Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mapping Transcriptomic Vector Field of Single Cells

ABSTRACT: Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

ORGANISM(S): Homo sapiens

PROVIDER: GSE193517 | GEO | 2022/02/02

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development

Project description:Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensionaltemporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identifies step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.

2020-04-23 | GSE133642 | GEO

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

Project description:Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally.

2017-10-17 | GSE105054 | GEO

Drug combination sci-Plex Data

Project description:Recent advances in multiplexed single-cell transcriptomics experiments are facilitating the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug responses across different cell type, dose, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, which enables drug similarity analysis and predictions for unseen dosage and drug combinations. We show that CPA accurately models single-cell perturbations across compounds, doses, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying that it captures features that distinguish different interaction programs. Finally, we demonstrate that CPA can generate in-silico 5,329 missing genetic combination perturbations ($97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design and hypothesis generation by enabling in-silico response prediction at the single-cell level, and thus accelerate therapeutic applications using single-cell technologies.

2022-06-25 | GSE206741 | GEO

Whole genome analysis of mouse B16 melanoma cancer cells in response to interfacial geometry

Project description:Analysis of the regulation of B16 gene expression in response to interfacial geometry. The hypothesis tested in the present study was that interfacial geometry modulates tumourigenicity of B16 cells. Results provide important information of the response of B16 cells to interfacial geometry especially, metastasis, MAP kinase, and STAT pathways.

2016-04-20 | GSE79085 | GEO

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [ST]

Project description:We designed a neural network-based computational method that learns transcriptome-to-space mapping and reconstructs 3D tissue organization by learning from scRNA-seq and spatial transcriptomic data.

2023-08-08 | GSE220572 | GEO

Reconstruction of 3-dimensional tissue organization at the single-cell resolution [scRNA-seq]

2023-08-08 | GSE220571 | GEO

MicroRNA programs in normal and aberrant stem and progenitor cells

Project description:Emerging evidence suggests that microRNAs (miRNAs), an abundant class of ~22-nt small regulatory RNAs, play key roles in controlling the post-transcriptional genetic programs in stem and progenitor cells. Here we systematically examined miRNA expression profiles in various adult tissue-specific stem cells and their differentiated counterparts. These analyses revealed miRNA programs that are common or unique to blood, muscle, and neural stem cell populations and miRNA signatures that mark the transitions from self-renewing and quiescent stem cells to proliferative and differentiating progenitor cells. Moreover, we found a stem/progenitor transition miRNA (SPT-miRNA) signature that predicts the effects of genetic perturbations, such as loss of PTEN and the Rb family, AML-ETO expression, and MLL-AF10 transformation, on self-renewal, proliferation, and/or differentiation potentials of mutant stem/progenitor cells. More importantly, some SPT-miRNAs can control rate of self-renewal in embryonic stem cells and the reconstitution potential of hematopoietic stem cells (HSCs). Finally, we found that some SPT-miRNAs may coordinately regulate genes that are known to play roles in controlling HSC self-renewal, such as Hoxb6 and Hoxa4. Together, these analyses revealed the miRNA programs that control key processes in normal and aberrant stem and progenitor cells, setting the foundations for dissecting post-transcriptional regulatory networks in stem cells. We used a multiplex protocol to amplify miRNAs from 20-1000 sorted stem and/or progenitor cells and then analyzed the expression of 425 mature miRNAs using TaqMan miRNA quantitative PCR analyses

2011-03-21 | E-GEOD-28036 | biostudies-arrayexpress

Strains of increasing genetic divergence to the reference strain

Project description:comparative genomic hybridization between Burkholderia cepacia sequenced strains to determine what are the limits to strain relativity in order to still provide accurate estimations of gene content

2010-02-06 | GSE20096 | GEO

MG1655_temperature_O2_response_normalized_data_Tavazoie

Project description:Microarray transcriptional profiling was utilized to observe the global cellular state correlates of the physiological responses to temperature and oxygen changes. We provide evidence for the existence of correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations-precisely mirroring the co-occurrence of these parameters upon transitions between the outside world and the mammalian gastrointestinal-tract. Keywords: time course Physiological perturbations were carried out under a controlled environment in the context of bioreactor (Bioflo 110, New Brunswick Scientific) growth. Thermoelectric sensors and heaters were used to shift temperature profiles between 25 C and 37 C, and polarographic dissolved oxygen sensors (Mettler Toledo) and nitrogen gas was used to rapidly change oxygen saturation between anaerobic (0% dissolved oxygen) and aerobic (16-21% dissolved oxygen) condition. The cultures were maintained in exponential phase (O.D.600 0.2-0.4) through controlled dilution, where fresh media is pumped in and spent media is pumped out at a controlled rate. Prior to the perturbations, cells were maintained in the pre-transition environment for at least eight generations (8-24 hours). All experiments were performed in duplicate.

2008-05-13 | E-GEOD-10855 | biostudies-arrayexpress

Chi3l1 is a modulator of glioma stem cell states and a therapeutic vulnerability for glioblastoma [scRNA-seq]

Project description:Chi3l1 (Chitinase 3-like 1) is a secreted protein highly expressed in glioblastoma. Here, we show that exposure of glioma stem cells (GSCs) to Chi3l1 reduces the CD133+/SOX2+ cells and increases the CD44+/Chi3l1+ cells. Single cell RNA-seq and RNA velocity following incubation of GSCs with Chi3l1 show significant changes in GSC state dynamics driving GSCs towards a mesenchymal expression profile and reducing transition probabilities towards terminal cellular states. ATAC-seq reveals that Chi3l1 increases accessibility of promoters containing MAZ transcription factor footprint. Inhibition of MAZ directly regulates genes with highest expression in cellular clusters exhibiting significant cell state transitions.

2022-10-24 | GSE184696 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data