Spectral Spatial Imaging
Spectral Spatial Imaging - Related Videos
08:52
Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
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Cited by 1 •
2025
This protocol details the key steps to enable rigorous and reproducible measurement of both glycogen and N-linked glycans using mass spectrometry imaging.
10:28
High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
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2025
Spectral Iterative Bleaching Extends Multiplexity (IBEX) builds upon the base IBEX technique by adding heparin blocking to minimize nonspecific binding and leveraging spectral detection with computational unmixing to suppress autofluorescence. This approach accelerates image acquisition while reducing sources of background, enabling robust multi-round, high-parameter spatial proteomic analyses.
11:37
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
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Cited by 36 •
2017
An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.
08:47
Spectral Confocal Imaging of Fluorescently tagged Nicotinic Receptors in Knock-in Mice with Chronic Nicotine Administration
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Cited by 12 •
2012
We have developed a novel technique of quantifying nicotinic acetylcholine receptor changes within subcellular regions of specific subtypes of CNS neurons to better understand the mechanisms of nicotine addiction by using a combination of approaches including fluorescent protein tagging of the receptor using the knock-in approach and spectral confocal imaging.
14:26
In vivo Quantification of G Protein Coupled Receptor Interactions using Spectrally Resolved Two-photon Microscopy
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Cited by 6 •
2011
By employing a spectrally resolved two-photon microscopy imaging system, pixel-level maps of Förster Resonance Energy Transfer (FRET) efficiencies are obtained for cells expressing membrane receptors hypothesized to form homo-oligomeric complexes. From the FRET efficiency maps, we are able to estimate stoichiometric information about the oligomer complex under study.
FAQs
Separation comes from comparing intensity patterns across wavelengths rather than relying only on signal strength at one band. Spectral analysis can distinguish signals with different wavelength-dependent behavior, even when they occupy nearby or overlapping locations in an image. The resulting maps preserve where each signal occurs, allowing investigators to examine co-located or heterogeneous biological features.
These components determine how wavelength-resolved light is selected or measured from each image location. Filters isolate selected wavelength ranges, dispersive elements separate light by wavelength, and tunable detectors acquire measurements under adjustable spectral conditions. Their role is to generate the wavelength-specific data required for subsequent spectral discrimination and spatial mapping.
Spatial registration connects a spectral signature to its physical location, so an averaged sample description does not obscure local variation. This is important when neighboring regions differ in composition or structure. In bioengineering, the combined view can expose heterogeneous tissue features, cellular variation, or nonuniform biomaterial behavior that would be difficult to interpret from spectral information without location.
An experiment first collects wavelength-resolved measurements across image locations using an appropriate optical or detector configuration. Spectral analysis then evaluates intensity variation across wavelengths, separates distinguishable signals, and assigns the resulting information back to spatial positions. The final dataset can be inspected as maps of spectral features, supporting comparisons among regions within a tissue or engineered material.
The approach is useful when researchers need structural and molecular information from the same sample while also tracking where those features occur. Bioengineering applications include label-free tissue characterization, biomaterial evaluation, and visualization of cellular or biochemical variation. It is especially relevant when spatial heterogeneity may influence interpretation or the performance of a diagnostic or engineered-tissue technology.
An output can link molecularly informative spectral signatures with structural locations, allowing researchers to identify spatially varying features rather than treating the sample as uniform. Such results may improve sample analysis, reveal biological heterogeneity, and provide evidence for evaluating biomaterials or refining diagnostic and engineered-tissue technologies.