Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA286742 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:A validated regulatory network for Th17 cell specification
| PRJNA175364 | ENA
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: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: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:Within the bone marrow, hematopoietic stem cells differentiate and give rise to diverse blood cell types and functions. Currently, hematopoietic progenitors are defined using surface markers combined with functional assays that are not directly linked with the in vivo potential or gene regulatory mechanisms. Here we comprehensively identify myeloid progenitor subpopulations by transcriptional sorting of single cells from the bone marrow. We describe multiple progenitor subgroups showing unexpected transcriptional priming towards seven differentiation fates, but no progenitors with a mixed state. Transcriptional differentiation is correlated with combinations of known and previously undefined transcription factors, suggesting the process is tightly regulated. Histone maps and knockout assays are consistent with the transcriptional states while traditional transplantation experiments are only partially overlapping myeloid transcriptional priming. Our analyses uncover the function of the underlying regulatory mechanisms for several sub groups and establishes a general framework for dissecting hematopoiesis. Bone marrow common myeloid progenitor H3K4me2 profiles were generated by deep sequencing of iChIP libraries on an Illumina NextSeq
2015-11-25 | E-GEOD-72858 | biostudies-arrayexpress
Project description:Tremendous progress has been made in identifying individual transcription factors that regulate hematopoietic differentiation, but many aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression patterns and sequence-based analysis of motifs in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly co-expressed genes, some of which are restricted to a single lineage, but most are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states, suggesting a more complex regulatory system than previously assumed. We functionally validated a subset of candidate factors using RNA interference and ChIP-Seq. Our dataset, analytic tools, and findings, available on a web-based portal, provide a unique resource to study the regulatory architecture of hematopoiesis. We defined 38 distinct hematopoietic cell states based on cell surface marker expression, representing hematopoietic stem and progenitor cells, terminally differentiated cells, and intermediate states. For each state, we purified samples separately from 4 to 7 independent donors by multiparameter flow cytometry, yielding 211 profiled samples. Cells from all stem and progenitor populations were purified from umbilical cord blood. Terminally differentiated lymphocyte populations were purified from peripheral blood.
2011-01-21 | E-GEOD-24759 | biostudies-arrayexpress
Project description:We performed the longitudinal peripheral blood transcriptome profiling after diagnosis in five patients, and found that patients' blood transcriptomes undergo dramatic and consistent changes in early recovery stage (ERS). Meanwhile, the blood transcriptome did not fully return to the healthy state even after more than one month of recovery. We also realized that differential expressed genes (DEGs) are enriched in biological processes of antigen processing and related processes, and promoter regions of these genes enrich key transcription factors (TFs) involved in T cell differentiation and activation. We further provided potential mechanisms underlying these orchestrated processes, by revealing these specific TFs mediated gene co-expression network.
2022-04-12 | GSE154272 | GEO
Project description:White adipose tissue (WAT) harbors functionally diverse subpopulations of adipose progenitor cells that differentially impact tissue plasticity in a sex- and depot-dependent manner. To date, the molecular basis of this cellular heterogeneity has not been fully defined. Here, we describe a multilayered omics approach to dissect adipose progenitor cell heterogeneity from in three dimensions: progenitor subpopulation, sex, and anatomical localization. We applied state-of-the-art mass spectrometry methods to quantify 4870 proteins in eight different stromal cell populations from perigonadal and inguinal WAT of male and female mice and acquired transcript expression levels of 15477 genes using RNA-seq. Notably, our data highlight the molecular signatures defining sex differences in PDGFR+ preadipocyte differentiation and identify regulatory pathways that functionally distinguish adipose tissue PDGFRb+ subpopulations. The data are freely accessible as a resource at "Pread Profiler. Together, the multilayered omics analysis provides unprecedented insights into adipose stromal cell heterogeneity.
2023-03-11 | PXD023829 | Pride
Project description:Transcriptome changes 1h or 4h following DELLA stabilisation in microdissected fully proliferating Arabidopsis leaves
2013-08-01 | GSE49399 | GEO