Fluorescence Spectral Imaging Research Articles

Improved Visualization of Oral Microbial Consortia.

Bacteria on the tongue dorsum (TD) form consortia tens to hundreds of microns in diameter organized around a core of epithelial cells. Whole-mount preparations have been instrumental in revealing their organization and specific microbial associations. However, their thickness and intricate 3-dimensional complexity present challenges for a comprehensive spatial analysis. To overcome these challenges, we employed a complementary approach: embedding in hydrophilic plastic followed by sectioning and postsectioning labeling. Samples were labeled by hybridization with multiplexed fluorescent oligonucleotide probes and visualized by spectral imaging and linear unmixing. Application of this strategy to TD biofilms improved the visualization of bacteria that were difficult to resolve in whole-mount imaging. Actinomyces, previously detected as patches, became resolved at the single-cell level. The filamentous taxa Leptotrichia and Lachnospiraceae, located at the core of the consortium, were regularly visualized whereas previously they were rarely detected when using whole mounts. Streptococcus salivarius, heterogeneously detected in whole mounts, were regularly and homogenously observed. Two-dimensional images provide valuable information about the organization of bacterial biofilms. However, they offer only a single plane of view for objects that can extend to hundreds of microns in thickness, and information obtained from such images may not always reflect the complexity of a 3-dimensional object. We combined serial physical sectioning with optical sectioning to facilitate the 3-dimensional reconstruction of consortia, spanning over 100 µm in thickness. Our work showcases the use of hydrophilic plastic embedding and sectioning for examining the structure of TD biofilms through spectral imaging fluorescence in situ hybridization. The result was improved visualization of important members of the human oral microbiome. This technique serves as a complementary method to the previously employed whole-mount analysis, offering its own set of advantages and limitations. Addressing the spatial complexity of bacterial consortia demands a multifaceted approach for a comprehensive and effective analysis.

Fluorescence Spectral Imaging Based on Computational Spectral Sensing

As a powerful optical tool, spectrometers are widely used in materials analysis and light-source characterization. Although commercial spectrometers based on dispersive diffraction or Fourier transform offer high resolution and broad dynamic range, spatial information of the sample is lost, limiting its application in snapshot hyperspectral imaging. Herein, we propose a computational spectrometer based on the tailored point spread function of the imaging system that uses disordered microstructures fabricated by low-cost photolithography. The spectrometer achieves a high quantum efficiency (${Q}_{\mathrm{avg}}\phantom{\rule{0.2em}{0ex}}\ensuremath{\sim}\phantom{\rule{0.2em}{0ex}}60\mathrm{%}$) and ultracompact footprint as low as $24.5\ifmmode\times\else\texttimes\fi{}24.5\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}{\mathrm{m}}^{2}$, exhibits outstanding performance in weak-light hyperspectral imaging (e.g., Raman or fluorescence spectroscopy) in the microscopic system. Apart from the advancements mentioned above, our spectrometer offers good compatibility with commercial microscopes, implying it is a promising candidate for spectral imaging in chemistry, biology, and others.

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging.

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the , and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the , , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

Coral holobiont research needs spatial analyses at the microbial scale.

Coral holobiont research needs spatial analyses at the microbial scale.

Phasor Analysis of Fluorescence Lifetime Enables Quantitative Multiplexed Molecular Imaging of Three Probes.

The excited-state lifetime is an intrinsic property of fluorescent molecules that can be leveraged for multiplexed imaging. An advantage of fluorescence lifetime-based multiplexing is that signals from multiple probes can be gathered simultaneously, whereas traditional spectral fluorescence imaging typically requires multiple images at different excitation and emission wavelengths. Additionally, lifetime and spectra could both be utilized to expand the multiplexing capacity of fluorescence. However, resolving exogenous molecular probes based exclusively on the fluorescence lifetime has been limited by technical challenges in analyzing lifetime data. The phasor approach to lifetime analysis offers a simple, graphical solution that has increasingly been used to assess endogenous cellular autofluorescence to quantify metabolic factors. In this study, we employed the phasor analysis of FLIM to quantitatively resolve three exogenous, antibody-targeted fluorescent probes with similar spectral properties based on lifetime information alone. First, we demonstrated that three biomarkers that were spatially restricted to the cell membrane, cytosol, or nucleus could be accurately distinguished using FLIM and phasor analysis. Next, we successfully resolved and quantified three probes that were all targeted to cell surface biomarkers. Finally, we demonstrated that lifetime-based quantitation accuracy can be improved through intensity matching of various probe-biomarker combinations, which will expand the utility of this technique. Importantly, we reconstructed images for each individual probe, as well as an overlay of all three probes, from a single FLIM image. Our results demonstrate that FLIM and phasor analysis can be leveraged as a powerful tool for simultaneous detection of multiple biomarkers with high sensitivity and accuracy.

