression of OCT4, SOX2, and KLF4 reprogrammed engineered human dermal fibroblasts and human embryonic kidney cell line into iPSCs. The generated iPSCs showed their potential to make embryoid bodies and differentiate into the derivatives of all three germ layers. Collectively, our data emphasize the exploitation of CASH-1 by CRISPR/Cas9 tool for therapeutic and biotechnological applications including but not limited to reprogramming of engineered cells into iPSCs.
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Rammohan J, Sarkar S, Ross D. Single-cell measurement quality in bits. PLoS One 2022; 17:e0269272. [PMID: 35951522 PMCID: PMC9371318 DOI: 10.1371/journal.pone.0269272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 05/17/2022] [Indexed: 11/18/2022] Open
Abstract
Single-cell measurements have revolutionized our understanding of heterogeneity in cellular response. However, there is no universally comparable way to assess single-cell measurement quality. Here, we show how information theory can be used to assess and compare single-cell measurement quality in bits, which provides a universally comparable metric for information content. We anticipate that the experimental and theoretical approaches we show here will generally enable comparisons of quality between any single-cell measurement methods.
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Affiliation(s)
- Jayan Rammohan
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Swarnavo Sarkar
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- * E-mail:
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Chen W, Guillaume-Gentil O, Rainer PY, Gäbelein CG, Saelens W, Gardeux V, Klaeger A, Dainese R, Zachara M, Zambelli T, Vorholt JA, Deplancke B. Live-seq enables temporal transcriptomic recording of single cells. Nature 2022; 608:733-740. [PMID: 35978187 PMCID: PMC9402441 DOI: 10.1038/s41586-022-05046-9] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 06/29/2022] [Indexed: 11/26/2022]
Abstract
Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell's ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell's trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.
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Affiliation(s)
- Wanze Chen
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Pernille Yde Rainer
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph G Gäbelein
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Wouter Saelens
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Amanda Klaeger
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Riccardo Dainese
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Magda Zachara
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tomaso Zambelli
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland
| | - Julia A Vorholt
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland.
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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Capp JP, Thomas F. From developmental to atavistic bet-hedging: How cancer cells pervert the exploitation of random single-cell phenotypic fluctuations. Bioessays 2022; 44:e2200048. [PMID: 35839471 DOI: 10.1002/bies.202200048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/08/2022]
Abstract
Stochastic gene expression plays a leading developmental role through its contribution to cell differentiation. It is also proposed to promote phenotypic diversification in malignant cells. However, it remains unclear if these two forms of cellular bet-hedging are identical or rather display distinct features. Here we argue that bet-hedging phenomena in cancer cells are more similar to those occurring in unicellular organisms than to those of normal metazoan cells. We further propose that the atavistic bet-hedging strategies in cancer originate from a hijacking of the normal developmental bet-hedging of metazoans. Finally, we discuss the constraints that may shape the atavistic bet-hedging strategies of cancer cells.
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Affiliation(s)
- Jean-Pascal Capp
- Toulouse Biotechnology Institute, INSA / University of Toulouse, CNRS, INRAE, Toulouse, France
| | - Frédéric Thomas
- CREEC, UMR IRD 224-CNRS 5290-University of Montpellier, Montpellier, France
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59
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Richard AC. Divide and Conquer: Phenotypic and Temporal Heterogeneity Within CD8+ T Cell Responses. Front Immunol 2022; 13:949423. [PMID: 35911755 PMCID: PMC9334874 DOI: 10.3389/fimmu.2022.949423] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
The advent of technologies that can characterize the phenotypes, functions and fates of individual cells has revealed extensive and often unexpected levels of diversity between cells that are nominally of the same subset. CD8+ T cells, also known as cytotoxic T lymphocytes (CTLs), are no exception. Investigations of individual CD8+ T cells both in vitro and in vivo have highlighted the heterogeneity of cellular responses at the levels of activation, differentiation and function. This review takes a broad perspective on the topic of heterogeneity, outlining different forms of variation that arise during a CD8+ T cell response. Specific attention is paid to the impact of T cell receptor (TCR) stimulation strength on heterogeneity. In particular, this review endeavors to highlight connections between variation at different cellular stages, presenting known mechanisms and key open questions about how variation between cells can arise and propagate.
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Zhu X, Li J, Lin Y, Zhao L, Wang J, Peng X. Dimensionality Reduction of Single-Cell RNA Sequencing Data by Combining Entropy and Denoising AutoEncoder. JOURNAL OF COMPUTATIONAL BIOLOGY : A JOURNAL OF COMPUTATIONAL MOLECULAR CELL BIOLOGY 2022; 29:1074-1084. [PMID: 35834604 DOI: 10.1089/cmb.2022.0118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
ABSTRACT Single-cell RNA sequencing (scRNA-seq) can present cellular heterogeneity at higher resolution when measuring the gene expression in an individual cell. However, there are still some computational problems in scRNA-seq data, including high dimensionality, high sparseness, and high noise. To solve them, dimensionality reduction is essential as it reduces dimensions and also removes most of the zeros and noise. Therefore, we propose a hybrid dimensionality reduction algorithm for scRNA-seq data by integrating binning-based entropy and a denoising autoencoder, named ScEDA. In ScEDA, a novel binning-based entropy estimation method is performed to select efficient genes, while removing noise. For each gene, binning-based entropy is designed to describe the differences in its expression across all cells, that is, the distribution of expression of each gene in all cells. Genes are regarded as inefficient and removed when they achieve low binning-based entropy. Moreover, by combining Kullback-Leibler (KL) divergence with the autoencoder, the objective function is reconstructed to maximize the similarity in distribution between input data and reconstructed data. Furthermore, by adding Poisson-distributed noise to the original input data, the denoising autoencoder is used to improve robustness. Compared with three other clustering methods, ScEDA provides superior average performance on 16 real scRNA-seq datasets, with obvious enhancement in large-scale datasets.
