Autofluorescence Lifetime Research Articles

Detection and characterization of colorectal cancer by autofluorescence lifetime imaging on surgical specimens.

Colorectal cancer (CRC) ranks among the most prevalent malignancies worldwide, driving a quest for comprehensive characterization methods. We report a characterization of the ex vivo autofluorescence lifetime fingerprint of colorectal tissues obtained from 73 patients that underwent surgical resection. We specifically target the autofluorescence characteristics of collagens, reduced nicotine adenine (phosphate) dinucleotide (NAD(P)H), and flavins employing a fiber-based dual excitation (375nm and 445nm) optical imaging system. Autofluorescence-derived parameters obtained from normal tissues, adenomatous lesions, and adenocarcinomas were analyzed considering the underlying clinicopathological features. Our results indicate that differences between tissues are primarily driven by collagen and flavins autofluorescence parameters. We also report changes in the autofluorescence parameters associated with NAD(P)H that we tentatively attribute to intratumoral heterogeneity, potentially associated to the presence of distinct metabolic subpopulations. Changes in autofluorescence signatures of malignant tumors were also observed with lymphatic and venous invasion, differentiation grade, and microsatellite instability. Finally, we characterized the impact of radiative treatment in the autofluorescence fingerprints of rectal tissues and observed a generalized increase in the mean lifetime of radiated adenocarcinomas, which is suggestive of altered metabolism and structural remodeling. Overall, our preliminary findings indicate that multiparametric autofluorescence lifetime measurements have the potential to significantly enhance clinical decision-making in CRC, spanning from initial diagnosis to ongoing management. We believe that our results will provide a foundational framework for future investigations to further understand and combat CRC exploiting autofluorescence measurements.

Exploring the structure, metabolism, and biochemistry of the neuronal microenvironment label-free using fast simultaneous multimodal optical microscopy

The technologies to examine the neuronal microenvironment label free remain critically underexplored. There is a gap in our knowledge of underlying metabolic, biochemical, and electrophysiological mechanisms behind several neurological processes at a cellular level, which can be traced to the lack of versatile and high-throughput tools to investigate neural networks. In this paper, four label-free contrasts were explored as mechanisms to study neuronal activity, namely, scattering, birefringence, autofluorescence from metabolic cofactors and molecules, and local biochemistry. To overcome challenges of observing neuronal activity spanning three orders of magnitude in space and time, microscopes had to be developed to simultaneously capture these contrasts quickly, with high resolution, and over a large FOV. We developed versatile autofluorescence lifetime, multiharmonic generation, polarization-sensitive interferometry, and Raman imaging in epi-detection (VAMPIRE) microscopy to simultaneously observe multiple facets of neuronal structure and dynamics. The accelerated computational-imaging-driven acquisition speeds, the utilization of a single light source to evoke all contrasts, the simultaneous acquisition that provides an otherwise impossible multimodal dynamic imaging capability, and the real-time processing of the data enable VAMPIRE microscopy as a powerful imaging platform for neurophotonics and beyond.

Drosophila melanogaster sperm turn more oxidative in the female.

Males and females of many species store sperm for extended periods. During storage, sperm are predicted to undergo cellular and functional changes, especially towards glycolytic energy metabolism because oxygen radicals derived from oxidative phosphorylation can affect sperm motility and fertilisation ability. However, not all species can use both major energy metabolism pathways. Here, we examined the fruit fly Drosophila melanogaster and asked whether sperm metabolism can be fuelled by both glycolysis and oxidative phosphorylation, and to what extent metabolism changes during storage. Inhibiting glycolysis in vitro led to a more oxidative state of sperm; inhibiting oxidative phosphorylation increased the glycolytic component, assessed by multi-photon autofluorescence lifetime imaging (FLIM) of NAD(P)H. We further examined sperm in male and female sperm storage organs using FLIM of NAD(P)H and FAD. In intact storage organs, we found that, unexpectedly, (i) sperm were more oxidative in females than in males, and (ii) oxidative phosphorylation increased with storage duration in females. Our observation that the relative contribution of the two major energy metabolic pathways in D. melanogaster sperm differs in males and females and over storage time has important evolutionary implications.