Deciphering modes of long-range energy transfer in perovskite crystals using confocal excitation and wide-field fluorescence spectral imaging

Excitation energy migration beyond mesoscale is of contemporary interest for both solar photovoltaic and light-emissive devices, especially in context of organometal halide perovskites (OMHPs) which have been shown to have very long (charge carrier) diffusion lengths. While understanding the energy propagation pathways in OMHPs is crucial for further advancement of material design and improvement of opto-electronic features, the simultaneous existence of multiple processes like carrier diffusion, photon recycling, and photon transport makes it often complex to differentiate them. In this study, we unravel the diverse yet dominant excitation energy transfer mode(s) in crystalline MAPbBr3 micron-sized 1D rods and plates by localized (confocal) laser excitation coupled with spectrally-resolved wide-field fluorescence imaging. While rarely used, this technique can efficiently probe excitation migration beyond the diffraction limit and can be realized by simple modification of existing epifluorescence microscopy setups. We find that in rods of length below ∼2 microns, carrier diffusion dominates amongst various energy transfer processes. However, the transient non-radiative defects severely inhibit the extent of carrier migration and also temporarily affect the radiative recombination dynamics of the photo-carriers. For MAPbBr3 plates of several tens of micrometers, we find that the photoluminescence (PL) spectral characteristics remain unaltered at short distances (< ∼3 μm) while at a larger distance, the spectral profile is gradually red-shifted. This implies that carrier diffusion dominates over small distances, while photon recycling, i.e., repeated re-absorption and re-emission of photons, propagates excitation energy transfer over extended length scales with assistance from wave-guided photon transport. Our findings can potentially be used for future studies on the characterization of energy transport mechanisms in semiconductor solids as well as for organic (molecular) self-assembled microstructures.

Live-cell fluorescence spectral imaging as a data science challenge.

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

Investigating the effects of alcohols and cholesterol manipulations on bovine aortic endothelial cell membrane properties using phasor analysis of laurdan fluorescence lifetime microscopy and spectral imaging

Endothelial cell membranes are made up of a heterogenous mixture of lipids and proteins and include liquid ordered (Lo) domains called lipid rafts and caveolae. These compartments have higher concentrations of cholesterol and sphingolipids and are concentrated in signaling molecules. Perturbations in membrane fluidity, including changes in cholesterol content can lead to changes in the structure of the plasma membrane and impair signaling mechanisms. In order to study how changing membrane fluidity can lead to changes in cellular signaling, we must be able to characterize changes in fluidity.

Phasor S-FLIM for the physiological profiling of living cells

Fluorescence Lifetime Imaging Microscopy (FLIM) and spectral imaging are two broadly applied methods for increasing dimensionality in microscopy. However, their combination is typically inefficient and slow in terms of both acquisition and processing. By integrating technological and computational advances, we developed a robust and unbiased Spectral FLIM (S-FLIM) system capable of extracting detailed and precise information about the photophysics of fluorescent specimens at optical resolution. The data are processed with the phasor approach to provide unbiased and fast analysis of S-FLIM data.

Mechanical disruption of E-cadherin complexes with epidermal growth factor receptor actuates growth factor–dependent signaling

