Browsing by Subject "multi-omics"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Deciphering Gene Regulatory Mechanisms Through Multi-omics Integration
    (2022-09) Chen, Duojiao; Liu, Yunlong; Wan, Jun; Zhang, Chi; Yan, Jingwen
    Complex biological systems are composed of many regulatory components, which can be measured with the advent of genomics technology. Each molecular assay is normally designed to interrogate one aspect of the cell state. However, a comprehensive understanding of the regulatory mechanism requires characterization from multiple levels such as genome, epigenome, and transcriptome. Integration of multi-omics data is urgently needed for understanding the global regulatory mechanism of gene expression. In recent years, single-cell technology offers unprecedented resolution for a deeper characterization of cellular diversity and states. High-quality single-cell suspensions from tissue biopsies are required for single-cell sequencing experiments. Tissue biopsies need to be processed as soon as being collected to avoid gene expression changes and RNA degradation. Although cryopreservation is a feasible solution to preserve freshly isolated samples, its effect on transcriptome profiles still needs to be investigated. Investigation of multi-omics data at the single-cell level can provide new insights into the biological process. In addition to the common method of integrating multi-omics data, it is also capable of simultaneously profiling the transcriptome and epigenome at single-cell resolution, enhancing the power of discovering new gene regulatory interactions. In this dissertation, we integrated bulk RNA-seq with ATAC-seq and several additional assays and revealed the complex mechanisms of ER–E2 interaction with nucleosomes. A comparison analysis was conducted for comparing fresh and frozen multiple myeloma single-cell RNA sequencing data and concluded that cryopreservation is a feasible protocol for preserving cells. We also analyzed the single-cell multiome data for mesenchymal stem cells. With the unified landscape from simultaneously profiling gene expression and chromatin accessibility, we discovered distinct osteogenic differentiation potential of mesenchymal stem cells and different associations with bone disease-related traits. We gained a deeper insight into the underlying gene regulatory mechanisms with this frontier single-cell mutliome sequencing technique.
  • Thumbnail Image
    Item
    Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer
    (Ivyspring International Publisher, 2020-09-02) Cao, Rui; Yang, Fan; Ma, Si-Cong; Liu, Li; Zhao, Yu; Li, Yan; Wu, De-Hua; Wang, Tongxin; Lu, Wei-Jia; Cai, Wei-Jing; Zhu, Hong-Bo; Guo, Xue-Jun; Lu, Yu-Wen; Kuang, Jun-Jie; Huan, Wen-Jing; Tang, Wei-Min; Huang, Kun; Huang, Junzhou; Yao, Jianhua; Dong, Zhong-Yi; Biostatistics, School of Public Health
    Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.