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Affiliation(s)
- Xiaoshu Zhu
- School of Computer Science and Engineering, Yulin Normal University, Yulin, China
| | - Jian Li
- School of Computer Science and Engineering, Yulin Normal University, Yulin, China
| | - Yongchang Lin
- School of Computer Science and Engineering, Yulin Normal University, Yulin, China
| | - Liquan Zhao
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xiaoqing Peng
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
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Zreika S, Fourneaux C, Vallin E, Modolo L, Seraphin R, Moussy A, Ventre E, Bouvier M, Ozier-Lafontaine A, Bonnaffoux A, Picard F, Gandrillon O, Gonin-Giraud S. Evidence for close molecular proximity between reverting and undifferentiated cells. BMC Biol 2022; 20:155. [PMID: 35794592 PMCID: PMC9258043 DOI: 10.1186/s12915-022-01363-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/27/2022] [Indexed: 11/28/2022] Open
Abstract
Background According to Waddington’s epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or “attractor states” to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 h the ability to self-renew when transferred back in self-renewal conditions. Despite those intriguing and promising results, the underlying molecular state of those “reverting” cells remains unexplored. The aim of the present study was therefore to molecularly characterize the T2EC reversion process by combining advanced statistical tools to make the most of single-cell transcriptomic data. For this purpose, T2EC, initially maintained in a self-renewal medium (0H), were induced to differentiate for 24H (24H differentiating cells); then, a part of these cells was transferred back to the self-renewal medium (48H reverting cells) and the other part was maintained in the differentiation medium for another 24H (48H differentiating cells). For each time point, cell transcriptomes were generated using scRT-qPCR and scRNAseq. Results Our results showed a strong overlap between 0H and 48H reverting cells when applying dimensional reduction. Moreover, the statistical comparison of cell distributions and differential expression analysis indicated no significant differences between these two cell groups. Interestingly, gene pattern distributions highlighted that, while 48H reverting cells have gene expression pattern more similar to 0H cells, they are not completely identical, which suggest that for some genes a longer delay may be required for the cells to fully recover. Finally, sparse PLS (sparse partial least square) analysis showed that only the expression of 3 genes discriminates 48H reverting and 0H cells. Conclusions Altogether, we show that reverting cells return to an earlier molecular state almost identical to undifferentiated cells and demonstrate a previously undocumented physiological and molecular plasticity during the differentiation process, which most likely results from the dynamic behavior of the underlying molecular network. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01363-7.
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Posseme C, Llibre A, Charbit B, Bondet V, Rouilly V, Saint-André V, Boussier J, Bergstedt J, Smith N, Townsend L, Sugrue JA, Ní Cheallaigh C, Conlon N, Rotival M, Kobor MS, Mottez E, Pol S, Patin E, Albert ML, Quintana-Murci L, Duffy D. Early IFNβ secretion determines variable downstream IL-12p70 responses upon TLR4 activation. Cell Rep 2022; 39:110989. [PMID: 35767946 PMCID: PMC9237956 DOI: 10.1016/j.celrep.2022.110989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 04/04/2022] [Accepted: 06/01/2022] [Indexed: 12/14/2022] Open
Abstract
The interleukin-12 (IL-12) family comprises the only heterodimeric cytokines mediating diverse functional effects. We previously reported a striking bimodal IL-12p70 response to lipopolysaccharide (LPS) stimulation in healthy donors. Herein, we demonstrate that interferon β (IFNβ) is a major upstream determinant of IL-12p70 production, which is also associated with numbers and activation of circulating monocytes. Integrative modeling of proteomic, genetic, epigenomic, and cellular data confirms IFNβ as key for LPS-induced IL-12p70 and allowed us to compare the relative effects of each of these parameters on variable cytokine responses. Clinical relevance of our findings is supported by reduced IFNβ-IL-12p70 responses in patients hospitalized with acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or chronically infected with hepatitis C (HCV). Importantly, these responses are resolved after viral clearance. Our systems immunology approach defines a better understanding of IL-12p70 and IFNβ in healthy and infected persons, providing insights into how common genetic and epigenetic variation may impact immune responses to bacterial infection.
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Affiliation(s)
- Celine Posseme
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France; Frontiers of Innovation in Research and Education PhD Program, CRI Doctoral School, Paris, France
| | - Alba Llibre
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | - Bruno Charbit
- Cytometry and Biomarkers UTechS, CRT, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | - Vincent Bondet
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | | | - Violaine Saint-André
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France; Bioinformatics and Biostatistics Hub, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | - Jeremy Boussier
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | - Jacob Bergstedt
- Human Evolutionary Genetics Unit, CNRS, Institut Pasteur, Université Paris Cité, UMR2000, 75015 Paris, France
| | - Nikaïa Smith
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | - Liam Townsend
- Department of Infectious Diseases, St. James's Hospital, Dublin, Ireland; Department of Clinical Medicine, School of Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Jamie A Sugrue
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Clíona Ní Cheallaigh
- Department of Infectious Diseases, St. James's Hospital, Dublin, Ireland; Department of Clinical Medicine, School of Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Niall Conlon
- Department of Immunology, St. James's Hospital, Dublin, Ireland; Department of Immunology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Maxime Rotival
- Human Evolutionary Genetics Unit, CNRS, Institut Pasteur, Université Paris Cité, UMR2000, 75015 Paris, France
| | - Michael S Kobor
- Department of Medical Genetics, Center for Molecular Medicine and Therapeutics, University of British Columbia/British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Estelle Mottez
- Cytometry and Biomarkers UTechS, CRT, Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | - Stanislas Pol
- Hepatology Unit, Hôpital Cochin, AP-HP, 27, rue du Fg Saint-Jacques, 75014 Paris, France
| | - Etienne Patin
- Human Evolutionary Genetics Unit, CNRS, Institut Pasteur, Université Paris Cité, UMR2000, 75015 Paris, France
| | | | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, CNRS, Institut Pasteur, Université Paris Cité, UMR2000, 75015 Paris, France; Human Genomics and Evolution, Collège de France, 75005 Paris, France
| | - Darragh Duffy
- Translational Immunology Unit, Institut Pasteur, Université Paris Cité, 75015 Paris, France; Cytometry and Biomarkers UTechS, CRT, Institut Pasteur, Université Paris Cité, 75015 Paris, France.
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63
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Shape asymmetry - what's new? Emerg Top Life Sci 2022; 6:285-294. [PMID: 35758318 DOI: 10.1042/etls20210273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022]
Abstract
Studies of shape asymmetry have become increasingly abundant as the methods of geometric morphometrics have gained widespread use. Most of these studies have focussed on fluctuating asymmetry and have largely obtained similar results as more traditional analyses of asymmetry in distance measurements, but several notable differences have also emerged. A key difference is that shape analyses provide information on the patterns, not just the amount of variation, and therefore tend to be more sensitive. Such analyses have shown that apparently symmetric structures in animals consistently show directional asymmetry for shape, but not for size. Furthermore, the long-standing prediction that phenotypic plasticity in response to environmental heterogeneity can contribute to fluctuating asymmetry has been confirmed for the first time for the shape of flower parts (but not for size). Finally, shape analyses in structures with complex symmetry, such as many flowers, can distinguish multiple types of directional asymmetry, generated by distinct direction-giving factors, which combine to the single component observable in bilaterally symmetric structures. While analyses of shape asymmetry are broadly compatible with traditional analyses of asymmetry, they incorporate more detailed morphological information, particularly for structures with complex symmetry, and therefore can reveal subtle biological effects that would otherwise not be apparent. This makes them a promising tool for a wide range of studies in the basic and applied life sciences.
