Huang X, Gao X, Fu L. BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images.
Bioinformatics 2024;
40:btae052. [PMID:
38291952 PMCID:
PMC10873573 DOI:
10.1093/bioinformatics/btae052]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/01/2024] Open
Abstract
MOTIVATION
Spectral imaging is often used to observe different objects with multiple fluorescent labels to reveal the development of the biological event. As the number of observed objects increases, the spectral overlap between fluorophores becomes more serious, and obtaining a "pure" picture of each fluorophore becomes a major challenge. Here, we propose a blind spectral unmixing algorithm called BINGO (Blind unmixing via SVD-based Initialization Nmf with project Gradient descent and spare cOnstrain), which can extract all kinds of fluorophores more accurately from highly overlapping multichannel data, even if the spectra of the fluorophores are extremely similar or their fluorescence intensity varies greatly.
RESULTS
BINGO can isolate up to 10 fluorophores from spectral imaging data for a single excitation. nine-color living HeLa cells were visualized distinctly with BINGO. It provides an important algorithmic tool for multiplex imaging studies, especially in intravital imaging. BINGO shows great potential in multicolor imaging for biomedical sciences.
AVAILABILITY AND IMPLEMENTATION
The source code used for this paper is available with the test data at https://github.com/Xinyuan555/BINGO_unmixing.
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