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Heydari T, Zandstra PW. Inferring Gene Regulatory Networks and Predicting the Effect of Gene Perturbations via IQCELL. Methods Mol Biol 2024; 2767:251-262. [PMID: 36790623 DOI: 10.1007/7651_2022_465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
IQCELL is a platform that infers Boolean gene regulatory networks from single-cell RNA sequencing data. Boolean networks can be simulated under normal and perturbed conditions. In this chapter, we provide a detailed guideline for implementing IQCELL from a raw dataset. The steps include processing data, inferring informative genes, inferring gene regulatory network, and simulating the resulted network under normal and perturbed conditions.
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Affiliation(s)
- Tiam Heydari
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Peter W Zandstra
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
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2
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Muñoz EM, Martínez Cerdeño V. Editorial: Transcription regulation - Brain development and homeostasis - A finely tuned and orchestrated scenario in physiology and pathology, volume II. Front Mol Neurosci 2023; 16:1280573. [PMID: 37736114 PMCID: PMC10509287 DOI: 10.3389/fnmol.2023.1280573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 09/23/2023] Open
Affiliation(s)
- Estela M. Muñoz
- Institute of Histology and Embryology of Mendoza (IHEM), National University of Cuyo (UNCuyo), National Scientific and Technical Research Council (CONICET), Mendoza, Argentina
| | - Verónica Martínez Cerdeño
- Department of Pathology and Laboratory Medicine, Institute for Pediatric Regenerative Medicine, Shriners Hospitals for Children of Northern California, and MIND Institute at the UC Davis Medical Center, University of California Davis School of Medicine, Sacramento, CA, United States
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3
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Joo JI, Park H, Cho K. Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207322. [PMID: 37269056 PMCID: PMC10460890 DOI: 10.1002/advs.202207322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/17/2023] [Indexed: 06/04/2023]
Abstract
Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.
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Affiliation(s)
- Jae Il Joo
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
- Present address:
biorevert IncDaejeon34051Republic of Korea
| | - Hwa‐Jeong Park
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
- Present address:
Promega Corporationan affiliate of PromegaSouth Korea
| | - Kwang‐Hyun Cho
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
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4
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Ceglia N, Sethna Z, Freeman SS, Uhlitz F, Bojilova V, Rusk N, Burman B, Chow A, Salehi S, Kabeer F, Aparicio S, Greenbaum BD, Shah SP, McPherson A. Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector. Nat Commun 2023; 14:4400. [PMID: 37474509 PMCID: PMC10359421 DOI: 10.1038/s41467-023-39985-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 07/04/2023] [Indexed: 07/22/2023] Open
Abstract
Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.
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Affiliation(s)
- Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Zachary Sethna
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel S Freeman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Florian Uhlitz
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoria Bojilova
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bharat Burman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Chow
- Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Farhia Kabeer
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Benjamin D Greenbaum
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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5
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Soares BX, Miranda CC, Fernandes TG. Systems bioengineering approaches for developmental toxicology. Comput Struct Biotechnol J 2023; 21:3272-3279. [PMID: 38213895 PMCID: PMC10781881 DOI: 10.1016/j.csbj.2023.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 01/13/2024] Open
Abstract
Developmental toxicology is the field of study that examines the effects of chemical and physical agents on developing organisms. By using principles of systems biology and bioengineering, a systems bioengineering approach could be applied to study the complex interactions between developing organisms, the environment, and toxic agents. This approach would result in a holistic understanding of the effects of toxic agents on organisms, by considering the interactions between different biological systems and the impacts of toxicants on those interactions. It would be useful in identifying key biological pathways and mechanisms affected by toxic agents, as well as in the development of predictive models to assess potential risks of exposure to toxicants during development. In this review, we discuss the relevance of systems bioengineering to the field of developmental toxicity and provide up-to-date examples that illustrate the use of engineering principles for this application.