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Wang Y, Chapman MP. Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal control. ARTIF INTELL 2022. [DOI: 10.1016/j.artint.2022.103743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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65
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Wang Y, Zhao J, Park HJ, Zhou D. Effect of dedifferentiation on noise propagation in cellular hierarchy. Phys Rev E 2022; 105:054409. [PMID: 35706189 DOI: 10.1103/physreve.105.054409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
Many fast renewing tissues have a hierarchical structure. Tissue-specific stem cells are at the root of this cellular hierarchy, which give rive to a whole range of specialized cells via cellular differentiation. However, increasing evidence shows that the hierarchical structure can be broken due to cellular dedifferentiation in which cells at differentiated stages can revert to the stem cell stage. Dedifferentiation has significant impacts on many aspects of hierarchical tissues. Here we investigate the effect of dedifferentiation on noise propagation by developing a stochastic model composed of different cell types. The moment equations are derived, via which we systematically investigate how the noise in the cell number is changed by dedifferentiation. Our results suggest that dedifferentiation have different effects on the noises in the numbers of stem cells and nonstem cells. Specifically, the noise in the number of stem cells is significantly reduced by increasing dedifferentiation probability. Due to the dual effect of dedifferentiation on nonstem cells, however, more complex changes could happen to the noise in the number of nonstem cells by increasing dedifferentiation probability. Furthermore, it is found that even though dedifferentiation could turn part of the noise propagation process into a noise-amplifying step, it is very unlikely to turn the entire process into a noise-amplifying cascade.
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Affiliation(s)
- Yuman Wang
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
| | - Jintong Zhao
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
| | - Hye Jin Park
- Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
- Department of Physics, Inha University, Incheon 22212, Republic of Korea
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
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66
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Jiao F, Tang M. Quantification of transcription noise’s impact on cell fate commitment with digital resolutions. Bioinformatics 2022; 38:3062-3069. [DOI: 10.1093/bioinformatics/btac277] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 02/18/2022] [Accepted: 04/12/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Gene transcription is a random and noisy process. Tremendous efforts in single cell studies have been mapping transcription noises to phenotypic variabilities between isogenic cells. However, the exact role of the noise in cell fate commitment remains largely descriptive or even controversial.
Results
For a specified cell fate, we define the jumping digit I of a critical gene as a statistical threshold that a single cell has approximately an equal chance to commit the fate as to have at least I transcripts of the gene. When the transcription is perturbed by a noise enhancer without changing the basal transcription level E 0, we find a crossing digit k such that the noise catalyzes cell fate change when I > k while stabilizes the current state when I < k; k remains stable against enormous variations of kinetic rates. We further test the reactivation of latent HIV in 22 integration sites by noise enhancers paired with transcriptional activators. Strong synergistic actions are observed when the activators increase transcription burst frequency, whereas no synergism, but antagonism, is often observed if activators increase burst size. The synergistic efficiency can be predicted accurately by the ratio I / E0. When the noise enhancers double the noise, the activators double the burst frequency, and I / E0 ≥ 7, their combination is 10 times more effective than their additive effects across all 22 sites.
Availability and implementation
The jumping digit I may provide a novel probe to explore the phenotypic consequences of transcription noise in cell functions. Code is freely available at http://cam.gzhu.edu.cn/info/1014/1223.htm.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, P. R. China
- College of Mathematics and Information Sciences, Guangzhou University, Guangzhou, 510006, P. R. China
| | - Moxun Tang
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
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67
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Pitruzzello G, Baumann CG, Johnson S, Krauss TF. Single‐Cell Motility Rapidly Quantifying Heteroresistance in Populations of
Escherichia coli
and
Salmonella typhimurium. SMALL SCIENCE 2022. [DOI: 10.1002/smsc.202100123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
| | | | - Steven Johnson
- Department of Electronic Engineering University of York York YO10 5DD UK
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68
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Abplanalp WT, Tucker N, Dimmeler S. Single-cell technologies to decipher cardiovascular diseases. Eur Heart J 2022; 43:4536-4547. [PMID: 35265972 PMCID: PMC9659476 DOI: 10.1093/eurheartj/ehac095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/30/2022] [Accepted: 02/15/2022] [Indexed: 12/14/2022] Open
Abstract
Cardiovascular disease remains the leading cause of death worldwide. A deeper understanding of the multicellular composition and molecular processes may help to identify novel therapeutic strategies. Single-cell technologies such as single-cell or single-nuclei RNA sequencing provide expression profiles of individual cells and allow for dissection of heterogeneity in tissue during health and disease. This review will summarize (i) how these novel technologies have become critical for delineating mechanistic drivers of cardiovascular disease, particularly, in humans and (ii) how they might serve as diagnostic tools for risk stratification or individualized therapy. The review will further discuss technical pitfalls and provide an overview of publicly available human and mouse data sets that can be used as a resource for research.
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Affiliation(s)
- Wesley Tyler Abplanalp
- Institute for Cardiovascular Regeneration, Centre of Molecular Medicine, Goethe University Frankfurt, Theodor Stern Kai 7, 60590 Frankfurt, Germany,German Center for Cardiovascular Research DZHK, Partner site Frankfurt Rhine-Main, Berlin, Germany,Cardiopulmonary Institute, Goethe University Frankfurt, Frankfurt, Germany
| | - Nathan Tucker
- Masonic Medical Research Institute, Utica, NY, USA,Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Stefanie Dimmeler
- Corresponding author. Tel: +49 69 6301 5158, Fax: +49 69 6301 83462,
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69
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Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level. BMC Biol 2022; 20:60. [PMID: 35260165 PMCID: PMC8905725 DOI: 10.1186/s12915-022-01264-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/22/2022] [Indexed: 12/11/2022] Open
Abstract
Background Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made. Results Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. Conclusions These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01264-9.
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70
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The ability to classify patients based on gene-expression data varies by algorithm and performance metric. PLoS Comput Biol 2022; 18:e1009926. [PMID: 35275931 PMCID: PMC8942277 DOI: 10.1371/journal.pcbi.1009926] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 03/23/2022] [Accepted: 02/15/2022] [Indexed: 01/02/2023] Open
Abstract
By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or patients most (or least) likely to respond to a particular therapy. Transcriptomic measurements reflect the downstream effects of genomic and epigenomic variations. However, high-throughput technologies generate thousands of measurements per patient, and complex dependencies exist among genes, so it may be infeasible to classify patients using traditional statistical models. Machine-learning classification algorithms can help with this problem. However, hundreds of classification algorithms exist-and most support diverse hyperparameters-so it is difficult for researchers to know which are optimal for gene-expression biomarkers. We performed a benchmark comparison, applying 52 classification algorithms to 50 gene-expression datasets (143 class variables). We evaluated algorithms that represent diverse machine-learning methodologies and have been implemented in general-purpose, open-source, machine-learning libraries. When available, we combined clinical predictors with gene-expression data. Additionally, we evaluated the effects of performing hyperparameter optimization and feature selection using nested cross validation. Kernel- and ensemble-based algorithms consistently outperformed other types of classification algorithms; however, even the top-performing algorithms performed poorly in some cases. Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies.
