NanoString CosMx SMI Spatial Transcriptomics Service
CD Genomics offers an end-to-end NanoString CosMx SMI (Spatial Molecular Imager) Spatial Transcriptomics Service — a single-cell-resolution, imaging-based platform that detects up to 6,000 RNA targets and 64 protein markers (with whole transcriptome WTx assay covering 18,000 genes available) directly in intact tissue sections. Built on cyclic fluorescent in situ hybridization (FISH) chemistry with AI-powered multi-modal cell segmentation, CosMx SMI delivers quantitative, spatially resolved transcriptomic maps at subcellular resolution — without reverse transcription or PCR amplification, eliminating amplification bias. Compatible with FFPE, fresh frozen, tissue microarrays (TMA), organoids, and cell smears.
- Up to 6,000 RNA + 64 protein per section; whole transcriptome WTx (18,000 genes) available
- Direct RNA counting — no reverse transcription, no PCR amplification
- FFPE, fresh frozen, TMA, organoids, and cell smears
- Multi-modal AI cell segmentation combining nuclear stain, membrane stain, and transcript distribution
CosMx SMI — Technology Overview
CosMx SMI (Spatial Molecular Imager, originally developed by NanoString and now under Bruker Spatial Biology) is an imaging-based spatial transcriptomics platform that uses cyclic fluorescent in situ hybridization to detect RNA and protein targets at single-cell resolution directly in tissue sections.
Cyclic FISH chemistry. Each RNA target is detected by a target-specific probe containing a unique fluorescent barcode readout domain. Multiple rounds of fluorescent reporter probe hybridization, imaging, and UV-induced fluorophore cleavage are performed. For a 1,000-gene panel, a complete run involves 16 cycles with 4 fluorescence channels per cycle. This cyclic approach decouples detection plex from the number of available fluorescence channels, enabling up to 6,000 RNA targets and 64 protein markers (measured via DNA-barcoded antibodies) on a single tissue section. Critically, CosMx chemistry directly counts individual RNA transcripts in situ — no reverse transcription, no cDNA synthesis, and no PCR amplification — eliminating amplification-associated bias and enabling accurate quantification.
Subcellular resolution and imaging. The system uses a high-numerical-aperture objective (≥1.1 NA) at ≥22.77× magnification to achieve ≤100 nm imaging resolution across a 0.51 × 0.51 mm field of view (FOV). Up to 4 samples can be processed per run, with the system capable of scanning over 100 mm² of tissue area. The UV-induced fluorophore cleavage module reduces background noise between cycles.
Multi-modal cell segmentation. Accurate cell boundary detection is performed by a three-component pipeline: (1) nuclear (DAPI) and membrane marker staining to delineate cell boundaries, (2) an AI-augmented machine-learning algorithm for automated boundary detection, and (3) transcript-distribution-based refinement that adjusts boundaries based on the spatial location of detected transcripts. This ensures high-quality single-cell feature extraction even in densely packed tissue regions.
Panels and plex options. Pre-designed panels are available for human immuno-oncology, universal cell characterization, mouse neuroscience, and custom gene sets. The CosMx 6K Discovery Panel covers 6,000 targets. For whole-transcriptome-level spatial coverage, the CosMx WTx assay expands detection to approximately 18,000 human protein-coding genes. Protein co-detection panels (up to 64 markers) can be run on the same tissue section. Custom panel design is available for focused gene sets.
Data analysis and the AtoMx platform. Raw imaging data is processed through the AtoMx Spatial Informatics Platform, which performs barcode decoding, transcript assignment to spatial coordinates, cell segmentation, and count matrix generation. Output is compatible with standard single-cell and spatial analysis ecosystems (Seurat, Scanpy, Giotto, Squidpy).
CosMx SMI Workflow
CD Genomics manages the complete CosMx SMI workflow.
- Study design and panel selection
We align on your biological questions, tissue type, and target pathways. The appropriate CosMx panel (pre-designed 6K Discovery, WTx, or custom) and optional protein co-detection markers are selected. ROI strategy is planned based on tissue architecture and study goals.
- Sample preparation and QC
FFPE sections (4–5 μm), fresh frozen sections (5–10 μm), TMA sections, or organoid preparations are mounted onto CosMx-compatible slides. Section quality is assessed for tissue integrity, adherence, and autofluorescence. Tissue placement must fit within the designated slide zone.
- Probe hybridization and morphology staining
Target-specific ISH probes (RNA) and/or DNA-barcoded antibodies (protein) are hybridized to the tissue section. Simultaneously, nuclear (DAPI) and membrane marker stains are applied to provide the morphological reference needed for downstream cell segmentation.
