Creative Proteomics' spatial multi-omics solutions deliver end-to-end spatial transcriptomics, proteomics, and metabolomics—combined with expert sample handling and integrated bioinformatics—to produce high-resolution spatial maps and actionable biomarkers that let researchers and pharma pinpoint cellular niches, uncover mechanisms, and accelerate target/biomarker decisions.
What is Spatial Multi-Omics
Spatial multi-omics integrates spatially resolved genome, transcriptome, proteome, and metabolome measurements to map molecular activity directly onto tissue architecture. By preserving spatial context, these technologies reveal how cell states, molecular pathways, and microenvironments vary across anatomical structures and pathological lesions. Unlike bulk or dissociated single-cell techniques, spatial multi-omics preserves spatial coordinates and morphological features, enabling: (1) correlation of molecular phenotypes with histology; (2) identification of micro-niches (e.g., immune cell clusters at tumor margins); and (3) inference of signaling and metabolic flux across tissue compartments. These layers trace information flow from genes → transcripts → proteins → metabolites in the native spatial context.
For biomedical researchers, pharma R&D and contract research organizations, spatial multi-omics moves beyond dissociated single-cell profiling to provide mechanistic insight into cell-cell interactions, microenvironmental niches, and localized molecular phenotypes that drive disease, development, and drug response.
Fig 1. The frontier of transitioning from monoomics omics to multiomics: techniques, data analysis pipeline, and applications (Huan C, et al., 2025).
Technical Workflow for Spatial Multi-Omics Solutions
Technical Advantages of Our Spatial Multi-Omics Solutions
- Preserves anatomical context: Molecular signals are interpreted in the native tissue frame, enabling direct linkage to histopathology.
- Identifies microenvironmental niches: Resolve immune infiltration, stromal patterns, and tumor–host interfaces essential for translational biology and biomarker discovery.
- Multi-layer mechanistic insight: Correlate gene expression with protein abundance and local metabolite pools to infer active pathways and post-transcriptional regulation.
- Single-slice multi-omic potential: When feasible, multiple omics outputs from the same section or closely registered sections reduce sample consumption and preserve rare material.
- Scalable and customizable: Workflows can be tuned for high-resolution discovery or higher-throughput screening depending on project needs and budgets.
Multi-Omics Analysis Service We Provide
- Spatial transcriptomics: Support for capture-based barcoded slides and high-plex in situ methods; full library prep and sequencing.
- Spatial proteomics: Multiplexed antibody panels, imaging mass cytometry, and optimized antigen retrieval for FFPE.
- Spatial metabolomics: MALDI-MSI/DESI-MS pipelines for mapping metabolites and lipids at tissue-scale.
- Integrated Spatial Multi-Omics: Combined analyses (transcriptomics + proteomics + metabolomics) from a single section to achieve a holistic understanding of tissue microenvironments.
Data Analysis and Interpretation
- Preprocessing and QC: Spot/feature filtering, normalization, and batch correction tailored to each modality.
- Cellular deconvolution and annotation: Integration with single-cell reference atlases to assign cell types to spatial locations.
- Spatial statistics and neighborhood analysis: Detection of statistically significant spatial domains, co-localization, and spatial autocorrelation metrics.
- Pathway and network analysis: Integrate transcript, protein, and metabolite signals to prioritize pathways and potential therapeutic targets.
- Machine learning & predictive modeling: Spatially aware models for classification, biomarker discovery, and response prediction.
Application for Spatial Multi-Omics
- Organ spatial heterogeneity: Define regional molecular programs across complex tissues (e.g., liver zonation, kidney cortex vs medulla).
- Tumor immune microenvironment: Map immune cell states, checkpoint expression, and metabolic niches that influence response to immunotherapy.
- Tissue atlas and in situ reference maps: Build high-resolution atlases that link histology to molecular phenotype.
- Disease progression and pathology: Localize disease-associated molecular signatures across stages and identify early mechanistic changes.
- Developmental biology: Chart spatial programs during organogenesis or differentiation.
Sample Requirements (Recommended)
| Sample type | Preferred omics | Embedding / prep | Size / quantity | Handling & shipping notes |
|---|---|---|---|---|
| Fresh tissue (surgical) | Spatial proteomics, metabolomics, transcriptomics | OCT / CMC preferred; FFPE acceptable for proteomics | Cross-section ≤ 2 cm × 6 cm (proteomics); ≤ 1.5 cm × 4.5 cm (metabolomics) | Remove excess blood; blot dry; orient and mark; individual embed blocks; ship on dry ice or cold pack; avoid freeze–thaw. |
| FFPE tissue block | Spatial proteomics, spatial transcriptomics (FFPE-compatible) | Standard formalin fixation and paraffin embedding | Block cross-section ≤ 1.5 cm × 4.5 cm | Record orientation; multiple blocks pack separately; ship at ambient with cushioning or per courier guidance. |
| FFPE slides | Spatial proteomics, spatial transcriptomics (platform dependent) | Slides cut at recommended thickness | Proteomics: ≥ 3 slides at 10 μm; Transcriptomics: 10–12 slides at 5 μm | Use anti-detachment slides for transcript assays; bake slides (e.g., 42 °C for 3 h) if requested; avoid mechanical shock; ship in rigid slide mailers with ice packs if required. |
Why Choose Creative Proteomics
- Comprehensive one-stop service: From sample receipt and serial sectioning to multimodal acquisition and integrated bioinformatics.