Increased intercellular tension is associated with enhanced cell proliferation and tissue growth. Here, we present evidence for a force-transduction mechanism that links mechanical perturbations of epithelial (E)-cadherin (CDH1) receptors to the force-dependent activation of epidermal growth factor receptor (EGFR, ERBB1)-a key regulator of cell proliferation. Here, coimmunoprecipitation studies first show that E-cadherin and EGFR form complexes at the plasma membrane that are disrupted by either epidermal growth factor (EGF) or increased tension on homophilic E-cadherin bonds. Although force on E-cadherin bonds disrupts the complex in the absence of EGF, soluble EGF is required to mechanically activate EGFR at cadherin adhesions. Fully quantified spectral imaging fluorescence resonance energy transfer further revealed that E-cadherin and EGFR directly associate to form a heterotrimeric complex of two cadherins and one EGFR protein. Together, these results support a model in which the tugging forces on homophilic E-cadherin bonds trigger force-activated signaling by releasing EGFR monomers to dimerize, bind EGF ligand, and signal. These findings reveal the initial steps in E-cadherin-mediated force transduction that directly link intercellular force fluctuations to the activation of growth regulatory signaling cascades.

Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis

Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA’s multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA’s analysis is strongly correlated with sequencing data (Pearson’s r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.

P120 catenin potentiates constitutive E-cadherin dimerization at the plasma membrane and regulates trans binding

Cadherins are essential adhesion proteins that regulate tissue cohesion and paracellular permeability by assembling dense adhesion plaques at cell-to-cell contacts. Adherens junctions are central to a wide range of tissue functions; identifying protein interactions that potentiate their assembly and regulation has been the focus of research for over 2 decades. Here, we present evidence for a new, unexpected mechanism of cadherin oligomerization on cells. Fully quantified spectral imaging fluorescence resonance energy transfer (FSI-FRET) and fluorescence intensity fluctuation (FIF) measurements directly demonstrate that E-cadherin forms constitutive lateral (cis) dimers at the plasma membrane. Results further show that binding of the cytosolic protein p120ctn binding to the intracellular region is required for constitutive E-cadherin dimerization. This finding differs from a model that attributes lateral (cis) cadherin oligomerization solely to extracellular domain interactions. The present, novel findings are further supported by studies of E-cadherin mutants that uncouple p120ctn binding or with cells in which p120ctn was knocked out using CRISPR-Cas9. Quantitative affinity measurements further demonstrate that uncoupling p120ctn binding reduces the cadherin trans binding affinity and cell adhesion. These findings transform the current model of cadherin assembly at cell surfaces and identify the core building blocks of cadherin-mediated intercellular adhesions. They also identify a new role for p120ctn and reconcile findings that implicate both the extracellular and intracellular cadherin domains in cadherin clustering and intercellular cohesion.

Phasor S-FLIM: a new paradigm for fast and robust spectral fluorescence lifetime imaging.

Fluorescence lifetime imaging microscopy (FLIM) and spectral imaging are two broadly applied methods for increasing dimensionality in microscopy. However, their combination is typically inefficient and slow in terms of acquisition and processing. By integrating technological and computational advances, we developed a robust and unbiased spectral FLIM (S-FLIM) system. Our method, Phasor S-FLIM, combines true parallel multichannel digital frequency domain electronics with a multidimensional phasor approach to extract detailed and precise information about the photophysics of fluorescent specimens at optical resolution. To show the flexibility of the Phasor S-FLIM technology and its applications to the biological and biomedical field, we address four common, yet challenging, problems: the blind unmixing of spectral and lifetime signatures from multiple unknown species, the unbiased bleedthrough- and background-free Förster resonance energy transfer analysis of biosensors, the photophysical characterization of environment-sensitive probes in living cells and parallel four-color FLIM imaging in tumor spheroids.

Automatic Image Segmentation Based on Phasor Space Clustering

We present our latest developments in automatic image segmentation for measurements on spectral and lifetime fluorescence imaging. The algorithm operates on the basis of the phasor approach and makes use of a well-established machine learning method for clustering N-dimensional data points, in our case the pixel data points on the phasor space. The technique is based on Gaussian mixture models solved by the use of the expectation-maximization algorithm. We show the method is the best candidate out of the many machine learning clustering techniques available due to the nature of the phasor data and also due to the spreading of points in the phasor space for the cases of low pixel photon counts. In order to quantify the power of the method we performed simulations with synthetic phasor data and we tested the method by measuring its performance against a series of established clustering techniques. We also show results on lifetime and spectral data obtained from samples of live cells. The samples were prepared by staining with a series of commercially-available organelle dyes in order to target distinct structures in the cells. We show how by taking a single spectral and lifetime acquisition we resolve up to six populations in the phasor space corresponding to the six cellular staining probes. Furthermore, we use this method in combination with our other recent work, where we make use of higher harmonics of the phasor transform in order to solve for the fractions of known components and also for lifetimes of unknown components. This multi-harmonic multi-component method is highly sensitive to noise and heterogeneous distributions and here is where our clustering method simplifies the problem by preprocessing the data and fragmenting it into pixels containing distinct lifetime/spectral characteristics. (Supported by NIH P41-GM103540).

