Spectral Mapping
Spectral Mapping - Related Videos
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.
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.
06:58
Fate Mapping
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2023
Fate mapping is a technique used to understand how embryonic cells divide, differentiate, and migrate during development. In classic fate mapping experiments, cells in different areas of an embryo are labeled with a chemical dye and then tracked to determine which tissues or structures they form. Technological improvements now allow for individual cells to be marked and traced throughout embryonic development and adulthood. This video reviews the concepts behind fate mapping, and then details a...
11:03
Motor Maps
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex...
FAQs
Each measured spectrum gains biological meaning when paired with its exact location. Absorption, emission, and scattering patterns can then be compared across pixels or sampling points, allowing spatial differences to be associated with materials, molecules, cell types, or tissue properties. The resulting map combines spectral information with anatomical or sample context.
Calibration and computational analysis make spectra comparable and interpretable across a sample. Calibration supports the measurement stage, while computational analysis compares wavelength-dependent patterns and links them to locations or biological features. This combination is important because an instrument can collect many spectra, but the map requires analysis to associate measurements with molecules, cell types, or tissue properties.
Absorption, emission, and scattering are distinct signal types that can supply the spectral patterns used in a map. The relevant wavelength-dependent pattern is compared across locations rather than relying on one universal signal. This allows investigators to examine biological samples through the signal characteristics available from the measurement system and relate those patterns to sample composition.
Yes, spectral mapping can support biological analysis without requiring labels in every case. Wavelength-dependent patterns may be associated computationally with molecules, cell types, or tissue properties, providing information about composition and structure through the measured signal itself. This label-independent capability is useful when researchers want to localize biomarkers or characterize tissue variation.
A basic workflow collects spectra from many points or pixels across the sample, applies calibration, and uses computational analysis to compare the resulting patterns. The analysis then connects spectral features with their spatial locations and selected biological interpretations. The output is a spatial representation that can support tissue characterization, biomarker localization, or quantitative study.
Spectral maps can reveal how molecular or biochemical composition varies across tissues and other biological samples. Differences between locations may be associated with molecules, cell types, tissue properties, or localized biomarkers. Because the measurements retain spatial information, researchers can examine both the presence of a spectral feature and where it occurs within the sample.
The approach is useful for microscopy, tissue characterization, biomarker localization, and analysis of cellular or biochemical composition. It can also contribute to diagnostic studies, environmental monitoring, and quantitative research. In these settings, the combination of spatial location and spectral information helps researchers investigate biological variation and relate measured patterns to structures or materials.