“Advancements in Single-Cell Transcriptomics Through Long-Read scRNA-seq, Spatial Analysis, and GPT”, Rui Chen, PhD, UCI Department of Ophthalmology

Single-cell sequencing has revolutionized the molecular profiling of tissues and organs. However, current technologies still face several limitations and challenges. In this presentation, I will discuss various technologies we have developed to address some of these limitations. To explore mRNA alternative splicing at the single-cell level, we have employed single-cell RNA sequencing using both short-read and long-read high-throughput methodologies. Notably, we have identified a large percentage of novel isoforms, with many showing significant variation among cell types. Another limitation of scRNA-seq is the loss of spatial information. We have explored several single-cell spatial transcriptomics profiling technologies in the lab, and I will share our experiences with these methods. Finally, to address the need for more robust and effective tools for cell annotation in scRNA-seq data, we have utilized high-resolution single-cell reference datasets. By fine-tuning single-cell foundation models with domain-specific data, we have observed significantly improved performance compared to the initial models. These technological advancements can be readily adopted across various systems, further strengthening our single-cell toolboxes