Life Science
Multimodal chemical tissue imaging & spatial biology
Integrate O-PTIR, Raman, and Fluorescence for comprehensive spatial biology—adding chemical context to spatial omics with perfect registration at submicron resolution
The power of integrated multimodal imaging
Multimodal O-PTIR imaging represents a paradigm shift in tissue analysis by seamlessly combining complementary spectroscopic techniques—infrared (O-PTIR), Raman, and fluorescence—into a single, integrated platform. This breakthrough enables researchers to extract comprehensive chemical, structural, and molecular information from tissue sections with perfect spatial registration and submicron resolution, all without moving the sample or changing objectives.
Traditional approaches require multiple instruments, separate measurements, and complex image registration—introducing artifacts and wasting precious time. The mIRage platform eliminates these challenges by delivering simultaneous IR+Raman from the exact same spot (~500 nm resolution) and co-located fluorescence imaging using the same optics. This inherent registration ensures that chemical images from brightfield, autofluorescence, and IR spectroscopy align perfectly, enabling direct correlation of label-free chemistry with morphology and fluorescence markers.
Unique Capability: The mIRage platform is the world’s first commercial system providing simultaneous submicron IR and Raman spectroscopy with co-located fluorescence imaging. No sample movement, no objective changes, no registration errors—just comprehensive multimodal data from your tissue section in minutes.
Why Multimodal O-PTIR Transforms Tissue Analysis
~500 nm
all modalities
Perfect
spatial registration
10’s min
label-free imaging
3 Modes
one platform
Mouse brain tissue section – multimodal, label-free chemical imaging
Label-free chemical imaging is essential in life sciences as it visualizes and characterizes biological samples without dyes, preserving their natural state and minimizing artifacts. It enables the direct identification of molecular composition, offering precise insights into biochemical processes with high specificity.
Shown here is a standard histological thin section of mouse brain, chemically imaged for key macromolecules and metabolites. Label free imaging in 10’s of minutes with high spatial resolution.
Initial brightfield (BF) and Autofluorescence (AutoFL) images are shown on the left hand side. All images, BF, AutoFL and IR chemical images are inherently perfectly spatially registered as there are no optics (objective) or sample movements between these images.
Medium FOV (672×672µm) at medium resolution, 2µm pixel size:
• Red: Cell nuclei highlighted by 1080/1140cm⁻¹ ratio (nucleic acid)
• Green: Creatine highlighted by 1404/1655cm⁻¹ ratio
• Blue: Lipid highlighted by 1738/1655cm⁻¹ ratio
Small FOV (200×200µm) at high resolution, 200nm pixel size: Zoomed region showing fine details of chemical distribution.
Bottom panel: Representative single point spectra (~400nm spot size) from each of these chemical images. Asterisks on spectra indicate the image ratio wavenumber positions described above. White circles on images mark spectral collection locations.
Perfect Registration: Because brightfield, autofluorescence, and IR chemical images are collected using the same objective without any sample or optics movement, all images are inherently and perfectly spatially registered. This eliminates alignment errors and enables confident correlation of morphology with chemistry.
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Discover how multimodal O-PTIR can provide the comprehensive chemical picture your spatial biology research demands—and how it perfectly complements your existing spatial omics workflows.
Multimodal Integration: Complementary Information
O-PTIR (Infrared)
- Protein (Amide I, II)
- Lipids (C-H, C=O)
- Nucleic acids (PO₂⁻)
- Carbohydrates
- Water content
Raman
- C-C stretching
- Aromatic compounds
- Carotenoids
- Double bonds
- Water insensitive
Fluorescence
- Targeted markers
- Autofluorescence
- Specific proteins
- Cell structures
- ROI identification
mIRage Platform
Simultaneous IR + Raman
Co-located Fluorescence
Perfect Spatial Registration
~500 nm Resolution (All Modes)
Synergistic Benefits
Comprehensive Chemistry
IR: Broad functional groups
Raman: Specific molecular bonds
Fluorescence: Targeted markers
Combined: Complete picture
No ambiguity
Enhanced Confidence
IR confirms Raman ID
Raman confirms IR ID
Fluorescence guides analysis
Cross-validation
Reduced false positives
Time & Cost Savings
One measurement session
One sample preparation
No registration needed
Higher productivity
Faster publication
Using fluorescence to localize O-PTIR measurements
An Alzheimer’s disease mouse model brain tissue section was stained with Amytracker 630 to highlight amyloid aggregates, AF488 to highlight proteins and DAPI for the nucleus.