Delineation of gastrointestinal tumors biopsies using a fluorescence lifetime imaging optical fiber probe.

Autofluorescence spectroscopy has emerged in recent years as a powerful tool to report label-free contrast between normal and diseased tissues, both in vivo and ex-vivo. We report the application of an instrument employing an optical fiber probe and capable of performing real-time autofluorescence lifetime imaging at a macroscopic scale, under bright background conditions. We validate and demonstrate the practicality of this technology to discriminate healthy against neoplastic tissue in freshly excised tumor biopsies. The capability of delineating tumor margins through processing the fluorescence decays in the phasors domain was demonstrated on four different types of cancer, highlighting the broad range of potential clinical applications for the proposed approach. The presented results suggest that our autofluorescence lifetime imaging probe, together with phasor analysis, can offer a real-time tool to observe lifetime contrast on tissues and, thus, is a suitable candidate for improving in situ tissue diagnostics during surgery.

Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging.

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.

Metabolic changes in Toxoplasma gondii-infected host cells measured by autofluorescence imaging.

Toxoplasma gondii, the causative agent of toxoplasmosis, is an obligate intracellular parasite that infects warm-blooded vertebrates across the world. In humans, seropositivity rates of T. gondii range from 10% to 90% across communities. Despite its prevalence, few studies address how T. gondii infection changes the metabolism of host cells. In this study, we investigate how T. gondii manipulates the host cell metabolic environment by monitoring the metabolic response over time using noninvasive autofluorescence lifetime imaging of single cells, metabolite analysis, extracellular flux analysis, and reactive oxygen species (ROS) production. Autofluorescence lifetime imaging indicates that infected host cells become more oxidized and have an increased proportion of bound NAD(P)H compared to uninfected controls. Over time, infected cells also show decreases in levels of intracellular glucose and lactate, increases in oxygen consumption, and variability in ROS production. We further examined changes associated with the pre-invasion "kiss and spit" process using autofluorescence lifetime imaging, which also showed a more oxidized host cell with an increased proportion of bound NAD(P)H over 48 hours compared to uninfected controls, suggesting that metabolic changes in host cells are induced by T. gondii kiss and spit even without invasion.IMPORTANCEThis study sheds light on previously unexplored changes in host cell metabolism induced by T. gondii infection using noninvasive, label-free autofluorescence imaging. In this study, we use optical metabolic imaging (OMI) to measure the optical redox ratio (ORR) in conjunction with fluorescence lifetime imaging microscopy (FLIM) to noninvasively monitor single host cell response to T. gondii infection over 48 hours. Collectively, our results affirm the value of using autofluorescence lifetime imaging to noninvasively monitor metabolic changes in host cells over the time course of a microbial infection. Understanding this metabolic relationship between the host cell and the parasite could uncover new treatment and prevention options for T. gondii infections worldwide.

Imaging immunometabolism in situ in live animals.

Immunometabolism is a rapidly developing field that holds great promise for diagnostic and therapeutic benefits to human diseases. The field has emerged based on seminal findings from in vitro and ex vivo studies that established the fundamental role of metabolism in immune cell effector functions. Currently, the field is acknowledging the necessity of investigating cellular metabolism within the natural context of biological processes. Examining cells in their native microenvironment is essential not only to reveal cell-intrinsic mechanisms but also to understand how cross-talk between neighboring cells regulates metabolism at the tissue level in a local niche. This necessity is driving innovation and advancement in multiple imaging-based technologies to enable analysis of dynamic intracellular metabolism at the single-cell level, with spatial and temporal resolution. In this review, we tally the currently available imaging-based technologies and explore the emerging methods of Raman and autofluorescence lifetime imaging microscopy, which hold significant potential and offer broad applications in the field of immunometabolism.

Autofluorescence lifetime flow cytometry with time-correlated single photon counting.

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375 nm picosecond-pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2-5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm2 that is well below damage thresholds (25 J/cm2 at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.

Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.

