Long-read Ribo-STAMP simultaneously measures transcription and translation at full length isoform resolution
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ABSTRACT:
PROVIDER: PRJNA1076859 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27982876_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR27982877_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR27982878_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR27982879_subreads.fastq.gz | Fastqsanger.gz | |||
| SRR27982880_subreads.fastq.gz | Fastqsanger.gz |
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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:Transcription and translation are intertwined processes in which mRNA isoforms are crucial intermediaries. However, methodological limitations in analyzing translation at the mRNA isoform level have left gaps in our understanding of critical biological processes. To address these gaps, we developed an integrated computational and experimental framework called long-read Ribo-STAMP (LR-Ribo-STAMP) that capitalizes on advancements in long-read sequencing and RNA-base editing-mediated technologies to simultaneously profile translation and transcription at both the gene and mRNA isoform levels. We also developed the EditsC metric to quantify editing and leverage the single-molecule, full-length transcript information provided by long-read sequencing. Here, we report concordance between gene-level translation profiles obtained with long-read and short-read Ribo-STAMP. We show that LR-Ribo-STAMP successfully profiles translation of mRNA isoforms and links regulatory features, such as upstream open reading frames (uORFs), to translation measurements. We apply LR-Ribo-STAMP to discovering translational differences at both the gene and isoform levels in a triple-negative breast cancer cell line under normoxia and hypoxia and find that LR-Ribo-STAMP effectively delineates orthogonal transcriptional and translation shifts between conditions. We also discover regulatory elements that distinguish translational differences at the isoform level. We highlight GRK6, in which hypoxia is observed to increase expression and translation of a shorter mRNA isoform, giving rise to a truncated protein without the AGC Kinase domain. Overall, LR-Ribo-STAMP is an important advance in our repertoire of methods that measures mRNA translation with isoform sensitivity.
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Project description:scDUAL-seq Simultaneously Measures RNA Synthesis and Degradation Rates in Single Cells
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Project description:Ribo-STAMP reveals translation status of RNAs bound by PABPN and PABPC
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Project description:mRNAs are generally assumed to be loaded instantly with ribosomes upon entry into the cytoplasm. To measure ribosome density on nascent mRNA, we developed nascent Ribo-Seq (nRibo-Seq) by combining Ribo-Seq with progressive 4-thiouridine labelling. In mouse macrophages, we experimentally determined, for the first time, the lag between the appearance of nascent RNA and its association with ribosomes, which was calculated to be 20 - 22 min for bulk mRNA, and approximated the time it takes for mRNAs to be fully loaded with ribosomes to be 41 - 44 min. Notably, ribosomal loading time is adapted to gene function as rapid loading was observed with highly regulated genes. The lag and ribosomal loading time correlate positively with ORF size and mRNA half-life, and negatively with tRNA adaptation index. Similar results were obtained in mouse embryonic stem cells, where the lag in ribosome loading was even more pronounced with 35 - 38 min. We validated our measurements after stimulation of macrophages with lipopolysaccharide, where the lag between cytoplasmic and translated mRNA leads to uncoupling between input and ribosome-protected fragments. Uncoupling is stronger for mRNAs with long ORFs or half-lives, a finding we also confirmed at the level of protein production by nascent chain proteomics. As a consequence of the lag in ribosome loading, ribosome density measurements are distorted when performed under conditions where mRNA levels are far from steady state expression, and transcriptional changes affect ribosome density in a passive way. This study uncovers an unexpected and considerable lag in ribosome loading, and provides guidelines for the interpretation of Ribo-Seq data taking passive effects on ribosome density into account.
2021-06-22 | PXD026828 | Pride
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:Integrated multi-omics analysis of zinc finger proteins uncovers roles in RNA regulation [Ribo-STAMP cell lines]
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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: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.
2025-07-03 | GSE301542 | GEO