Multiscale profiling of enzyme activity in cancer [scRNA-seq]
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PROVIDER: PRJNA789581 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Multiscale profiling of enzyme activity in cancer
| PRJNA789579 | ENA
Project description:Multiscale profiling of enzyme activity in cancer [Bulk-seq]
| PRJNA789563 | ENA
Project description:Purpose: The goal of this study was to use enzyme activity as a proxy for profiling tumor progression and treatment response in an autochthonous mouse model of Alk-mutant non-small-cell lung cancer (NSCLC). The Eml4-Alk model was originally described in Maddalo et al., Nature 2014. Single-cell transcriptomic profiling of the Eml4-Alk NSCLC model was performed for unbiased discovery of the phenotypic landscape of Eml4-Alk and wild type mice. Methods: Single cell suspensions were prepared from excised lungs of Eml4-Alk and sex- and age-matched healthy, wild type C57/Bl6J mice. Suspensions were pooled from n=3 mice per condition, and then were enriched for cell viability and depleted for CD45+ cells. Single cells were processed using the 10X Genomics Single Cell 3’ platform using the Chromium Single Cell 3’ Library & Gel Bead Kit V2 kit (10X Genomics), per manufacturer’s protocol. Approximately 10,000 cells were loaded onto each channel and partitioned into Gel Beads in Emulsion (GEMs) in the 10x Chromium instrument. Following lysis of the captured cells, the released RNA was barcoded through reverse transcription in individual GEMs, and complementary DNA was generated and amplified. Libraries were constructed using a Single Cell 3’ Library and Gel Bead kit. The libraries were sequenced using an Illumina NovaSeq6000 sequencer on an Illumina NovaSeq SP flow cell. Results: Gene expression matrices were generated for each sample by the Cell Ranger (v.3.0.2) Pipeline coupled with mouse reference version GRCm38. The output filtered gene expression matrices were analyzed by Python software (v.3.9.0) with the scanpy package (v.1.7.2). For final analysis, genes expressed in at least three cells in the data and cells with > 200 genes detected were selected for further analyses, and low quality cells were removed based on number of total counts and percentage of mitochondrial genes expressed. Conclusions: This study provides the first (to the best of our knowledge) single-cell RNA-seq dataset of the Eml4-Alk autochthonous model of NSCLC.
2021-12-20 | GSE191078 | GEO
Project description:Purpose: The goal of this study was to use enzyme activity as a proxy for profiling tumor progression and treatment response in an autochthonous mouse model of Alk-mutant non-small-cell lung cancer (NSCLC). The Eml4-Alk model was originally described in Maddalo et al., Nature 2014. This dataset describes bulk RNA-seq profiling of cells from Eml4-Alk lungs following administration of a protease-activatable probe, QZ1, and sorting single cells based on signal from this probe. Methods: Eml4-Alk mice were anesthetized, and QZ1-(PEG2K) was administered intravenously via tail vein injection. Lungs were excised two hours later, separated into lobes, tumors were microdissected, and single cell suspensions were prepared. FACS sorting was performed on a FACSAria II (BD). Flow cytometry data was analyzed by the FlowJo software (Treestar). At least 100,000 cells from each of the QZ1+ and QZ1- compartments were collected into RPMI-1640 + 2% heat-inactivated FBS and pelleted via centrifugation at 1800 rpm for 5 minutes. Cell pellets were lysed in Trizol (ThermoFisher), and RNA was extracted using RNEasy Mini Kits (Qiagen), and bulk RNA sequencing was performed. Libraries were prepared using the Clontech SMARTer Stranded Total RNAseq Kit (Clontech), precleaned, and sequenced using an Illumina NextSeq500 on an Illumina NextSeq flow cell. Results: Feature counting was performed on BAM files using the Rsubread package. Differential expression analysis on QZ1+ vs QZ1- cells was performed using the DESeq2 package in R. GSEA was performed using GenePattern, and results were visualized using the clusterProfiler R package. Conclusions: This study demonstrates a method for activity-based cell sorting that can be coupled to downstream phenotypic characterization via RNA-seq.
2021-12-20 | GSE191071 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-12-20 | GSE191079 | GEO
Project description:Massively parallel characterization of CYP2C9 variant enzyme activity and abundance
| PRJNA694476 | ENA
Project description:In order to analyze the changes of G6PD enzyme activity regulating the metabolism and proliferation pathways in HCC cell. After G6PD enzyme activity inhibitor RRx-1 treated with 20μM concentrations for 24h in Huh7 cell. Differential expression of genes were selected after RNA sequencing. Then David Bioinformatics was utilized to identify the most significantly regulated GOTERMs and KEGG pathways based on the transcriptional changes.
2020-11-12 | E-MTAB-9712 | biostudies-arrayexpress
Project description:Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT). We aimed to study the underlying mechanism by which ARF1 has been predicted playing crucial role during carcinogenesis and metastasis. To attest the key function of ARF1 during epithelial mesenchymal transition (EMT), loss of function in vitro study by means of siRNA knockdown of ARF1 in MDA-MB-231 breast cancer cell line was designed. Genome-wide gene expression of nine ARF1 knockdown samples and three control samples were profiled by using the Illumina HumanHT-12 V4.0 expression beadchip platform.
2013-07-18 | E-GEOD-49005 | biostudies-arrayexpress
Project description:We studied the membrane protein compositions of Streptococcus pneumoniae WT and scRNA mutant strains, with or without the CSP1 induction into competence state.
2024-08-19 | PXD054463 | Pride
Project description:The organization of mammalian genomes within the nucleus features a complex, multiscale three-dimensional (3D) architecture. The functional significance of these 3D genome features, however, remains largely elusive due to limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we report GAGE-seq, a highly scalable, robust single-cell co-assay that simultaneously measures 3D genome structure and transcriptome within the same cell. Employing GAGE-seq on mouse brain cortex and human bone marrow CD34+ cells, we comprehensively characterized the intricate relationships between 3D genome and gene expression. We found that these multiscale 3D genome features collectively inform cell type-specific gene expressions, hence contributing to defining cell identity at the single-cell level. Integration of GAGE-seq data with spatial transcriptomic data revealed in situ variations of the 3D genome in mouse cortex. Moreover, our observations of lineage commitment in normal human hematopoiesis unveiled notable discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level that is more nuanced than previously appreciated. Together, GAGE-seq provides a powerful, cost-effective approach for interrogating genome structure and gene expression relationships at the single-cell level across diverse biological contexts.
2024-03-27 | GSE238001 | GEO