Multi-spectral Imaging Platform
Multi-spectral Imaging Platform - Related Videos
18:07
Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry
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Cited by 60 •
2012
In this article, we describe a method utilizing multi-spectral imaging flow cytometry to quantify the internalization of polyanhydride nanoparticles or bacteria by RAW 264.7 cells.
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.
09:40
Identification of a Murine Erythroblast Subpopulation Enriched in Enucleating Events by Multi-spectral Imaging Flow Cytometry
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Cited by 6 •
2014
The present protocol describes a novel method of identifying a population of enucleating orthochromatic erythroblasts by multi-spectral imaging flow cytometry, providing a visualization of the erythroblast enucleation process.
13:24
A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform
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Cited by 18 •
2009
We developed a novel multi-compartment neuron co-culture microsystem platform for in vitro CNS axon-glia interaction research. The platform is capable of conducting up to six independent experiments in parallel and was fabricated using a newly developed macro/micro hybrid fabrication method.
FAQs
The platform records signals from selected wavelength bands and preserves their spatial location within the sample. Computational analysis then compares the spectral information across those locations, helping distinguish tissue components that produce overlapping signals in a conventional image. This separation allows researchers to relate specific spectral signatures to molecular, structural, or functional features within defined regions.
Different wavelength bands can reveal different characteristics of the same sample. By illuminating or detecting tissue across selected spectral ranges, the system gathers complementary information rather than relying on one broad signal. Combining these measurements increases the ability to compare regions and identify features associated with biomarkers, tissue organization, or functional differences that standard color imaging may not resolve.
Standard color images primarily show visible appearance, whereas a Multi-spectral Imaging Platform adds measurements across multiple wavelength bands. The additional spectral information supports computational separation of tissue components and comparison of biologically distinct regions. As a result, researchers can examine tumor areas, surrounding cells, and other tissue features quantitatively rather than relying only on visual color differences.
A typical workflow begins by preparing the sample for imaging, followed by illumination or detection across the selected spectral ranges. The system captures spatially resolved signals for each range, then combines the measurements computationally. Researchers can compare the resulting images to identify tissue components, map biomarkers or architecture, and evaluate differences between regions or experimental conditions.
In cancer research, the method can characterize tumor regions and compare malignant cells with surrounding tissue. It can also support analysis of biomarkers and tissue architecture, allowing researchers to connect spatial patterns with biological features. These capabilities make the approach useful when the research question depends on distinguishing nearby regions that may appear similar in a standard image.
The platform can provide spatially resolved comparisons of tissue features before or after an experimental treatment, when those features are represented by measurable spectral signatures. Researchers may examine changes in tumor regions, biomarkers, or tissue architecture and relate them to treatment effects. This supports quantitative pathology and can help evaluate therapeutic outcomes alongside disease mechanisms.