High-plex Multiomic Analysis in FFPE at Subcellular Level by Spatial Molecular Imaging

Abstract

Spatial Molecular Imager (SMI) is an automated microscope imaging system with microfluidic reagent cycling, for high-plex, spatial in-situ detection of multiomic targets (RNA and protein) on FFPE and other intact samples with subcellular resolution. The key attributes of the CosMxTM SMI platform (NanoString®, Seattle, WA) include: 1) high-plex and high-sensitivity imaging chemistry that works for both RNA and protein detection, 2) three-dimensional subcellular-resolution image analysis with a target localization accuracy of ∼50 nm in the XY plane, 3) large Hamming-distance encoding scheme with low error rate (0.0092 false calls per cell per gene) and low background (∼ 0.04 counts per cell per gene), 4) high-throughput (up to 1 million cells per sample, four samples per run), 5) antibody-based cell segmentation methods, and 6) compatibility with formalin-fixed, paraffin-embedded (FFPE) samples.In this study, 980 RNAs and 80 proteins were measured at subcellular resolution in FFPE cultured cell pellets, as well as FFPE tissues from biobanked samples of non-small cell lung cancer (NSCLC) and breast cancer. Cross-platform analysis using 16 cancer cell lines validated high-correlation (R2 ∼0.77) and high sensitivity (∼1.44 FPKM/TPM; roughly 1 to 2 copies of RNA per cell) when compared to RNA-seq. Real-world archived NSCLC FFPE tumor sections revealed greater than 94% cell detection efficiency for RNA, despite the low RNA quality QV200 20% to the medium quality 65%. The accuracy of protein expression measurements was independent of the level of multiplexing, as demonstrated by the linear behavior of nested multiplexing panels (R2 > 0.9). At 980-plex RNA detection, data analysis allowed identification of over 18 distinct cell types, at least 10 unique tumor microenvironment neighborhoods, and over 100 pairwise ligand-receptor interactions. Data from 8 NSCLC samples comprising over 800,000 single cells and ∼260 million transcripts are released into the public domain (www.nanostring.com) to allow for extended data analysis by the entire spatial biology research community.

Molecular analysis of vascular gene expression

“…For example, NanoString’s GeoMX Digital Spatial Profiler can detect RNA and protein within the same region of interest, 92 and has been recently used to generate a spatial atlas of lung samples from patients with COVID‐19 73,74 . The second‐generation CosMX Spatial Molecular Imager expands on this technique by providing (sub)single cell resolution 93 . Multiplexed error‐robust fluorescence in situ hybridization also provides direct detection of predefined RNA targets 94 and is commercially available in the Vizgen’s MERSCOPE platform.…”

Community assessment of methods to deconvolve cellular composition from bulk gene expression

“…Newly emerging spatial transcriptomics technologies are able to measure expression for thousands of genes, are applicable to formalin fixed paraffin embedded as well as fresh frozen tissue, and are rapidly advancing to single-cell resolution. [16][17][18] Beyond these experimental and analytical challenges, the number of clinically annotated single cell-based samples is dwarfed by those derived from bulk genomic and transcriptomic data. Consequently, to leverage the large databases of clinically-annotated samples with bulk data such as The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), methods for computational "deconvolution" of bulk tissue transcriptomic profiles have been developed to estimate the relative amounts of specific cell types in an admixture leveraging both RNA-seq and legacy microarray platforms.…”

“…Newly emerging spatial transcriptomics technologies are able to measure expression for thousands of genes, are applicable to formalin fixed paraffin embedded as well as fresh frozen tissue, and are rapidly advancing to single-cell resolution. 16–18…”

Editorial: Defining the Spatial Organization of Immune Responses to Cancer and Viruses In Situ

“…Multiplexed tissue imaging methods—including imaging mass cytometry (IMC) ( 1 ), multiplex ion beam imaging (MIBI) ( 2 , 3 ), multiplex immunohistochemistry (mIHC) ( 4 ), CO-Detection by indEXing (CODEX) ( 5 , 6 ), cyclic immunofluorescence (CyCIF) ( 7 , 8 ), and spatial transcriptomics ( 9 – 12 )—allow for the simultaneous detection of more than 50 proteins and 1000s of transcripts, thereby empowering the interrogation of spatial organization within tissues. Importantly, these technologies retain the native tissue context of each individual cell, while enabling deep phenotypical and functional interrogation.…”

“…Orthogonal interrogation with synergistic tools, such as RNA quantification methods including spatial transcriptomics, single-cell RNA sequencing, and other advanced techniques are needed to precisely define the genetic and protein topographies of human tissues. The development of multi-omic measurements in situ is a key fundamental advancement in this regard, including the quantification of metabolic states ( 21 ), nucleic acids and proteins ( 12 , 23 , 25 ), clonality ( 26 ), and epigenetic states ( 27 ). The continued advancement of computational methods is also vital for multi-scalar inferential analysis across different measurement modalities ( 28 ).…”