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71
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Topolewski P, Zakrzewska KE, Walczak J, Nienałtowski K, Müller-Newen G, Singh A, Komorowski M. Phenotypic variability, not noise, accounts for most of the cell-to-cell heterogeneity in IFN-γ and oncostatin M signaling responses. Sci Signal 2022; 15:eabd9303. [PMID: 35167339 DOI: 10.1126/scisignal.abd9303] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Cellular signaling responses show substantial cell-to-cell heterogeneity, which is often ascribed to the inherent randomness of biochemical reactions, termed molecular noise, wherein high noise implies low signaling fidelity. Alternatively, heterogeneity could arise from differences in molecular content between cells, termed molecular phenotypic variability, which does not necessarily imply imprecise signaling. The contribution of these two processes to signaling heterogeneity is unclear. Here, we fused fibroblasts to produce binuclear syncytia to distinguish noise from phenotypic variability in the analysis of cytokine signaling. We reasoned that the responses of the two nuclei within one syncytium could approximate the signaling outcomes of two cells with the same molecular content, thereby disclosing noise contribution, whereas comparison of different syncytia should reveal contribution of phenotypic variability. We found that ~90% of the variance in the primary response (which was the abundance of phosphorylated, nuclear STAT) to stimulation with the cytokines interferon-γ and oncostatin M resulted from differences in the molecular content of individual cells. Thus, our data reveal that cytokine signaling in the system used here operates in a reproducible, high-fidelity manner.
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Affiliation(s)
- Piotr Topolewski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Karolina E Zakrzewska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Jarosław Walczak
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Karol Nienałtowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Gerhard Müller-Newen
- Institute of Biochemistry and Molecular Biology, RWTH Aachen University, 52074 Aachen, Germany
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
| | - Michał Komorowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
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72
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Adnane S, Marino A, Leucci E. LncRNAs in human cancers: signal from noise. Trends Cell Biol 2022; 32:565-573. [DOI: 10.1016/j.tcb.2022.01.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/08/2022] [Accepted: 01/14/2022] [Indexed: 12/31/2022]
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73
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Arora V, Sanguinetti G. Challenges for machine learning in RNA-protein interaction prediction. Stat Appl Genet Mol Biol 2022; 21:sagmb-2021-0087. [PMID: 35073469 DOI: 10.1515/sagmb-2021-0087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/02/2022] [Indexed: 11/15/2022]
Abstract
RNA-protein interactions have long being recognised as crucial regulators of gene expression. Recently, the development of scalable experimental techniques to measure these interactions has revolutionised the field, leading to the production of large-scale datasets which offer both opportunities and challenges for machine learning techniques. In this brief note, we will discuss some of the major stumbling blocks towards the use of machine learning in computational RNA biology, focusing specifically on the problem of predicting RNA-protein interactions from next-generation sequencing data.
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Affiliation(s)
- Viplove Arora
- Data Science, Department of Physics, International School for Advanced Studies (SISSA), Trieste 34136, Italy
| | - Guido Sanguinetti
- Data Science, Department of Physics, International School for Advanced Studies (SISSA), Trieste 34136, Italy
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74
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O'Callaghan A, Eling N, Marioni JC, Vallejos CA. BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data. F1000Res 2022; 11:59. [PMID: 38779464 PMCID: PMC11109695 DOI: 10.12688/f1000research.74416.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/14/2021] [Indexed: 05/25/2024] Open
Abstract
Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.
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Affiliation(s)
- Alan O'Callaghan
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zürich, CH-8057, Switzerland
- Institute for Molecular Health Sciences, ETH Zürich, Zürich, 8093, Switzerland
| | - John C. Marioni
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, CB10 1SD, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Catalina A. Vallejos
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- The Alan Turing Institute, The Alan Turing Institute, London, NW1 2DB, UK
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75
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Chesnais F, Hue J, Roy E, Branco M, Stokes R, Pellon A, Le Caillec J, Elbahtety E, Battilocchi M, Danovi D, Veschini L. High content Image Analysis to study phenotypic heterogeneity in endothelial cell monolayers. J Cell Sci 2022; 135:273879. [PMID: 34982151 DOI: 10.1242/jcs.259104] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 12/15/2021] [Indexed: 11/20/2022] Open
Abstract
Endothelial cells (EC) are heterogeneous across and within tissues, reflecting distinct, specialised functions. EC heterogeneity has been proposed to underpin EC plasticity independently from vessel microenvironments. However, heterogeneity driven by contact-dependent or short-range cell-cell crosstalk cannot be evaluated with single cell transcriptomic approaches as spatial and contextual information is lost. Nonetheless, quantification of EC heterogeneity and understanding of its molecular drivers is key to developing novel therapeutics for cancer, cardiovascular diseases and for revascularisation in regenerative medicine. Here, we developed an EC profiling tool (ECPT) to examine individual cells within intact monolayers. We used ECPT to characterise different phenotypes in arterial, venous and microvascular EC populations. In line with other studies, we measured heterogeneity in terms of cell cycle, proliferation, and junction organisation. ECPT uncovered a previously under-appreciated single-cell heterogeneity in NOTCH activation. We correlated cell proliferation with different NOTCH activation states at the single cell and population levels. The positional and relational information extracted with our novel approach is key to elucidating the molecular mechanisms underpinning EC heterogeneity.
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Affiliation(s)
- Francois Chesnais
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Jonas Hue
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Errin Roy
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Marco Branco
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Ruby Stokes
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Aize Pellon
- Centre for host-microbiome interactions, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Juliette Le Caillec
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Eyad Elbahtety
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
| | - Matteo Battilocchi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Davide Danovi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK.,bit.bio, Babraham Research Campus, The Dorothy Hodgkin Building, Cambridge CB22 3FH, UK
| | - Lorenzo Veschini
- Academic centre of reconstructive science, Faculty of Dentistry Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK
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Shafiekhani S, Jafari A, Jafarzadeh L, Sadeghi V, Gheibi N. Predicting efficacy of 5-fluorouracil therapy via a mathematical model with fuzzy uncertain parameters. JOURNAL OF MEDICAL SIGNALS & SENSORS 2022; 12:202-218. [PMID: 36120402 PMCID: PMC9480509 DOI: 10.4103/jmss.jmss_92_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 11/12/2021] [Accepted: 01/21/2022] [Indexed: 11/08/2022]
Abstract
Background: Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of tumor-immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5-fluorouracil (5-FU) chemotherapy. Result: FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5-FU treatment. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid-derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band.
