Multiscale profiling of enzyme activity in cancer [scRNA-seq]
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ABSTRACT:
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:Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. High-fidelity methods to profile enzyme activity are therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we present an integrated set of methods for measuring specific protease activities across scales, and deploy these methods to study treatment response in an autochthonous model of Alk-mutant lung cancer. We leverage multiplexed nanosensors and machine learning to analyze in vivo protease activity dynamics in lung cancer, identifying significant dysregulation that includes enhanced cleavage of a peptide, S1, which rapidly returns to healthy levels with targeted therapy. Through direct on-tissue localization of protease activity, we pinpoint S1 cleavage to the tumor vasculature. To link protease activity to cellular function, we design a high-throughput method to isolate and characterize proteolytically active cells, uncovering a pro-angiogenic phenotype in S1-cleaving cells. These methods provide a framework for functional, multiscale characterization of protease dysregulation in cancer.
| S-EPMC9530178 | biostudies-literature
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Project description:A tRNA modifying enzyme facilitates RNase P activity in Arabidopsis nuclei
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Project description:ERK- and USP9X-coupled regulation of thymidine kinase 1 promotes both its enzyme activity-dependent and enzyme activity-independent functions for tumor growth
2024-12-05 | PXD058602 |
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