Unsupervised clustering of multiparametric fluorescent images extends the spectrum of detectable cell membrane phases with sub-micrometric resolution

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.

Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM

Multiplexed fluorescence detection has become increasingly important in the fields of biosensing and bioimaging. Although a variety of excitation/detection optical designs and fluorescence unmixing schemes have been proposed to allow for multiplexed imaging, rapid and reliable differentiation and quantification of multiple fluorescent species at each imaging pixel is still challenging. Here we present a pulsed interleaved excitation spectral fluorescence lifetime microscopic (PIE-sFLIM) system that can simultaneously image six fluorescent tags in live cells in a single hyperspectral snapshot. Using an alternating pulsed laser excitation scheme at two different wavelengths and a synchronized 16-channel time-resolved spectral detector, our PIE-sFLIM system can effectively excite multiple fluorophores and collect their emission over a broad spectrum for analysis. Combining our system with the advanced live-cell labeling techniques and the lifetime/spectral phasor analysis, our PIE-sFLIM approach can well unmix the fluorescence of six fluorophores acquired in a single measurement, thus improving the imaging speed in live-specimen investigation.

Unveiling nanoscale optical signatures of cytokine-induced β-cell dysfunction

Pro-inflammatory cytokines contribute to β-cell failure in both Type-1 and Type-2 Diabetes. Data collected so far allowed to dissect the genomic, transcriptomic, proteomic and biochemical landscape underlying cytokine-induced β-cell progression through dysfunction. Yet, no report thus far complemented such molecular information with the direct optical nanoscopy of the β-cell subcellular environment. Here we tackle this issue in Insulinoma 1E (INS-1E) β-cells by label-free fluorescence lifetime imaging microscopy (FLIM) and fluorescence-based super resolution imaging by expansion microscopy (ExM). It is found that 24-h exposure to IL-1β and IFN-γ is associated with a neat modification of the FLIM signature of cell autofluorescence due to the increase of either enzyme-bound NAD(P)H molecules and of oxidized lipid species. At the same time, ExM-based direct imaging unveils neat alteration of mitochondrial morphology (i.e. ~ 80% increase of mitochondrial circularity), marked degranulation (i.e. ~ 40% loss of insulin granules, with mis-localization of the surviving pool), appearance of F-actin-positive membrane blebs and an hitherto unknown extensive fragmentation of the microtubules network (e.g. ~ 37% reduction in the number of branches). Reported observations provide an optical-microscopy framework to interpret the amount of molecular information collected so far on β-cell dysfunction and pave the way to future ex-vivo and in-vivo investigations.

Metabolic Imaging and Molecular Biology Reveal the Interplay between Lipid Metabolism and DHA-Induced Modulation of Redox Homeostasis in RPE Cells

Diabetes-induced oxidative stress induces the development of vascular complications, which are significant causes of morbidity and mortality in diabetic patients. Among these, diabetic retinopathy (DR) is often caused by functional changes in the blood-retinal barrier (BRB) due to harmful oxidative stress events in lipids, proteins, and DNA. Docosahexaenoic acid (DHA) has a potential therapeutic effect against hyperglycemia-induced oxidative damage and apoptotic pathways in the main constituents of BRB, retinal pigment epithelium cells (ARPE-19). Effective antioxidant response elicited by DHA is driven by the activation of the Nrf2/Nqo1 signaling cascade, which leads to the formation of NADH, a reductive agent found in the cytoplasm. Nrf2 also induces the expression of genes encoding enzymes involved in lipid metabolism. This study, therefore, aims at investigating the modulation of lipid metabolism induced by high-glucose (HG) on ARPE-19 cells through the integration of metabolic imaging and molecular biology to provide a comprehensive functional and molecular characterization of the mechanisms activated in the disease, as well the therapeutic role of DHA. This study shows that HG augments RPE metabolic processes by enhancing lipid metabolism, from fatty acid uptake and turnover to lipid biosynthesis and β-oxidation. DHA exerts its beneficial effect by ameliorating lipid metabolism and reducing the increased ROS production under HG conditions. This investigation may provide novel insight for formulating novel treatments for DR by targeting lipid metabolism pathways.

Spatial Reorganization of Liquid Crystalline Domains of Red Blood Cells in Type 2 Diabetic Patients with Peripheral Artery Disease

In this work, we will investigate if red blood cell (RBC) membrane fluidity, influenced by several hyperglycemia-induced pathways, could provide a complementary index of HbA1c to monitor the development of type 2 diabetes mellitus (T2DM)-related macroangiopathic complications such as Peripheral Artery Disease (PAD). The contextual liquid crystalline (LC) domain spatial organization in the membrane was analysed to investigate the phase dynamics of the transition. Twenty-seven patients with long-duration T2DM were recruited and classified in DM, including 12 non-PAD patients, and DM + PAD, including 15 patients in any stage of PAD. Mean values of RBC generalized polarization (GP), representative of membrane fluidity, together with spatial organization of LC domains were compared between the two groups; p-values < 0.05 were considered statistically significant. Although comparable for anthropometric characteristics, duration of diabetes, and HbA1c, RBC membranes of PAD patients were found to be significantly more fluid (GP: 0.501 ± 0.026) than non-PAD patients (GP: 0.519 ± 0.007). These alterations were shown to be triggered by changes in both LC microdomain composition and distribution. We found a decrease in Feret diameter from 0.245 ± 0.281 μm in DM to 0.183 ± 0.124 μm in DM + PAD, and an increase in circularity. Altered RBC membrane fluidity is correlated to a spatial reconfiguration of LC domains, which, by possibly altering metabolic function, are associated with the development of T2DM-related macroangiopathic complications.

