Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging

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 techniq...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 40; H. 1
Hauptverfasser: Cang, Hu, Liu, Yang, Xing, Jianhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 02.01.2024
Oxford Publishing Limited (England)
Schlagworte:
Availability
Chemical compounds
Demixing
Fluorescence
Fluorophores
Imaging
Mosaics
Multiplexing
Original Paper
ISSN:1367-4811, 1367-4803, 1367-4811
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btad784