Multimodal Multiphoton Tomography with a Compact Femtosecond Fiber Laser

Multiphoton tomography (MPT) based on near infrared (NIR) femtosecond laser technology has become a versatile high-resolution clinical and research imaging tool. We report on multimodal MPT with the air-cooled fiber laser tomograph MPTcompact. The ultracompact passively mode-locked erbium-doped 50/80 MHz laser operating at 780 nm is directly integrated into the 360° imaging head mounted on a flexible mechanical arm. The tomograph provides optical biopsies with subcellular resolution and optical metabolic imaging capability based on two-photon autofluorescence (AF), second harmonic generation, fluorescence lifetime imaging (FLIM) by time-correlated single photon counting with 250 ps temporal resolution, reflectance confocal microscopy, and white LED light CMOS camera imaging. For the first time, NIR femtosecond laser pulses have been used on human skin to realize simultaneous one-photon confocal imaging and two-photon imaging. Most useful information is provided by the two-photon excited intratissue AF of the coenzymes NAD(P)H and flavins. The signals of both types of coenzymes can be separated by FLIM. Furthermore, the free and protein-bound forms can be distinguished by time-resolved AF detection because the protein-bound NADH has one order higher AF lifetime than free (non-bound) NADH. The tomograph contains onboard storage batteries so that it can operate for up to a few hours independently from external power supply. Applications include in vivo cancer detection and in situ evaluation of anti-aging drugs and pharmaceuticals. The tomograph MPTcompact has been successfully tested in a clinical multicenter study for the diagnosis of malignant melanoma on 100 patients with suspicious pigmented lesions. MPT has the potential to realize non-invasive high-resolution label-free in vivo histology within minutes and, therefore, to reduce the number of physically taken biopsies.

Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues.

Advancements in optical imaging techniques have revolutionized the field of biomedical research, allowing for the comprehensive characterization of tissues and their underlying biological processes. Yet, there is still a lack of tools to provide quantitative and objective characterization of tissues that can aid clinical assessment in vivo to enhance diagnostic and therapeutic interventions. Here, we present a clinically viable fiber-based imaging system combining time-resolved spectrofluorimetry and reflectance spectroscopy to achieve fast multiparametric macroscopic characterization of tissues. An essential feature of the setup is its ability to perform dual wavelength excitation in combination with recording time-resolved fluorescence data in several spectral intervals. Initial validation of this bimodal system was carried out in freshly resected human colorectal cancer specimens, where we demonstrated the ability of the system to differentiate normal from malignant tissues based on their autofluorescence and reflectance properties. To further highlight the complementarity of autofluorescence and reflectance measurements and demonstrate viability in a clinically relevant scenario, we also collected in vivo data from the skin of a volunteer. Altogether, integration of these modalities in a single platform can offer multidimensional characterization of tissues, thus facilitating a deeper understanding of biological processes and potentially advancing diagnostic and therapeutic approaches in various medical applications.

Two-photon autofluorescence lifetime assay of rabbit photoreceptors and retinal pigment epithelium during light-dark visual cycles in rabbit retina

Two-photon excited fluorescence (TPEF) is a powerful technique that enables the examination of intrinsic retinal fluorophores involved in cellular metabolism and the visual cycle. Although previous intensity-based TPEF studies in non-human primates have successfully imaged several classes of retinal cells and elucidated aspects of both rod and cone photoreceptor function, fluorescence lifetime imaging (FLIM) of the retinal cells under light-dark visual cycle has yet to be fully exploited. Here we demonstrate a FLIM assay of photoreceptors and retinal pigment epithelium (RPE) that reveals key insights into retinal physiology and adaptation. We found that photoreceptor fluorescence lifetimes increase and decrease in sync with light and dark exposure, respectively. This is likely due to changes in all-trans-retinol and all-trans-retinal levels in the outer segments, mediated by phototransduction and visual cycle activity. During light exposure, RPE fluorescence lifetime was observed to increase steadily over time, as a result of all-trans-retinol accumulation during the visual cycle and decreasing metabolism caused by the lack of normal perfusion of the sample. Our system can measure the fluorescence lifetime of intrinsic retinal fluorophores on a cellular scale, revealing differences in lifetime between retinal cell classes under different conditions of light and dark exposure.

