Application of non-negative matrix factorization to multispectral FLIM data analysis

Multivariate unmixing approaches on Raman images of plant cell walls: new insights or overinterpretation of results?

BACKGROUND

Plant cell walls are nanocomposites based on cellulose microfibrils embedded in a matrix of polysaccharides and aromatic polymers. They are optimized for different functions (e.g. mechanical stability) by changing cell form, cell wall thickness and composition. To reveal the composition of plant tissues in a non-destructive way on the microscale, Raman imaging has become an important tool. Thousands of Raman spectra are acquired, each one being a spatially resolved molecular fingerprint of the plant cell wall. Nevertheless, due to the multicomponent nature of plant cell walls, many bands are overlapping and classical band integration approaches often not suitable for imaging. Multivariate data analysing approaches have a high potential as the whole wavenumber region of all thousands of spectra is analysed at once.

RESULTS

Three multivariate unmixing algorithms, vertex component analysis, non-negative matrix factorization and multivariate curve resolution-alternating least squares were applied to find the purest components within datasets acquired from micro-sections of spruce wood and Arabidopsis. With all three approaches different cell wall layers (including tiny S1 and S3 with 0.09-0.14 µm thickness) and cell contents were distinguished and endmember spectra with a good signal to noise ratio extracted. Baseline correction influences the results obtained in all methods as well as the way in which algorithm extracts components, i.e. prioritizing the extraction of positive endmembers by sequential orthogonal projections in VCA or performing a simultaneous extraction of non-negative components aiming at explaining the maximum variance in NMF and MCR-ALS. Other constraints applied (e.g. closure in VCA) or a previous principal component analysis filtering step in MCR-ALS also contribute to the differences obtained.

CONCLUSIONS

VCA is recommended as a good preliminary approach, since it is fast, does not require setting many input parameters and the endmember spectra result in good approximations of the raw data. Yet the endmember spectra are more correlated and mixed than those retrieved by NMF and MCR-ALS methods. The latter two give the best model statistics (with lower lack of fit in the models), but care has to be taken about overestimating the rank as it can lead to artificial shapes due to peak splitting or inverted bands.

Defect detection of capacitive touch panel using a nonnegative matrix factorization and tolerance model

Capacitive touch panels (CTPs), as a medium of information interactions, have become essential parts in many consumer electronics. However, current methods such as image edge matching and frequency notch filter cannot suit the defect detection for the new-type complex CTP patterns, which have neither basic primitives nor periodicity. For solving the issues, we proposed a nonnegative matrix factorization (NMF)-based large-size image registration method, and combined it with image tolerance models to detect defects in such CTP patterns. The NMF-based image registration method can fast extract each CTP from a large image. And then, any three of registered images are selected as reference images, which are further processed by threshold processing and simple mathematical morphological operation to obtain tolerance models. Afterward, we can use the tolerance models to obtain a nondefective template. In the normal inspection stage, the defects in CTP patterns can be identified as long as comparing the tolerance models of the template and sensed images. The experimental results show that the proposed method can efficiently and accurately detect various types of defects in CTP patterns. Moreover, the detection results are robust under different illuminations. Therefore, this algorithm can be reliably applied in actual inspection of such new-type CTP patterns.

Extended output phasor representation of multi-spectral fluorescence lifetime imaging microscopy

In this paper, we investigate novel low-dimensional and model-free representations for multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) data. We depart from the classical definition of the phasor in the complex plane to propose the extended output phasor (EOP) and extended phasor (EP) for multi-spectral information. The frequency domain properties of the EOP and EP are analytically studied based on a multiexponential model for the impulse response of the imaged tissue. For practical implementations, the EOP is more appealing since there is no need to perform deconvolution of the instrument response from the measured m-FLIM data, as in the case of EP. Our synthetic and experimental evaluations with m-FLIM datasets of human coronary atherosclerotic plaques show that low frequency indexes have to be employed for a distinctive representation of the EOP and EP, and to reduce noise distortion. The tissue classification of the m-FLIM datasets by EOP and EP also improves with low frequency indexes, and does not present significant differences by using either phasor.