Proinflammatory and Cancer-Promoting Pathobiont Fusobacterium nucleatum Directly Targets Colorectal Cancer Stem Cells

Intestinal bacterial communities participate in gut homeostasis and are recognized as crucial in bowel inflammation and colorectal cancer (CRC). Fusobacterium nucleatum (Fn), a pathobiont of the oral microflora, has recently emerged as a CRC-associated microbe linked to disease progression, metastasis, and a poor clinical outcome; however, the primary cellular and/or microenvironmental targets of this agent remain elusive. We report here that Fn directly targets putative colorectal cancer stem cells (CR-CSCs), a tumor cell subset endowed with cancer re-initiating capacity after surgery and chemotherapy. A patient-derived CSC line, highly enriched (70%) for the stem marker CD133, was expanded as tumor spheroids, dissociated, and exposed in vitro to varying amounts (range 100–500 MOI) of Fn. We found that Fn stably adheres to CSCs, likely by multiple interactions involving the tumor-associated Gal-GalNac disaccharide and the Fn-docking protein CEA-family cell adhesion molecule 1 (CEACAM-1), robustly expressed on CSCs. Importantly, Fn elicited innate immune responses in CSCs and triggered a growth factor-like, protein tyrosine phosphorylation cascade largely dependent on CEACAM-1 and culminating in the activation of p42/44 MAP kinase. Thus, the direct stimulation of CSCs by Fn may contribute to microbiota-driven colorectal carcinogenesis and represent a target for innovative therapies.

Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness

VO₂max index has a significant impact on overall health. Its estimation through wearables notifies the user of his level of fitness but cannot provide a detailed analysis of the time intervals in which heartbeat dynamics are changed and/or fatigue is emerging. Here, we developed a multiple modality biosignal processing method to investigate running sessions to characterize in real time heartbeat dynamics in response to external energy demand. We isolated dynamic regimes whose fraction increases with the VO₂max and with the emergence of neuromuscular fatigue. This analysis can be extremely valuable by providing personalized feedback about the user’s fitness level improvement that can be realized by developing personalized exercise plans aimed to target a contextual increase in the dynamic regime fraction related to VO₂max increase, at the expense of the dynamic regime fraction related to the emergence of fatigue. These strategies can ultimately result in the reduction in cardiovascular risk.

Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data

Self-monitoring of weight, diet and physical activity is a valuable component of behavioral weight loss treatment. The validation and user-friendliness of this approach is not optimal since users are selected from homogeneous pools and rely on different applications, increasing the burden and achieving partial, generic and/or unrelated information about their metabolic state. Moreover, studies establishing type, time, duration, and adherence criteria for self-monitoring are lacking. In this study, we developed a digital web-based application (ArmOnIA), which integrates dietary, anthropometric, and physical activity data and provides a personalized estimation of energy balance. Moreover, we determined type, time, duration, and adherence criteria for self-monitoring to achieve significant weight loss in a highly heterogeneous group. A single-arm, uncontrolled prospective study on self-monitored voluntary adults for 7 months was performed. Hierarchical clustering of adherence parameters yielded three behavioral approaches: high (HA), low (LA), and medium (MA) adherence. Average BMI decrease is statistically significant between LA and HA. Moreover, we defined thresholds for the minimum frequencies and duration of dietary and weight self-monitoring. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim of achieving long-lasting results when pursuing a healthy lifestyle.

Phasor-based image segmentation: machine learning clustering techniques

The phasor approach is a well-established method for data visualization and image analysis in spectral and lifetime fluorescence microscopy. Nevertheless, it is typically applied in a user-dependent manner by manually selecting regions of interest on the phasor space to find distinct regions in the fluorescence images. In this paper we present our work on using machine learning clustering techniques to establish an unsupervised and automatic method that can be used for identifying populations of fluorescent species in spectral and lifetime imaging. We demonstrate our method using both synthetic data, created by sampling photon arrival times and plotting the distributions on the phasor plot, and real live cells samples, by staining cellular organelles with a selection of commercial probes.

Investigation of the Membrane Fluidity Regulation of Fatty Acid Intracellular Distribution by Fluorescence Lifetime Imaging of Novel Polarity Sensitive Fluorescent Derivatives

Free fatty acids are essential structural components of the cell, and their intracellular distribution and effects on membrane organelles have crucial roles in regulating the metabolism, development, and cell cycle of most cell types. Here we engineered novel fluorescent, polarity-sensitive fatty acid derivatives, with the fatty acid aliphatic chain of increasing length (from 12 to 18 carbons). As in the laurdan probe, the lipophilic acyl tail is connected to the environmentally sensitive dimethylaminonaphthalene moiety. The fluorescence lifetime imaging analysis allowed us to monitor the intracellular distribution of the free fatty acids within the cell, and to simultaneously examine how the fluidity and the microviscosity of the membrane environment influence their localization. Each of these probes can thus be used to investigate the membrane fluidity regulation of the correspondent fatty acid intracellular distribution. We observed that, in PC-12 cells, fluorescent sensitive fatty acid derivatives with increased chain length compartmentalize more preferentially in the fluid regions, characterized by a low microviscosity. Moreover, fatty acid derivatives with the longest chain compartmentalize in lipid droplets and lysosomes with characteristic lifetimes, thus making these probes a promising tool for monitoring lipophagy and related events.