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Affiliation(s)
- Beatriz Xavier Soares
- Department of Bioengineering and iBB – Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Associate Laboratory i4HB – Institute for Health and Bioeconomy, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Cláudia C. Miranda
- Department of Bioengineering and iBB – Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Associate Laboratory i4HB – Institute for Health and Bioeconomy, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- AccelBio, Collaborative Laboratory to Foster Translation and Drug Discovery, Cantanhede, Portugal
| | - Tiago G. Fernandes
- Department of Bioengineering and iBB – Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
- Associate Laboratory i4HB – Institute for Health and Bioeconomy, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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6
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Singh A, Tiwari VK. Transcriptional networks of transient cell states during human prefrontal cortex development. Front Mol Neurosci 2023; 16:1126438. [PMID: 37138706 PMCID: PMC10150774 DOI: 10.3389/fnmol.2023.1126438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 03/15/2023] [Indexed: 05/05/2023] Open
Abstract
The human brain is divided into various anatomical regions that control and coordinate unique functions. The prefrontal cortex (PFC) is a large brain region that comprises a range of neuronal and non-neuronal cell types, sharing extensive interconnections with subcortical areas, and plays a critical role in cognition and memory. A timely appearance of distinct cell types through embryonic development is crucial for an anatomically perfect and functional brain. Direct tracing of cell fate development in the human brain is not possible, but single-cell transcriptome sequencing (scRNA-seq) datasets provide the opportunity to dissect cellular heterogeneity and its molecular regulators. Here, using scRNA-seq data of human PFC from fetal stages, we elucidate distinct transient cell states during PFC development and their underlying gene regulatory circuitry. We further identified that distinct intermediate cell states consist of specific gene regulatory modules essential to reach terminal fate using discrete developmental paths. Moreover, using in silico gene knock-out and over-expression analysis, we validated crucial gene regulatory components during the lineage specification of oligodendrocyte progenitor cells. Our study illustrates unique intermediate states and specific gene interaction networks that warrant further investigation for their functional contribution to typical brain development and discusses how this knowledge can be harvested for therapeutic intervention in challenging neurodevelopmental disorders.
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Affiliation(s)
- Aditi Singh
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Science, Queens University, Belfast, United Kingdom
| | - Vijay K. Tiwari
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Science, Queens University, Belfast, United Kingdom
- Institute of Molecular Medicine, University of Southern Denmark, Odense C, Denmark
- Danish Institute for Advanced Study (DIAS), Odense M, Denmark
- Department of Clinical Genetics, Odense University Hospital, Odense C, Denmark
- *Correspondence: Vijay K. Tiwari, ;
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7
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Virtual cells in a virtual microenvironment recapitulate early development-like patterns in human pluripotent stem cell colonies. Stem Cell Reports 2022; 18:377-393. [PMID: 36332630 PMCID: PMC9859929 DOI: 10.1016/j.stemcr.2022.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022] Open
Abstract
The mechanism by which morphogenetic signals engage the regulatory networks responsible for early embryonic tissue patterning is incompletely understood. Here, we developed a minimal gene regulatory network (GRN) model of human pluripotent stem cell (hPSC) lineage commitment and embedded it into "cellular" agents that respond to a dynamic morphogenetic signaling microenvironment. Simulations demonstrated that GRN wiring had significant non-intuitive effects on tissue pattern order, composition, and dynamics. Experimental perturbation of GRN connectivities supported model predictions and demonstrated the role of OCT4 as a master regulator of peri-gastrulation fates. Our so-called GARMEN strategy provides a multiscale computational platform to understand how single-cell-based regulatory interactions scale to tissue domains. This foundation provides new opportunities to simulate the impact of network motifs on normal and aberrant tissue development.
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Bocci F, Zhou P, Nie Q. spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data. Mol Syst Biol 2022; 18:e11176. [PMID: 36321549 PMCID: PMC9627675 DOI: 10.15252/msb.202211176] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
Extracting dynamical information from single-cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory-oriented, bottom-up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single-cell RNA sequencing (scRNA-seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state-specific gene-gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial-mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre-existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.
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Affiliation(s)
- Federico Bocci
- Department of MathematicsUniversity of CaliforniaIrvineCAUSA
- NSF‐Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCAUSA
| | - Peijie Zhou
- Department of MathematicsUniversity of CaliforniaIrvineCAUSA
| | - Qing Nie
- Department of MathematicsUniversity of CaliforniaIrvineCAUSA
- NSF‐Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCAUSA
- Department of Developmental and Cell BiologyUniversity of CaliforniaIrvineCAUSA
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9
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Tran A, Yang P, Yang JYH, Ormerod J. OUP accepted manuscript. Brief Funct Genomics 2022; 21:270-279. [PMID: 35411370 PMCID: PMC9328023 DOI: 10.1093/bfgp/elac008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
Abstract
Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.
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Affiliation(s)
- Andy Tran
- School of Mathematics and Statistics, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
| | | | | | - John Ormerod
- Corresponding author: John Ormerod, School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia. Tel.: +61293515787; Fax: +61293514534; E-mail:
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