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A common feature of morphogenesis is the formation of three-dimensional structures from the folding of two-dimensional epithelial sheets, aided by cell shape changes at the cellular-level. Changes in cell shape must be studied in the context of cell-polarised biomechanical processes within the epithelial sheet. In epithelia with highly curved surfaces, finding single-cell alignment along a biological axis can be difficult to automate in silico. We present ‘Origami’, a MATLAB-based image analysis pipeline to compute direction-variant cell shape features along the epithelial apico-basal axis. Our automated method accurately computed direction vectors denoting the apico-basal axis in regions with opposing curvature in synthetic epithelia and fluorescence images of zebrafish embryos. As proof of concept, we identified different cell shape signatures in the developing zebrafish inner ear, where the epithelium deforms in opposite orientations to form different structures. Origami is designed to be user-friendly and is generally applicable to fluorescence images of curved epithelia., Author summary During embryonic development, two-dimensional epithelial sheets bend and fold into complex three-dimensional structures–like paper in the origami art form. The genetic and biomechanical processes driving epithelial folding can be polarised in the epithelium, leading to asymmetric shape changes at the single cell level. Defects in such epithelial shaping have been linked to many developmental anomalies and diseases. It is, therefore, important not only to quantify shape change at the single cell level, but also to orientate these asymmetrical changes along an epithelial axis of polarity when studying morphogenetic processes. Origami is a MATLAB-based software that has been developed to automatically extract such single-cell asymmetrical shape features along the epithelial apico-basal axis from fluorescence microscopy images of folding epithelia. Origami provides a solution to computing directional vectors along the epithelial apico-basal axis followed by extracting direction-variant shape features of each segmented cell. It is generally applicable to epithelial structures regardless of complexity or direction of folding and is robust to imaging conditions. As proof of concept, Origami successfully differentiated between different cell shape signatures in highly curved structures at different developmental timepoints in the zebrafish inner ear.

Cellular force generation and force transmission are of fundamental importance for numerous biological processes and can be studied with the methods of Traction Force Microscopy (TFM) and Monolayer Stress Microscopy. Traction Force Microscopy and Monolayer Stress Microscopy solve the inverse problem of reconstructing cell-matrix tractions and inter- and intra-cellular stresses from the measured cell force-induced deformations of an adhesive substrate with known elasticity. Although several laboratories have developed software for Traction Force Microscopy and Monolayer Stress Microscopy computations, there is currently no software package available that allows non-expert users to perform a full evaluation of such experiments. Here we present pyTFM, a tool to perform Traction Force Microscopy and Monolayer Stress Microscopy on cell patches and cell layers grown in a 2-dimensional environment. pyTFM was optimized for ease-of-use; it is open-source and well documented (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background. pyTFM can be used as a standalone Python package or as an add-on to the image annotation tool ClickPoints. In combination with the ClickPoints environment, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing forces, stresses, and their distribution. In this work, we also thoroughly analyze the accuracy and performance of the Traction Force Microscopy and Monolayer Stress Microscopy algorithms of pyTFM using synthetic and experimental data from epithelial cell patches., Author summary The analysis of cellular force generation and transmission is an increasingly important aspect in the field of biological research. However, most methods for studying cellular force generation or transmission require complex calculations and have not yet been implemented in comprehensive, easy-to-use software. This is a major hurdle preventing a wider application in the field. Here we present pyTFM, an open-source Python package with a graphical user interface that can be used to evaluate cellular force generation in cells and cell colonies and force transfer within small cell patches and larger cell layers grown on the surface of an elastic substrate. In combination with the image annotation and tool ClickPoints, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing cellular forces, stresses, and their distribution. Additionally, pyTFM can be used as standalone python library. pyTFM comes with an extensive documentation (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background.