In the figure to the right is shown a brightfield image, top left, of the stained sample. In the top right is the RGB composite fluorescence image, which highlights in red/orange the regions of amyloid aggregation. Note how some amyloid aggregates highlighted in the fluorescence image are not readily distinguishable in the brightfield image.
At the bottom is an averaged O-PTIR spectra, from the line profile indicated in the fluorescence image, with spectra averaged on (in blue) and off (in red) the aggregate. The average spectrum of the aggregate shows distinct spectral differences in the amide I band with a significant spectral feature at 1631cm⁻¹, typical of protein beta sheet structures.
This clearly demonstrates the utility of combining fluorescence imaging to highlight regions of amyloid aggregation, some of which cannot be readily seen in brightfield microscopy, with submicrometer O-PTIR spectroscopy which can then provide the molecular compositional information, in this case, being particularly sensitive to protein secondary structure, a characteristic strength of IR spectroscopy.
Fluorescence-Guided Workflow: Use fluorescence to rapidly identify regions of interest across large tissue areas, then apply high-resolution O-PTIR to chemically characterize these features with submicron precision. This strategy combines the speed and specificity of fluorescence with the chemical detail of IR spectroscopy.
Key applications of multimodal tissue imaging
Neurodegenerative Disease
Fluorescence highlights amyloid plaques and tau tangles, O-PTIR reveals β-sheet content and secondary structure, Raman identifies carotenoid associations—comprehensive pathology analysis.
Cancer Pathology
Combine immunofluorescence markers with label-free chemical imaging to correlate protein expression with tissue chemistry. Identify tumor margins, grade malignancy, assess treatment response.
Drug Penetration Studies
Use fluorescent drug analogs to map distribution, O-PTIR to quantify drug concentration via IR signatures, and Raman for formulation analysis—complete pharmacokinetic picture.
Metabolic Mapping
Track isotope-labeled metabolites (¹³C, ²H) with O-PTIR while using autofluorescence to identify cellular compartments. Correlate metabolic activity with tissue structure.
Connective Tissue Analysis
Combine collagen autofluorescence with polarization-sensitive O-PTIR for fiber orientation and Raman for collagen type identification. Complete structural characterization.
Microplastics in Tissue
Autofluorescence reveals plastic particles, O-PTIR provides polymer ID via IR fingerprint, Raman confirms composition—comprehensive microplastic characterization in biological samples.
Typical multimodal workflow
1. Brightfield
- Locate features
- Cell structure
- Tissue organization
No Movement
2. Fluorescence
- Target markers
- Autofluorescence
- Identify ROIs
No Movement
3. O-PTIR
- Protein structure
- Lipid content
- Nucleic acids
No Movement
4. Raman
(Simultaneous with O-PTIR)
- Carotenoids
- Aromatic rings
- Confirms IR ID
Same Time
Result
Comprehensive
Multimodal
Dataset
Perfect
Registration
- Morphology
- Fluorescence
- IR Chemistry
- Raman Chemistry
One Session
One Sample
Complete Picture
Why multimodal O-PTIR?
Technical Advantages
- Perfect Registration: All images inherently aligned—no optics or sample movement
- Simultaneous IR+Raman: Same spot, same time, same ~500 nm resolution
- Co-Located Fluorescence: Same objective, immediate correlation
- No Artifacts: Single measurement eliminates registration errors
- Time Efficient: Complete multimodal dataset in 10’s of minutes
- Sample Preservation: Non-destructive, label-free core measurements
- Flexible Configuration: Add Raman and/or fluorescence as needed
Scientific Impact
- Comprehensive Chemistry: IR + Raman cover full molecular landscape
- Cross-Validation: Confirm chemical ID with multiple techniques
- Reduced Ambiguity: Complementary information resolves uncertainty
- Targeted Analysis: Fluorescence guides high-resolution spectroscopy
- Discovery Potential: Reveal relationships invisible to single techniques
- Publication Quality: Multi-technique data strengthens findings
- Higher Confidence: Multiple orthogonal measurements validate results
Multimodal O-PTIR vs. separate instruments
| Feature | Multimodal O-PTIR (mIRage) | Separate Instruments |
|---|---|---|
| Spatial Registration | Perfect (inherent) | Manual (error-prone) |
| IR + Raman Collection | Simultaneous, same spot | Sequential, different spots |
| Spatial Resolution | ~500 nm (all modes) | Variable (10-30 µm IR) |
| Sample Movement | None required | Transfer between instruments |
| Objective Changes | None | Multiple |
| Total Time | 10's of minutes | Hours to days |
| Data Correlation | Immediate | Post-processing required |
| Sample Integrity | Preserved | Risk of damage/contamination |
Clinical & research applications
Alzheimer's Research
Fluorescence localizes amyloid plaques, O-PTIR reveals β-sheet content, Raman identifies carotenoid associations. Complete characterization of pathological features.