- Platform diversity and flexibility: Compatibility with leading spatial transcriptomics, imaging mass cytometry, and MSI platforms to match resolution and throughput demands.
- Deep sample handling expertise: Extensive experience with animal, plant, and human tissues ensures preservation of spatial integrity and data quality.
- Advanced bioinformatics integration: Spatially aware fusion of transcript, protein, and metabolite layers with industry-grade statistical rigor.
- Actionable deliverables: Publication-quality figures, interactive maps, and reproducible data packages that feed downstream validation or regulatory pipelines.
References
- Liu X, et al. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. Journal of Hematology & Oncology, 2024, 17(1): 72.
- Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome medicine, 2024, 16(1): 14.
- Huan C, et al. Spatially resolved multiomics: data analysis from monoomics to multiomics. BME frontiers, 2025, 6: 0084.
Spatial multi-omic map of human myocardial infarction
Journal: Nature
Impact factor: 48.5
Published: 2022
DOI: 10.1038/s41586-022-05060-x
Backgrounds
Myocardial infarction (MI) triggers complex tissue remodeling involving multiple cell types and states across distinct anatomical zones. A high-resolution, multimodal map that links transcriptional, epigenetic and spatial organization in human MI tissue was lacking; the study aimed to create an integrated spatial multi-omic reference to reveal injury, repair and remodeling programs in human hearts.
Materials & Methods
- Human samples & spatial design: Myocardial samples were collected from defined physiological/lesion zones (e.g., ischemic zone, fibrotic zone, border zone, remote/control) from MI patients and controls.
- Modalities generated: Single-nucleus RNA-seq (snRNA-seq), single-nucleus ATAC-seq (snATAC-seq) for chromatin accessibility, and spatial transcriptomics (barcoded capture slides) on tissue sections; histology (H&E/IHC) was used for registration. Processed datasets were made available (cellxgene / Zenodo).
- Computational integration: The study applied cross-modal integration methods to map snRNA/snATAC cell states onto spatial transcriptomics spots, performed spatial niche and neighbourhood modeling, TF/regulatory analyses, and pathway enrichment to link cell states with spatial context.
Results
- High-resolution cell atlas: Integration of snRNA-seq and snATAC-seq with spatial transcriptomics refined cardiac cell-type and cell-state definitions across MI and control tissue, revealing disease-associated states not apparent from bulk assays.
- Spatial niches and tissue structures: The study identified distinct spatial niches (e.g., myogenic vs. fibroblast/myeloid–enriched areas) and characteristic tissue structures associated with injury, repair, and remodelling. Quantitative neighborhood models showed how abundances of specific cell types in local and extended neighborhoods predicted pathway activities (hypoxia, WNT, JAK-STAT, NF-κB).
- Disease-specific cell states: The authors discovered and validated disease-specific cell states among cardiomyocytes, fibroblasts, and myeloid cells (for example, SPP1⁺ macrophage states and multiple fibroblast subtypes) and mapped their spatial co-localizations and inferred interactions.
- Genetic associations & regulatory features: GWAS-SNP enrichment analyses and integrated snATAC data linked cell types to genetic risk loci and transcription factor activity, providing mechanistic hypotheses for cell-type–specific regulation in post-MI remodelling.
Fig 2. Spatial multi-omic profiling of human myocardial infarction.
Fig 3. Characterization of tissue organization using spatial transcriptomics data.
Conclusions
This study provides an integrative spatial multi-omic reference of the human heart after myocardial infarction. By fusing single-nucleus transcriptomes and chromatin accessibility with spatial gene expression, the work elucidates spatially organized cell states, molecular programs, and inter-cellular dependencies that underlie injury and repair. The atlas serves as a public resource and a mechanistic foundation for future translational and therapeutic investigations into cardiac remodelling.
What are the key technological innovations enabling spatial integration?
DBiT-seq delivers dual-coordinate spatial barcodes via microchannels to co-map transcripts and proteins. MiP-seq (multi-omics in situ pairwise sequencing) integrates DNA, RNA, and proteins at subcellular resolution using padlocked probes and dual barcodes, reducing sequencing cycles and improving accuracy
How does spatial resolution impact biological insight?
Higher spatial resolution enables single-cell or even subcellular insights, revealing fine-grained interactions such as immune cell infiltration, tumor heterogeneity, and neuron connectivity. Lower-resolution approaches may be more cost-effective but less detailed.
What are the limitations of spatial multi-omics?
Limitations include high cost, complex data integration, limited throughput for certain platforms, and technical biases in capturing specific molecules (e.g., low-abundance proteins or metabolites).
Demo: High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue
Fig 4. Spatial Multi-Omics Mapping of Whole Mouse Embryos (Liu Y, et al., 2020).
Fig 5. Spatial Multi-Omics Mapping of an Embryonic Mouse Brain (Liu Y, et al., 2020).