3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data.

Fluorescence lifetime imaging of the co-enzyme reduced nicotinamide adenine dinucleotide (NADH) offers a label-free approach for detecting cellular metabolic perturbations. However, the relationships between variations in NADH lifetime and metabolic pathway changes are complex, preventing robust interpretation of NADH lifetime data relative to metabolic phenotypes. Here, a three-dimensional convolutional neural network (3D CNN) trained at the cell level with 3D NAD(P)H lifetime decay images (two spatial dimensions and one time dimension) was developed to identify metabolic pathway usage by cancer cells. NADH fluorescence lifetime images of MCF7 breast cancer cells with three isolated metabolic pathways, glycolysis, oxidative phosphorylation, and glutaminolysis were obtained by a multiphoton fluorescence lifetime microscope and then segmented into individual cells as the input data for the classification models. The 3D CNN models achieved over 90% accuracy in identifying cancer cells reliant on glycolysis, oxidative phosphorylation, or glutaminolysis. Furthermore, the model trained with human breast cancer cell data successfully predicted the differences in metabolic phenotypes of macrophages from control and POLG-mutated mice. These results suggest that the integration of autofluorescence lifetime imaging with 3D CNNs enables intracellular spatial patterns of NADH intensity and temporal dynamics of the lifetime decay to discriminate multiple metabolic phenotypes. Furthermore, the use of 3D CNNs to identify metabolic phenotypes from NADH fluorescence lifetime decay images eliminates the need for time- and expertise-demanding exponential decay fitting procedures. In summary, metabolic-prediction CNNs will enable live-cell and in vivo metabolic measurements with single-cell resolution, filling a current gap in metabolic measurement technologies.

Probing cell metabolism using the two-photon excitation autofluorescence lifetime of tryptophan.

Compared to intensity detection, fluorescence lifetime has the advantage of being unaffected by variations in excitation intensity, fluorophore concentration, or attenuation due to biological absorption and scattering. In this Letter, to the best of our knowledge, we present the use of the two-photon excitation autofluorescence lifetime imaging of tryptophan (TRP) to probe cell metabolism for the first time. Tests of pure chemical samples showed that the fluorescence lifetime of TRP was highly sensitive to changes in molecular conformation and the environment. In in vitro cell experiments, we successfully utilized the fluorescence lifetime of TRP to distinguish tumor cells from healthy cells, track the therapeutic effect of the tumor immunotherapy drug 1-MT for HeLa cells, and monitor cells in response to carbonyl cyanide 3-chlorophenylhydrazone (CCCP)-induced cell apoptosis. These results reveal that the two-photon excitation autofluorescence lifetime of TRP could be a sensitive natural probe of cell metabolism in living cells.

Label-free spatially maintained measurements of metabolic phenotypes in cells.

Metabolic reprogramming at a cellular level contributes to many diseases including cancer, yet few assays are capable of measuring metabolic pathway usage by individual cells within living samples. Here, autofluorescence lifetime imaging is combined with single-cell segmentation and machine-learning models to predict the metabolic pathway usage of cancer cells. The metabolic activities of MCF7 breast cancer cells and HepG2 liver cancer cells were controlled by growing the cells in culture media with specific substrates and metabolic inhibitors. Fluorescence lifetime images of two endogenous metabolic coenzymes, reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), were acquired by a multi-photon fluorescence lifetime microscope and analyzed at the cellular level. Quantitative changes of NADH and FAD lifetime components were observed for cells using glycolysis, oxidative phosphorylation, and glutaminolysis. Conventional machine learning models trained with the autofluorescence features classified cells as dependent on glycolytic or oxidative metabolism with 90%-92% accuracy. Furthermore, adapting convolutional neural networks to predict cancer cell metabolic perturbations from the autofluorescence lifetime images provided improved performance, 95% accuracy, over traditional models trained via extracted features. Additionally, the model trained with the lifetime features of cancer cells could be transferred to autofluorescence lifetime images of T cells, with a prediction that 80% of activated T cells were glycolytic, and 97% of quiescent T cells were oxidative. In summary, autofluorescence lifetime imaging combined with machine learning models can detect metabolic perturbations between glycolysis and oxidative metabolism of living samples at a cellular level, providing a label-free technology to study cellular metabolism and metabolic heterogeneity.

Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes.

Introduction: Autofluorescence imaging of the coenzymes reduced nicotinamide (phosphate) dinucleotide (NAD(P)H) and oxidized flavin adenine dinucleotide (FAD) provides a label-free method to detect cellular metabolism and phenotypes. Time-domain fluorescence lifetime data can be analyzed by exponential decay fitting to extract fluorescence lifetimes or by a fit-free phasor transformation to compute phasor coordinates. Methods: Here, fluorescence lifetime data analysis by biexponential decay curve fitting is compared with phasor coordinate analysis as input data to machine learning models to predict cell phenotypes. Glycolysis and oxidative phosphorylation of MCF7 breast cancer cells were chemically inhibited with 2-deoxy-d-glucose and sodium cyanide, respectively; and fluorescence lifetime images of NAD(P)H and FAD were obtained using a multiphoton microscope. Results: Machine learning algorithms built from either the extracted lifetime values or phasor coordinates predict MCF7 metabolism with a high accuracy (∼88%). Similarly, fluorescence lifetime images of M0, M1, and M2 macrophages were acquired and analyzed by decay fitting and phasor analysis. Machine learning models trained with features from curve fitting discriminate different macrophage phenotypes with improved performance over models trained using only phasor coordinates. Discussion: Altogether, the results demonstrate that both curve fitting and phasor analysis of autofluorescence lifetime images can be used in machine learning models for classification of cell phenotype from the lifetime data.

Light-sheet autofluorescence lifetime imaging with a single-photon avalanche diode array.

Fluorescence lifetime imaging microscopy (FLIM) of the metabolic co-enzyme nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] is a popular method to monitor single-cell metabolism within unperturbed, living 3D systems. However, FLIM of NAD(P)H has not been performed in a light-sheet geometry, which is advantageous for rapid imaging of cells within live 3D samples. We aim to design, validate, and demonstrate a proof-of-concept light-sheet system for NAD(P)H FLIM. A single-photon avalanche diode camera was integrated into a light-sheet microscope to achieve optical sectioning and limit out-of-focus contributions for NAD(P)H FLIM of single cells. An NAD(P)H light-sheet FLIM system was built and validated with fluorescence lifetime standards and with time-course imaging of metabolic perturbations in pancreas cancer cells with 10s integration times. NAD(P)H light-sheet FLIM in vivo was demonstrated with live neutrophil imaging in a larval zebrafish tail wound also with 10s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM were compared across laser scanning and light-sheet geometries, indicating a to acquisition speed advantage for the light sheet compared to the laser scanning geometry. FLIM of NAD(P)H is feasible in a light-sheet geometry and is attractive for 3D live cell imaging applications, such as monitoring immune cell metabolism and migration within an organism.

Application of Nanosecond Pulsed Electric Field and Autofluorescence Lifetime Microscopy of FAD in Lung Cells.

Exposure of nanosecond pulsed electric fields (nsPEFs) to live cells is an increasing research interest in biology and medicine. Despite extensive studies, a question still remains as to how effects of application of nsPEF on intracellular functions are different between cancerous cells and normal cells and how the difference can be detected. Herein, we have presented an approach of autofluorescence lifetime (AFL) microscopy of flavin adenine dinucleotide (FAD) to detect effects of application of nsPEF having 50 ns of a pulse width, nsPEF(50), on intracellular function in lung cancerous cells, A549 and H661, which show nsPEF(50)-induced apoptosis, and normal cells, MRC-5, in which the field effect is less or not induced. Then, the application of nsPEF(50) is shown to increase the lifetime of FAD autofluorescence in lung cancerous cells, whereas the electric field effects on the autofluorescence of FAD was not significant in normal healthy cells, which indicates that the lifetime measurements of FAD autofluorescence are applicable to detect the field-induced change in intracellular functions. Lifetime and intensity microscopic images of FAD autofluorescence in these lung cells were also acquired after exposure to the apoptosis-inducer staurosporine (STS). Then, it was found that the AFL of FAD became longer after exposure not only in the cancerous cells but also in the normal cells. These results indicate that nsPEF(50) applied to lung cells induced apoptotic cell death only in lung cancerous cells (H661 and A549) but not in lung normal cells (MRC-5), whereas STS induced apoptotic cell death both in lung cancerous cells and in lung normal cells. The lifetime microscopy of FAD autofluorescence is suggested to be very useful as a sensitive detection method of nsPEF-induced apoptotic cell death.