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77
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Garcia I, Orellana-Muñoz S, Ramos-Alonso L, Andersen AN, Zimmermann C, Eriksson J, Bøe SO, Kaferle P, Papamichos-Chronakis M, Chymkowitch P, Enserink JM. Kel1 is a phosphorylation-regulated noise suppressor of the pheromone signaling pathway. Cell Rep 2021; 37:110186. [PMID: 34965431 DOI: 10.1016/j.celrep.2021.110186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/01/2021] [Accepted: 12/07/2021] [Indexed: 11/26/2022] Open
Abstract
Mechanisms have evolved that allow cells to detect signals and generate an appropriate response. The accuracy of these responses relies on the ability of cells to discriminate between signal and noise. How cells filter noise in signaling pathways is not well understood. Here, we analyze noise suppression in the yeast pheromone signaling pathway and show that the poorly characterized protein Kel1 serves as a major noise suppressor and prevents cell death. At the molecular level, Kel1 prevents spontaneous activation of the pheromone response by inhibiting membrane recruitment of Ste5 and Far1. Only a hypophosphorylated form of Kel1 suppresses signaling, reduces noise, and prevents pheromone-associated cell death, and our data indicate that the MAPK Fus3 contributes to Kel1 phosphorylation. Taken together, Kel1 serves as a phospho-regulated suppressor of the pheromone pathway to reduce noise, inhibit spontaneous activation of the pathway, regulate mating efficiency, and prevent pheromone-associated cell death.
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Affiliation(s)
- Ignacio Garcia
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Sara Orellana-Muñoz
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Lucía Ramos-Alonso
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway; Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Aram N Andersen
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway
| | - Christine Zimmermann
- Institute for Virology, University Medical Center of the Johannes Gutenberg-University, 55131 Mainz, Germany
| | - Jens Eriksson
- Department of Medical Biochemistry and Microbiology, Uppsala University, 752 37 Uppsala, Sweden
| | - Stig Ove Bøe
- Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Petra Kaferle
- Institut Curie, PSL Research University, CNRS, UMR3664, Sorbonne Universities, Paris, France
| | - Manolis Papamichos-Chronakis
- Department of Molecular Physiology and Cell Signalling Institute of Systems, Molecular and Integrative Biology University of Liverpool, L69 7BE Liverpool, UK
| | - Pierre Chymkowitch
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway; Department of Microbiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Jorrit M Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, 0379 Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway; Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0316 Oslo, Norway.
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78
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A Novel Approach for Calculating Exact Forms of mRNA Distribution in Single-Cell Measurements. MATHEMATICS 2021. [DOI: 10.3390/math10010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Gene transcription is a stochastic process manifested by fluctuations in mRNA copy numbers in individual isogenic cells. Together with mathematical models of stochastic transcription, the massive mRNA distribution data that can be used to quantify fluctuations in mRNA levels can be fitted by Pm(t), which is the probability of producing m mRNA molecules at time t in a single cell. Tremendous efforts have been made to derive analytical forms of Pm(t), which rely on solving infinite arrays of the master equations of models. However, current approaches focus on the steady-state (t→∞) or require several parameters to be zero or infinity. Here, we present an approach for calculating Pm(t) with time, where all parameters are positive and finite. Our approach was successfully implemented for the classical two-state model and the widely used three-state model and may be further developed for different models with constant kinetic rates of transcription. Furthermore, the direct computations of Pm(t) for the two-state model and three-state model showed that the different regulations of gene activation can generate discriminated dynamical bimodal features of mRNA distribution under the same kinetic rates and similar steady-state mRNA distribution.
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79
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Mircea M, Semrau S. How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification. Biochem Soc Trans 2021; 49:2509-2525. [PMID: 34854897 PMCID: PMC8786291 DOI: 10.1042/bst20210135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 12/13/2022]
Abstract
On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.
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Affiliation(s)
- Maria Mircea
- Leiden Institute of Physics, Leiden University, Leiden, The Netherlands
| | - Stefan Semrau
- Leiden Institute of Physics, Leiden University, Leiden, The Netherlands
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80
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Klein B, Holmér L, Smith KM, Johnson MM, Swain A, Stolp L, Teufel AI, Kleppe AS. A computational exploration of resilience and evolvability of protein-protein interaction networks. Commun Biol 2021; 4:1352. [PMID: 34857859 PMCID: PMC8639913 DOI: 10.1038/s42003-021-02867-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 11/03/2021] [Indexed: 11/09/2022] Open
Abstract
Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.
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Affiliation(s)
- Brennan Klein
- Network Science Institute, Northeastern University, Boston, MA, USA. .,Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA, USA.
| | - Ludvig Holmér
- grid.419684.60000 0001 1214 1861Center for Data Analytics, Stockholm School of Economics, Stockholm, Sweden
| | - Keith M. Smith
- grid.12361.370000 0001 0727 0669Department of Physics and Mathematics, Nottingham Trent University, Nottingham, UK
| | - Mackenzie M. Johnson
- grid.89336.370000 0004 1936 9924Department of Integrative Biology, University of Texas at Austin, Austin, TX USA
| | - Anshuman Swain
- grid.164295.d0000 0001 0941 7177Department of Biology, University of Maryland, College Park, MD USA
| | - Laura Stolp
- grid.7177.60000000084992262Graduate School of Science, University of Amsterdam, Amsterdam, The Netherlands
| | - Ashley I. Teufel
- grid.89336.370000 0004 1936 9924Department of Integrative Biology, University of Texas at Austin, Austin, TX USA ,grid.209665.e0000 0001 1941 1940Santa Fe Institute, Santa Fe, NM USA ,grid.469272.c0000 0001 0180 5693Texas A&M University, San Antonio, San Antonio, TX USA
| | - April S. Kleppe
- grid.5949.10000 0001 2172 9288Institute for Evolution and Biodiversity, University of Münster, Münster, Germany ,grid.7048.b0000 0001 1956 2722Department of Clinical Medicine (MOMA), Aarhus University, Aarhus, Denmark
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81
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Burns JJR, Shealy BT, Greer MS, Hadish JA, McGowan MT, Biggs T, Smith MC, Feltus FA, Ficklin SP. Addressing noise in co-expression network construction. Brief Bioinform 2021; 23:6446269. [PMID: 34850822 PMCID: PMC8769892 DOI: 10.1093/bib/bbab495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.
