Fluorescence Microscopy Analysis
Fluorescence Microscopy Analysis - Related Videos
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
09:59
Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy
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Cited by 3 •
2013
Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.
09:22
Introduction to Fluorescence Microscopy
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2023
Fluorescence microscopy is a very powerful analytical tool that combines the magnifying properties of light microscopy with visualization of fluorescence. Fluorescence is a phenomenon that involves absorbance and emission of a small range of light wavelengths by a fluorescent molecule known as a fluorophore. Fluorescence microscopy is accomplished in conjunction with the basic light microscope by the addition of a powerful light source, specialized filters, and a means of fluorescently...
13:28
Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry
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Cited by 33 •
2012
Microbial biofilms are generally constituted by distinct subpopulations of specialized cells. Single-cell analysis of these subpopulations requires the use of fluorescent reporters. Here we describe a protocol to visualize and monitor several subpopulationswithin B. subtilis biofilms using fluorescence microscopy and flow cytometry.
09:30
Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
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Cited by 14 •
2017
We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using Förster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in µManager and data analysis is undertaken in FLIMfit.
FAQs
Background correction separates fluorescence attributed to labeled structures from unwanted signal in an image. This step matters because raw intensity can otherwise exaggerate or obscure differences between samples. Applying it before comparing signal intensity, localization, or changes over time makes measurements more consistent and helps distinguish biological or material responses from imaging-related background.
Localization identifies where fluorescent signal appears within an image, while colocalization examines whether signals from different labels occupy related spatial regions. Intensity measurements quantify signal strength. Together, these readouts can show not only whether a target is present, but also where it is and whether its distribution relates to another component or changes over time.
Fluorophores absorb excitation light at defined wavelengths and emit light at longer wavelengths. Optical filters direct the emitted signal toward the camera while limiting unwanted wavelengths, allowing the recorded image to represent the labeled feature more selectively. These optical relationships affect whether structures, molecules, or cellular behaviors can be visualized and compared across images.
Reliable analysis begins with careful image acquisition, followed by background correction and calibration before measurements are compared. Statistical analysis then helps evaluate whether observed differences are consistent across the data. This workflow is important when fluorescence microscopy analysis is used to compare engineered cells, biomaterials, tissue constructs, or delivery systems.
This approach is useful when bioengineers need to assess how engineered cells behave, how biomaterials perform, or how tissue constructs respond. It can also support evaluation of biosensors and delivery systems. The measurements may connect fluorescently observed localization, intensity, colocalization, or time-dependent changes with the performance or biological response of an engineered system.
Qualitative assessment describes visible patterns such as the presence, distribution, or apparent organization of fluorescent signal. Quantitative assessment converts image features into measurements of intensity, localization, colocalization, or change over time. Using both perspectives provides a fuller interpretation, linking visual observations with numerical evidence when studying cellular function or engineered biological systems.