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Advances in genomics technology, from PCR to NGS through single-cell analysis, have been critical for deepening our understanding of biology, but without spatial context, a crucial aspect of biology is missing. Understanding how cells function in their native environment is now changing the way researchers understand the complexity of different tissues throughout the body.
Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH), a proven spatial genomics technology, was developed at Harvard University in the lab of Xiaowei Zhuang, PhD.1,2 MERFISH uses a novel combinatorial barcoding scheme to spatially localize transcripts at subcellular resolution, mapping gene expression across whole tissues and detailing the complex arrangement of cell types and states.
MERSCOPE, powered by MERFISH, was the first-ever single-cell, high-plex spatial genomics platform with flexible sample input delivering highly sensitive, specific, and biologically accurate data. With seamless workflow from sample prep to data visualization, the MERSCOPE® Platform is a comprehensive in situ single-cell spatial genomics solution with integrated high-resolution imaging, fluidics, image processing, and automation.
One challenge facing researchers entering the spatial genomics field is the massive scale of the datasets that can be generated, with potentially billions of transcripts and millions of cells mapped in a single 1 cm2 tissue sample. MERSCOPE is specifically designed to enable users to navigate these large datasets and fully explore their samples.
The process begins with the Gene Panel Portal, a web-based tool for designing and building customized gene panels comprised of up to 500 genes, streamlined with real-time feedback and guidance. Vizgen® has developed the first two of a planned series of predesigned gene panels as a convenient off-the-shelf option. The 500-gene panels, PanNeuro Cell Type (Mouse) and PanCancer Pathways (Human), enable the cellular and subcellular analysis of neuronal signaling, activity, and connectivity, and the characterization of tumor behavior across multiple different types of human cancers.
MERSCOPE has integrated Watershed and, more recently, Cellpose cell segmentation methodologies enabled by the MERSCOPE Cell Boundary Staining Kit. Cellpose uses machine learning for highly accurate cell identification across tissue types, providing an alternative segmentation algorithm through reduction of artifacts and improved cell identification in high-density regions. The instrument can automatically process raw images into a format ready for downstream spatial analysis, enabling faster biological discovery. In addition to providing the raw data files, Vizgen bundles the data into a proprietary file type for exploring in the MERSCOPE® Vizualizer, an easy-to-use desktop spatial analysis software for interacting with MERFISH datasets.
The Vizgen Post-processing Tool enables efficient data reprocessing and refinement of experiment results, including manual fine-tuning of cell segmentation, regenerating new single-cell data, and changing image formats for use with external tools. Older MERFISH data can be re-run to take advantage of newer, fully compatible segmentation algorithms. In addition, the open-source software promotes transparency and usability, while supporting seamless integration with third-party tools such as Scanpy/Squidpy, Seurat, Voyager, and Giotto, as well as providing native plugin development.
Spatial genomics is a rapidly evolving field. MERFISH technology has been leveraged to generate data for over 100 scientific papers and pre-prints across fields such as oncology, neuroscience, developmental biology, and immunology. Recent landmark papers featuring MERSCOPE-generated MERFISH data include a joint venture between the Icahn School of Medicine at Mt. Sinai and Regeneron looking at responses to checkpoint inhibitors in cancer patients,3 and a collaboration between the Allen Institute for Brain Science, Harvard, the University of Pennsylvania and Genentech, analyzing a total of 4.3 million cells to create a mouse brain atlas.4
Over the next few years, Vizgen predicts there will be widespread adoption of spatial genomics, requiring innovation in the development of user-friendly, intuitive analysis tools to support more complex and larger spatial datasets.
References
1. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015). RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science (New York, N.Y.), 348(6233), aaa6090.
2. Method of the Year 2020: spatially resolved transcriptomics. (2021). Nature methods, 18(1), 1.
3. Magan, A., et al. (2022). Intratumoral mregDC and CXCL13 T helper niches enable local differentiation of CD8 T cells following PD-1 blockade. bioRxiv 2022.06.22.497216
4. Yao, Z, et al. (2023). A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. bioRxiv 2023.03.06.531121.