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Affiliation(s)
- Joshua J R Burns
- Department of Horticulture, 149 Johnson Hall. Washington State University, Pullman, WA 99164. USA
| | - Benjamin T Shealy
- Department of Electrical & Computer Engineering, 105 Riggs Hall. Clemson University, Clemson, SC 29631. USA
| | - Mitchell S Greer
- School of Electrical Engineering and Computer Science, EME 102. Washington State University, Pullman, WA 99164. USA
| | - John A Hadish
- Molecular Plant Sciences Program, French Ad 324g. Washington State University, Pullman, WA 99164. USA
| | - Matthew T McGowan
- Molecular Plant Sciences Program, French Ad 324g. Washington State University, Pullman, WA 99164. USA
| | - Tyler Biggs
- Department of Horticulture, 149 Johnson Hall. Washington State University, Pullman, WA 99164. USA
| | - Melissa C Smith
- Department of Electrical & Computer Engineering, 105 Riggs Hall. Clemson University, Clemson, SC 29631. USA
| | - F Alex Feltus
- Department of Genetics and Biochemistry, 130 McGinty Court. Clemson University, Clemson, SC 29634. USA.,Biomedical Data Science & Informatics Program, 100 McAdams Hall. Clemson University, Clemson, SC 29634. USA.,Clemson Center for Human Genetics, 114 Gregor Mendel Circle, Greenwood, SC 29646. USA
| | - Stephen P Ficklin
- Department of Horticulture, 149 Johnson Hall. Washington State University, Pullman, WA 99164. USA.,School of Electrical Engineering and Computer Science, EME 102. Washington State University, Pullman, WA 99164. USA
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82
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Joly-Smith E, Wang ZJ, Hilfinger A. Inferring gene regulation dynamics from static snapshots of gene expression variability. Phys Rev E 2021; 104:044406. [PMID: 34781497 DOI: 10.1103/physreve.104.044406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 08/27/2021] [Indexed: 11/07/2022]
Abstract
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of covariability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate timescales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle-dependent transcription rates can be identified from the variability of coregulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.
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Affiliation(s)
- Euan Joly-Smith
- Department of Physics, University of Toronto, 60 St. George Street, Toronto, Ontario, Canada M5S 1A7
| | - Zitong Jerry Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Andreas Hilfinger
- Department of Physics, University of Toronto, 60 St. George Street, Toronto, Ontario, Canada M5S 1A7.,Department of Mathematics, University of Toronto, 40 St. George Street, Toronto, Ontario, Canada M5S 2E4.,Department of Cell & Systems Biology, University of Toronto, 25 Harbord Street, Toronto, Ontario, Canada M5S 3G5
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83
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Resztak JA, Wei J, Zilioli S, Sendler E, Alazizi A, Mair-meijers HE, Wu P, Slatcher RB, Zhou X, Luca F, Pique-regi R. Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution.. [PMID: 35313584 PMCID: PMC8936121 DOI: 10.1101/2021.09.30.462672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Synthetic glucocorticoids, such as dexamethasone, have been used as treatment for many immune conditions, such as asthma and more recently severe COVID-19. Single cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.
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84
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Dankó B, Szikora P, Pór T, Szeifert A, Sebestyén E. SplicingFactory-splicing diversity analysis for transcriptome data. Bioinformatics 2021; 38:384-390. [PMID: 34499147 PMCID: PMC8722757 DOI: 10.1093/bioinformatics/btab648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 07/31/2021] [Accepted: 09/06/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements. RESULTS We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations. AVAILABILITY AND IMPLEMENTATION The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Benedek Dankó
- Department of Genetics, Eötvös Loránd University, Budapest H-1053, Hungary
| | - Péter Szikora
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest H-1085, Hungary
| | - Tamás Pór
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest H-1085, Hungary
| | - Alexa Szeifert
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest H-1083, Hungary
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85
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Li J, Singh U, Arendsee Z, Wurtele ES. Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data. Front Genet 2021; 12:722981. [PMID: 34484307 PMCID: PMC8415361 DOI: 10.3389/fgene.2021.722981] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 07/26/2021] [Indexed: 12/13/2022] Open
Abstract
The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.
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Affiliation(s)
- Jing Li
- Genetics and Genomics Graduate Program, Iowa State University, Ames, IA, United States
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, United States
- Center for Metabolic Biology, Iowa State University, Ames, IA, United States
| | - Urminder Singh
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, United States
- Center for Metabolic Biology, Iowa State University, Ames, IA, United States
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States
| | - Zebulun Arendsee
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, United States
- Center for Metabolic Biology, Iowa State University, Ames, IA, United States
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States
| | - Eve Syrkin Wurtele
- Genetics and Genomics Graduate Program, Iowa State University, Ames, IA, United States
- Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, IA, United States
- Center for Metabolic Biology, Iowa State University, Ames, IA, United States
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States
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86
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The switch of DNA states filtering the extrinsic noise in the system of frequency modulation. Sci Rep 2021; 11:16309. [PMID: 34381062 PMCID: PMC8357933 DOI: 10.1038/s41598-021-95365-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/20/2021] [Indexed: 02/07/2023] Open
Abstract
There is a special node, which the large noise of the upstream element may not always lead to a broad distribution of downstream elements. This node is DNA, with upstream element TF and downstream elements mRNA and proteins. By applying the stochastic simulation algorithm (SSA) on gene circuits inspired by the fim operon in Escherichia coli, we found that cells exchanged the distribution of the upstream transcription factor (TF) for the transitional frequency of DNA. Then cells do an inverse transform, which exchanges the transitional frequency of DNA for the distribution of downstream products. Due to this special feature, DNA in the system of frequency modulation is able to reset the noise. By probability generating function, we know the ranges of parameter values that grant such an interesting phenomenon.
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87
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Wang IF, Wang Y, Yang YH, Huang GJ, Tsai KJ, Shen CKJ. Activation of a hippocampal CREB-pCREB-miRNA-MEF2 axis modulates individual variation of spatial learning and memory capability. Cell Rep 2021; 36:109477. [PMID: 34348143 DOI: 10.1016/j.celrep.2021.109477] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/07/2021] [Accepted: 07/13/2021] [Indexed: 11/30/2022] Open
Abstract
Phenotypic variation is a fundamental prerequisite for cell and organism evolution by natural selection. Whereas the role of stochastic gene expression in phenotypic diversity of genetically identical cells is well studied, not much is known regarding the relationship between stochastic gene expression and individual behavioral variation in animals. We demonstrate that a specific miRNA (miR-466f-3p) is upregulated in the hippocampus of a portion of individual inbred mice upon a Morris water maze task. Significantly, miR-466f-3p positively regulates the neuron morphology, function and spatial learning, and memory capability of mice. Mechanistically, miR-466f-3p represses translation of MEF2A, a negative regulator of learning/memory. Finally, we show that varied upregulation of hippocampal miR-466f-3p results from randomized phosphorylation of hippocampal cyclic AMP (cAMP)-response element binding (CREB) in individuals. This finding of modulation of spatial learning and memory via a randomized hippocampal signaling axis upon neuronal stimulation represents a demonstration of how variation in tissue gene expression lead to varied animal behavior.
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Affiliation(s)
- I-Fang Wang
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan
| | - Yihan Wang
- Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan
| | - Yi-Hua Yang
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan; Research Center of Clinical Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan
| | - Guo-Jen Huang
- Department and Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou 33302, Taiwan
| | - Kuen-Jer Tsai
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan; Research Center of Clinical Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan.
| | - Che-Kun James Shen
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan.
