Living Image Software
Living Image Software - Related Videos
08:32
Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software
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Cited by 8 •
2011
The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.
07:42
Live Cell Imaging during Mechanical Stretch
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Cited by 12 •
2015
A novel imaging protocol was developed using a custom motor-driven mechanical actuator to allow the measurement of real time responses to mechanical strain in live cells. Relevant to mechanobiology, the system can apply strains up to 20% while allowing near real-time imaging with confocal or atomic force microscopy.
09:57
Live Cell Imaging of Mitosis
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2023
Mitosis is a form of cell division in which a cell’s genetic material is divided equally between two daughter cells. Mitosis can be broken down into six phases, during each of which the cell’s components, such as its chromosomes, show visually distinct characteristics. Advances in fluorescence live cell imaging have allowed scientists to study this process in great detail, providing important insights into the biological control of this process and how it might go wrong in diseases such as...
07:27
Live Confocal Imaging of Developing Arabidopsis Flowers
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Cited by 16 •
2017
Live confocal imaging provides biologists with a powerful tool to study development. Here, we present a detailed protocol for the live confocal imaging of developing Arabidopsis flowers.
06:10
Live Imaging of Mouse Secondary Palate Fusion
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Cited by 11 •
2017
Here, we present a protocol for live imaging of mouse secondary palate fusion using confocal microscopy. This protocol can be used in combination with a variety of fluorescent reporter mouse lines, and with pathway inhibitors for mechanistic insight. This protocol can be adapted for live imaging in other developmental systems.
FAQs
Calibration and background correction make optical measurements more suitable for quantitative comparison. Calibration supports consistent interpretation of image intensity, while background correction helps distinguish signal from labeled cells, organisms, or tissues from unrelated image background. Together, these steps improve the reliability of measurements used to evaluate changes between images, subjects, or treatment conditions.
Regions of interest, or ROIs, let researchers focus analysis on a defined area within an image rather than treating the entire field as one measurement. Selecting an ROI around relevant tissue, an organism, or another target supports spatially specific quantification. This helps connect signal intensity with the biological location being investigated.
By applying comparable image-analysis steps to measurements collected at different time points, the software can quantify changing optical signals in the same living model. Researchers can then follow increases or decreases associated with disease progression, gene expression, or treatment response. This time-based analysis supports longitudinal interpretation rather than relying on a single endpoint.
A typical analysis begins with acquired bioluminescence or fluorescence images, followed by calibration and selection of appropriate regions of interest. Researchers then apply background correction and quantify the resulting signal. Comparing these measurements across animals, locations, or time points produces spatial and quantitative data for interpreting biological changes under the conditions of the study.
The platform supports several preclinical questions, including whether disease-related signals change over time, whether labeled cells or tissues can be followed in living animals, and whether a drug or cell-based therapy alters those signals. It can also help monitor gene expression, allowing investigators to compare treatment responses through repeated optical measurements.
Its value in preclinical medicine comes from combining spatial signal information with repeated measurements from living models. Researchers can monitor disease progression or therapeutic responses without depending solely on repeated tissue collection. This enables longitudinal study designs in which changes are followed over time and treatment groups can be compared using quantified optical data.