Global PUS1 mRNA targets detection by STAMP
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA956927 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24208226_1.fastq.gz | Fastqsanger.gz | |||
| SRR24208226_2.fastq.gz | Fastqsanger.gz | |||
| SRR24208227_1.fastq.gz | Fastqsanger.gz | |||
| SRR24208227_2.fastq.gz | Fastqsanger.gz | |||
| SRR24208228_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 8 |
Similar Datasets
Project description:Recently, several studies revealed that pseudourine exists on mRNA. However, identification of ψ sites for specific PUS remains a certain ambiguity. PUS1, an important pseudouridine synthase, plays a critical role in cell growth and tumorigenesis of HCC. Here, we utilized an IP-free approach, Surveying Targets by APOBEC-Mediated Profiling (STAMP) and analyzed C-to-U editing sites to realize PUS1-bound mRNAs. And then, we identified the potential mRNA pseudouridylation targets of PUS1 by intersecting editing sites’ host genes with these that known to pseudouridylated.
2024-02-26 | GSE229980 | GEO
Project description:Global analysis of enhancer targets
| PRJNA532921 | ENA
Project description:Tubulin tyrosine ligase-like family member 5 (TTLL5/STAMP) has multiple activities in cells. TTLL5 is one of 13 TTLLs, has polyglutamylation activity, augments the activity of p160 coactivators (SRC-1 and TIF2) in glucocorticoid receptor-regulated gene induction and repression, and displays steroid-independent growth activity with several cell types. To examine TTLL5/STAMP functions in whole animals, mice were prepared with an internal deletion that eliminated several activities of the Stamp gene. This mutation causes both reduced levels of STAMP mRNA and C-terminal truncation of STAMP protein. Homozygous targeted mutant (Stamptm/tm) mice appear normal except for marked decreases in male fertility associated with defects in progressive sperm motility. Abnormal axonemal structures with loss of tubulin doublets occur in most Stamptm/tm sperm tails in conjunction with substantial reduction in tubulin polyglutamylation, which closely correlates with the reduction in mutant STAMP mRNA. The axonemes in other structures appear unaffected. There is no obvious change in the organs for sperm development of wt vs. Stamptm/tm males despite the levels of wt STAMP mRNA in testes being 20-fold higher than in any other organ examined. This defect in male fertility is unrelated to other Ttll genes or 24 genes previously identified as important for sperm function. Thus, STAMP appears to participate in a unique, tissue-selective TTLL-mediated pathway for tubulin polyglutamylation that is required for sperm maturation and motility and may be relevant for male fertility. Testes RNA samples for EXON arrays from WT and KO with three biological replications
2013-03-28 | E-GEOD-45546 | biostudies-arrayexpress
Project description:Gene expression analysis requires accurate measurements of global RNA degradation rates, earlier problematic with methods disruptive to cell physiology. Recently, metabolic RNA labeling emerged as an efficient and minimally invasive technique applied in mammalian cells. Here, we have adapted SH-Linked Alkylation for the Metabolic Sequencing of RNA (SLAM-Seq) for a global mRNA stability study in yeast using 4-thiouracil pulse-chase labeling. We assign high-confidence half-life estimates for 67.5 % of expressed ORFs, and measure a median half-life of 9.4 min. For mRNAs where half-life estimates exist in the literature, their ranking order was in good agreement with previous data, indicating that SLAM-Seq efficiently classifies stable and unstable transcripts. We then leveraged our yeast protocol to identify targets of the Nonsense-mediated decay (NMD) pathway by measuring the change in RNA half-lives; instead of steady-state RNA level changes. With SLAM-Seq, we assign 580 transcripts as putative NMD targets, based on their measured half-lives in wild-type and upf3Δ mutants. We find 225 novel targets, and observe a strong agreement with previous reports of NMD targets, 61.2 % of our candidates being identified in previous studies.This indicates that SLAM-Seq is a simpler and more economic method for global quantification of mRNA half-lives. Our adaptation for yeast yielded global quantitative measures of the NMD effect on transcript half-lives, high correlation with RNA half-lives measured previously with more technically challenging protocols, and identification of novel NMD regulated transcripts that escaped prior detection.
