Discovery and Validation of Cancer Therapeutic Targets Through the Single-Cell Long-Read RNA-seq Analysis.
The innovation of single-cell long-read RNA-seq technology has advanced our capacity to characterize the transcriptome at the isoform and single-cell levels, surmounting substantial challenges in detecting and analyzing molecular alteration in cancer cells. Our goal is to devise computational methodologies to expedite the individual single-cell-level identification of concealed oncogenic modifications such as alternative splicing variants, novel isoforms, and mutation profiles based on this state-of-the-art technique. By annotating known and novel isoforms, quantifying isoform expressions, and profiling mutation isoforms with single-cell precision, and subsequently validating the functional implications of discovered candidates, we aim to facilitate the discovery of novel targets for cancer therapy and innovative diagnostic approaches. Furthermore, the resultant bioinformatics tools and our findings will be disseminated to the medical scientific community, potentially contributing to the development of effective diagnostic and therapeutic regimens in the clinic.
Learn More About Their Work:
- Cells:
Context-Dependent Distinct Roles of SOX9 in Combined Hepatocellular Carcinoma-Cholangiocarcinoma
https://pubmed.ncbi.nlm.nih.gov/39273023/ - American Journal of Pathology:
Hepatic Nuclear Receptors in Cholestasis-to-Cholangiocarcinoma
https://pmc.ncbi.nlm.nih.gov/articles/PMC11983697/ - GMC Genomics:
Utility Analyses of AVITI Sequencing Chemistry
https://link.springer.com/article/10.1186/s12864-024-10686-4 - Frontiers In Cell Development Biology:
Deep Spatial Sequencing Revealing Differential Immune Responses in Human Hepatocellular Carcinoma
https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1600129/full - Cancer Research Communications:
SALL4 Is Required for YAP1-Dependent Malignant and Regenerative Hepatocyte-to=Cholangiocyte Reprogramming
https://pubmed.ncbi.nlm.nih.gov/40932240/
Presentations, abstract submissions and posters related to this project:
- Presentation and abstract submission:
Silvia Liu. Isoform and fusion detection on bulk and single-cell long-read RNA-seq data. Great Lakes Bioinformatics Conference. 2024/05 - Presentation and abstract submission:
Wenjia Wang (A graduate student in Dr. Liu's lab). IFDlong: an isoform fusion detector on long-read RNA-seq data. The Joint Statistical Meetings. 2024/08 - Poster:
Jia-Jun Liu (A Data Scientist in Dr. Liu's lab). Meta-analytic framework for robust biomarker and cell subtype identification on single-cell and spatial transcriptomics data. Hillman Cancer Center retreat. 2024/09. - Poster:
Jia-Jun Liu (A Data Scientist in Dr. Liu's lab). Integrative single-cell and spatial transcriptomics analysis to characterize liver cellular biomarkers and tissue microenvironment. Pittsburgh Liver Research Center retreat. 2024/10. - Invited talk:
Sungjin Ko. Co-repression of Yap1 and Sox9 abrogate advanced intrahepatic cholangiocarcinoma by eliminating transcriptional compensation. PSI seminar, University of Southern California. 2024/03 - Poster:
Jia-Jun Liu, Silvia Liu. Computational integrative Analysis of Single-cell and Spatial Transcriptomics Reveals Biomarkers in Liver Diseases. Pittsburgh Liver Research Center retreat. 2025/10 - Invited talk:
Silvia Liu. Computational Single-cell, Long-read, and Spatial transcriptomics data analysis. Cancer Biology Seminar Series, University of Pittsburgh. 2025/03 - Invited talk:
Silvia Liu. Computational multi-omics integration: long-read, single-cell, and spatial transcriptomics. Department of Computational and Systems Biology, University of Pittsburgh. 2026/01.