High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.

UNASSIGNED: Heart disease is the leading cause of death in the United States, yet research is limited by the inability to culture primary cardiac cells. Cardiomyocytes (CMs) derived from human induced pluripotent stem cells (iPSCs) are a promising solution for drug screening and disease modeling.
UNASSIGNED: Induced pluripotent stem cell-derived CM (iPSC-CM) differentiation and maturation studies typically use heterogeneous substrates for growth and destructive verification methods. Reproducible, tunable substrates and touch-free monitoring are needed to identify ideal conditions to produce homogenous, functional CMs.
UNASSIGNED: We generated synthetic polyethylene glycol-based hydrogels for iPSC-CM differentiation and maturation. Peptide concentrations, combinations, and gel stiffness were tuned independently. Label-free optical redox imaging (ORI) was performed on a widefield microscope in a 96-well screen of gel formulations. We performed live-cell imaging throughout differentiation and early to late maturation to identify key metabolic shifts.
UNASSIGNED: Label-free ORI confirmed the expected metabolic shifts toward oxidative phosphorylation throughout the differentiation and maturation processes of iPSC-CMs on synthetic hydrogels. Furthermore, ORI distinguished high and low differentiation efficiency cell batches in the cardiac progenitor stage.
UNASSIGNED: We established a workflow for medium throughput screening of synthetic hydrogel conditions with the ability to perform repeated live-cell measurements and confirm expected metabolic shifts. These methods have implications for reproducible iPSC-CM generation in biomanufacturing.

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.

UNASSIGNED: To report a case of ophthalmomyiasis interna posterior which was asymptomatic and had pigment clumps in the inner retina at the macula.
UNASSIGNED: Single-centre, observational, retrospective case report.
UNASSIGNED: A routine refractive error check-up for an asymptomatic 52-year-old Asian Indian woman, who had relied on glasses for 8 years, unfolded a captivating narrative within her retina. This coloured fundus photo unveils mid-peripheral retinal disease with multiple outer retinal atrophic tracts, circumlinear patterns, and intricately intertwined RPE atrophic tracts. These were hyper-autofluorescent on blue autofluorescence. The inferonasal periphery had two-disc diameters of pigmented retinal-choroidal atrophic scar. The macula revealed a collection of black intraretinal pigments in parafoveal areas. The distinct clinical presentation, marked by multiple tracts and unilateral manifestation without disc pallor, hinted at the intriguing possibility of self-resolved \"Ophthalmomyiasis interna posterior.\"
UNASSIGNED: The course of disease in ophthalmomyiasis interna posterior can be self-limiting and asymptomatic. The presence of inner retinal pigments at foveal and parafoveal areas, possibly due to pigment migration from the peripheral outer retinal tracts, is a rare presentation.

Microglia, the brain\'s primary resident immune cell, exists in various phenotypic states depending on intrinsic and extrinsic signaling. Distinguishing between these phenotypes can offer valuable biological insights into neurodevelopmental and neurodegenerative processes. Recent advances in single-cell transcriptomic profiling have allowed for increased granularity and better separation of distinct microglial states. While techniques such as immunofluorescence and single-cell RNA sequencing (scRNA-seq) are available to differentiate microglial phenotypes and functions, these methods present notable limitations, including challenging quantification methods, high cost, and advanced analytical techniques. This protocol addresses these limitations by presenting an optimized cell preparation procedure that prevents ex vivo activation and a flow cytometry panel to distinguish four distinct microglial states from murine brain tissue. Following cell preparation, fluorescent antibodies were applied to label 1) homeostatic, 2) disease-associated (DAM), 3) interferon response (IRM), and 4) lipid-droplet accumulating (LDAM) microglia, based on gene markers identified in previous scRNA-Seq studies. Stained cells were analyzed by flow cytometry to assess phenotypic distribution as a function of age and sex. A key advantage of this procedure is its adaptability, allowing the panel provided to be enhanced using additional markers with an appropriate cell analyzer (i.e., Cytek Aurora 5 laser spectral flow cytometer) and interrogating different brain regions or disease models. Additionally, this protocol does not require microglial cell sorting, resulting in a relatively quick and straightforward experiment. Ultimately, this protocol can compare the distribution of microglial phenotypic states between various experimental groups, such as disease state or age, with a lower cost and higher throughput than scRNA-seq. Key features • Analysis of microglial phenotypes from murine brain without the need for cell sorting, imaging, or scRNA-seq. • This protocol can distinguish between homeostatic, disease-associated (DAM), lipid-droplet accumulating (LDAM), and interferon response (IRM) microglia from any murine brain region and/or disease model of interest. • This protocol can be modified to incorporate additional markers of interest or dyes when using a cell analyzer capable of multiple color detections.

Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

The impact of age on mesenchymal stromal cell (MSC) characteristics has been well researched. However, increased age is concomitant with increased prevalence of polypharmacy. This adjustable factor may have further implications for the functionality of MSCs and the effectiveness of autologous MSC procedures. We applied hyperspectral microscopy of cell autofluorescence-a non-invasive imaging technique used to characterise cytometabolic heterogeneity-to identify changes in the autofluorescence signals of MSCs from (1) young mice, (2) old mice, (3) young mice randomised to receive polypharmacy (9-10 weeks of oral therapeutic doses of simvastatin, metoprolol, oxycodone, oxybutynin and citalopram), and (4) old mice randomised to receive polypharmacy. Principal Component Analysis and Logistic Regression Analysis were used to assess alterations in spectral and associated metabolic characteristics. Modelling demonstrated that cells from young mice receiving polypharmacy had less NAD(P)H and increased porphyrin relative to cells from old control mice, allowing for effective separation of the two groups (AUC of ROC curve > 0.94). Similarly, cells from old polypharmacy mice were accurately separated from those from young controls due to lower levels of NAD(P)H (p < 0.001) and higher porphyrin (p < 0.001), allowing for an extremely accurate logistic regression (AUC of ROC curve = 0.99). This polypharmacy regimen may have a more profound impact on MSCs than ageing, and can simultaneously reduce optical redox ratio (ORR) and increase porphyrin levels. This has implications for the use of autologous MSCs for older patients with chronic disease.

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome\'s unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as \"autofluorescence signatures\" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.

The European brittle star Amphiura filiformis emits blue light, via a Renilla-like luciferase, which depends on the dietary acquisition of coelenterazine. Questions remain regarding luciferin availability across seasons and the persistence of luminous capabilities after a single boost of coelenterazine. To date, no study has explored the seasonal, long-term monitoring of these luminous capabilities or the tracking of luciferase expression in photogenic tissues. Through multidisciplinary analysis, we demonstrate that luminous capabilities evolve according to the exogenous acquisition of coelenterazine throughout adult life. Moreover, no coelenterazine storage forms are detected within the arms tissues. Luciferase expression persists throughout the seasons, and coelenterazine\'s presence in the brittle star diet is the only limiting factor for the bioluminescent reaction. No seasonal variation is observed, involving a continuous presence of prey containing coelenterazine. The ultrastructure description provides a morphological context to investigate the green autofluorescence signal attributed to coelenterazine during luciferin acquisition. Finally, histological analyses support the hypothesis of a pigmented sheath leading light to the tip of the spine. These insights improve our understanding of the bioluminescence phenomenon in this burrowing brittle star.

The revolution that we are seeing in the world of surgery will determine the way we understand surgical approaches in coming years. Since the implementation of minimally invasive surgery, innovations have constantly been developed to allow the laparoscopic approach to go further and be applied to more and more procedures. In recent years, we have been in the middle of another revolutionary era, with robotic surgery, the application of artificial intelligence and image-guided surgery. The latter includes 3D reconstructions for surgical planning, virtual reality, holograms or tracer-guided surgery, where ICG-guided fluorescence has provided a different perspective on surgery. ICG has been used to identify anatomical structures, assess tissue perfusion, and identify tumors or tumor lymphatic drainage. But the most important thing is that this technology has come hand in hand with the potential to develop other types of tracers that will facilitate the identification of tumor cells and ureters, as well as different light beams to identify anatomical structures. These will lead to other types of systems to assess tissue perfusion without the use of tracers, such as hyperspectral imaging. Combined with the upcoming introduction of ICG quantification, these developments represent a real revolution in the surgical world. With the imminent implementation of these technological advances, a review of their clinical application in general surgery is timely, and this review serves that aim.