2022-02-28 | GSE196690 | GEO
Project description:Global analysis of enhancer targets: Mosaic-seq
| PRJNA532919 | ENA
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-06-30 | GSE301112 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-07-06 | GSE301544 | GEO
Project description:Transcription and translation are intertwined processes where mRNA isoforms are crucial intermediaries. However, methodological limitations in analyzing translation at the mRNA isoform level have impaired our ability to comprehensively establish links between the full-length transcripts and the translatome. This has left gaps in our understanding of critical biological processes, regulatory mechanisms, and disease progression. To address this, we develop an integrated computational and experimental framework called long-read Ribo-STAMP (LR-Ribo-STAMP). LR-Ribo-STAMP capitalizes on advancements in long-read sequencing and RNA-base editing-mediated technologies to simultaneously and scalably profile translation and transcription at both gene and mRNA isoform levels for the first time. In this report, we show agreement between gene-level translation profiles obtained with LR-Ribo-STAMP and those from previously validated short-read Ribo-STAMP data in unperturbed cells. At the mRNA isoform level, we show that LR-Ribo-STAMP successfully profiles translation in unperturbed cells and links mRNA isoforms and regulatory features, such as upstream ORFs (uORFs) and regulatory sequences, to translation measurements. We further demonstrate the method’s effectiveness in profiling disease models by profiling translation at gene and isoform levels in a triple-negative breast cancer cell line under normoxia and hypoxia. Here, we find that LR-Ribo-STAMP effectively delineates orthogonal transcriptional and translation shifts between conditions at gene and isoform levels. At the isoform level, LR-Ribo-STAMP uniquely identifies key regulatory elements and shifts in mRNA isoform transcription that correlate with changes in translational, providing an example of insight that can inform the development of novel therapeutics. Overall, LR-Ribo-STAMP is a significant advancement in translation methods and can have profound implications for basic research and clinical applications.
2024-05-20 | GSE255844 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-07-03 | GSE301542 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity, but remains constrained by scalability, high costs, and the destruction of cells during analysis. To overcome these challenges, we developed STAMP (Single-Cell Transcriptomics Analysis and Multimodal Profiling), a highly scalable approach for the profiling of single cells. By leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, enabling single-cell genomics of hundreds to millions of cells at an unprecedented affordability. Immobilizing (‘stamping’) cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA, protein and H&E) profiling, while retaining cellular structure and morphology. Its flexible, ultra-high-throughput formats facilitate the analysis of single or multiple samples in the same experiment, enhancing experimental scalability and adaptability. We demonstrate STAMP's versatility across diverse experimental contexts, including the profiling of peripheral blood mononuclear cells (PBMCs), cell lines and stem cells. We also stamped cells and nuclei from dissociated tissues from mouse organs to simulate the generation of cell atlases. Accessibility was further enlarged by analyzing nuclei from archival formalin fixed and paraffin embedded (FFPE) tissue samples. Combining RNA and protein profiling, we applied STAMP for high-throughput immuno-phenotyping of millions of blood cells, providing multimodal insights into cellular heterogeneity. We highlight the capability of STAMP to identify ultra-rare cell populations, simulating clinical applications for detecting circulating tumor cells (CTCs). By capturing lineage dynamics during stem cell differentiation and subtle changes in in vitro activated PBMCs, we further showed its utility for large-scale perturbation studies. These results validate STAMP as a first-of-its-kind single-cell imaging analysis strategy. We present data for 10,962,092 high quality cells/nuclei and 6,030,429,954 high quality transcripts. By replacing sequencing with imaging, STAMP enables high-resolution cellular profiling that is more accessible, scalable, and cost-effective. STAMP has the potential to transform our ability to map biological diversity and dynamics, significantly advancing research and clinical applications.
2025-06-23 | GSE290466 | GEO