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88
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Higgins-Chen AT, Thrush KL, Levine ME. Aging biomarkers and the brain. Semin Cell Dev Biol 2021; 116:180-193. [PMID: 33509689 PMCID: PMC8292153 DOI: 10.1016/j.semcdb.2021.01.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/15/2022]
Abstract
Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.
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Affiliation(s)
- Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, 300 George St, Suite 901, New Haven, CT 06511, USA.
| | - Kyra L Thrush
- Program in Computational Biology and Bioinformatics, Yale University, 300 George St, Suite 501, New Haven, CT 06511, USA.
| | - Morgan E Levine
- Department of Pathology, Yale University School of Medicine, 310 Cedar Street, Suite LH 315A, New Haven, CT 06520, USA.
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89
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Loughrey C, Fitzpatrick P, Orr N, Jurek-Loughrey A. The topology of data: Opportunities for cancer research. Bioinformatics 2021; 37:3091-3098. [PMID: 34320632 PMCID: PMC8504620 DOI: 10.1093/bioinformatics/btab553] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/14/2021] [Accepted: 07/28/2021] [Indexed: 01/20/2023] Open
Abstract
Motivation Topological methods have recently emerged as a reliable and interpretable framework for extracting information from high-dimensional data, leading to the creation of a branch of applied mathematics called Topological Data Analysis (TDA). Since then, TDA has been progressively adopted in biomedical research. Biological data collection can result in enormous datasets, comprising thousands of features and spanning diverse datatypes. This presents a barrier to initial data analysis as the fundamental structure of the dataset becomes hidden, obstructing the discovery of important features and patterns. TDA provides a solution to obtain the underlying shape of datasets over continuous resolutions, corresponding to key topological features independent of noise. TDA has the potential to support future developments in healthcare as biomedical datasets rise in complexity and dimensionality. Previous applications extend across the fields of neuroscience, oncology, immunology and medical image analysis. TDA has been used to reveal hidden subgroups of cancer patients, construct organizational maps of brain activity and classify abnormal patterns in medical images. The utility of TDA is broad and to understand where current achievements lie, we have evaluated the present state of TDA in cancer data analysis. Results This article aims to provide an overview of TDA in Cancer Research. A brief introduction to the main concepts of TDA is provided to ensure that the article is accessible to readers who are not familiar with this field. Following this, a focussed literature review on the field is presented, discussing how TDA has been applied across heterogeneous datatypes for cancer research.
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Affiliation(s)
- Ciara Loughrey
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, BT9 5BN, United Kingdom
| | - Padraig Fitzpatrick
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, BT9 5BN, United Kingdom
| | - Nick Orr
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, BT9 7AE, United Kingdom
| | - Anna Jurek-Loughrey
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, BT9 5BN, United Kingdom
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90
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The effect of natural selection on the propagation of protein expression noise to bacterial growth. PLoS Comput Biol 2021; 17:e1009208. [PMID: 34280182 PMCID: PMC8321134 DOI: 10.1371/journal.pcbi.1009208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 07/29/2021] [Accepted: 06/22/2021] [Indexed: 11/19/2022] Open
Abstract
In bacterial cells, protein expression is a highly stochastic process. Gene expression noise moreover propagates through the cell and adds to fluctuations in the cellular growth rate. A common intuition is that, due to their relatively high noise amplitudes, proteins with a low mean expression level are the most important drivers of fluctuations in physiological variables. In this work, we challenge this intuition by considering the effect of natural selection on noise propagation. Mathematically, the contribution of each protein species to the noise in the growth rate depends on two factors: the noise amplitude of the protein’s expression level, and the sensitivity of the growth rate to fluctuations in that protein’s concentration. We argue that natural selection, while shaping mean abundances to increase the mean growth rate, also affects cellular sensitivities. In the limit in which cells grow optimally fast, the growth rate becomes most sensitive to fluctuations in highly abundant proteins. This causes abundant proteins to overall contribute strongly to the noise in the growth rate, despite their low noise levels. We further explore this result in an experimental data set of protein abundances, and test key assumptions in an evolving, stochastic toy model of cellular growth. Gene expression in bacterial cells is intrinsically stochastic: copy numbers of all proteins vary between genetically identical cells, even in a homogeneous environment. Such noise in gene expression affects metabolic fluxes and even propagates to the cellular level. Indeed, also the growth rate of individual cells, an important proxy for fitness, fluctuates strongly. This beckons the question as to which protein species contribute most to the noise in the cell’s growth rate. We here derive general mathematical predictions stating that if cells have been optimised by evolution to grow fast, abundant protein species contribute most to the noise in the growth rate. This result is counter-intuitive, because the noise levels in the expression of those highly expressed proteins are small.
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91
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Richter ML, Deligiannis IK, Yin K, Danese A, Lleshi E, Coupland P, Vallejos CA, Matchett KP, Henderson NC, Colome-Tatche M, Martinez-Jimenez CP. Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy. Nat Commun 2021; 12:4264. [PMID: 34253736 PMCID: PMC8275628 DOI: 10.1038/s41467-021-24543-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 06/24/2021] [Indexed: 12/19/2022] Open
Abstract
Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity, we have developed a single-nucleus RNA-seq2 method tailored for the comprehensive analysis of the nuclear transcriptome from frozen tissues, allowing the dissection of all cell types present in the liver, regardless of cell size or cellular fragility. We use this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that ploidy states are associated with different metabolic potential, and gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobule. Our work reveals a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.
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Affiliation(s)
- M L Richter
- Helmholtz Pioneer Campus (HPC), Helmholtz Zentrum München, Neuherberg, Germany
| | - I K Deligiannis
- Helmholtz Pioneer Campus (HPC), Helmholtz Zentrum München, Neuherberg, Germany
| | - K Yin
- Helmholtz Pioneer Campus (HPC), Helmholtz Zentrum München, Neuherberg, Germany
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, United Kingdom
| | - A Danese
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - E Lleshi
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, United Kingdom
| | - P Coupland
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, United Kingdom
| | - C A Vallejos
- MRC Human Genetics Unit, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - K P Matchett
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Little France Crescent, Edinburgh, United Kingdom
| | - N C Henderson
- MRC Human Genetics Unit, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Little France Crescent, Edinburgh, United Kingdom
| | - M Colome-Tatche
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Munich, Germany.
| | - C P Martinez-Jimenez
- Helmholtz Pioneer Campus (HPC), Helmholtz Zentrum München, Neuherberg, Germany.
- TUM School of Medicine, Technical University of Munich, Munich, Germany.
