Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

ABSTRACT: Single cell RNA-sequencing analysis of zebrafish embryos with wild type and crispant sample.

ORGANISM(S): Mus musculus Danio rerio

PROVIDER: GSE145298 | GEO | 2020/02/15

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Dissecting cell identity via network inference and in silico gene perturbation.

Project description:Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.

| S-EPMC9946838 | biostudies-literature

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

Project description:CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

| PRJNA606682 | ENA

Project description:In triplicate for each condition, 12 WT and acbd6 F0 crispant Danio rerio (zebrafish) embryos were incubated with 20 μM YnMyr for 24 h, either between 48-72 hpf or 96-120 hpf. After labelling, zebrafish were washed twice with fresh egg water, deyolked, flash frozen in liquid nitrogen and stored at -80°C until further analysis.

2024-06-16 | PXD043679 | Pride

2024-06-16 | PXD043676 | Pride

Project description:In quadruplicate for each condition, 10 acbd6 WT, 68 acbd6 crispant and 71 crispant X. tropicalis embryos were incubated with 20 μM YnMyr for 17 h. After labelling, embryos were washed twice with fresh 0.05 x MMR, flash frozen in liquid nitrogen and stored at -80°C until further analysis.

2024-06-16 | PXD043677 | Pride

2024-06-16 | PXD043680 | Pride

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms.

Project description:The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.

| S-EPMC7447758 | biostudies-literature

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Project description:The project consisted of single and double knockdown experiments involving forty-five genes in the human squamous carcinoma cell culture line A431. During the knockdown phase, the cells were transfected with siRNAs which decreased target gene expression. After 72 hours the cells were deemed to have reached a new steady-state and approximate cell counts were calculated using resazurin. At this point the cells were lysed using CelluLyser, and a proprietary RNA spike was added to each sample relative to the cell count to act as a reference gene for the qPCR analysis. Then cDNA was prepared from the RNA and preamplified in preparation for the high-throughput qPCR screening. The transcript profiles, with respect to the forty-five target genes, were determined using TaqMan assays on Fluidigm's Biomark platform using 96x96 Dynamic Array IFCs.

2020-07-06 | GSE125958 | GEO

Project description:Targeted sequencing of lamprey PRC2 crispant embryos

| PRJNA1013422 | ENA

In silico prediction of chemical mechanism of action via an improved network-based inference method.

Project description:Background and purposeDeciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost.Experimental approachIn this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays.Key resultsHigh performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor ? with IC50 or EC50 values ?10 ?M. Furthermore, two dual ligands with both agonistic and antagonistic activities ?1 ?M would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis.Conclusion and implicationsIn summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.

| S-EPMC5738663 | biostudies-literature

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