Multiplexed bioluminescence imaging with a substrate unmixing platform

Bioluminescent tools can illuminate cellular features in whole organisms. Multi-component tracking remains challenging, though, owing to a lack of well-resolved probes and long imaging times. To address the need for more rapid, quantitative, and multiplexed bioluminescent readouts, we developed an analysis pipeline featuring sequential substrate administration and serial image acquisition. Light output from each luciferin is layered on top of the previous image, with minimal delay between substrate delivery. A MATLAB algorithm was written to analyze bioluminescent images generated from the rapid imaging protocol and deconvolute (i.e., unmix) signals from luciferase-luciferin pairs. Mixtures comprising three to five luciferase reporters were readily distinguished in under 50 min; this same experiment would require days using conventional workflows. We further showed that the algorithm can be used to accurately quantify luciferase levels in heterogeneous mixtures. Based on its speed and versatility, the multiplexed imaging platform will expand the scope of bioluminescence technology.

A comparison of spectral unmixing to conventional compensation for the calculation of fluorochrome abundances from flow cytometric data

Traditionally, flow cytometers acquired data using the same number of detectors as fluorochromes being measured in the experiment. More recently, spectral flow cytometers utilize a larger number of detectors than fluorochromes. This seemingly small difference opens the door to a wide variety of mathematical tools for the calculation of the true fluorochrome abundances from the raw detector values as compared with traditional compensation. This review will provide a brief overview of the mathematics and theory underlying traditional compensation and unmixing focusing on the differences between them and the additional information provided by unmixing approaches.

The evolution of spectral flow cytometry

This special issue of Cytometry marks the transition of spectral flow cytometry from an emerging technology into a transformative force that will shape the fields of cytometry and single-cell analysis for some time to come. Tracing its roots to the earliest years of flow cytometry, spectral flow cytometry has evolved from the domain of individual researchers pushing the limits of hardware, reagents, and software to the mainstream, where it is being harnessed and adapted to meet the analytical challenges presented by modern biomedical research. In particular, the current form of spectral flow technology has arisen to address the needs of multiparameter immunophenotyping of immune cells in basic and translational research, and much of the current instrumentation and software reflects the needs of those applications. Yet, the possibilities enabled by high-resolution analysis of the spectral properties of optical absorbance, scatter, and emission have only begun to be exploited. In this brief review, the author highlights the origins and early milestones of single-cell spectral analysis, assesses the current state of instrumentation and software, and speculates as to future directions of spectral flow cytometry technology and applications.

Recent Advances in Multi- and Hyperspectral Image Analysis

Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.

A Cost-Efficient Multiwavelength LED-Based System for Quantitative Photoacoustic Measurements

The unique ability of photoacoustic (PA) sensing to provide optical absorption information of biomolecules deep inside turbid tissues with high sensitivity has recently enabled the development of various novel diagnostic systems for biomedical applications. In many cases, PA setups can be bulky, complex, and costly, as they typically require the integration of expensive Q-switched nanosecond lasers, and also presents limited wavelength availability. This article presents a compact, cost-efficient, multiwavelength PA sensing system for quantitative measurements, by utilizing two high-power LED sources emitting at central wavelengths of 444 and 628 nm, respectively, and a single-element ultrasonic transducer at 3.5 MHz for signal detection. We investigate the performance of LEDs in pulsed mode and explore the dependence of PA responses on absorber's concentration and applied energy fluence using tissue-mimicking phantoms demonstrating both optical absorption and scattering properties. Finally, we apply the developed system on the spectral unmixing of two absorbers contained at various relative concentrations in the phantoms, to provide accurate estimations with absolute deviations ranging between 0.4 and 12.3%. An upgraded version of the PA system may provide valuable in-vivo multiparametric measurements of important biomarkers, such as hemoglobin oxygenation, melanin concentration, local lipid content, and glucose levels.