Prostate cancer cells control energy metabolism by chelating intracellular zinc. Thus, zinc delivery has been a popular therapeutic approach for prostate cancer. Here, we propose the use of the membrane-penetrating peptide Novicidin connected to zinc-Schiff base as a carrier vehicle for the delivery of zinc to prostate cells. Mass spectrometry, electrochemistry and spectrophotometry confirmed the formation/stability of this complex and provided insight regarding the availability of zinc for complex interactions. This delivery system showed minor toxicity in normal PNT1A cells and high potency towards PC3 tumor cells. The complex preferentially penetrated PC3 tumor cells in contrast to confinement to the membranes of PNT1A. Furthermore, zinc uptake was confirmed in both cell lines. Molecular analysis was used to confirm the activation of zinc stress (e.g., ZnT-1) and apoptosis (e.g., CASP-1). Our results strongly suggest that the zinc-Schiff base-Novicidin complex has great potential as a novel anticancer drug.

Background Intraoperative frozen section (FS) consultation is an important tool in surgical oncology that suffers from sampling error because the pathologist does not always know where to perform a biopsy of the surgical specimen. Intraoperative molecular imaging is a technology used in the OR to visualize lesions during surgery. We hypothesized that molecular imaging can address this pathology challenge in FS by visualizing the cancer cells in the specimen in the pathology suite. Here, we report the development and validation of a molecular-imaging capable cryostat called Smart-Cut. Methods A molecular imaging capable cryostat prototype was developed and tested using a murine model. Tumors grown in mice were targeted with a NIR contrast agent, indocyanine green (ICG), via tail vein injection. Tumors and adjacent normal tissue samples were frozen sectioned with Smart-Cut. Fluorescent sections and non-fluorescent sections were prepared for H&E and fluorescent microscopy. Fluorescent signal was quantified by tumor-to-background ratio (TBR). NIR fluorescence was tested in one patient enrolled in a clinical trial. Results The Smart-Cut prototype has a small footprint and fits well in the pathology suite. Fluorescence imaging with Smart-Cut identified cancerous tissue in the specimen in all 12 mice. No false positives or false negatives were seen, as confirmed by H&E. The mean TBR in Smart-Cut positive tissue sections was 6.8 (SD±3.8). In a clinical application in the pathology suite, NIR imaging identified two lesions in a pulmonary resection specimen, where traditional grossing only identified one. Conclusion Molecular imaging can be integrated into the pathology suite via the Smart-Cut device, and can detect cancer in frozen tissue sections using molecular imaging in a murine model.

Advances in intravital microscopy (IVM) have enabled the studies of cellular organization and dynamics in the native microenvironment of intact organisms with minimal perturbation. The abilities to track specific cell populations and monitor their interactions have opened up new horizons for visualizing cell biology in vivo, yet the success of standard fluorescence cell labeling approaches for IVM comes with a “dark side” in that unlabeled cells are invisible, leaving labeled cells or structures to appear isolated in space, devoid of their surroundings and lacking proper biological context. Here we describe a novel method for “filling in the void” by harnessing the ubiquity of extracellular (interstitial) fluid and its ease of fluorescence labelling by commonly used vascular and lymphatic tracers. We show that during routine labeling of the vasculature and lymphatics for IVM, commonly used fluorescent tracers readily perfuse the interstitial spaces of the bone marrow (BM) and the lymph node (LN), outlining the unlabeled cells and forming negative contrast images that complement standard (positive) cell labeling approaches. The method is simple yet powerful, offering a comprehensive view of the cellular landscape such as cell density and spatial distribution, as well as dynamic processes such as cell motility and transmigration across the vascular endothelium. The extracellular localization of the dye and the interstitial flow provide favorable conditions for prolonged Intravital time lapse imaging with minimal toxicity and photobleaching.

PLOS ONE 16(7), e0254739 (2021). doi:10.1371/journal.pone.0254739, Published by PLOS, San Francisco, California, US

Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images using virtual staining (also known as “label-free prediction” and “in-silico labeling”) can get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.

