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What will you discover?

CosMx Spatial Molecular Imager (available 2nd half of 2022) represents the next evolution in spatial biology. CosMx SMI is a true multiomic, single-cell solution that enables researchers to investigate morphologically intact tissues at unprecedented resolution. ​

​We have generated this open-source dataset on non-small-cell lung cancer (NSCLC) tissue which represents the largest single-cell and subcellular analysis on Formalin-Fixed Paraffin-Embedded (FFPE) samples to date and highlights the power of CosMx SMI. CosMx combines superior sensitivity, robust cell identification and a broad target panel to enable researchers to explore the cell atlas, cell-cell interactions and phenotypes of the tissue microenvironment.​

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Data Summary

This data set contains 8 different samples from 5 NSCLC tissues

  • Sex: 3 female, 2 male
  • Race: White
  • Age: 60+
  • Grade: G1-G3
  • Stage: 4 IIIA, 1 IIB
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How it Works

This dataset was generated using a 960-plex gene expression assay. The assay content focuses on cell type deconvolution of multiple tissue types and key aspects of cell signaling and cell state, with a focus on ligands and receptors that enable communication between cells. ​

Discover and map cell types

SMI data creates a comprehensive spatial cell atlas of NSCLC tissue by defining the subcellular expression map of 960 genes, identifying 18 different cell types, including four sub-types of T-cells, and mapping cell types in eight FFPE tissue sections.  ​

Mapping FFPE sections is easy with the spatial molecular imager

Cell type map of NSCLC tissues. The map displays 135,707 cells across ~20 mm2 tissue section. Color denotes cell type. (A) UMAP projection. (B) Spatially resolved cell-type map. 


Phenotyping of the tissue microenvironment

Spatial location of cell types enables characterization of the tissue microenvironment. Based on analysis of each cell’s 200 closest neighbors, SMI data partitioned NSCLC tissues into ten distinct neighborhood clusters or niches. Some of these neighborhoods are specific to a single tissue, while others are shared across all five tissues. 

 SMI data partitioned FFPE tissue sections into multiple distinct clusters

Organizational map of NSCLC tissue. Color denotes neighborhood cluster, “niche”. (A) UMAP projection. (B) Spatially resolved neighborhood cluster map. 


In-depth characterization of the tumor microenvironment

SMI data allows tumor characterizations not just by gene expression, cell types, and spatial features, like niches, but also by the tendency of immune cells to invade into the tumor. This data indicates, as an example, macrophages in Lung 6 tissue are primarily surrounded by non-tumor cells, while mast cells are more likely to be surrounded by tumor cells.

From FFPE section to single cells abundance

High-level tumor characteristics. (A) Abundance of each cell type within each tissue. (B) Abundance of each niche within each tissue. (C) Invasiveness of each cell type within each tumor. Cells are scored for the frequency of tumor cells within their 100 nearest neighbors. Shapes show the density of this score within each cell type. 


Differential expression pattern of a cell type based on spatial location

The data can be used to analyze changes in gene expression patterns of any given cell type based on spatial context as shown here with macrophages. For example, macrophages express more SPP1 in the tumor interior and tumor-stroma boundary than they do in more immune-rich settings. 

changes in gene expression patterns of any given cell type based on spatial context on a FFPE section

Gene expression of macrophages based on spatial context. (A) Heat map for expression of 960 genes across all niches. (B) Spatial map for expression of SPP1. Color shows SPP1 expression level.


Ligand receptor analysis

SMI enables analysis for the enrichment of pairwise ligand-receptor expression between interacting cell types. The SMI RNA panel has 100 canonical ligand-receptor partners. Of these, 16 were significantly enriched at the interface between tumor cells and T cells. Interestingly one of these pairs, the immune checkpoint PD-L1/PD-1 (CD74/PDCD1), exhibited a high degree of variation in Tumor to T cell co-expression.

Spatial molecular imager shows paired ligand-receptor expression between tumor and T cells

Paired ligand-receptor expression between interacting tumor and T-cells. (A) 100 unique ligand-receptor pairs are included in the CosMx 1000 plex panel. (B) 16 ligand-receptor pairs exhibited spatial significance in lung cancer tumors. 


Detect with Confidence

Single-cell in situ data was generated from three serial sections of the “Lung 5” sample. While these three FFPE sections include different cells, they capture the same regions of the tumor microenvironment. The analysis of these three replicates shows high correlation between each section, indicating a high level of reproducibility.

three FFPE sections include different cells, capturing the same regions of the tumor microenvironment. Here we demonstrate reproducibility.

SMI generates highly reproducible data from 3 replicates. For each replicate, total transcripts of each gene were recorded and compared.  

Technology Publication:
High-Plex Multiomic Analysis in FFPE Tissue at Single-Cellular and Subcellular Resolution by Spatial Molecular Imaging

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