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BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images.

Xinyuan Huang^1,2,3, Xiujuan Gao^1,2,3, Ling Fu^1,2,3,4,5,6

¹Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Bioinformatics (Oxford, England)

|January 31, 2024

Summary

A new algorithm, BINGO, accurately separates multiple fluorophores from complex spectral imaging data. This blind spectral unmixing tool enhances multicolor imaging, even with highly overlapping spectra.

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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Area of Science:

Biomedical imaging
Spectroscopy
Computational biology

Background:

Spectral imaging is crucial for observing biological events using multiple fluorescent labels.
Increasing numbers of fluorophores lead to significant spectral overlap, challenging the isolation of individual signals.
Accurate separation of fluorophore signals is essential for detailed biological analysis.

Purpose of the Study:

To develop a blind spectral unmixing algorithm for accurate fluorophore extraction from highly overlapping multichannel data.
To address the challenge of spectral overlap in multicolor imaging, particularly in biological studies.
To provide a robust computational tool for analyzing complex spectral imaging data.

Main Methods:

Proposed a novel blind spectral unmixing algorithm named BINGO (Blind unmixing via SVD-based Initialization Nmf with project Gradient descent and spare cOnstrain).
Utilized Singular Value Decomposition (SVD)-based initialization, Non-negative Matrix Factorization (NMF), projected gradient descent, and sparsity constraints.
Applied the algorithm to highly overlapping multichannel spectral imaging data.

Main Results:

BINGO successfully isolated up to 10 fluorophores from spectral imaging data with a single excitation.
Demonstrated distinct visualization of nine-color living HeLa cells using the BINGO algorithm.
Showcased the algorithm's effectiveness even with extremely similar fluorophore spectra and varying fluorescence intensities.

Conclusions:

BINGO is a powerful algorithmic tool for multiplex imaging, particularly valuable in intravital imaging.
The algorithm significantly improves the accuracy of fluorophore separation in complex biological samples.
BINGO holds substantial potential for advancing multicolor imaging applications in biomedical sciences.