Date: 9 May 2024 (Thursday)
Time: 5:00 pm – 6:00 pm
Venue: Cheung Kung Hai Lecture Theatre 1, G/F, William M.W. Mong Block, 21 Sassoon Road  

5:00 p.m.

Presenter: Wenyuan XIE (PhD candidate)
Primary Supervisor: Prof. Cora LAI
Presentation Title: The role of parvalbumin inhibitory interneuron in the pathogenesis of Alzheimer’s disease
Abstract: Alzheimer’s disease (AD) is a common neurodegenerative disorder. Recent studies have demonstrated that AD patients and animal models exhibit hyperactivity of hippocampal excitatory neurons caused by GABAergic inhibitory interneurons dysfunction and reduced gamma oscillations. Parvalbumin interneurons (PVINs), a type of GABAergic inhibitory interneuron, play a crucial role in generating gamma oscillations and regulating sleep states. However, the roles of PVINs in the early progression of AD remain unknown. Through in vivo studies, we found 5XFAD mouse develop early sleep disturbance, followed by progressive plaque accumulation, vascular degeneration, and neuronal damage in the dorsal hippocampus, leading to later cognitive impairment. Interestingly, amyloid plaque in the hippocampus was partially cleared by non-invasive gamma frequency stimulation during sleep. The current project aims to study the involvement of PVINs in the sleep disturbance, early pathogenesis, and treatment of AD.

5:30 p.m.

Presenter: Mingze GAO (PhD candidate)
Primary Supervisor: Prof. Yuanhua HUANG
Presentation Title: Machine learning based cellular differentiation inference from snapshot or lineage-barcoded time-series scRNA-seq data
Abstract: The dynamic behavior of cell proliferation and differentiation process is vital to understand their molecular mechanism under various conditions. For cell population snapshots, we recently developed UniTVelo, a unified time-based RNA velocity method, that achieves powerful inference of directed cell differentiation across various systems (Nat Comm 2022). However, for complex biological processes, especially in a clone-specific manner, time-series scRNA-seq with barcoded lineage tracing techniques is a desired design. Prominent examples include the LARRY barcoded hematopoiesis datasets on both mouse and human. Here, we further developed CloneTracer, a non-linear time-variate dynamical system to faithfully estimate the clone-specific transition rates among cell states. With stochastic simulation algorithm, we were able to further add a new quantitative lens of division summary of progenitor cells as well as the fate bias for all progenitor-fate pairs to the field. In conclusion, our work could elucidate the kinetics among meta-clones at any given time point and fate decision bias analysis between a progenitor and a progeny.

ALL ARE WELCOME

Should you have any enquiries, please feel free to contact Jerry Siu at 3917 6912.