UROP Proceedings 2022-23

School of Science Division of Life Science 30 G Proteins and their Regulators in Cancer Biology Supervisor: WONG, Yung Hou / LIFS Student: RAG, Adwika / BCB Course: UROP1100, Fall G-protein coupled receptors (GPCR) are a 7 transmembrane domain receptor quite important in regulating several biological functions in the body and found in most human cells. Two receptors part of this family are melatonin receptors type 1 and 2, as well as dopamine receptors, type D1-D5. Melatonin most notably regulates the body’s circadian rhythm and dopamine controls pleasure and reward. Homodimerization and heterodimerization is a new paradigm in GPCR biology with various studies proving its existence in different cell types. We try to dock these proteins using computational techniques to understand the 3D confirmation of the heterodimer of MT1 and D3. Big Data: Bioinformatic Analysis of Single-cell Genomic Data Supervisor: WU, Angela Ruohao / LIFS Student: KIM, Hyunggyu / BCB Course: UROP1100, Summer A three-dimensional cerebral organoid is a crucial model in brain research to study human brain development and possible dysfunction. However, the complexity of the brain currently limits the proceeding research. Hence, single-cell RNA sequencing (ScRNA-Seq) arises as a useful tool to obtain the cDNA library of the cultured brain organoid. For further simplicity, a bioinformatic tool such as Seurat is used to identify the cell types in brain organoids (Fiorenzano et al., 2021). Despite the disadvantage of limited reproducibility and incomplete maturation of brain organoids, we still observed the significance of the brain organoid model due to its comprehensiveness in modelling. Hence, in my UROP project, I would identify cell types in day-30 cerebral organoids using 10x Genomics and bioinformatic tools. Big Data: Bioinformatic Analysis of Single-cell Genomic Data Supervisor: WU, Angela Ruohao / LIFS Student: YU, Jiamu / BCB-IRE Course: UROP2100, Fall UROP3100, Summer The field of spatial transcriptomics offers a range of methods for capturing and analysing the spatial organization of gene expression, but there has not been an unbiased and comprehensive comparison among these approaches. Existing metrics used for comparison between different methods were limited to hardware specifications and did not account for differences in the capability of recovering meaningful biological information. A systematic benchmarking of spatial transcriptomic methods would provide practical guidance for experimental settings based on different considerations, and would also facilitate the development of new algorithms. This report focuses on benchmarking different cell segmentation algorithms used in spatial transcriptomics experiments. Preliminary results are presented herein, which will be further analyzed in the coming months to provide more comprehensive insights.