Dataset Information
Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging.
Ontology highlight
ABSTRACT: Motivation
Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information (MI)-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, MI-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection.Results
We found that selecting regions within a multiplex image with low-spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging.Availability and implementation
The codes are available at https://github.com/xing-lab-pitt/mosaic-picasso.
SUBMITTER: Cang H
PROVIDER: S-EPMC10781941 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Publications
Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging.
Bioinformatics (Oxford, England) 20240101 1
<h4>Motivation</h4>Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identif ...[more]
