Browsing by Author "Sun, Liang"

Now showing 1 - 3 of 3
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    A single-cell level comparison of human inner ear organoids with the human cochlea and vestibular organs
    (Cell Press, 2023) van der Valk, Wouter H.; van Beelen, Edward S. A.; Steinhart, Matthew R.; Nist-Lund, Carl; Osorio, Daniel; de Groot, John C. M. J.; Sun, Liang; van Benthem, Peter Paul G.; Koehler, Karl R.; Locher, Heiko; Otolaryngology -- Head and Neck Surgery, School of Medicine
    Inner ear disorders are among the most common congenital abnormalities; however, current tissue culture models lack the cell type diversity to study these disorders and normal otic development. Here, we demonstrate the robustness of human pluripotent stem cell-derived inner ear organoids (IEOs) and evaluate cell type heterogeneity by single-cell transcriptomics. To validate our findings, we construct a single-cell atlas of human fetal and adult inner ear tissue. Our study identifies various cell types in the IEOs including periotic mesenchyme, type I and type II vestibular hair cells, and developing vestibular and cochlear epithelium. Many genes linked to congenital inner ear dysfunction are confirmed to be expressed in these cell types. Additional cell-cell communication analysis within IEOs and fetal tissue highlights the role of endothelial cells on the developing sensory epithelium. These findings provide insights into this organoid model and its potential applications in studying inner ear development and disorders.
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    Mapping oto-pharyngeal development in a human inner ear organoid model
    (The Company of Biologists, 2023) Steinhart, Matthew R.; van der Valk, Wouter H.; Osorio, Daniel; Serdy, Sara A.; Zhang, Jingyuan; Nist-Lund, Carl; Kim, Jin; Moncada-Reid, Cynthia; Sun, Liang; Lee, Jiyoon; Koehler, Karl R.; Otolaryngology -- Head and Neck Surgery, School of Medicine
    Inner ear development requires the coordination of cell types from distinct epithelial, mesenchymal and neuronal lineages. Although we have learned much from animal models, many details about human inner ear development remain elusive. We recently developed an in vitro model of human inner ear organogenesis using pluripotent stem cells in a 3D culture, fostering the growth of a sensorineural circuit, including hair cells and neurons. Despite previously characterizing some cell types, many remain undefined. This study aimed to chart the in vitro development timeline of the inner ear organoid to understand the mechanisms at play. Using single-cell RNA sequencing at ten stages during the first 36 days of differentiation, we tracked the evolution from pluripotency to various ear cell types after exposure to specific signaling modulators. Our findings showcase gene expression that influences differentiation, identifying a plethora of ectodermal and mesenchymal cell types. We also discern aspects of the organoid model consistent with in vivo development, while highlighting potential discrepancies. Our study establishes the Inner Ear Organoid Developmental Atlas (IODA), offering deeper insights into human biology and improving inner ear tissue differentiation.
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    Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer
    (Springer, 2018) Shao, Wei; Cheng, Jun; Sun, Liang; Han, Zhi; Feng, Qianjin; Zhang, Daoqiang; Huang, Kun; Medicine, School of Medicine
    Existing studies have demonstrated that combining genomic data and histopathological images can better stratify cancer patients with distinct prognosis than using single biomarker, for different biomarkers may provide complementary information. However, these multi-modal data, most high-dimensional, may contain redundant features that will deteriorate the performance of the prognosis model, and therefore it has become a challenging problem to select the informative features for survival analysis from the redundant and heterogeneous feature groups. Existing feature selection methods assume that the survival information of one patient is independent to another, and thus miss the ordinal relationship among the survival time of different patients. To solve this issue, we make use of the important ordinal survival information among different patients and propose an ordinal sparse canonical correlation analysis (i.e., OSCCA) framework to simultaneously identify important image features and eigengenes for survival analysis. Specifically, we formulate our framework basing on sparse canonical correlation analysis model, which aims at finding the best linear projections so that the highest correlation between the selected image features and eigengenes can be achieved. In addition, we also add constrains to ensure that the ordinal survival information of different patients is preserved after projection. We evaluate the effectiveness of our method on an early-stage renal cell carcinoma dataset. Experimental results demonstrate that the selected features correlated strongly with survival, by which we can achieve better patient stratification than the comparing methods.