Cancer Diagnosis
Immunofluorescence marks tumor cells, O-PTIR characterizes tissue chemistry, Raman confirms molecular composition. Enhanced diagnostic accuracy.
Drug Development
Fluorescent drug tracking + label-free chemical quantification + formulation analysis = comprehensive pharmacokinetic understanding.
Bone & Cartilage
Collagen autofluorescence + polarization-sensitive O-PTIR + Raman type identification = complete structural and chemical characterization.
Microplastics
Autofluorescence detection + O-PTIR polymer ID + Raman confirmation = accurate identification of sub-micron particles in tissue.
Metabolomics
Isotope labeling (¹³C/²H) with O-PTIR + autofluorescence compartment ID + Raman metabolite fingerprinting = spatial metabolic mapping.
The field of spatial biology has revolutionized how we understand tissue organization by preserving the spatial context of molecular information. Spatial omics technologies—including spatial transcriptomics, spatial proteomics, and spatial genomics—map RNA, proteins, and DNA distributions while maintaining tissue architecture. However, spatial metabolomics and chemical characterization remain critical missing pieces in comprehensive spatial profiling.
Multimodal O-PTIR uniquely fills this gap by providing label-free spatial metabolomics and chemistry alongside transcriptomic and proteomic data. While spatial transcriptomics reveals “where genes are expressed” and spatial proteomics shows “where proteins are located,” O-PTIR answers “what is the chemical composition and metabolic state?” at the same spatial coordinates with submicron resolution.
Complementary to Spatial Omics: O-PTIR provides orthogonal chemical information that validates and extends spatial transcriptomic/proteomic findings. Map lipid distributions, protein secondary structures, metabolic heterogeneity, and tissue chemistry—all registered to the same tissue coordinates used by 10X Visium, GeoMx DSP, MERFISH, or other spatial omics platforms.
Multimodal O-PTIR for spatial biology & spatial omics
Adding Chemical Context to Spatial Biology Workflows
Spatial biology applications
Spatial Metabolic Heterogeneity
Map metabolic states across tumor microenvironments, identify metabolically distinct regions, correlate metabolic activity with gene expression patterns from spatial transcriptomics. Reveal metabolic gradients invisible to genomic techniques.
Tumor Microenvironment Characterization
Chemically characterize immune cell infiltration zones, identify lipid-rich tumor margins, map protein aggregation in necrotic cores, analyze extracellular matrix composition—all integrated with spatial transcriptomics data.
Co-Registration with Spatial Omics Platforms
Perfect integration with 10X Visium, GeoMx DSP, MERFISH, CODEX, and other platforms. Use same tissue sections or serial sections with shared coordinates for multi-platform spatial analysis and validation.
Spatial Tissue Architecture
Define tissue domains based on chemistry, correlate structural features with gene expression zones, map extracellular matrix composition across developmental or disease gradients. Reveal chemical organization principles.
Hypothesis-Driven Spatial Interrogation
Use spatial transcriptomics to identify regions of interest, then apply high-resolution O-PTIR to test chemical hypotheses. Validate metabolic predictions from gene expression. Close the loop from genes to metabolism.
Spatial Drug Distribution
Map drug penetration gradients in tumors, correlate with spatial gene expression responses, identify resistant vs. responsive zones based on chemistry and transcriptomics. Understand pharmacokinetic-pharmacodynamic relationships spatially.
Integrating O-PTIR into spatial biology workflows
Complete Spatial Multi-Omics Workflow
Tissue Sample Preparation
Serial sections or same section (for sequential analysis)
Spatial coordinates maintained across all modalities
Spatial Transcriptomics
(10X Visium, GeoMx, etc.)
