Drosophila melanogaster
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA89925 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:FACS sorted Drosophila Embryo Cultured Neurons allowed to differentiate 3-5 days in culture. Positively sorted samples Keywords = Cholinergic Neurons Keywords: other
2004-07-04 | GSE1047 | GEO
Project description:Drosophila Embryo Culture Gad1 Expressin Neurons
| PRJNA89927 | ENA
Project description:Drosophila Embryo Cell Culture samples allowed to differentiate 3-5 days in culture. Samples express a GFP reporter in cholinergic neurons (7.4 kb ChaGal4:UAS-GFP) or an RFP reporter (2.1 kb Gad1-RFP) in glutamic acid decarboxylase 1 expressing Neurons. This group of samples contains the negatively sorted FACS fractions (2 Cha and 2 Gad negative samples). Keywords: other
2004-07-04 | GSE1046 | GEO
Project description:Positively sorted Drosophila Embryo Culture Gad1 (glutamic acid decarboxylase 1) expressing neurons. FACS sorted after 3-5 days in culture. Labeled with 2.1 kb Gad1-RFP reporter gene. Keywords = Gad1 Gad Expressing Neurons Motor Keywords: other
2004-07-04 | GSE1048 | GEO
Project description:Transcription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 57,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a ‘random forest’ model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. Therefore, the same terminal characters can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.
2018-06-14 | GSE103772 | GEO
Project description:Transcription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 57,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a ‘random forest’ model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. Therefore, the same terminal characters can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.
2018-06-14 | GSE103771 | GEO
Project description:Expression data from early Drosophila embryo
| PRJNA96091 | ENA
Project description:Expression data from early Drosophila embryo
| PRJNA94207 | ENA
Project description:In this study we use a combination of proteomics Label-Free quantification methods to monitor protein expression changes over a time course of more than 20 hours of embryo development in Drosophila melanogaster.
2016-04-11 | PXD003178 | Pride