Long-lived epigenetic states convert all-or-none single-cell responses to graded population responses in mammalian expression systems
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE345733 | GEO | 2026/09/11
REPOSITORIES: GEO
Similar Datasets
Project description:Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In a study of the metabolic response of a clonal Saccharomyces cerevisiae population challenged by the glycolytic inhibitor 2-deoxy-D-glucose (2-DG), this variability is shown here, by comparing measurements at the population and the single-cell level. A novel microarray platform and MALDI mass spectrometric read-out allow us to follow 24 parameters simultaneously, in up to 100 single-cell measurements. Correlation plots for metabolites from the glycolytic pathway, the pentose phosphate pathway, and the observed ATP/ADP ratio as a measure of cellular energy charge give new insight into biochemical and metabolic pathways, which would not be accessible from population-level metabolomics data. Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study we demonstrate that with the Microarrays for Mass Spectrometry (MAMS) platform we are able to observe this variability. We monitor environmentally-(2-deoxy-D-glucose) and genetically-(∆PFK2) perturbed Saccharomyces cerevisiae cells both at the population and near single-cell levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge give new biological insight that is not accessible from population-level metabolomic data.
2013-05-22 | MTBLS29 | MetaboLights
Project description:This model is from the article:
A regulatory role for repeated decoy transcription factor binding sites in target gene expression.
Lee TH, Maheshri N. Mol Syst Biol.
2012 Mar 27;8:576. 22453733
,
Abstract:
Tandem repeats of DNA that contain transcription factor (TF) binding sites could serve as decoys, competitively binding to TFs and affecting target gene expression. Using a synthetic system in budding yeast, we demonstrate that repeated decoy sites inhibit gene expression by sequestering a transcriptional activator and converting the graded dose-response of target promoters to a sharper, sigmoidal-like response. On the basis of both modeling and chromatin immunoprecipitation measurements, we attribute the altered response to TF binding decoy sites more tightly than promoter binding sites. Tight TF binding to arrays of contiguous repeated decoy sites only occurs when the arrays are mostly unoccupied. Finally, we show that the altered sigmoidal-like response can convert the graded response of a transcriptional positive-feedback loop to a bimodal response. Together, these results show how changing numbers of repeated TF binding sites lead to qualitative changes in behavior and raise new questions about the stability of TF/promoter binding.
Note:
This model corresponds to the basic model for gene expression in the presence of decoys, described in the paper.
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.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1202270000 | BioModels
Project description:This model is from the article:
A regulatory role for repeated decoy transcription factor binding sites in target gene expression.
Lee TH, Maheshri N. Mol Syst Biol.
2012 Mar 27;8:576. 22453733
,
Abstract:
Tandem repeats of DNA that contain transcription factor (TF) binding sites could serve as decoys, competitively binding to TFs and affecting target gene expression. Using a synthetic system in budding yeast, we demonstrate that repeated decoy sites inhibit gene expression by sequestering a transcriptional activator and converting the graded dose-response of target promoters to a sharper, sigmoidal-like response. On the basis of both modeling and chromatin immunoprecipitation measurements, we attribute the altered response to TF binding decoy sites more tightly than promoter binding sites. Tight TF binding to arrays of contiguous repeated decoy sites only occurs when the arrays are mostly unoccupied. Finally, we show that the altered sigmoidal-like response can convert the graded response of a transcriptional positive-feedback loop to a bimodal response. Together, these results show how changing numbers of repeated TF binding sites lead to qualitative changes in behavior and raise new questions about the stability of TF/promoter binding.
Note:
This model corresponds to the comprehensive model encompassing the basic model (MODEL1202270000) as well as the tTA/dox (tet-transcriptional activator/doxycycline) interaction, described in the paper.
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.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1202270001 | BioModels
Project description:Here, we introduce an in-silico algorithm demuxlet that harnesses naturally occurring genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These two capabilities enable a simple multiplexing design that increases single cell library construction throughput by experimental design where cells from genetically diverse samples are multiplexed and captured at 2-10x over standard workflows. We further demonstrate the utility of sample multiplexing by characterizing the interindividual variability in cell type-specific responses of ~15k PBMCs to interferon-beta, a potent cytokine. Our computational tool enables sample multiplexing of droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.
2017-06-13 | GSE96583 | GEO
Project description:Tissue regeneration requires de novo patterning, which has been proposed to be facilitated by cellular heterogeneity. Yet, how such heterogeneities are integrated with the mechanochemical state of the tissue, and stabilized at the chromatin level into stable and spatially organised fates, remains poorly understood. Using in vivo mouse intestinal regeneration models and organoids, we identify a critical density regime which produces a permissive window of heterogeneity in the mechanosensor YAP1. We show that YAP1 heterogeneity is coupled to lineage-biased chromatin accessibility and is decoded through FOXA1, which integrates the permissive chromatin state to Delta-Notch supra-cellular feedback and lineage commitment. This circuit generates fate bistability and preserves a memory of transient YAP1 activity, maintaining spatial patterning as tissues return to homeostasis after injury. Together, our findings establish a multiscale framework in which tissue-scale mechanics tune single-cell competence and, through FOXA1-mediated bistability, convert transient heterogeneity into stable, reproducible, and self-organised tissue architecture.