A rapid and cost-effective system is vital for the detection of harmful algae that causes environmental problems in terms of water quality. The approach for algae detection was to capture images based on hyperspectral fluorescence imaging microscope by detecting specific fluorescence signatures. With the high degree of overlapping spectra of algae, the distribution of pigment in the region of interest was unknown according to a previous report. We propose an optimization method of multivariate curve resolution (MCR) to improve the performance of pigment analysis. The reconstruction image described location and concentration of the microalgae pigments. This result indicated the cyanobacterial pigment distribution and mapped the relative pigment content. In conclusion, with the advantage of acquiring two-dimensional images across a range of spectra, HSI conjoining spectral features with spatial information efficiently estimated specific features of harmful microalgae in MCR models.

Providing surgical margin information during breast cancer surgery is crucial for the success of the procedure. The margin is defined as the distance from the tumor to the cut surface of the resection specimen. The consensus among surgeons and radiation oncologists is that there should be no tumor left within 1 to maximum 2 mm from the surface of the surgical specimen. If a positive margin remains, there is substantial risk for tumor recurrence, which may also result in potentially reduced cosmesis and eventual need for mastectomy. In this paper we report a novel multimodal optical imaging instrument based on combined high-resolution confocal microscopy-optical coherence tomography imaging for assessing the presence of potential positive margins on surgical specimens. Since rapid specimen analysis is critical during surgery, this instrument also includes a fluorescence imaging channel to enable rapid identification of the areas of the specimen that have potential positive margins. This is possible by specimen incubation with a cancer specific agent prior to imaging. In this study we used a quenched contrast agent, which is activated by cancer specific enzymes, such as urokinase plasminogen activators (uPA). Using this agent or a similar one, one may limit the use of high-resolution optical imaging to only fluorescence-highlighted areas for visualizing tissue morphology at the sub-cellular scale and confirming or ruling out cancer presence. Preliminary evaluation of this technology was performed on 20 surgical specimens and testing of the optical imaging findings was performed against histopathology. The combination of the three imaging modes allowed for high correlation between optical image analysis and histological ground-truth. The initial results are encouraging, showing instrument capability to assess margins on clinical specimens with a positive predictive value of 1.0 and a negative predictive value of 0.83.

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively., Author summary To analyze cell infection and changes in cell morphology in fluorescence microscopy images, proper cell detection and segmentation are required. In automated fluorescence microscopy image analysis, the separation of signals in close proximity is a challenging problem. High cell densities or cluster formations increase the probability of such situations on the cellular level. Another limitation is the detection of morphologically complex cells, such as macrophages or neurons. Their indefinite morphology causes identification issues when looking for slight variations of fixed shapes. Compared to merely segmenting cytoplasm, instance-based segmentation is a much harder task, since the assignment of a cell instance identity to every pixel of the image is required. We present a new deep learning approach for cell detection and segmentation that efficiently utilizes the nucleus channel for improving segmentation and detection performance of cells by incorporating previously learned nucleus features. This is achieved by a fusion of feature pyramids for nucleus detection and segmentation. The performance is evaluated on a microscopic image dataset, containing both nucleus and cell signals.

Accurate quantification and characterization of atherosclerotic plaques with MRI requires high spatial resolution acquisitions with excellent image quality. The intrinsically better signal-to-noise ratio (SNR) at high-field clinical 7T compared to the widely employed lower field strengths of 1.5 and 3T may yield significant improvements to vascular MRI. However, 7T atherosclerosis imaging also presents specific challenges, related to local transmit coils and B1 field inhomogeneities, which may overshadow these theoretical gains. We present the development and evaluation of 3D, black-blood, ultra-high resolution vascular MRI on clinical high-field 7T in comparison lower-field 3T. These protocols were applied for in vivo imaging of atherosclerotic rabbits, which are often used for development, testing, and validation of translatable cardiovascular MR protocols. Eight atherosclerotic New Zealand White rabbits were imaged on clinical 7T and 3T MRI scanners using 3D, isotropic, high (0.63 mm3) and ultra-high (0.43 mm3) spatial resolution, black-blood MR sequences with extensive spatial coverage. Following imaging, rabbits were sacrificed for validation using fluorescence imaging and histology. Image quality parameters such as SNR and contrast-to-noise ratio (CNR), as well as morphological and functional plaque measurements (plaque area and permeability) were evaluated at both field strengths. Using the same or comparable imaging parameters, SNR and CNR were in general higher at 7T compared to 3T, with a median (interquartiles) SNR gain of +40.3 (35.3–80.1)%, and a median CNR gain of +68.1 (38.5–95.2)%. Morphological and functional parameters, such as vessel wall area and permeability, were reliably acquired at 7T and correlated significantly with corresponding, widely validated 3T vessel wall MRI measurements. In conclusion, we successfully developed 3D, black-blood, ultra-high spatial resolution vessel wall MRI protocols on a 7T clinical scanner. 7T imaging was in general superior to 3T with respect to image quality, and comparable in terms of plaque area and permeability measurements.

