Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

14.7K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
14.7K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

21.6K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
21.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Glycosylated extracellular matrix drives immune suppression by modulating macrophage-T cell crosstalk in triple-negative breast cancer.

Nature communications·2026
Same author

Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.

Frontiers in bioinformatics·2026
Same author

Synthetic Inducible Bacteria Normalize the Microenvironment of Refractory Tumors, Potentiating Checkpoint Inhibitor Responses.

Molecular cancer therapeutics·2026
Same author

Synthetic inducible bacteria normalize the microenvironment of refractory tumors, potentiating checkpoint inhibitor responses.

Molecular cancer therapeutics·2026
Same author

Computational delineation and cellular profiling of murine cortical cell layers using multiplex immunofluorescence imaging.

Journal of neuroscience methods·2026
Same author

Tumor-restraining fibroblasts emerge after chemotherapy specifically in responders.

Cancer cell·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 5, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.5K

Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy.

Murad Megjhani1, Pedro Correa de Sampaio2, Julienne Leigh Carstens2

  • 1Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.

Bioinformatics (Oxford, England)
|March 24, 2017
PubMed
Summary
This summary is machine-generated.

Adaptive Morphologically Constrained Spectral Unmixing (MCSU) improves multiplex fluorescence microscopy by analyzing spatial context and morphology. This novel approach enhances spectral unmixing performance, outperforming existing methods in complex biological samples.

More Related Videos

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis
08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

4.3K
High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
10:28

High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence

Published on: October 28, 2025

667

Related Experiment Videos

Last Updated: Mar 5, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.5K
Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis
08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

4.3K
High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence
10:28

High-plex Imaging using Spectral Confocal Microscopy to Minimize Non-specific Tissue Fluorescence

Published on: October 28, 2025

667

Area of Science:

  • Microscopy and Imaging
  • Computational Biology
  • Biophysics

Background:

  • Current spectral unmixing methods struggle with high spectral overlap in multiplex fluorescence microscopy due to pixel-level analysis.
  • Exploiting spatial context and morphological information is crucial for improving unmixing accuracy.

Purpose of the Study:

  • To develop and evaluate adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms.
  • To overcome limitations of traditional spectral unmixing by incorporating spatial and morphological constraints.

Main Methods:

  • Developed MCSU algorithms integrating dictionary-based models for morphology and total variation for spatial context.
  • Learned morphological and spatial context models directly from image data.
  • Applied the method to multi-spectral images of multiplex-labeled pancreatic ductal adenocarcinoma (PDAC) tissue samples.

Main Results:

  • The proposed MCSU method demonstrated improved spectral unmixing performance.
  • Achieved a 39.6% reduction in Mean Squared Error (MSE) compared to non-constrained methods.
  • Showed an 8% increase in Signal Reconstruction Error (SRE) ratio, indicating better signal preservation.

Conclusions:

  • Adaptive MCSU effectively leverages morphological and spatial information for enhanced spectral unmixing.
  • The method offers significant improvements in accuracy and signal reconstruction for multiplex fluorescence microscopy.
  • This approach holds promise for analyzing complex biological samples with high spectral overlap.