- Gene expression maps
- Cell type identification
- Transcriptional zones
- Pathway activity
WHERE genes expressed
Spatial Proteomics
(CODEX, MIBI, IHC)
- Protein localization
- Biomarker distribution
- Cell phenotypes
- Immune infiltration
WHERE proteins located
O-PTIR Spatial Chemistry
(Label-Free Metabolomics)
- Metabolite distribution
- Lipid composition
- Protein structure
- Chemical heterogeneity
WHAT chemical composition
Integrated Spatial Multi-Omics Analysis
- Correlate gene expression with metabolic state
- Link protein markers to chemical composition
- Identify metabolically distinct cell populations
- Map tumor microenvironment chemical-molecular landscape
Why O-PTIR complements spatial omics technologies
Fills Critical Gaps
- Spatial Metabolomics: Direct chemical detection of metabolites without labels or mass spec sample prep
- Lipid Mapping: Comprehensive lipid distribution and saturation state mapping
- Protein Structure: Secondary structure information (α-helix, β-sheet) beyond protein presence/absence
- Chemical Validation: Confirm metabolic predictions from gene expression data
- Label-Free: No need for isotope labeling, antibodies, or chemical tags
- Non-Destructive: Tissue preserved for additional spatial omics analyses
Perfect Integration
- Same Coordinates: Register O-PTIR data to spatial transcriptomics coordinate systems
- Serial Sections: Compatible with sequential spatial omics analysis workflows
- Same Section: Can measure after spatial transcriptomics on identical tissue
- Submicron Resolution: ~500 nm matches or exceeds spatial transcriptomics platforms
- Rapid Analysis: 10’s of minutes for comprehensive chemical maps
- Standard Substrates: Works on glass slides and spatial omics capture arrays
Example spatial biology research questions enabled by multimodal O-PTIR
- Tumor Metabolism & TME: “Do regions with high glycolytic gene expression (GLUT1, HK2, LDHA) show corresponding lipid accumulation and lactate signatures detectable by O-PTIR?”
- Immune Cell Metabolic States: “What is the metabolic state (lipid vs. glucose utilization) of T cells in different tumor microenvironment zones, and how does it correlate with their exhaustion markers (PD-1, TIM-3, LAG-3)?”
- Developmental Metabolic Gradients: “How do lipid and metabolite gradients guide developmental gene expression patterns during organogenesis? Do metabolic zones predict transcriptional boundaries?”
- Neurodegeneration Progression: “Where do metabolic changes (lipid oxidation, energy depletion) precede or follow transcriptional changes in Alzheimer’s disease progression across hippocampal regions?”
- Spatial Drug Response Heterogeneity: “Does spatial drug distribution correlate with gene expression changes (drug target, resistance genes) and metabolic reprogramming in resistant vs. sensitive tumor regions?”
- Stem Cell Niche Chemistry: “What chemical microenvironment features (metabolites, ECM composition, oxygen gradients) distinguish stem cell niches from differentiated tissue zones identified by spatial transcriptomics?”
- Spatial Immunometabolism: “How do metabolically distinct tumor regions (oxidative vs. glycolytic) shape immune cell infiltration patterns and activation states revealed by spatial proteomics?”
- ECM Remodeling in Fibrosis: “Can we spatially map collagen maturation (crosslinking, composition) alongside fibroblast gene expression signatures during progressive liver or lung fibrosis?”
Real-World Integration Strategies
Strategy 1: Serial Section Workflow
Best for: Maximizing data from each modality without compromise
- Cut adjacent 5-10 µm sections from tissue block
- Section 1 → 10X Visium spatial transcriptomics
- Section 2 → O-PTIR multimodal chemical imaging
- Section 3 → Optional: IHC, H&E, or other validation
- Computationally register sections using tissue landmarks
- Overlay gene expression with chemical maps
Advantages: Each platform optimized independently, no workflow conflicts
Considerations: Requires computational registration (readily achievable with modern tools)
Strategy 2: Sequential Same-Section
Best for: Perfect spatial correlation, limited tissue availability
- Mount tissue on compatible substrate (glass, CaF₂)
- Perform O-PTIR imaging first (non-destructive)
- Mark regions of interest based on chemistry
- Apply spatial transcriptomics workflow (e.g., GeoMx DSP)
- Target ROIs for transcriptomic analysis based on O-PTIR
- Direct correlation—no registration needed
Advantages: Perfect pixel-level correlation, hypothesis-driven targeting
Considerations: Workflow optimization required, substrate compatibility
Webinars
- Life Science | Microplastics
- May 28, 2026
- Life Science | Microplastics
- April 1, 2026
- Life Science
- February 12, 2026
- Life Science
- November 13, 2025
- Life Science
- October 2, 2025
- Life Science | Microplastics
- June 26, 2025
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