Förster Resonance Energy Transfer (FRET) has become an immensely powerful tool to profile intra- and inter-molecular interactions. Through fusion of genetically encoded fluorescent proteins (FPs) researchers have been able to detect protein oligomerization, receptor activation, and protein translocation among other biophysical phenomena. Recently, two bright monomeric red fluorescent proteins, mRuby3 and mScarlet-I, have been developed. These proteins offer much improved physical properties compared to previous generations of monomeric red FPs that should help facilitate more general adoption of Green/Red FRET. Here we assess the ability of these two proteins, along with mCherry, to act as a FRET acceptor for the bright, monomeric, green-yellow FP mNeonGreen using intensiometric FRET and 2-photon Fluorescent Lifetime Imaging Microscopy (FLIM) FRET techniques. We first determined that mNeonGreen was a stable donor for 2-photon FLIM experiments under a variety of imaging conditions. We then tested the red FP’s ability to act as FRET acceptors using mNeonGreen-Red FP tandem construct. With these constructs we found that mScarlet-I and mCherry are able to efficiently FRET with mNeonGreen in spectroscopic and FLIM FRET. In contrast, mNeonGreen and mRuby3 FRET with a much lower efficiency than predicted in these same assays. We explore possible explanations for this poor performance and determine mRuby3’s protein maturation properties are a major contributor. Overall, we find that mNeonGreen is an excellent FRET donor, and both mCherry and mScarlet-I, but not mRuby3, act as practical FRET acceptors, with the brighter mScarlet-I out performing mCherry in intensiometric studies, but mCherry out performing mScarlet-I in instances where consistent efficiency in a population is critical.

Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these false-coloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra- and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.

The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.

Accurate detection of extracellular chemical gradients is essential for many cellular behaviors. Gradient sensing is challenging for small cells, which can experience little difference in ligand concentrations on the up-gradient and down-gradient sides of the cell. Nevertheless, the tiny cells of the yeast Saccharomyces cerevisiae reliably decode gradients of extracellular pheromones to find their mates. By imaging the behavior of polarity factors and pheromone receptors, we quantified the accuracy of initial polarization during mating encounters. We found that cells bias the orientation of initial polarity up-gradient, even though they have unevenly distributed receptors. Uneven receptor density means that the gradient of ligand-bound receptors does not accurately reflect the external pheromone gradient. Nevertheless, yeast cells appear to avoid being misled by responding to the fraction of occupied receptors rather than simply the concentration of ligand-bound receptors. Such ratiometric sensing also serves to amplify the gradient of active G protein. However, this process is quite error-prone, and initial errors are corrected during a subsequent indecisive phase in which polarity clusters exhibit erratic mobile behavior., Cells use surface receptors to decode spatial information from chemical gradients, but accurate decoding is hampered by small cell size and the presence of molecular noise. This study shows that yeast cells decode pheromone gradients by measuring the local ratio of bound to unbound receptors. This mechanism corrects for uneven receptor density at the surface and amplifies the gradient transmitted to downstream components.

