Multispectral Imaging Flow Cytometry
Multispectral Imaging Flow Cytometry - Related Videos
11:39
Assessing Autophagic Flux by Measuring LC3, p62, and LAMP1 Co-localization Using Multispectral Imaging Flow Cytometry
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Cited by 94 •
2017
Here, multispectral imaging flow cytometry with an analytical feature that compares bright detail images of 3 autophagy markers and quantifies their co-localization, along with LC3 spot counting, was used to measure autophagy in an objective, quantitative, and statistically robust manner.
14:09
Localization and Relative Quantification of Carbon Nanotubes in Cells with Multispectral Imaging Flow Cytometry
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Cited by 18 •
2013
In this study, the main purpose was to monitor the cellular uptake and eventual exocytosis of carbon nanotubes (CNTs). From this perspective, we proposed here a unique method using multispectral imaging flow cytometry allowing a quantification and localization of CNTs in a statistically relevant number of cells.
09:11
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Cited by 5 •
2023
The micronucleus (MN) assay is a well-established test for quantifying DNA damage. However, scoring the assay using conventional techniques such as manual microscopy or feature-based image analysis is laborious and challenging. This paper describes the methodology to develop an artificial intelligence model to score the MN assay using imaging flow cytometry data.
12:56
An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry
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Cited by 10 •
2019
The in vitro micronucleus assay is a well-established method for evaluating genotoxicity and cytotoxicity but scoring the assay using manual microscopy is laborious and suffers from subjectivity and inter-scorer variability. This paper describes the protocol developed to perform a fully automated version of the assay using multispectral imaging flow cytometry.
10:43
Flow Cytometry Purification of Mouse Meiotic Cells
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Cited by 65 •
2011
An efficient method to obtain highly purified viable meiotic fractions from mouse testis is described, which combines a refined cell dissociation protocol with fluorescent activated cell sorting (FACS). This method takes advantage of differences in the DNA content and nuclear density of discrete meiotic fractions.
FAQs
Spectral unmixing computationally separates overlapping fluorescent signals recorded across multiple spectral channels. This distinction helps associate individual signals with different molecular markers even when their emitted light is not completely isolated. By resolving these overlaps, the method supports more informative characterization of heterogeneous cell populations and strengthens comparisons between cellular phenotype, morphology, and marker expression.
Spatial information shows where features occur within an individual cell, rather than reporting only the presence or intensity of a signal. Combining morphology and localization with fluorescence and scattered light can reveal relationships between cell structure and molecular-marker expression. This integrated view is particularly relevant when engineered cells or cellular responses must be evaluated at single-cell resolution.
Conventional flow cytometry contributes high-throughput analysis, whereas microscopy provides spatially resolved cellular features. Multispectral imaging flow cytometry brings these measurement perspectives together while also recording information across multiple spectral channels. The result is a combined dataset that can connect cell morphology, phenotype, localization, and molecular-marker expression instead of emphasizing only one measurement dimension.
Cells first pass through a focused interrogation region in suspension, where illumination produces fluorescent and scattered light. An imaging system then records spatial features across multiple spectral channels for each cell. Computational spectral unmixing can subsequently distinguish overlapping signals, allowing the resulting measurements to be interpreted jointly for morphology, localization, phenotype, and marker expression.
Bioengineering researchers can apply the approach to heterogeneous cell populations, engineered cells, biomaterials interactions, and cellular responses. Its value comes from measuring structural and molecular characteristics in the same individual-cell dataset. That combination can help evaluate how engineered biological systems vary across cells and can connect observed cellular features with specific marker-expression patterns.
The integrated measurements provide several descriptors of each cell, including morphology, phenotype, localization, and molecular-marker expression. These descriptors can improve the information available for designing cell sorting strategies and for monitoring engineered biological processes. They also support quantitative evaluation of engineered systems by revealing variation within populations rather than relying only on aggregate measurements.