NanoString Begins Commercial Shipments of CosMx Spatial Molecular Imager and AtoMx Spatial Informatics Platform
NanoString Technologies, Inc. (NASDAQ: NSTG), a leading provider of life science tools for discovery and translational research, today announced the first commercial shipment of the CosMx Spatial Molecular Imager (SMI) and the AtoMx Spatial Informatics Platform (SIP).
CosMx SMI enables high-resolution imaging of more than 1,000 RNA and over 64 protein analytes within morphologically intact whole tissue sections. The CosMx instrument allows researchers to visualize and quantify gene and protein expression at single cell and subcellular resolutions within both fresh frozen and formalin-fixed paraffin-embedded (FFPE) tissue samples. Using a multi-modality approach including protein imaging, CosMx SMI delivers best-in-class cell segmentation. With high-plex in situ analysis, researchers can perform cell typing, cell state, functional, and cell-cell interaction analyses in a single experiment.
The CosMx SMI serves scientists across the continuum of research using a tunable workflow that can prioritize either unbiased whole-slide imaging at high-plex for discovery or high-throughput biology-driven analysis for translational research. Researchers can choose from a wide variety of CosMx assays, including pre-defined panels, pre-defined panels combined with custom content, or fully customized assays supporting any species.
The AtoMx SIP is the first cloud-based informatics platform to provide the secure, scalable storage and analysis that spatial biology researchers need to drive their workflow from study design to peer-reviewed publication. AtoMx SIP is compatible with both the CosMx SMI platform and the GeoMx Digital Spatial Profiler (DSP), and stores data in a flexible 'data lakehouse’ structure which can be readily examined using artificial intelligence and machine learning approaches. A cloud-based platform obviates the need for laboratories to invest in their own costly informatics infrastructure and reduces spatial biology analysis compute times from days to hours. Users have the flexibility to apply a pre-defined data analysis pipeline, to customize these pipelines using their own code, and to access open-source tools developed by the bio-informatics community.
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