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images., Author summary To understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

ContextUpon palmitate oversupply, membrane fatty acid-transporter CD36 (SR-B2) permanently translocates from endosomal storage to the sarcolemma, inducing lipotoxicity. CD36 trans location results from endosomal alkalinisation elicited by palmitate-induced disattachment of the cytoplasmic V-1-subcomplex from the membrane-integrated V-0-subcomplex of vacuolar-type H+-ATPase.ObjectiveDevelop a CD36 fluorescent labeling technique as initial step towards live cell imaging.MethodsThree human CD36 (hCD36) mutants were constructed via insertion of a tetracysteine motif at different positions within the extracellular domain. Constructs were lentivirally transduced for subsequent CD36 labeling with fluorescein-arsenical hairpin-binder (FlAsH). Cell imaging was combined with V-0/V-1 immunostaining and Western blotting.ResultsTransduction of hCD36-wildtype and mutants yielded corresponding proteins in HL-1 cardiomyocytes. Tetracysteine mutant-2 (hCD36-TC2) showed similar fatty acid uptake to wild type. FlAsH staining revealed a speckled pattern reminiscent of endosomes. We found decreased V-1 co-localization with CD36 upon high-palmitate culturing. Conversely, V-0 consistently co-localized with CD36.ConclusionhCD36-TC2 is a possible candidate for application of biarsenical dyes in live imaging studies pending further investigation. Our data is compatible with V-0/V-1 disassembly in high-palmitate-treated cells.

Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores’ spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.

We have developed a new method for selectively sorting droplets containing growing bacteria using a fluorescence resonance energy transfer (FRET)-based RNA probe. Bacteria and the FRET-based RNA probe are encapsulated into nanoliter-scale droplets, which are incubated to allow for cell growth. The FRET-based RNA probe is cleaved by RNase derived from the bacteria propagated in the droplets, resulting in an increase in fluorescence intensity. The fluorescent droplets containing growing bacteria are distinguishable from quenching droplets, which contain no cells. We named this method FNAP-sort based on the use of a fluorescent nucleic acid probe in droplets for bacterial sorting. Droplets containing the FRET-based RNA probe and four species of pure cultures, which grew in the droplets, were selectively enriched on the basis of fluorescence emission. Furthermore, fluorescent droplets were sorted from more than 500,000 droplets generated using environmental soil bacteria and the FRET-based RNA probe on days 1, 3, and 7 with repeated incubation and sorting. The bacterial compositions of sorted droplets differed on days 1, 3, and 7; moreover, on day 7, the bacterial composition of the fluorescent droplets was drastically different from that of the quenching droplets. We believe that FNAP-sort is useful for high-throughput cultivation and sorting of environmental samples containing bacteria with various growth rates, including slow-growing microbes that require long incubation times.

Graphene Oxide (GO) has recently attracted substantial attention in biomedical field as an effective platform for biological sensing, tissue scaffolds and in vitro fluorescence imaging. However, the targeting modality and the capability of its in vivo detection have not been explored. To enhance the functionality of GO, we combine it with superparamagnetic iron oxide nanoparticles (Fe3O4 NPs) serving as a biocompatible magnetic drug delivery addends and magnetic resonance contrast agent for MRI. Synthesized GO-Fe3O4 conjugates have an average size of 260 nm and show low cytotoxicity comparable to that of GO. Fe3O4 nanoparticles provide superparamagnetic properties for magnetic targeted drug delivery allowing simple manipulation by the magnetic field and magnetic resonance imaging with high r2/r1 relaxivity ratios of ~10.7. GO-Fe3O4 retains pH-sensing capabilities of GO used in this work to detect cancer versus healthy environments in vitro and exhibits fluorescence in the visible for bioimaging. As a drug delivery platform GO-Fe3O4 shows successful fluorescence-tracked transport of hydrophobic doxorubicin non-covalently conjugated to GO with substantial loading and 2.5-fold improved efficacy. As a result, we propose GO-Fe3O4 nanoparticles as a novel multifunctional magnetic targeted platform for high efficacy drug delivery traced in vitro by GO fluorescence and in vivo via MRI capable of optical cancer detection.