Evaluating spectral cytometry for immune profiling in viral disease

In conventional fluorescence cytometry, each fluorophore present in a panel is measured in a target detector, through the use of wide band-pass optical filters. In contrast, spectral cytometry uses a large number of detectors with narrow band-pass filters to measure a fluorophore's signal across the spectrum, creating a more detailed fluorescent signature for each fluorophore. The spectral approach shows promise in adding flexibility to panel design and improving the measurement of fluorescent signal. However, few comparisons between conventional and spectral systems have been reported to date. We therefore sought to compare a modern conventional cytometry system with a modern spectral system, and to assess the quality of resulting datasets from the point of view of a flow cytometry user. Signal intensity, spread, and resolution were compared between the systems. Subsequently, the different methods of separating fluorophore signals were compared, where compensation mathematically separates multiple overlapping fluorophores and unmixing relies on creating a detailed fluorescent signature across the spectrum to separate the fluorophores. Within the spectral data set, signal spread and resolution were comparable between compensation and unmixing. However, for some highly overlapping fluorophores, unmixing resolved the two fluorescence signals where compensation did not. Finally, data from mid- to large-size panels were acquired and were found to have comparable resolution for many fluorophores on both instruments, but reduced levels of spreading error on our spectral system improved signal resolution for a number of fluorophores, compared with our conventional system. Furthermore, autofluorescence extraction on the spectral system allowed for greater population resolution in highly autofluorescent samples. Overall, the implementation of a spectral cytometry approach resulted in data that are comparable to that generated on conventional systems, with a number of potential advantages afforded by the larger number of detectors, and the integration of the spectral unmixing approach. © 2020 International Society for Advancement of Cytometry.

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary Du. The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

Optical pH measurement system using a single fluorescent dye for assessing susceptibility to dental caries

Sugar-rich diets and poor dental hygiene promote the formation of a biofilm (plaque) that strongly adheres to the dental enamel surface and fosters the evolution of aciduric bacteria. The acid contributes to demineralization of the exterior tooth enamel, which accelerates after the pH drops below a critical value (∼5.5) for extended time periods resulting in the need for restorative procedures. Preventative techniques to alert the dentist and caries-susceptible patients regarding vulnerability to dental decay require a clinical measure of plaque activity. Therefore, there is a need to evaluate the acid production capability of plaque deposits in the pits and fissures of occlusal and interproximal regions. A ratiometric fluorescence pH-sensing device has been developed using an FDA-approved dye and LED excitation. Fluorescein spectral profiles were collected using a spectrometer and analyzed with a spectral unmixing algorithm for calibration over the pH range of 4.5 to 7. An in vivo pilot study on human subjects was performed using a sucrose rinse to accelerate bacterial metabolism and to measure the time-dependent drop in pH. The optical system is relatively immune to confounding factors such as photobleaching, dye concentration, and variation in excitation intensity associated with earlier dye-based pH measurement techniques.

Unmixing EEG Inverse Solutions Based on Brain Segmentation

Due to its low resolution, any EEG inverse solution provides a source estimate at each voxel that is a mixture of the true source values over all the voxels of the brain. This mixing effect usually causes notable distortion in estimates of source connectivity based on inverse solutions. To lessen this shortcoming, an unmixing approach is introduced for EEG inverse solutions based on piecewise approximation of the unknown source by means of a brain segmentation formed by specified Regions of Interests (ROIs). The approach is general and flexible enough to be applied to any inverse solution with any specified family of ROIs, including point, surface and 3D brain regions. Two of its variants are elaborated in detail: arbitrary piecewise constant sources over arbitrary regions and sources with piecewise constant intensity of known direction over cortex surface regions. Numerically, the approach requires just solving a system of linear equations. Bounds for the error of unmixed estimates are also given. Furthermore, insights on the advantages and of variants of this approach for connectivity analysis are discussed through a variety of designed simulated examples.

Fluorescence excitation analysis by two-photon confocal laser scanning microscopy: a new method to identify fluorescent nanoparticles on histological tissue sections

In the present study, we make use of the ability of two-photon confocal laser scanning microscopes (CLSMs) equipped with tunable lasers to produce spectral excitation image sequences. Furthermore, unmixing, which is usually performed on emission image sequences, is performed on these excitation image sequences. We use factor analysis of medical image sequences (FAMIS), which produces factor images, to unmix spectral image sequences of stained structures in tissue sections to provide images of characterized stained cellular structures. This new approach is applied to histological tissue sections of mouse aorta containing labeled iron nanoparticles stained with Texas Red and counterstained with SYTO13, to obtain visual information about the accumulation of these nanoparticles in the arterial wall. The possible presence of Texas Red is determined using a two-photon CLSM associated with FAMIS via the excitation spectra. Texas Red and SYTO13 are thus differentiated, and corresponding factor images specify their possible presence and cellular localization. In conclusion, the designed protocol shows that sequences of images obtained by excitation in a two-photon CLSM enables characterization of Texas Red-stained nanoparticles and other markers. This methodology offers an alternative and complementary solution to the conventional use of emission spectra unmixing to localize fluorescent nanoparticles in tissue samples.