Blind end-member and abundance extraction for multispectral fluorescence lifetime imaging microscopy data

This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear mixture model with positivity and sum-to-one restrictions on the abundances and end-members to compensate for signature variability. The synthesis procedure depends on a quadratic optimization problem, which is solved by an alternating least-squares structure over convex sets. The BEAE strategy only assumes that the number of components in the analyzed sample is known a spriori. The proposed method is first validated by using synthetic m-FLIM datasets at 15, 20, and 25 dB signal-to-noise ratios. The samples simulate the mixed response of tissue containing multiple fluorescent intensity decays. Furthermore, the results were also validated with six m-FLIM datasets from fresh postmortem human coronary atherosclerotic plaques. A quantitative evaluation of the BEAE was made against two popular techniques: minimum volume constrained nonnegative matrix factorization (MVC-NMF) and multivariate curve resolution-alternating least-squares (MCR-ALS). Our proposed method (BEAE) was able to provide more accurate estimations of the end-members: 0.32% minimum relative error and 13.82% worst-case scenario, despite different initial conditions in the iterative optimization procedure and noise effect. Meanwhile, MVC-NMF and MCR-ALS presented more variability in estimating the end-members: 0.35% and 0.34% for minimum errors and 15.31% and 13.25% in the worst-case scenarios, respectively. This tendency was also maintained for the abundances, where BEAE obtained 0.05 as the minimum absolute error and 0.12 in the worst-case scenario; MCR-ALS and MVC-NMF achieved 0.04 and 0.06 for the minimum absolute errors, and 0.15 and 0.17 under the worst-case conditions, respectively. In addition, the average computation time was evaluated for the synthetic datasets, where MVC-NMF achieved the fastest time, followed by BEAE and finally MCR-ALS. Consequently, BEAE improved MVC-NMF in convergence to a local optimal solution and robustness against signal variability, and it is roughly 3.6 time faster than MCR-ALS.

Fluorescence lifetime excitation cytometry by kinetic dithering

Flow cytometers are powerful high-throughput devices that capture spectroscopic information from individual particles or cells. These instruments provide a means of multi-parametric analyses for various cellular biomarkers or labeled organelles and cellular proteins. However, the spectral overlap of fluorophores limits the number of fluorophores that can be used simultaneously during experimentation. Time-resolved parameters enable the quantification of fluorescence decay kinetics, thus circumventing common issues associated with intensity-based measurements. This contribution introduces fluorescence lifetime excitation cytometry by kinetic dithering (FLECKD) as a method to capture multiple fluorescence lifetimes using a hybrid time-domain approach. The FLECKD approach excites fluorophores by delivering short pulses of light to cells or particles by rapid dithering and facilitates measurement of complex fluorescence decay kinetics by flow cytometry. Our simulations demonstrated a resolvable fluorescence lifetime value as low as 1.8 ns (±0.3 ns) with less than 20% absolute error. Using the FLECKD instrument, we measured the shortest average fluorescence lifetime value of 2.4 ns and found the system measurement error to be ±0.3 ns (SEM), from hundreds of monodisperse and chemically stable fluorescent microspheres. Additionally, we demonstrate the ability to detect two distinct excited state lifetimes from fluorophores in single cells using FLECKD. This approach presents a new ability to resolve multiple fluorescence lifetimes while retaining the fluidic throughput of a cytometry system. The ability to discriminate more than one average fluorescence lifetime expands the current capabilities of high-throughput and intensity-based cytometry assays as the need to tag one single cell with multiple fluorophores is now widespread.

Photo-induced processes in collagen-hypericin system revealed by fluorescence spectroscopy and multiphoton microscopy

Collagen is the main structural protein and the key determinant of mechanical and functional properties of tissues and organs. Proper balance between synthesis and degradation of collagen molecules is critical for maintaining normal physiological functions. In addition, collagen influences tumor development and drug delivery, which makes it a potential cancer therapy target. Using second harmonic generation, two-photon excited fluorescence microscopy, and spectrofluorimetry, we show that the natural pigment hypericin induces photosensitized destruction of collagen-based tissues. We demonstrate that hypericin-mediated processes in collagen fibers are irreversible and may be used for the treatment of cancer and collagen-related disorders.

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

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.