In this paper, we present a new multi-focus microscope (MFM) system based on a phase mask and HiLo algorithm, achieving high-speed (20 volumes per second), high-resolution, low-noise 3-D fluorescent imaging. During imaging, the emissions from the specimen at nine different depths are simultaneously modulated and focused to different regions on a single CCD chip, i.e., the CCD chip is subdivided into nine regions to record images from the different selected depths. Next, HiLo algorithm is applied to remove the background noises and to form clean 3-D images. To visualize larger volumes, the nine layers are scanned axially, realizing fast 3-D imaging. In the imaging experiments, a mouse kidney sample of ~ 60 × 60 × 16 μm3 is visualized with only 10 raw images, demonstrating substantially enhanced resolution and contrast as well as suppressed background noises. The new method will find important applications in 3-D fluorescent imaging, e.g., recording fast dynamic events at multiple depths in vivo.

We have previously demonstrated that the use of a commercially-available immersion-based optical clearing agent (OCA) enables, within 3-6 hours, three-dimensional visualization of subsurface exogenous fluorescent and absorbing markers of vascular architecture and neurodegenerative disease in thick (0.5-1.0mm) mouse brain sections. Nonetheless, the relative performance of immersion-based OCAs has remained unknown. Here, we show that immersion of brain sections in specific OCAs (FocusClear, RIMS, sRIMS, or ScaleSQ) affects both their transparency and volume; the optical clearing effect occurs over the entire visible spectrum and is reversible; and that ScaleSQ had the highest optical clearing potential and increase in imaging depth of the four evaluated OCAs, albeit with the largest change in sample volume and a concomitant decrease in apparent microvascular density of the sample. These results suggest a rational, quantitative framework for screening and characterization of the impact of optical clearing, to streamline experimental design and enable a cost-benefit assessment.

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is largely independent of the specific imaging method (bright-field/phase) and objective used (40X/63X). We validate our algorithm’s performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.

Freely crawling cells are often viewed as randomly moving Brownian particles but they generally exhibit some directional persistence. This property is often related to their zigzag motile behaviors that can be described as a noisy but temporally structured sequence of “runs” and “turns.” However, its underlying biophysical mechanism is largely unexplored. Here, we carefully investigate the collective actin wave dynamics associated with the zigzag-crawling movements of microglia (as primary brain immune cells) employing a number of different quantitative imaging modalities including synthetic aperture microscopy and optical diffraction tomography, as well as conventional fluorescence imaging and scanning electron microscopy. Interestingly, we find that microglia exhibit two distinct types of actin waves working at two quite different time scales and locations, and they seem to serve different purposes. One type of actin waves is fast “peripheral ruffles” arising spontaneously with an oscillating period of about 6 seconds at some portion of the leading edge of crawling microglia, where the vigorously biased peripheral ruffles seem to set the direction of a new turn (in 2-D free space). When the cell turning events are inhibited with a physical confinement (in 1-D track), the peripheral ruffles still exist at the leading edge with no bias but showing phase coherence in the cell crawling direction. The other type is “dorsal actin waves” which also exhibits an oscillatory behavior but with a much longer period of around 2 minutes compared to the fast “peripheral ruffles”. Dorsal actin waves (whether the cell turning events are inhibited or not) initiate in the lamellipodium just behind the leading edge, travelling down toward the core region of the cell and disappear. Such dorsal wave propagations seem to be correlated with migration of the cell. Thus, we may view the dorsal actin waves are connected with the “run” stage of cell body, whereas the fast ruffles at the leading front are involved in the “turn” stage.