Z-STED Imaging and Spectroscopy to Investigate Nanoscale Membrane Structure and Dynamics

Super-resolution stimulated emission depletion (STED) microcopy provides optical resolution beyond the diffraction limit. The resolution can be increased laterally (xy) or axially (z). Two-dimensional STED has been extensively used to elucidate the nanoscale membrane structure and dynamics via imaging or combined with spectroscopy techniques such as fluorescence correlation spectroscopy (FCS) and spectral imaging. On the contrary, z-STED has not been used in this context. Here, we show that a combination of z-STED with FCS or spectral imaging enables us to see previously unobservable aspects of cellular membranes. We show that thanks to an axial resolution of ∼100 nm, z-STED can be used to distinguish axially close-by membranes, early endocytic vesicles, or tubular membrane structures. Combination of z-STED with FCS and spectral imaging showed diffusion dynamics and lipid organization in these structures, respectively.

Spatial Ecology of the Human Tongue Dorsum Microbiome

SUMMARYA fundamental question in microbial ecology is how microbes are spatially organized with respect to each other and their host. A test bed for examining this question is the tongue dorsum, which harbors a complex and important microbial community. Here, we use multiplexed fluorescence spectral imaging to investigate the organization of the tongue microbiome at micron to hundred-micron scales. We design oligonucleotide probes for taxa both abundant and prevalent, as determined by sequence analysis. Imaging reveals a highly structured spatial organization of microbial consortia, ranging in linear dimension from tens to hundreds of microns. The consortia appear to develop from a core of epithelial cells, with taxa clustering in domains suggestive of clonal expansion. Quantitative proximity analysis provides the basis for a model of tongue dorsum microbiome organization and dynamics. Our work illustrates how high-resolution analysis of micron-scale organization provides insights into physiological functions and microbiome-host interactions.

LED illumination module for fluorescence and spectroscopic studies in microscopy

In this report we produce a microscopic illuminator consisting of four light emitting diodes (LEDs) with different wavelength. The radiation power of each LED could be controlled independently. The dichroic mirrors are used as a beam combiner. So, it is possible to create a highlight of the required spectral composition on the object under study. This illuminator is useful both for fluorescent analysis or spectral imaging.

Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method.

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the "spectral + spatial" features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.

Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine.

This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early-stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non-atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100-pixel points were randomly extracted after binarization. Diagnostic models of non-atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial-least-square discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms. The prediction effects of PLS-DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS-DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early-stage gastric cancer.

A Label-free Multicolor Optical Surface Tomography (ALMOST) imaging method for nontransparent 3D samples

BackgroundCurrent mesoscale 3D imaging techniques are limited to transparent or cleared samples or require the use of X-rays. This is a severe limitation for many research areas, as the 3D color surface morphology of opaque samples—for example, intact adult Drosophila, Xenopus embryos, and other non-transparent samples—cannot be assessed. We have developed “ALMOST,” a novel optical method for 3D surface imaging of reflective opaque objects utilizing an optical projection tomography device in combination with oblique illumination and optical filters.ResultsAs well as demonstrating image formation, we provide background information and explain the reconstruction—and consequent rendering—using a standard filtered back projection algorithm and 3D software. We expanded our approach to fluorescence and multi-channel spectral imaging, validating our results with micro-computed tomography. Different biological and inorganic test samples were used to highlight the versatility of our approach. To further demonstrate the applicability of ALMOST, we explored the muscle-induced form change of the Drosophila larva, imaged adult Drosophila, dynamically visualized the closure of neural folds during neurulation of live Xenopus embryos, and showed the complementarity of our approach by comparison with transmitted light and fluorescence OPT imaging of a Xenopus tadpole.ConclusionThus, our new modality for spectral/color, macro/mesoscopic 3D imaging can be applied to a variety of model organisms and enables the longitudinal surface dynamics during development to be revealed.