Similar Datasets
Project description:We adapted sgRNA lentiviral infection and Cas9 electroporation (SLICE) to allow for CROP-Seq (Datlinger et al, Nat Med 2017) in primary cells. We used a library of 48 sgRNA, derived from GW screens, to explore transcriptional changes downstream of CRISPR-KO.
2018-09-06 | GSE119450 | GEO
Project description:T cell receptor (TCR) signaling in Jurkat cells was investigated using the CROP-seq method for CRISPR single-cell sequencing. In CROP-seq, genetic perturbations are introduced into single cells in a pooled fashion, and single-cell RNA-seq is used to determine the transcriptional response to the CRISPR-induced perturbation in a large number of single cells in parallel. Importantly, the CROP-seq vector makes individual guide-RNAs detectable using standard single-cell RNA-seq technology. The dataset presented here is based on CROP-seq in combination with single-cell RNA-seq using the 10x Genomics v2 chemistry. It recapitulates a previously published CROP-seq dataset (Datlinger et al. 2017 Nature Methods; GEO: GSE92872) that used the Drop-seq protocol as the single-cell RNA-seq readout. Additional information on CROP-seq are available from the following website: http://crop-seq.computational-epigenetics.org/ .
2019-10-08 | GSE137554 | GEO
Project description:The study of the compond PIP-RBPJ-1‘ function on neural stem cell. Synthetic DNA-binding inhibitors capable of gaining precise control over neurogenesis factors could obviate the current clinical barriers associated with small molecule use in regenerative medicine. Here, we show the design and bio-efficacy of a synthetic ligand PIP-RBPJ-1 to cause promoter-specific suppression of neurogenesis-associated HES1, and its downstream genes. Furthermore, PIP-RBPJ-1 alone could alter the neural system-associated notch signaling factors and remarkably induce neurogenesis with an efficiency that is comparable to a conventional approach. Here is one day treatment of the PIP-RBPJ-1 on neural stem cells.
2018-07-19 | GSE109916 | GEO
Project description:To better detect CROP-seq mRNAs in our CROP-seq experiment, we performed amplicon sequencing on the 10X single-cell cDNA libraries
2022-08-15 | E-MTAB-12005 | biostudies-arrayexpress
Project description:Background The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted a combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. Results Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP-] cells were identified. Functional and regulatory network analyses based on knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The networks identified appear associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines. Finally, the STAT5 motif was identified in promoters of an important part of genes belonging to the PIP networks. Conclusion Our global exploratory approach was found to be an effective strategy to identify the biological pathways modulated along with the PIP expression, thus supporting good prognostic value of disease-free survival time in breast cancer based on previous reports focusing on PIP’s favorable signature. Moreover, our data allowed us to provide the first insight in its regulatory subnetwork in which STAT5 appears as a potential key regulator.
2009-03-06 | GSE11627 | GEO
Project description:To investigate the influence of transcription factor knockouts in cell fate decision-making, we performed a CROP-seq screen of 20 transcription factors in brain organoids.
2022-08-18 | E-MTAB-11999 | biostudies-arrayexpress
Project description:iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin.
2024-10-15 | MODEL2204040001 | BioModels
Project description:CROP-seq of hiPSC-derived astrocytes
| PRJNA755719 | ENA
Project description:Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low-complexity rather than genome-wide assays. To address this limitation, we introduce Pooled Library Amplification for Transcriptome Expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening (HTS) assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10 to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS).
2017-04-07 | GSE97460 | GEO