- Multi-cycle imaging and decoding
The slide undergoes multiple rounds of fluorescent reporter hybridization, imaging, and UV cleavage inside the CosMx SMI instrument. Each cycle captures a subset of the total barcode library. Barcodes are decoded and transcripts assigned to subcellular spatial coordinates.
- Cell segmentation and data analysis
The multi-modal segmentation pipeline delineates single cells. A gene–cell expression matrix with spatial coordinates is generated. Spatial data analysis follows our customized bioinformatics workflow. A comprehensive report with publication-ready figures is delivered.
CosMx SMI Sample Requirements
To ensure optimal data quality, please prepare samples according to the following guidelines:
| Sample type | Requirement |
|---|---|
| FFPE tissue | Standard FFPE blocks or pre-cut sections (4–5 μm) on CosMx-compatible slides; CosMx ISH chemistry tolerates degraded RNA typical of FFPE |
| Fresh frozen tissue | OCT-embedded, cryosectioned (5–10 μm) onto CosMx-compatible slides; −80°C storage |
| Tissue microarray (TMA) | Standard TMA sections on CosMx-compatible slides |
| Organoids / cell smears | Fixed and prepared according to project-specific protocols |
Bioinformatics & Spatial Data Analysis
Our spatial bioinformatics pipeline transforms CosMx imaging data into biological insight.
Standard analysis
- Raw data QC: transcript count and spatial distribution metrics, per-FOV and per-cell QC filtering
- Dimensionality reduction and spatial clustering: PCA, UMAP, and spatially-aware clustering
- Cell-type annotation: semi-supervised clustering combined with spatial context and marker gene validation
- Spatial feature expression maps: visualization of individual gene expression across tissue coordinates
- Cell composition and differential expression: per-region cell-type proportions and spatially contextualized differential expression
Advanced analysis
- Spatial neighborhood and niche analysis: identification of recurrent multicellular neighborhoods using community detection and spatial enrichment methods
- Cell–cell proximity and colocalization analysis: nearest-neighbor distance distributions, pairwise spatial enrichment scores
- Ligand–receptor spatial interaction: spatially informed cell–cell communication inference
- Tissue region annotation: classification of tumor core, invasive margin, stroma, TLS, and other histologically defined regions
- Spatial biomarker discovery: identification of spatially restricted gene expression signatures and cell-state markers
- Multi-sample comparative spatial analysis: cross-condition comparison of spatial features including niche composition and cell-type spatial distributions
Deliverables
Our CosMx SMI spatial transcriptomics service provides imaging data, expression matrices, and structured analysis reports that can be used directly for interpretation and publication.
- Raw and processed imaging data
- Decoded transcript coordinates, cell segmentation masks, per-cell feature tables
- Gene–cell expression matrix
- Count matrices with spatial coordinates (.h5ad, .rds, .csv)
- QC report
- Per-FOV and per-cell QC metrics, segmentation quality assessment, transcript density maps
- Spatial analysis report
- Spatial clustering, cell-type annotation with spatial validation, niche/neighborhood analysis, publication-ready spatial maps
- Publication-ready figures
- High-resolution spatial expression maps, cell-type spatial distributions, niche composition charts (vector PDF/SVG and raster PNG/TIFF)
- Reproducible analysis code
- Fully documented scripts with session information
CosMx SMI Applications
CosMx SMI single-cell spatial transcriptomics is suited to studies where resolving individual cell identities and spatial relationships within intact tissue is essential.
Tumor microenvironment characterization
Map tumor, immune, stromal, and vascular cells at single-cell resolution with spatial context. Distinguish immune-excluded, immune-inflamed, and immune-desert regions; characterize tertiary lymphoid structures; quantify spatial colocalization patterns. See also: Tumor Microenvironment Solutions.
FFPE cohort and archival studies
CosMx's ISH chemistry directly hybridizes to RNA without requiring cDNA synthesis or amplification, making it robust to the RNA degradation typical of FFPE specimens. This supports spatial analysis of archival clinical cohorts, retrospective studies, and multi-center consortia. Data quality has been demonstrated to remain stable across variation in fixation protocols.
Immuno-oncology and biomarker discovery
Spatially resolve immune checkpoint expression and immunosuppressive cell states. Combine RNA and protein co-detection for multi-analyte spatial profiling in a single section. Identify spatially restricted biomarkers.
Neuroscience
Map neuronal and glial cell types, resolve layer-specific gene expression, and characterize disease-associated spatial reorganization in brain tissue.
Inflammatory and autoimmune disease
Characterize immune cell spatial organization in IBD, rheumatoid arthritis, psoriasis, and other chronic inflammatory conditions.