The ability to make transgenic Hydra lines has allowed for quantitative in vivo studies of Hydra regeneration and physiology. These studies commonly include excision, grafting and transplantation experiments along with high-resolution imaging of live animals, which can be challenging due to the animal's response to touch and light stimuli. While various anesthetics have been used in Hydra studies, they tend to be toxic over the course of a few hours or their long-term effects on animal health are unknown. Here, we show that the monoterpenoid alcohol linalool is a useful anesthetic for Hydra. Linalool is easy to use, non-toxic, fast acting, and reversible. It has no detectable long-term effects on cell viability or cell proliferation. We demonstrate that the same animal can be immobilized in linalool multiple times at intervals of several hours for repeated imaging over 2-3 days. This uniquely allows for in vivo imaging of dynamic processes such as head regeneration. We directly compare linalool to currently used anesthetics and show its superior performance. Linalool will be a useful tool for tissue manipulation and imaging in Hydra research in both research and teaching contexts.

Correlative light, electron, and ion microscopy (CLEIM) offers huge potential to track the intracellular fate of antibiotics, with organelle-level resolution. However, a correlative approach that enables subcellular antibiotic visualisation in pathogen-infected tissue is lacking. Here, we developed correlative light, electron, and ion microscopy in tissue (CLEIMiT) and used it to identify the cell type–specific accumulation of an antibiotic in lung lesions of mice infected with Mycobacterium tuberculosis. Using CLEIMiT, we found that the anti-tuberculosis (TB) drug bedaquiline (BDQ) is localised not only in foamy macrophages in the lungs during infection but also accumulate in polymorphonuclear (PMN) cells., This study uses correlative light, electron and ion microscopy (CLEIM) in vivo to reveal the intracellular fate of an antibiotic in lung lesions of mice infected with Mycobacterium tuberculosis, with organelle-level resolution.

In vivo two-photon microscopy utilizing a nonlinear optical process enables, in living mouse brains, not only the visualization of morphologies and functions of neural networks in deep regions but also their optical manipulation at targeted sites with high spatial precision. Because the two-photon excitation efficiency is proportional to the square of the photon density of the excitation laser light at the focal position, optical aberrations induced by specimens mainly limit the maximum depth of observations or that of manipulations in the microscopy. To increase the two-photon excitation efficiency, we developed a method for evaluating the focal volume in living mouse brains. With this method, we modified the beam diameter of the excitation laser light and the value of the refractive index in the immersion liquid to maximize the excitation photon density at the focal position. These two modifications allowed the successful visualization of the finer structures of hippocampal CA1 neurons, as well as the intracellular calcium dynamics in cortical layer V astrocytes, even with our conventional two-photon microscopy system. Furthermore, it enabled focal laser ablation dissection of both single apical and single basal dendrites of cortical layer V pyramidal neurons. These simple modifications would enable us to investigate the contributions of single cells or single dendrites to the functions of local cortical networks.

Rapid detection of bacterial pathogens is a critical unmet need for both food and environmental samples such as irrigation water. As a part of the Food safety Modernization Act (FSMA), The Produce Safety rule has established several requirements for testing for the presence of generic Escherichia coli in water, but the current method available for testing (EPA M1603) demands specified multiple colony verification and highly trained personnel to perform these tests. The purpose of the study was to assess a phage induced bacterial lysis using quantitative image analysis to achieve rapid detection of E. coli at low concentrations within 8 hours. This study aimed to develop a simple yet highly sensitive and specific approach to detect target bacteria in complex matrices. In the study, E. coli cells were first enriched in tryptic soy broth (TSB), followed by T7 phage induced lysis, concentration, staining and fluorescent imaging. Image analysis was conducted including image pre-processing, image segmentation and quantitatively analysis of cellular morphological features (area, eccentricity and full width at half maximum). Challenge experiments using realistic matrices, including simulated fresh produce wash water, coconut water and spinach wash water, demonstrated the method can be applied for use in situations that occur in food processing facilities. The results indicated E. coli cells that are lysed by T7 phages demonstrated significantly (P < 0.05) higher extracellular DNA release, altered cellular shape (from rod to circular) and diffused fluorescent signal intensity. Using this biosensing strategy, a sensitivity to detect Escherichia coli at 10 CFU/ml within 8 hours was achieved, both in laboratory medium and in complex matrices. The proposed phage based biosensing strategy enables rapid detection of bacteria and is applicable to analysis of food systems. Furthermore, the steps involved in this assay can be automated to enable detection of target bacteria in food facilities without extensive resources.