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92
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Fischer S, Dinh M, Henry V, Robert P, Goelzer A, Fromion V. BiPSim: a flexible and generic stochastic simulator for polymerization processes. Sci Rep 2021; 11:14112. [PMID: 34238958 PMCID: PMC8266833 DOI: 10.1038/s41598-021-92833-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 06/07/2021] [Indexed: 11/30/2022] Open
Abstract
Detailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology.
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Affiliation(s)
- Stephan Fischer
- INRAE, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Marc Dinh
- INRAE, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Vincent Henry
- INRAE, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | | | - Anne Goelzer
- INRAE, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France
| | - Vincent Fromion
- INRAE, MaIAGE, Université Paris-Saclay, Jouy-en-Josas, France.
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93
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Nienałtowski K, Rigby RE, Walczak J, Zakrzewska KE, Głów E, Rehwinkel J, Komorowski M. Fractional response analysis reveals logarithmic cytokine responses in cellular populations. Nat Commun 2021; 12:4175. [PMID: 34234126 PMCID: PMC8263596 DOI: 10.1038/s41467-021-24449-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 06/17/2021] [Indexed: 01/10/2023] Open
Abstract
Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose–response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes. Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.
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Affiliation(s)
- Karol Nienałtowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Rachel E Rigby
- Medical Research Council Human Immunology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jarosław Walczak
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Karolina E Zakrzewska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Edyta Głów
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Jan Rehwinkel
- Medical Research Council Human Immunology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Michał Komorowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.
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94
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Rammohan J, Lund SP, Alperovich N, Paralanov V, Strychalski EA, Ross D. Comparison of bias and resolvability in single-cell and single-transcript methods. Commun Biol 2021; 4:659. [PMID: 34079048 PMCID: PMC8172639 DOI: 10.1038/s42003-021-02138-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 04/16/2021] [Indexed: 11/17/2022] Open
Abstract
Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.
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Affiliation(s)
- Jayan Rammohan
- National Institute of Standards and Technology, Gaithersburg, MD, USA.
| | - Steven P Lund
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Vanya Paralanov
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, USA.
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95
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A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability. Sci Rep 2021; 11:11075. [PMID: 34040065 PMCID: PMC8155031 DOI: 10.1038/s41598-021-90353-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 04/30/2021] [Indexed: 12/30/2022] Open
Abstract
Recent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.
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96
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Liu L, Warmflash A. Self-organized signaling in stem cell models of embryos. Stem Cell Reports 2021; 16:1065-1077. [PMID: 33979594 PMCID: PMC8185436 DOI: 10.1016/j.stemcr.2021.03.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/17/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Mammalian embryonic development is a complex process driven by self-organization. Understanding how a fertilized egg develops into an embryo composed of more than 200 cell types in precise spatial patterns remains one of the fundamental challenges in biology. Pluripotent stem cells have been used as in vitro models for investigating mammalian development, and represent promising building blocks for regenerative therapies. Recently, sophisticated stem cell-based models that recapitulate early embryonic fate patterning and morphogenesis have been developed. In this article, we review recent advances in stem cell models of embryos in particular focusing on signaling activities underpinning cell fate decisions in space and time.
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Affiliation(s)
- Lizhong Liu
- Department of Biosciences, Rice University, Houston, TX 77005, USA
| | - Aryeh Warmflash
- Department of Biosciences, Rice University, Houston, TX 77005, USA; Department of Bioengineering, Rice University, Houston, TX 77005, USA.
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97
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Vershinina O, Bacalini MG, Zaikin A, Franceschi C, Ivanchenko M. Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear. Sci Rep 2021; 11:9201. [PMID: 33911141 PMCID: PMC8080842 DOI: 10.1038/s41598-021-88504-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 04/06/2021] [Indexed: 12/30/2022] Open
Abstract
DNA methylation variability arises due to concurrent genetic and environmental influences. Each of them is a mixture of regular and noisy sources, whose relative contribution has not been satisfactorily understood yet. We conduct a systematic assessment of the age-dependent methylation by the signal-to-noise ratio and identify a wealth of "deterministic" CpG probes (about 90%), whose methylation variability likely originates due to genetic and general environmental factors. The remaining 10% of "stochastic" CpG probes are arguably governed by the biological noise or incidental environmental factors. Investigating the mathematical functional relationship between methylation levels and variability, we find that in about 90% of the age-associated differentially methylated positions, the variability changes as the square of the methylation level, whereas in the most of the remaining cases the dependence is linear. Furthermore, we demonstrate that the methylation level itself in more than 15% cases varies nonlinearly with age (according to the power law), in contrast to the previously assumed linear changes. Our findings present ample evidence of the ubiquity of strong DNA methylation regulation, resulting in the individual age-dependent and nonlinear methylation trajectories, whose divergence explains the cross-sectional variability. It may also serve a basis for constructing novel nonlinear epigenetic clocks.
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Affiliation(s)
- O. Vershinina
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950
| | - M. G. Bacalini
- grid.492077.fUniversity of Bologna and IRCCS Istituto delle Scienze Neurologiche di Bologna (ISNB), 40139 Bologna, Italy
| | - A. Zaikin
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950 ,grid.83440.3b0000000121901201Department of Mathematics and Institute for Women’s Health, University College London, London, WC1H 0AY UK ,grid.448878.f0000 0001 2288 8774Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia 119992
| | - C. Franceschi
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950 ,grid.492077.fUniversity of Bologna and IRCCS Istituto delle Scienze Neurologiche di Bologna (ISNB), 40139 Bologna, Italy
| | - M. Ivanchenko
- grid.28171.3d0000 0001 0344 908XDepartment of Applied Mathematics, Mathematics of Future Technologies Center, Laboratory of Systems Medicine of Healthy Aging, Lobachevsky University, Nizhny Novgorod, Russia 603950
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98
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Kapourani CA, Argelaguet R, Sanguinetti G, Vallejos CA. scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution. Genome Biol 2021; 22:114. [PMID: 33879195 PMCID: PMC8056718 DOI: 10.1186/s13059-021-02329-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/25/2021] [Indexed: 02/06/2023] Open
Abstract
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET .
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Affiliation(s)
- Chantriolnt-Andreas Kapourani
- MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | | | - Guido Sanguinetti
- School of Informatics, University of Edinburgh, Edinburgh, UK.
- SISSA, International School of Advanced Studies, Trieste, Italy.
| | - Catalina A Vallejos
- MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
- The Alan Turing Institute, London, UK.
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99
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Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021; 22:459-476. [PMID: 33875884 PMCID: PMC10152720 DOI: 10.1038/s41576-021-00341-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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100
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Capp J. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. Evol Appl 2021; 14:893-901. [PMID: 33897810 PMCID: PMC8061278 DOI: 10.1111/eva.13204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 12/11/2022] Open
Abstract
Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.
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Affiliation(s)
- Jean‐Pascal Capp
- Toulouse Biotechnology InstituteINSACNRSINRAEUniversity of ToulouseToulouseFrance
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