scholarly journals Identification of the macrophage-specific promoter signature in FANTOM5 mouse embryo developmental time course data

2017 ◽  
Vol 102 (4) ◽  
pp. 1081-1092 ◽  
Author(s):  
Kim M. Summers ◽  
David A. Hume
Keyword(s):  
Mouse Embryo ◽  
Time Course ◽  
Developmental Time ◽  
Time Course Data

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

1125 DEVELOPMENTAL TIME COURSE OF VENO-OCCLUSIVE DYSFUNCTION AFTER BILATERAL CAVERNOSAL NERVE RESECTION DETERMINED BY TELEMETRY

2011 ◽  
Vol 185 (4S) ◽  
Author(s):  
Keisha K. King ◽  
Ramon Martinez ◽  
Istvan Kovanecz ◽  
Leah A. Garcia ◽  
Sateysh Sinha ◽  
...  
Keyword(s):  
Time Course ◽  
Developmental Time ◽  
Cavernosal Nerve

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

scholarly journals Stoichiometric Relationship between Na+ Ions Transported and Glucose Consumed in Human Erythrocytes: Bayesian Analysis of 23Na and 13C NMR Time Course Data

2013 ◽  
Vol 104 (8) ◽  
pp. 1676-1684 ◽  
Author(s):  
Max Puckeridge ◽  
Bogdan E. Chapman ◽  
Arthur D. Conigrave ◽  
Stuart M. Grieve ◽  
Gemma A. Figtree ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
13C Nmr ◽  
Time Course ◽  
Human Erythrocytes ◽  
Na Ions ◽  
Time Course Data

scholarly journals Inhibition of mTOR during a postnatal critical sensitive window rescues deficits in GABAergic PV cell connectivity and social behavior caused by loss of TSC1

2020 ◽  
Author(s):  
Mayukh Choudhury ◽  
Clara A. Amegandjin ◽  
Vidya Jadhav ◽  
Josianne Nunes Carriço ◽  
Ariane Quintal ◽  
...  
Keyword(s):  
Social Behavior ◽  
Time Course ◽  
Cell Types ◽  
Developmental Time ◽  
Adult Mice ◽  
Mtorc1 Signaling ◽  
Pv Cell ◽  
Mechanistic Basis

ABSTRACTMutations in regulators of the Mechanistic Target Of Rapamycin Complex 1 (mTORC1), such as Tsc1/2, lead to neurodevelopmental disorders associated with autism, intellectual disabilities and epilepsy. Whereas the effects of mTORC1 signaling dysfunction within diverse cell types are likely critical for the onset of the diverse neurological symptoms associated with mutations in mTORC1 regulators, they are not well understood. In particular, the effects of mTORC1 dys-regulation in specific types of inhibitory interneurons are unclear.Here, we showed that Tsc1 haploinsufficiency in parvalbumin (PV)-positive GABAergic interneurons either in cortical organotypic cultures or in vivo caused a premature increase in their perisomatic innervations, followed by a striking loss in adult mice. This effects were accompanied by alterations of AMPK-dependent autophagy in pre-adolescent but not adult mice. PV cell-restricted Tsc1 mutant mice showed deficits in social behavior. Treatment with the mTOR inhibitor Rapamycin restricted to the third postnatal week was sufficient to permanently rescue deficits in both PV cell innervation and social behavior in adult conditional haploinsufficient mice. All together, these findings identify a novel role of Tsc1-mTORC1 signaling in the regulation of the developmental time course and maintenance of cortical PV cell connectivity and provide a mechanistic basis for the targeted rescue of autism-related behaviors in disorders associated with deregulated mTORC1 signaling.

scholarly journals Emergence of neuronal diversity during vertebrate brain development

2019 ◽  
Author(s):  
Bushra Raj ◽  
Jeffrey A. Farrell ◽  
Aaron McKenna ◽  
Jessica L. Leslie ◽  
Alexander F. Schier
Keyword(s):  
Brain Development ◽  
Single Cell ◽  
Time Course ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Developmental Time ◽  
Neural Progenitors ◽  
Neuronal Diversity ◽  
Gene Markers ◽  
Vertebrate Brain

ABSTRACTNeurogenesis in the vertebrate brain comprises many steps ranging from the proliferation of progenitors to the differentiation and maturation of neurons. Although these processes are highly regulated, the landscape of transcriptional changes and progenitor identities underlying brain development are poorly characterized. Here, we describe the first developmental single-cell RNA-seq catalog of more than 200,000 zebrafish brain cells encompassing 12 stages from 12 hours post-fertilization to 15 days post-fertilization. We characterize known and novel gene markers for more than 800 clusters across these timepoints. Our results capture the temporal dynamics of multiple neurogenic waves from embryo to larva that expand neuronal diversity from ∼20 cell types at 12 hpf to ∼100 cell types at 15 dpf. We find that most embryonic neural progenitor states are transient and transcriptionally distinct from long-lasting neural progenitors of post-embryonic stages. Furthermore, we reconstruct cell specification trajectories for the retina and hypothalamus, and identify gene expression cascades and novel markers. Our analysis reveal that late-stage retinal neural progenitors transcriptionally overlap cell states observed in the embryo, while hypothalamic neural progenitors become progressively distinct with developmental time. These data provide the first comprehensive single-cell transcriptomic time course for vertebrate brain development and suggest distinct neurogenic regulatory paradigms between different stages and tissues.

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