Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mapping convergent regulators of melanoma drug resistance by PerturbFate

ABSTRACT: High-throughput genomic studies have uncovered associations between diverse genetic alterations and disease phenotypes; however, elucidating how perturbations in functionally disparate genes give rise to convergent cellular states remains challenging. Here, we present PerturbFate, a high-throughput, cost-effective, combinatorial-indexing single-cell platform that enables systematic interrogation of massively parallel CRISPR interference (CRISPRi) perturbations across the full spectrum of gene regulation, from chromatin remodeling and nascent transcription to steady-state transcriptomic phenotypes. Using PerturbFate, we profiled over 300,000 cultured melanoma cells to characterize multi-modal phenotypic and gene regulatory responses to perturbations in more than 140 Vemurafenib resistance-associated genes. We uncovered a shared dedifferentiated cell state marked by convergent cooperative transcription factor (TF) activities across diverse genetic perturbations. We further dissected phenotypic responses to perturbations in Mediator Complex components, linking module-specific biochemical properties to convergent transcriptional activations. Together, we reveal common regulatory nodes that drive similar phenotypic outcomes across distinct genetic perturbations. We also delineate how perturbations in functionally unrelated genes reshape cell state. PerturbFate thus establishes a versatile platform for identifying key molecular regulators by anchoring multi-modal regulatory dynamics to disease-relevant phenotypes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE291147 | GEO | 2026/02/06

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Quantitative proteomic analyses of budding yeast cells harboring an additional chromosome

Project description:Phenotypic variability is a hallmark of diseases involving chromosome gains and losses, such as Down Syndrome and cancer. Allelic variances have been thought to be the sole cause of this heterogeneity. Here, we systematically examine the consequences of gaining and losing single or multiple chromosomes to show that the aneuploid state causes non-genetic phenotypic variability. Yeast cell populations harboring the same defined aneuploidy exhibit heterogeneity in cell cycle progression and response to environmental perturbations, which we show to be partly due to gene copy number imbalances. Thus, subtle changes in gene expression severely impact the robustness of biological networks and cause alternate behaviors when they occur at a large scale. Because trisomic mice also exhibit variable phenotypes, we further propose that non-genetic individuality is a universal characteristic of the aneuploid state that could contribute to variability in presentation and treatment responses of diseases caused by aneuploidy.

2017-04-10 | PXD006009 | Pride

Genetic properties underlying transcriptional plasticity and evolvability in E. coli

Project description:The rate and direction of phenotypic evolution depends on the availability of phenotypic variants induced either genetically or environmentally for selection act upon. Theoretical suggest that genetic interactions between genes governing phenotypes could explain how the bias in phenotypic variability arises. However, it remains unknown whether the phenotypic variability is explained by real known genetic interactions as expected. To address this question, we analyzed transcriptional variability in a model microorganism, Escherichia coli, in response to different environmental and genetic perturbations. Thanks to the rich knowledge of the genetic interactions of E. coli, we identified common genetic properties that potentially affect transcriptional variability in both environmental and genetic causes. These empirical evidences support the relevance of the genetic interactions to shape phenotypic variability that is shared by different perturbations. Our results provide a mechanistic insight into how evolution learns from past environments to generalize to new environments.

2024-11-24 | GSE260863 | GEO

Phenotypic Proteomic Profiling Reveals Convergent Kinase Targets for Circadian Clock Modulators

Project description:Determining the mechanisms of action of drug molecules that modulate circadian rhythms is critical to develop novel compounds to treat clock disorders. Here we employed Phenotypic Proteomic Profiling (PPP) integrating multipronged proteomics approaches including global proteome, phosphoproteome, kinome mapping, and proteome-wide profiling of thermal stability (TPP) to systematically determine convergent molecular targets of four circadian period lengthening compounds (Longdaysin, Roscovitine, Purvalanol A, and SP600125) in human cells. We demonstrate convergent changes in phosphorylation level and activity of several proteins and kinases involved in vital signaling pathways including MAPK, NGF, BCR, AMPK, and mTOR signaling by the compounds. Kinome profiling using desthiobiotin-ATP enrichment quantitative proteomics and radiometric assays further indicated inhibition of CKId, ERK1/2, CDK2/7, TNIK and STK26 activity as a common mechanism of action for the compounds. Pharmacological or genetic inhibition of several convergent kinases resulted in circadian period lengthening, establishing them as novel bone fide circadian targets. TPP analysis using live cells revealed binding of these drugs to clock regulatory kinases, signaling molecules, and ubiquitination mediator (F-box) proteins. Phenotypic proteomic profiling thus establishes a set of novel circadian clock effectors.

2019-11-25 | PXD014186 | Pride

Endogenous fine-mapping of functional regulatory elements in complex genetic loci in HAP1 [scRNA-seq]

Project description:The majority of genetic loci linked to polygenic complex traits are found in non-coding regions of the human genome. These loci often exhibit complex gene regulatory relationships and linkage disequilibrium (LD) configurations, making it challenging to accurately identify causal variants and their target genes. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and target gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait locus (eQTL) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one third of the causal CREs lack classical epigenetic markers, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms in molecular trait formation and disease development.

2024-12-31 | GSE285461 | GEO

Endogenous fine-mapping of functional regulatory elements in complex genetic loci in HAP1 [ATAC-seq]

2024-12-31 | GSE285458 | GEO

Endogenous fine-mapping of functional regulatory elements in complex genetic loci in HAP1 [microC]

2025-01-10 | GSE286224 | GEO

Endogenous fine-mapping of functional regulatory elements in complex genetic loci in HAP1 [4C]

2025-01-03 | GSE285760 | GEO

Endogenous fine-mapping of functional regulatory elements in complex genetic loci in HAP1 [scMulti]

2025-01-10 | GSE286205 | GEO

Endogenous fine-mapping of functional regulatory elements in complex genetic loci in HAP1 [CUT&Tag]

2025-01-03 | GSE285718 | GEO

Label-free melanoma phenotype classification using AI-based morphological profiling

Project description:Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Within tumors, cells coexist in different mutable phenotypes that exhibit differential functional properties and drug responses. The definition of these phenotypic states has been challenging to rigorously define with conventional marker-based methods, and more high-parameter molecular methods are cell-destructive, labor-intensive, and can take days to weeks to obtain a readout. To overcome these technical and practical limitations, we utilized the Deepcell platform to perform real-time classification of unlabeled melanoma cells into Melanocytic and Mesenchymal phenotypes. We used 19 patient-derived cell lines with known Melanocytic or Mesenchymal transcription scores to develop the ‘Melanoma Phenotype Classifier’ to phenotype melanoma cells based on morphology alone. A Classifier accuracy of >88% was achieved, and morphology analysis of the images revealed distinct morphotypes for each phenotype, highlighting distinct morphological differences. To further link phenotypic state with multi-dimensional morphological profiles, we performed genetic and chemical perturbations known to shift the phenotypic state. The AI Classifier successfully predicted shifts in phenotype driven by the perturbations. These results further demonstrate how phenotype is linked to distinct morphological changes that are detectable by AI. Lastly, we applied the Melanoma Phenotype Classifier to dissociated biopsy samples, which revealed phenotypic heterogeneity that was confirmed by single cell RNASeq. This work establishes a link between morphology and Melanoma phenotype, and lays the groundwork for the use of morphology as a label-free method of phenotyping viable melanoma cells combined with additional analyses.

2025-07-25 | GSE273247 | 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