Spatial Molecular Imaging Technology Overview
Helping Your Research
Answering any key tissue biology question requires comprehensive understanding of the cell types that compose it, their functions and the relationship among them. The current single-cell analysis technologies are applied to dissociated cells, which lose valuable information associated with cells anatomical and spatial contexts. NanoString’s Spatial Molecular Imaging technology enables the analysis of hundreds to thousands of RNAs or proteins directly from single cells, down to subcellular resolution, within morphologically intact tissue samples. The Spatial Molecular Imaging combines the power of high-plex profiling with high-resolution imaging and allows researchers to visualize and quantify targeted protein and gene expression on tissue slices.
The Spatial Molecular Imaging technology has compelling performance metrics, including:
- Large Panel: pre-designed panels with up to 1,000-plex and the option to customize
- High Sensitivity: accurately detect low copy number genes
- High resolution: subcellular with 3-Dimensional resolution
- Any analyte analysis: true spatial-omic capabilities, enabling analysis of both RNA and protein simultaneously on same tissue section
- Any sample type analysis: Formalin-Fixed Paraffin-Embedded, fresh frozen, and organoid
1,000-plex RNA Expression on FFPE
High Sensitivity with Very Low ERCC Negative Control
3D Mapping with Subcellular Resolution
How it Works
The Spatial Molecular Imaging technology is being developed as an integrated system with mature cyclic in situ hybridization chemistry, ultra high-resolution imaging readout instrument, and interactive data analysis and visualization software.
Sample preparation involves standard ISH (in situ hybridization) processing steps and allows direct use of pathology lab-standard glass slides without any tissue expansion or cleaning and does not require any cDNA synthesis or amplification.
The Spatial Molecular Imaging is based on highly multiplexed in-situ hybridization chemistry and direct single molecule imaging and counting. RNA or protein targets in individual cells are identified through hybridization of targets with specific probes or antibodies labelled with unique barcode system, followed by readout of barcode with cyclic rounds of fluorescently labeled reporter probes imaging. Each RNA or protein appears as a single spot in cells and digitally quantified by counting the number of imaged spots. The Spatial Molecular Imaging technology involves no reverse transcription or amplification or enzyme that provides high detection efficiency and unbiased quantification.
Easy Sample Preparation, Compatible with Any Sample Type
Automated Cyclic in situ Hybridization Chemistry
Fully Validated Image Processing & Analysis Pipeline – Acquisition to Target Identification
In-situ visualization and measurement of tumor infiltrating TCR clones on intact FFPE renal cell carcinoma (RCC) tissue using spatial molecular imager – AGBT 2021
Highly sensitive transcriptomic-based pooled CRISPR screening on 1 million cells enabled by spatial molecular imager and GeoMx Digital Spatial Profiler (DSP) – AGBT 2021
In-situ measurement of gene expression on intact FFPE tissue with subcellular resolution using spatial molecular imager powered by Hyb & Seq chemistry – AGBT 2021
Mapping cell type, cell state, and cell-cell interactions with 1000-plex single cell gene expression assay using spatial molecular imaging – AGBT 2021
Unsupervised discovery of tissue substructures using spatial molecular imaging of gene expression – AGBT 2021
Reference-based cell type classification using spatial molecular imaging of gene expression – AGBT 2021
The Spatial Molecular Imaging is the most flexible and robust spatial biology solution for:
- Cell Atlasing: mapping known and unknown cell types in tissue
- Cell Characterization: understanding cells states, cellular pathway activities and their spatial location
- Cell-cell Interaction: how cell-cell interaction and biological process dysfunction cause disease
- Spatial Biomarkers: quantifying single-cell biomarkers with spatial context