2026-06-30 | GSE283661 | GEO
Project description:Tissue regeneration requires de novo patterning, which has been proposed to be facilitated by cellular heterogeneity. Yet, how such heterogeneities are integrated with the mechanochemical state of the tissue, and stabilized at the chromatin level into stable and spatially organised fates, remains poorly understood. Using in vivo mouse intestinal regeneration models and organoids, we identify a critical density regime which produces a permissive window of heterogeneity in the mechanosensor YAP1. We show that YAP1 heterogeneity is coupled to lineage-biased chromatin accessibility and is decoded through FOXA1, which integrates the permissive chromatin state to Delta-Notch supra-cellular feedback and lineage commitment. This circuit generates fate bistability and preserves a memory of transient YAP1 activity, maintaining spatial patterning as tissues return to homeostasis after injury. Together, our findings establish a multiscale framework in which tissue-scale mechanics tune single-cell competence and, through FOXA1-mediated bistability, convert transient heterogeneity into stable, reproducible, and self-organised tissue architecture.
2026-06-30 | GSE283660 | GEO
Project description:Long-lived epigenetic states convert all-or-none single-cell responses to graded population responses in mammalian expression systems
| PRJNA1521776 | ENA
Project description:Yao2016_Calcium_Signaling
This model is described in the article:
Distinct cellular states
determine calcium signaling response
Jason Yao, Anna Pilko, Roy
Wollman
Molecular Systems Biology
Abstract:
The heterogeneity in mammalian cells signaling response is
largely a result of preexisting cell to cell variability. It is
unknown whether cell to cell variability rises from biochemical
stochastic fluctuations or distinct cellular states. Here, we
utilize calcium response to adenosine trisphosphate as a model
for investigating the structure of heterogeneity within a
population of cells and analyze whether distinct cellular
response states coexist. We use a functional definition of
cellular state that is based on a mechanistic dynamical systems
model of calcium signaling. Using Bayesian parameter inference,
we obtain high confidence parameter value distributions for
several hundred cells, each fitted individually. Clustering the
inferred parameter distributions revealed three major distinct
cellular states within the population. The existence of
distinct cellular states raises the possibility that the
observed variability in response is a result of structured
heterogeneity between cells. The inferred parameter
distribution predicts, and experiments confirm that variability
in IP3R response explains the majority of calcium
heterogeneity. Our work shows how mechanistic models and single
cell parameter fitting can uncover hidden population structure
and demonstrate the need for parameter inference at the single
cell level.
This model is hosted on
BioModels Database
and identified by:
MODEL1611150001.
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.
2017-06-01 | MODEL1611150001 | BioModels
Project description:Tumor microenvironment contains abundant quantities of Tumor necrosis factor alpha (TNFα) secreted by a battery of immune cells. Signal flow through TNFα stimulated TNFR1 signaling, which is supposed to maintain a fine balance between survival and cell-death phenotypes, is often sacrificed in a diseased tissue, such as that of a cancer. Strategies to tilt this balance towards cell-death in a tumor that can help improve the therapeutic efficiency are often remain ineffective due to the cell-to-cell variability in expressing different phenotypes. This variability during TNFR1 signaling stems from the heterogeneity in signal flow through intracellular signaling entities that regulate pro-survival and apoptosis responses. This stochastic Boolean dynamic model of TNFR1 signaling focuses on understanding the dynamic cross-talk regulation of apoptosis at single-cell level. We demonstrate with this model that the signal flow path variability can be modulated to enable cells favour apoptosis. We use BM-ProSPR algorithm (GitHub - ganeshIITB/BMProSPR: Codes pertaining to BM-ProSPR) to compute reliable partial state transition graph (pSTG) for the systematic analysis of the dynamical properties of the network at single-cell level (pSTGs of two different conditions are provided as .mat files). Using the pSTG, we derive a comprehensive way to construct ensemble-level dynamics of intracellular signaling entities in order to understand the cross-talk between the pathways (Methods section in manuscript- Time varying conditional probability of finding a node being active). Model analysis juxtaposed with the experimental observations revealed that NFκB and PI3K transient responses guide XIAP to coordinate the crucial dynamic cross-talk between the pro-survival and apoptotic arms at the single-cell level. Model predicted ~31% increase in apoptosis can be achieved by arresting Comp1-IKK* activity. In summary, the analysis of TNFR1 signaling network model provides insights into
1. What are the central regulators for regulating phenotypic response at single-cell level?
2. How do these regulators coordinate the cross-talk between the pathways of different phenotypes?
3. How do the ensemble-level signal flow paths shift towards a phenotype by arresting an entity that regulates the central regulators’ levels?
2025-04-22 | MODEL2504180001 | BioModels
Project description:Virus and host factors contribute to cell-to-cell variation in viral infection and determine the outcome of the overall infection. However, the extent of the variability at the single cell level and how it impacts virus-host interactions at a systems level are not well understood. To characterize the dynamics of viral transcription and host responses, we used single-cell RNA sequencing to quantify at multiple time points the host and viral transcriptomes of human A549 cells and primary bronchial epithelial cells infected with influenza A virus. We observed substantial variability of viral transcription between cells, including the accumulation of defective viral genomes (DVGs) that impact viral replication. We show a correlation between DVGs and viral-induced variation of the host transcriptional program and an association between differential induction of innate immune response genes and attenuated viral transcription in subpopulations of cells. These observations at the single cell level improve our understanding of the complex virus-host interplay during influenza infection.
2019-12-10 | GSE118773 | GEO