Smart Laser Capturing Microscope for Genome Wide Single Cell Spatial Transcriptomics - PROJECT SUMMARY We introduce an innovative AI-Assisted Laser Capture Microdissection (AI-LCM) approach for Spatial Transcriptomics, designed to overcome the limitations of existing spatial biology methods. Current techniques like MERFISH, Visium, and traditional LCM have distinct strengths, but they fall short in providing both single- cell resolution and genome-wide coverage efficiently. Our AI-LCM technique combines microscopy, Next Generation Sequencing (NGS), and LCM to enable high-throughput, comprehensive spatial transcriptomics analysis. Our method utilizes DNA nanoballs tagged with unique molecular identifiers (UMIs), readable via both microscopy and NGS. This dual-readout allows for an 'imaging-followed-by-sequencing' workflow, ensuring each cell's omics profile is linked to its spatial coordinate through the UMI-tagged nanoballs. We will achieve this through three strategic aims: 1. Optimizing in situ generation and sequencing of DNA nanoballs for diverse sample types. 2. Enhancing AI-assisted LCM to efficiently dissociate nanoball-tagged single cells at high- throughput. 3. Refining sequencing protocols to accommodate nanoballs, linking genomic data with precise spatial origins. This approach promises true single-cell spatial resolution with genome-wide coverage, exploiting the dual-functionality of DNA nanoballs for concurrent processing of thousands of cells, vastly improving LCM's throughput. Building on established technologies, our protocol is both feasible and innovative. DNA nanoballs are already integral in microscopy-based spatial transcriptomics, and LCM's robustness for single-cell profiling is well-documented. Our integration of microscopy and genomics draws on the PIs' extensive experience in commercial SBS products, automated microscopy, and AI-based image analysis. The proposed AI-LCM method not only aims to transcend the boundaries of existing spatial transcriptomics but also seeks to expand the possibilities for a wide range of single-cell assays, adding valuable spatial context to complex genomic data. This project stands to revolutionize the field by providing a novel tool for comprehensive, high-resolution mapping of cellular environments.
