Articles by Morgan Wu
Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues using STARmap PLUS, RIBOmap and TEMPOmap - Nature Protocols
Abstract Controlled gene expression programs have a crucial role in shaping cellular functions and activities. At the core of this process lies the RNA life cycle, ensuring protein products are synthesized in the right place at the right time.
Search and match across spatial omics samples at single-cell resolution - Nature Methods
Abstract Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking.
Publisher Correction: Spatial atlas of the mouse central nervous system at molecular resolution - Nature
Correction to: Nature https://doi.org/10.1038/s41586-023-06569-5 Published online 27 September 2023 This article was originally published under a standard Springer Nature licence (© The Author(s), under exclusive licence to Springer Nature Limited). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International licence, © The Author(s). The change has been made in the HTML and PDF versions of the article.
Spatial atlas of the mouse central nervous system at molecular resolution - Nature
Abstract Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain1,2, but cannot capture their spatial organization.
Mitigating autocorrelation during spatially resolved transcriptomics data analysis
Abstract Several computational methods have recently been developed for characterizing molecular tissue regions in spatially resolved transcriptomics (SRT) data. However, each method fundamentally relies on spatially smoothing transcriptomic features across neighboring cells. Here, we demonstrate that smoothing increases autocorrelation between neighboring cells, causing latent space to encode physical adjacency rather than spatial transcriptomic patterns.
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