An isoform-resolution transcriptomic atlas of colorectal cancer from long-read single-cell sequencing
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ABSTRACT:
PROVIDER: PRJEB68074 | ENA |
REPOSITORIES: ENA
Dataset's files
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| ERR12186700.fastq.gz | Fastqsanger.gz | |||
| ERR12186701.fastq.gz | Fastqsanger.gz | |||
| ERR12186702.fastq.gz | Fastqsanger.gz | |||
| ERR12186703.fastq.gz | Fastqsanger.gz | |||
| ERR12186704.fastq.gz | Fastqsanger.gz |
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Project description:An isoform-resolution transcriptomic atlas of colorectal cancer from single-cell long-read sequencing
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Project description:Colorectal cancer (CRC) ranks as the second leading cause of cancer deaths globally. In recent years, short-read single-cell RNA sequencing (scRNA-seq) has been instrumental in deciphering tumor heterogeneities. However, these studies only enable gene-level quantification but neglect alterations in transcript structures arising from alternative end processing or splicing. In this study, we integrated short- and long-read scRNA-seq of CRC samples to build an isoform-resolution CRC transcriptomic atlas. We identified 394 dysregulated transcript structures in tumor epithelial cells, including 299 resulting from various combinations of splicing events. Second, we characterized genes and isoforms associated with epithelial lineages and subpopulations exhibiting distinct prognoses. Among 31,935 isoforms with novel junctions, 330 were supported by The Cancer Genome Atlas RNA-seq and mass spectrometry data. Finally, we built an algorithm that integrated novel peptides derived from open reading frames of recurrent tumor-specific transcripts with mass spectrometry data and identified recurring neoepitopes that may aid the development of cancer vaccines.
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Project description:Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.
2020-04-14 | E-MTAB-8735 | biostudies-arrayexpress
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:Long-read Ribo-STAMP simultaneously measures transcription and translation at full length isoform resolution
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Project description:Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing methods using either short-read (SR) or long-read (LR) RNA sequencing have significant limitations: SR sequencing provides high depth but struggles with isoform deconvolution, while LR sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. Applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of mRNA abundance determinants, reveals the role of untranslated regions (UTRs) in isoform regulation through isoform-specific interactions with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.
2024-07-10 | GSE271528 | GEO
Project description:Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing methods using either short-read (SR) or long-read (LR) RNA sequencing have significant limitations: SR sequencing provides high depth but struggles with isoform deconvolution, while LR sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. Applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of mRNA abundance determinants, reveals the role of untranslated regions (UTRs) in isoform regulation through isoform-specific interactions with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.
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Project description:BACKGROUND: Alternative splicing plays crucial roles in normal heart development and cardiac disease by influencing protein-coding sequences, functional domains, and molecular networks. However, a detailed characterization of the human heart isoform landscape remains incomplete. METHODS: Leveraging long-read single-nucleus RNA sequencing and computational analysis, we dissected full-length isoform heterogeneities, expression patterns, and usage shifts across cell types, cell states, and cardiac conditions of the adult left ventricle. We applied in silico approaches to assess the functional relevance of identified isoforms; validated isoform compositions of representative cardiac genes using reverse transcription quantitative polymerase chain reaction and targeted amplicon sequencing; and developed a web server for interactive navigation of our results. RESULTS: The data revealed that isoform heterogeneity is widespread in the cardiac cellular system, serving as a posttranscriptional buffer system that calibrates the molecule reservoirs in human hearts. In healthy left ventricles, ≈30% of cell type–specific genes were polyform, using multiple isoforms tailored to cell type–specific programs. Among ubiquitously expressed genes, >300 showed differential isoform usage with cell type specificity. Compared with heart failure, 379 genes in cardiomyocytes demonstrated marked isoform usage shifts, most of which are predicted to change protein coding outcomes through direct changes in protein coding sequences and switches between intron retention and non–protein-coding biotypes. In contrast, cell state–specific programs tend to operate on monoform genes associated with changes among cell states. In addition, our data revealed heart failure–associated differential isoform usage events in stromal and immune cell types in the cardiac microenvironment. CONCLUSIONS: We present a comprehensive atlas of splicing isoforms in the normal adult heart and heart failure through long-read single-nucleus RNA sequencing and comprehensive computational analyses. The results suggest crucial roles of isoforms in buffering core cellular programs and contributing to disease-associated cell states. The full-length details of these cell-specific isoforms serve as an important reference for downstream translational and mechanistic studies and are available on our online data portal at https://github.com/gaolabtools/heart-isoform-atlas. Github repo: https://github.com/gaolabtools/heart-isoform-atlas doi: 10.1161/CIRCULATIONAHA.125.074959
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Project description:CRC long-read single-cell sequencing
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Project description:In recent years, long-read sequencing technologies have detected transcript isoforms with unprecedented accuracy and resolution. However, it remains unclear whether long-read sequencing can effectively disentangle the isoform landscape of complex allele-specific loci that arise from genetic or epigenetic differences between alleles. Here, we combine the PacBio Iso-Seq workflow with the established phasing approach WhatsHap to assign long reads to the corresponding allele in polymorphic F1 mouse hybrids. Upon comparing the long-read sequencing results with matched short reads, we observed general consistency in the allele-specific information and were able to confirm the imprinting status of known imprinted genes. We then explored the complex imprinted Gnas locus known for allele-specific non-coding and coding isoforms and were able to benchmark historical observations. This approach also allowed us to detect isoforms from both the active and inactive X chromosomes of genes that escape X chromosome inactivation. The described workflow offers a promising framework and demonstrates the power of long-read transcriptomic data to provide mechanistic insight into complex allele-specific loci.
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