Label-Free Characterization of Atherosclerotic Plaques Via High-Resolution Multispectral Fluorescence Lifetime Imaging Microscopy.

Autofluorescence lifetime (AFL) imaging, a robust technique that enables label-free molecular investigation of biological tissues, is being introduced into the field of cardiovascular diagnostics. However, detailed AFL characteristics of coronary arteries remain elusive and there is a lack of methodology enabling such characterization. We developed multispectral fluorescence lifetime imaging microscopy (FLIM) based on analog-mean-delay. Freshly sectioned coronary arteries and atheromas, harvested from 5 swine models, were imaged using FLIM and stained to label lipids, macrophages, collagen, and smooth muscle cells. The components were quantitated from digitized histological images and compared with the corresponding FLIM. Multispectral AFL parameters derived from 2 different spectral bands (390 nm and 450 nm) were analyzed. FLIM provided a wide field-of-view, high-resolution AFL imaging of frozen sections. Principal compositions of coronary arteries, such as tunica media, tunica adventitia, elastic laminas, smooth muscle cell-enriched fibrous plaque, lipid-rich core, and foamy macrophages, were well visualized in FLIM images and were found to have each different AFL spectra. In particular, proatherogenic components including lipids and foamy macrophages exhibited significantly different AFL values compared with plaque-stabilizing collagen- or smooth muscle cell-enriched tissues (P<0.0001). Pairwise comparisons showed that each composition was distinguishable from another by the difference in multispectral AFL parameters. Pixel-level analysis based on coregistered FLIM-histology dataset showed that each component of atherosclerosis (lipids, macrophages, collagen, and smooth muscle cells) had distinct correlation pattern with AFL parameters. Random forest regressors trained with the dataset allowed automated, simultaneous visualization of the key atherosclerotic components with high accuracy (r>0.87). FLIM provided detailed pixel-level AFL investigation of the complex composition of coronary artery and atheroma. Our FLIM strategy enabling an automated, comprehensive visualization of multiple plaque components from unlabeled sections will be highly useful to efficiently evaluate ex vivo samples without the need for histological staining and analysis.

Label-free autofluorescence lifetime reveals the structural dynamics of ataxin-3 inside droplets formed via liquid–liquid phase separation

Liquid–liquid phase separation is a phenomenon that features the formation of liquid droplets containing concentrated solutes. The droplets of neurodegeneration-associated proteins are prone to generate aggregates and cause diseases. To uncover the aggregation process from the droplets, it is necessary to analyze the protein structure with keeping the droplet state in a label-free manner, but there was no suitable method. In this study, we observed the structural changes of ataxin-3, a protein associated with Machado–Joseph disease, inside the droplets, using autofluorescence lifetime microscopy. Each droplet showed autofluorescence due to tryptophan (Trp) residues, and its lifetime increased with time, reflecting structural changes toward aggregation. We used Trp mutants to reveal the structural changes around each Trp and showed that the structural change consists of several steps on different timescales. We demonstrated that the present method visualizes the protein dynamics inside a droplet in a label-free manner. Further investigations revealed that the aggregate structure formed in the droplets differs from that formed in dispersed solutions and that a polyglutamine repeat extension in ataxin-3 hardly modulates the aggregation dynamics in the droplets. These findings highlight that the droplet environment facilitates unique protein dynamics different from those in solutions.