Dr. Taiwen Li earned his Ph.D. in Oral Basic Science from the West China School of Stomatology at Sichuan University. He honed his expertise in Computational Biology in Xiaole Shirley Liu's lab at the Dana-Farber Cancer Institute and the Harvard T.H. Chan School of Public Health. Dr. Li's lab specializes in cancer system immunology and has developed several innovative tools for analyzing tumor immune infiltration, including TIMER, TISCH, and TRUST. Additionally, his lab employs multi-omics and computational approaches to explore the mechanisms underlying oral cancer initiation. Dr. Li's research has garnered significant recognition, with his papers being cited over 16,000 times. He has been consistently acknowledged for his scientific contributions, being listed among the Top 1% of Scientists on the Stanford List and as one of Elsevier's Most Cited Chinese Researchers.
Research Interests
Our lab specializes in cancer system immunology and oral carcinogenesis, employing a multi-omics and computational approach to unravel the complexities of tumor initiation and progression. We have developed advanced computational tools to analyze the tumor microenvironment and immune infiltration, providing insights into the immune landscape's role in cancer development and therapy response. Combining multi-omics and molecular biology techniques, we conduct in vivo and in vitro studies and explore immunotherapy, identifying key molecules with potential for cancer prevention.
Our lab focuses on advancing the field of cancer system immunology by developing and refining computational tools to analyze the tumor microenvironment. Notable contributions include TIMER, TISCH, and TRUST, which facilitate the comprehensive assessment of tumor immune infiltration and the dynamic interactions within the tumor microenvironment. These tools are instrumental in elucidating the roles of various immune cells in cancer progression and response to therapies, such as immune checkpoint blockade and photodynamic therapy. By integrating single-cell transcriptomics, spatial transcriptomics, mass cytometry, and other high-throughput techniques, we aim to provide a deeper understanding of the immune landscape in different cancer types and develop strategies to enhance immunotherapeutic efficacy.
Our research in oral carcinogenesis leverages multi-omics and computational approaches to uncover the molecular mechanisms underlying the initiation and progression of oral cancer. We employ single-cell transcriptomics, spatial transcriptomics, and other advanced methodologies to dissect the cellular and molecular changes in the oral tissue microenvironment during carcinogenesis. Our studies have highlighted the impact of genetic mutations, immune responses, and signaling pathways in the development of oral cancers. By integrating diverse data types, we aim to identify potential biomarkers and therapeutic targets, ultimately contributing to improved diagnosis and treatment strategies for oral cancer.
Tools:
- TIMER1/TIMER2 TIMER provides a robust platform for estimating immune infiltration levels in cancer using six advanced algorithms. It includes modules for exploring associations between immune infiltrates and genetic or clinical features, as well as cancer-related associations in TCGA cohorts, all presented in functional heatmap tables for easy comparison across multiple cancer types. (Cancer res. 2017 Nucleic Acids Res. 2020, Hot Paper, Over 8.3k citations)
- TISCH1/TISCH2 TISCH is an updated resource for single-cell RNA-seq data from human and mouse tumors, featuring 190 datasets encompassing 6 million cells across 50 cancer types. New functionalities include analyses of cell-cell communication and significant ligand-receptor pairs, transcription factor enrichment visualizations, and tools for identifying correlated genes with survival data. TISCH2 offers a user-friendly platform for comprehensive gene expression analysis in the tumor microenvironment. (Nucleic Acids Res. 2021 2023, Highly Cited Paper)
- HUSCH HUSCH is a curated database integrating nearly 3 million single-cell RNA-seq profiles from 185 datasets across 45 human tissues. It provides interactive visualization of gene expression, differential gene analysis, and functional insights for each cell type cluster, along with data integration and harmonization for comprehensive tissue-level analysis. (Nucleic Acids Res. 2023)
- TRUST TRUST is a computational algorithm that can extract TCR/BCR immune repertoires from tumor bulk or single cell RNA-seq data. (Nat Genet. 2016) The latest version is TRUST4.
- OralDB OralDB stands out as the first comprehensive analytical platform dedicated to omics data for oral diseases, featuring robust visualization capabilities. Key functional modules include dataset overviews, gene expression analysis, differential gene expression analysis, pathway enrichment analysis, cell-cell interaction inference, and gene correlation analysis.
- SELINA SELINA presents an automated framework for annotating single-cell RNA-seq data using a reference atlas of 1.7 million cells across 230 human cell types. SELINA removes batch effects, enhances rare cell type annotation, and aligns query data with an autoencoder. It is robust across various tissues and accurately annotates cells in different disease contexts. (Cell Rep Methods. 2023)