Mus musculus
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ABSTRACT:
PROVIDER: PRJNA328869 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Hamey2017 - Blood stem cell regulatory
network (LMPP network)
This model is described in the article:
Reconstructing blood stem
cell regulatory network models from single-cell molecular
profiles
Fiona K. Hamey, Sonia Nestorowa,
Sarah J. Kinston, David G. Kent, Nicola K. Wilson, and Berthold
Göttgens
Proceedings of the National Academy of
Sciences of the United States of America
Abstract:
Adult blood contains a mixture of mature cell types, each
with specialized functions. Single hematopoietic stem cells
(HSCs) have been functionally shown to generate all mature cell
types for the lifetime of the organism. Differentiation of HSCs
toward alternative lineages must be balanced at the population
level by the fate decisions made by individual cells.
Transcription factors play a key role in regulating these
decisions and operate within organized regulatory programs that
can be modeled as transcriptional regulatory networks. As
dysregulation of single HSC fate decisions is linked to fatal
malignancies such as leukemia, it is important to understand
how these decisions are controlled on a cell-by-cell basis.
Here we developed and applied a network inference method,
exploiting the ability to infer dynamic information from
single-cell snapshot expression data based on expression
profiles of 48 genes in 2,167 blood stem and progenitor cells.
This approach allowed us to infer transcriptional regulatory
network models that recapitulated differentiation of HSCs into
progenitor cell types, focusing on trajectories toward
megakaryocyte–erythrocyte progenitors and lymphoid-primed
multipotent progenitors. By comparing these two models, we
identified and subsequently experimentally validated a
difference in the regulation of nuclear factor, erythroid 2
(Nfe2) and core-binding factor, runt domain, alpha subunit 2,
translocated to, 3 homolog (Cbfa2t3h) by the transcription
factor Gata2. Our approach confirms known aspects of
hematopoiesis, provides hypotheses about regulation of HSC
differentiation, and is widely applicable to other hierarchical
biological systems to uncover regulatory relationships.
This model is hosted on
BioModels Database
and identified by:
MODEL1610060001.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-09 | MODEL1610060001 | BioModels
Project description:Hamey2017 - Blood stem cell regulatory
network
This model is described in the article:
Reconstructing blood stem
cell regulatory network models from single-cell molecular
profiles
Fiona K. Hamey, Sonia Nestorowa,
Sarah J. Kinston, David G. Kent, Nicola K. Wilson, and Berthold
Göttgens
Proceedings of the National Academy of
Sciences of the United States of America
Abstract:
Adult blood contains a mixture of mature cell types, each
with specialized functions. Single hematopoietic stem cells
(HSCs) have been functionally shown to generate all mature cell
types for the lifetime of the organism. Differentiation of HSCs
toward alternative lineages must be balanced at the population
level by the fate decisions made by individual cells.
Transcription factors play a key role in regulating these
decisions and operate within organized regulatory programs that
can be modeled as transcriptional regulatory networks. As
dysregulation of single HSC fate decisions is linked to fatal
malignancies such as leukemia, it is important to understand
how these decisions are controlled on a cell-by-cell basis.
Here we developed and applied a network inference method,
exploiting the ability to infer dynamic information from
single-cell snapshot expression data based on expression
profiles of 48 genes in 2,167 blood stem and progenitor cells.
This approach allowed us to infer transcriptional regulatory
network models that recapitulated differentiation of HSCs into
progenitor cell types, focusing on trajectories toward
megakaryocyte–erythrocyte progenitors and lymphoid-primed
multipotent progenitors. By comparing these two models, we
identified and subsequently experimentally validated a
difference in the regulation of nuclear factor, erythroid 2
(Nfe2) and core-binding factor, runt domain, alpha subunit 2,
translocated to, 3 homolog (Cbfa2t3h) by the transcription
factor Gata2. Our approach confirms known aspects of
hematopoiesis, provides hypotheses about regulation of HSC
differentiation, and is widely applicable to other hierarchical
biological systems to uncover regulatory relationships.
This model is hosted on
BioModels Database
and identified by:
MODEL1610060000.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-06-09 | MODEL1610060000 | BioModels
Project description:Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors (TFs) in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterised by distinctive TF expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated TF pairings, including previously unrecognised relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a novel regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease. Examination of Gata2 and Gfi1 binding patterns in murine mast cells
2013-04-24 | E-GEOD-42518 | biostudies-arrayexpress
Project description:Blood and cardiovascular development represent paradigms of embryonic organ formation, with increasingly specialised cells generated from early mesoderm. Here, we map the progression of mesoderm towards blood by single cell expression analysis of 3,934 cells, aiming to capture an entire embryo equivalent of cells with blood-forming potential at four sequential developmental stages. Using novel computational approaches, we reconstruct the developmental journey at single cell resolution, which reveals asynchrony of maturation and sequential waves of expression for major regulators. Considering transitions between individual cellular states, we synthesise executable transcriptional regulatory network models that recapitulate blood development. Using mouse embryos and embryonic stem cells, we validate model predictions by showing that Sox7 inhibits primitive erythropoiesis, and that Sox and Hox factors control early expression of Erg. We therefore demonstrate that single cell analysis of a developing organ coupled with novel computational approaches can reveal the transcriptional programs that control organogenesis. Transcriptome analysis in populations of 50 cells from each of 5 mouse embryos at primitive streak, neural plate and head fold stages of development
2015-02-09 | E-GEOD-61470 | biostudies-arrayexpress
Project description:Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors (TFs) in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterised by distinctive TF expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated TF pairings, including previously unrecognised relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a novel regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.
2013-04-24 | GSE42518 | GEO
Project description:A fully validated blood stem/progenitor cell regulatory network reveals mechanisms of cell state stabilisation
| PRJNA286742 | ENA
Project description:Endurance exercise in horses implies adaptive processes involving affective, physiological, biochemical, and cognitive-behavioral response in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome and miRNome during endurance exercise could provide significant insights into the molecular response to intense exercise or prediction of this response at basal status. In this perspective, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from 10 horses before and after a 160 km endurance competition. Results: We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre- endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. This suggested that single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 39 animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses at T1. Multiple factorial analyses also identified potential biomarkers at T0 for an increased possibility of failure to finish an endurance competition.
2016-11-18 | ST000503 | MetabolomicsWorkbench
Project description:Decoding the regulatory network of early blood development from single-cell gene expression measurements.
| PRJNA261245 | ENA
Project description:Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.
2015-02-09 | GSE61470 | GEO
Project description:In this study, we revealed the molecular network governing the differentiation of CAR T cells into transcriptionally and epigenetically distinct subsets. Using two mouse cancer models with different sensitivities to CAR T-cell therapy, we showed that CD8+ CAR T cells transitioned from the stem-like to effector-like subset in B-cell ALL but developed into exhausted T cells in the solid tumor. By simultaneously profiling transcriptomic and epigenomic analyses in single cells, we demonstrated that lineage-defining TFs were often controlled by exceptionally high numbers of cis-regulatory elements and regulated distinct chromatin states foreshadowing transcriptional changes during T cell differentiation. Different CAR T-cell subsets were governed by distinct gene regulatory networks with TFs as hubs. We showed that FOXP1 was a hub TF in the stem-like network and promoted the antitumor response and stemness of CAR T cells while limiting their transition to the effector-like subset. In contrast, KLF2, a hub TF in the effector-like network, controlled the lineage choice between effector-like and exhausted subsets by driving the effector program and suppressing the exhaustion program.
2023-10-09 | GSE202543 | GEO