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Efficient blind spectral unmixing of fluorescently labeled samples using multi-layer non-negative matrix factorization.

ABSTRACT: The ample variety of labeling dyes and staining methods available in fluorescence microscopy has enabled biologists to advance in the understanding of living organisms at cellular and molecular level. When two or more fluorescent dyes are used in the same preparation, or one dye is used in the presence of autofluorescence, the separation of the fluorescent emissions can become problematic. Various approaches have been recently proposed to solve this problem. Among them, blind non-negative matrix factorization is gaining interest since it requires little assumptions about the spectra and concentration of the fluorochromes. In this paper, we propose a novel algorithm for blind spectral separation that addresses some of the shortcomings of existing solutions: namely, their dependency on the initialization and their slow convergence. We apply this new algorithm to two relevant problems in fluorescence microscopy: autofluorescence elimination and spectral unmixing of multi-labeled samples. Our results show that our new algorithm performs well when compared with the state-of-the-art approaches for a much faster implementation.

SUBMITTER: Pengo T

PROVIDER: S-EPMC3832632 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Efficient blind spectral unmixing of fluorescently labeled samples using multi-layer non-negative matrix factorization.

Pengo Thomas T Muñoz-Barrutia Arrate A Zudaire Isabel I Ortiz-de-Solorzano Carlos C

PloS one 20131108 11

The ample variety of labeling dyes and staining methods available in fluorescence microscopy has enabled biologists to advance in the understanding of living organisms at cellular and molecular level. When two or more fluorescent dyes are used in the same preparation, or one dye is used in the presence of autofluorescence, the separation of the fluorescent emissions can become problematic. Various approaches have been recently proposed to solve this problem. Among them, blind non-negative matrix ...[more]

PMID: 24260120

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Project description:Environmental biofilms represent a complex mixture of different microorganisms. Their identity is usually analyzed by means of nucleic acid-based techniques. However, these biofilms are also composed of a highly complex extracellular matrix produced by the microbes within a particular biofilm system. The biochemical identity of this extracellular matrix remains in many cases an intractable part of biofilms and bioaggregates. Consequently, there is a need for an approach that will give access to the fully hydrated structure of the extracellular matrix or at least a major part of it. A crucial compound of the matrix identified as carbohydrate-based polymers represents major structural and functional constituents. These glycoconjugates can be characterized by using fluorescently-labeled lectins in combination with confocal laser scanning microscopy. The lectin approach is defined previously, as fluorescence lectin barcoding (FLBC) and fluorescence lectin-binding analysis (FLBA), where FLBC is equal to the screening of a particular sample with all the commercially available lectins and FLBA is the actual analysis of the matrix throughout an experiment with a selected panel of lectins. As the application of immune-based techniques in environmental biofilm systems is impossible, the lectin approach is currently the only option for probing lectin-specific glycoconjugates in complex biofilms and bioaggregates. From all the commercially available lectins tested, the lectins such as AAL, HAA, WGA, ConA, IAA, HPA, and LEA showed the highest binding efficiency. Furthermore, 20 of the overall lectins tested showed an intermediate signal intensity, nevertheless very useful for the assessment of matrix glycoconjugates. With the data compiled, we shall virtually shed more light on the dark matter of the extracellular matrix and their 3-dimensional distribution in environmental biofilm systems. The results will be helpful in future studies with a focus on the extracellular matrix glycoconjugates present in environmental microbial communities.

Dataset Information

Efficient blind spectral unmixing of fluorescently labeled samples using multi-layer non-negative matrix factorization.

Publications

Efficient blind spectral unmixing of fluorescently labeled samples using multi-layer non-negative matrix factorization.

Similar Datasets

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