Title: A Spatial Biologist's Guide to Decode Multiplexed Image Analysis
Abstract: Over recent years, spatial omics has gained prominence as a leading-edge technology, illuminating the critical importance of spatial cell coordination in addressing multifaceted biological challenges, such as cancer prognosis, embryonic development, and the success of immunotherapy. There are two primary branches of spatial technology: one based on sequencing and the other on imaging. Notably, the latter imposes a substantial computational burden, necessitating users with extensive experience in machine learning and programming. As spatial technology becomes more accessible to a broader audience, it is imperative to equip scientists with basic bioinformatics training to harness the full potential of their datasets. Despite the emergence of various spatial analysis pipelines, many are tailored to specific data types. Few have addressed the intricacies of CODEX data as comprehensively as the Nolan lab, which pioneered CODEX in 2018. Over the years, we have optimized key aspects of CODEX analysis, focusing on three crucial aspects: 1) image segmentation, 2) quality assessment and normalization, and 3) spatial-resolved single-cell and tissue-level analysis. Drawing from our extensive experience and the latest computational tools, we introduce a user-friendly, end-to-end Python workflow for CODEX analysis. This workflow can be easily extended to other spatial multiplexed-image data, empowering researchers to unlock valuable insights from their own experiments.
Bio: Yuqi Tan, a postdoctoral researcher at Stanford University, holds a BSc in Cell and Molecular Biology from the Chinese University of Hong Kong and a PhD in Computational Biology from Johns Hopkins University. With a record of 20 scholarships and awards, she specializes in crafting computational tools that integrate and transfer knowledge across various single-cell data modalities, whether in two or three dimensions. Her expertise finds practical applications in determining cell type identities and elucidating the spatial orchestration of cell types, especially within the domains of stem cell engineering, cancer immunotherapy, cancer initiation, and psychiatric diseases.
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