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Karakulak T, Zajac N, Bolck HA, Bratus-Neuenschwander A, Zhang Q, Qi W, Basu D, Oltra TC, Rehrauer H, von Mering C, Moch H, Kahraman A. Heterogeneous and novel transcript expression in single cells of patient-derived clear cell renal cell carcinoma organoids. Genome Res 2025; 35:698-711. [PMID: 40107723 DOI: 10.1101/gr.279345.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 02/20/2025] [Indexed: 03/22/2025]
Abstract
Splicing is often dysregulated in cancer, leading to alterations in the expression of canonical and alternatively spliced isoforms. We used the multiplexed arrays sequencing (MAS-seq) protocol of PacBio to sequence full-length transcripts in patient-derived organoid (PDO) cells of clear cell renal cell carcinoma (ccRCC). The sequencing revealed a heterogeneous dysregulation of splicing across 2599 single ccRCC cells. The majority of novel transcripts could be removed with stringent filtering criteria. The remaining 31,531 transcripts (36.6% of the 86,182 detected transcripts on average) were previously uncharacterized. In contrast to known transcripts, many of the novel transcripts have cell-specific expression. Novel transcripts common to ccRCC cells belong to genes involved in ccRCC-related pathways, such as hypoxia and oxidative phosphorylation. A novel transcript of the ccRCC-related gene nicotinamide N-methyltransferase is validated using PCR. Moreover, >50% of novel transcripts possess a predicted complete protein-coding open reading frame. An analysis of the most dominant transcript-switching events between ccRCC and non-ccRCC cells shows many switching events that are cell- and sample-specific, underscoring the heterogeneity of alternative splicing events in ccRCC. Overall, our study elucidates the intricate transcriptomic architecture of ccRCC, underlying its aggressive phenotype and providing insights into its molecular complexity.
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Affiliation(s)
- Tülay Karakulak
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University of Zurich and University Hospital Zurich, 8091 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Natalia Zajac
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Functional Genomics Center Zurich, ETH, 8057 Zurich, Switzerland
| | - Hella Anna Bolck
- Department of Pathology and Molecular Pathology, University of Zurich and University Hospital Zurich, 8091 Zurich, Switzerland
- Centre for AI, School of Engineering, Zurich University of Applied Sciences (ZHAW), 8400 Winterthur, Switzerland
| | | | - Qin Zhang
- Functional Genomics Center Zurich, ETH, 8057 Zurich, Switzerland
| | - Weihong Qi
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Functional Genomics Center Zurich, ETH, 8057 Zurich, Switzerland
| | - Debleena Basu
- Department of Pathology and Molecular Pathology, University of Zurich and University Hospital Zurich, 8091 Zurich, Switzerland
| | | | - Hubert Rehrauer
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Functional Genomics Center Zurich, ETH, 8057 Zurich, Switzerland
| | - Christian von Mering
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University of Zurich and University Hospital Zurich, 8091 Zurich, Switzerland
| | - Abdullah Kahraman
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland;
- School for Life Sciences, Institute for Chemistry and Bioanalytics, University of Applied Sciences Northwestern Switzerland, 4132 Muttenz, Switzerland
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Tung KF, Pan CY, Lin WC. Housekeeping protein-coding genes interrogated with tissue and individual variations. Sci Rep 2024; 14:12454. [PMID: 38816574 PMCID: PMC11139953 DOI: 10.1038/s41598-024-63269-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024] Open
Abstract
Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw .
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Affiliation(s)
- Kuo-Feng Tung
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C
| | - Chao-Yu Pan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C
| | - Wen-Chang Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C..
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Tung KF, Lin WC. TEx-MST: tissue expression profiles of MANE select transcripts. Database (Oxford) 2022; 2022:6726258. [PMID: 36170113 PMCID: PMC9518666 DOI: 10.1093/database/baac089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 12/05/2022]
Abstract
Recently, a new reference transcript dataset [Matched Annotation from the NCBI and EMBL-EBI (MANE) select] was released by NCBI and EMBL-EBI to make available a new unified representative transcript for human protein-coding genes. While the main purpose of MANE project is to provide a harmonized gene and transcript information standard, there is no explicit tissue expression information about these MANE select transcripts. In this report, we tried to provide useful expression profiles of MANE select transcripts in various normal human tissues to allow further interrogation of their molecular modulations and functional significance. We obtained the new V9 transcript expression dataset from the Genotype-Tissue Expression (GTEx) web portal. This new GTEx dataset, based on a long-read sequencing platform, affords better assessment of the expression of alternative spliced transcripts. This tissue expression profiles of MANE select transcripts (TEx-MST) database not only provides the basic information of MANE select transcripts but also tissue expression profiles on alternative transcripts in protein-coding genes. Users can initiate the interrogation by gene symbol searches or by browsing the MANE genes with various criteria (such as genome locations or expression rankings). We further utilized the GENCODE biotype feature to identify the top-ranked protein-coding transcripts by choosing the most expressed protein-coding transcripts from GTEx datasets (both V8 and V9 datasets). In summary, there are 18 083 genes matched between MANE and GTEx. Among them, 13 245 MANE select transcripts matched with the top-ranked protein-coding transcripts in GTEx V9 dataset, which underlined the dominate expression of MANE select transcripts. This TEx-MST web bioinformatic database provides a visualized user interface for the normal tissue expression patterns of MANE select transcripts using the newly released GTEx dataset. Database URL: TEx-MST is available at https://texmst.ibms.sinica.edu.tw/
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Affiliation(s)
- Kuo-Feng Tung
- Institute of Biomedical Sciences, Academia Sinica , Taipei 115, Taiwan, R.O.C
| | - Wen-chang Lin
- Institute of Biomedical Sciences, Academia Sinica , Taipei 115, Taiwan, R.O.C
- Institute of Biomedical Informatics, National Yang-Ming Chiao Tung University , Taipei 112, Taiwan, R.O.C
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