Case Study: Spatial Immune Microenvironment in Atherosclerotic Coronary Artery
Source: Campos J, McMurray JL, Certo M, et al., EMBO Molecular Medicine, 2025
Background
Atherosclerosis is characterized by the accumulation of lipids and immune cells in the arterial wall, driving disease progression from early fatty streaks to advanced fibroatheromatous plaques. Although innate and adaptive immunity are known to participate in atherogenesis, the spatial organization of immune cell populations within the arterial wall — and how these spatial relationships change with disease severity — remains poorly understood. Single-cell spatial mapping is essential for identifying immune-evasive niches and adaptive lymphoid structures that may influence plaque stability.
Methods
The study applied CosMx SMI (970-plex RNA panel, including 950 core probes plus 20 custom targets) to 5 μm FFPE sections of human coronary arteries from 3 donors (9 cross-sectional segments), spanning near-normal to advanced atherosclerotic lesions. A total of 235 fields of view were captured, and over 11 cell types were identified through supervised classification. Spatial cell neighborhoods were defined based on 20 μm cell proximity using k-means clustering. GeoMx DSP (whole transcriptome panel) was also applied to serial sections for complementary ROI-level profiling.
Results
CosMx SMI identified 11 distinct cell types and revealed that advanced lesions were enriched in adaptive immune cells — including B cells, CD4⁺ memory/helper T cells, and CD8⁺ cytotoxic T cells — alongside vascular smooth muscle cell phenotypic modulation. A key discovery was the identification of artery tertiary lymphoid organ (ATLO)–like structures in the adventitia of severe lesions, composed primarily of B cells and CD4⁺ memory/helper T cells. Spatial neighborhood analysis defined 10 cell neighborhoods; near-normal arteries were dominated by myofibroblast and endothelial neighborhoods, while advanced lesions shifted toward macrophage- and B cell–enriched neighborhoods.
Conclusion
This study demonstrates CosMx SMI's capability to resolve spatially organized immune microenvironments in FFPE cardiovascular tissue at single-cell resolution. The identification of ATLO-like structures, disease-stage–dependent neighborhood remodeling, and spatially restricted gene expression illustrates the platform's value for spatial biomarker discovery and tissue microenvironment characterization in archival clinical specimens.
Adapted from Campos et al., EMBO Molecular Medicine, 2025, doi:10.1038/s44321-025-00280-w, Figure 5E.
CosMx SMI vs Xenium, GeoMx, and Visium HD
Different spatial transcriptomics platforms suit different research needs. The table below compares key technical dimensions across four commonly used platforms.
| Dimension | CosMx SMI | Xenium (10x) | GeoMx DSP | Visium HD (10x) |
|---|---|---|---|---|
| Method | Imaging (cyclic FISH) | Imaging (padlock probe) | ROI-based, NGS readout | Sequencing (spatial barcodes) |
| Resolution | Single-cell, subcellular (≤100 nm) | Single-cell, subcellular | ROI (not single-cell) | 2 μm bins |
| RNA plex | Up to 6,000 (targeted); WTx 18,000 | Up to 5,000 (targeted) | Whole transcriptome (~18,000) | Whole transcriptome |
| Protein | Up to 64 markers, same section | Limited | Up to ~100 (separate) | N/A |
| Amplification | Direct RNA counting (amplification-free) | Padlock probe ligation + RCA | cDNA synthesis + PCR | cDNA synthesis + PCR |
| FFPE | ✅ Robust to RNA degradation | ✅ Validated | ✅ Core platform | ✅ Validated |
| Best for | High-plex single-cell spatial mapping in FFPE; multi-analyte (RNA+protein) co-detection; multi-center cohort studies | Large continuous tissue areas; 10x ecosystem integration | Whole transcriptome from user-selected ROIs; high throughput | Unbiased whole transcriptome discovery; sequencing-based integration |
How to choose. CosMx SMI is recommended when you need single-cell resolution with high-plex RNA and protein co-detection in FFPE tissue, particularly for multi-center cohort studies. Its amplification-free direct RNA counting chemistry provides robust performance on archival specimens with variable RNA quality. See also: 10x Xenium, 10x Visium HD, and our spatial transcriptomics overview.
Frequently Asked Questions (FAQ)
References
- Campos J, McMurray JL, Certo M, et al. "Spatial transcriptomics elucidates localized immune responses in atherosclerotic coronary artery." EMBO Molecular Medicine, vol. 17, 2025, pp. 2827–2846.
- Wang H, et al. "Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues." Nature Communications, 2025.