PurposeRapid, intra-operative identification of tumor tissue in the margins of excised specimens has become an important focus in the pursuit of reducing re-excision rates, especially for breast conserving surgery. Dual-probe difference specimen imaging (DDSI) is an emerging approach that uses the difference in uptake/clearance kinetics between a pair of fluorescently-labeled stains, one targeted to a biomarker-of-interest and the other an untargeted isotype, to reveal receptor-specific images of the specimen. Previous studies using antibodies labeled with either enhanced Raman particles or organic fluorophores have shown promising tumor vs. normal diagnostic performance. Yet, the unique properties of quantum dot-labeled antibody complexes (QDACs), which provide spectrally-distinct fluorescence emission from a common excitation source, make them ideal candidates for this application. Herein, we evaluate the diagnostic performance of QDAC-based DDSI in excised xenografts.ProceduresExcised fresh specimens of normal tissue and human tumor xenografts with elevated expression of HER2 were stained with a HER2-targeted QDAC and an untargeted QDAC isotype. Stained specimens were imaged on a custom hyperspectral imaging system capable of spectrally separating the quantum dot signatures, and images processed using the DDSI approach. The diagnostic performance of this technique under different incubation temperatures and probe concentrations was evaluated using receiver-operator characteristic analysis.ResultsHER2-targeted QDAC-DDSI was able to distinguish HER2(+) tumors from normal tissue with reasonably high diagnostic performance; however, this performance was sensitive to temperature during the staining procedure. Area under the curve values were 0.61 when staining at room temperature but increased to over 0.81 when staining at 37 °C. Diagnostic performance was not affected by increasing stain concentration.ConclusionsThis study is the first to report dual-probe difference imaging of specimens using QDACs and hyperspectral imaging. Our results show promising diagnostic performance under certain conditions, and compel further optimization and evaluation of this intra-operative margin assessment technique.

In order to achieve deep tissue imaging, a number of optical clearing agents have been developed. However, in a conventional microscopy setup, an objective lens can only be moved until it is in contact with a coverslip, which restricts the maximum focusing depth into a cleared tissue specimen. Until now, it is still a fact that the working distance of a high magnification objective lens with a high numerical aperture is always about 100 μm. In this study, a polymer thin film (also called as nanosheet) composed of fluoropolymer with a thickness of 130 nm, less than one-thousandth that of a 170 μm thick coverslip, is employed to replace the coverslip. Owing to its excellent characteristics, such as high optical transparency, mechanical robustness, chemical resistance, and water retention ability, nanosheet is uniquely capable of providing a coverslip-free imaging. By wrapping the tissue specimen with a nanosheet, an extra distance of 170 μm for the movement of objective lens is obtained. Results show an equivalently high resolution imaging can be obtained if a homogenous refractive index between immersion liquid and mounting media is adjusted. This method will facilitate a variety of imaging tasks with off-the-shelf high magnification objectives.

Holo-tomographic microscopy (HTM) is a label-free microscopy method reporting the fine changes of a cell’s refractive indices (RIs) in three dimensions at high spatial and temporal resolution. By combining HTM with epifluorescence, we demonstrate that mammalian cellular organelles such as lipid droplets (LDs) and mitochondria show specific RI 3D patterns. To go further, we developed a computer-vision strategy using FIJI, CellProfiler3 (CP3), and custom code that allows us to use the fine images obtained by HTM in quantitative approaches. We could observe the shape and dry mass dynamics of LDs, endocytic structures, and entire cells’ division that have so far, to the best of our knowledge, been out of reach. We finally took advantage of the capacity of HTM to capture the motion of many organelles at the same time to report a multiorganelle spinning phenomenon and study its dynamic properties using pattern matching and homography analysis. This work demonstrates that HTM gives access to an uncharted field of biological dynamics and describes a unique set of simple computer-vision strategies that can be broadly used to quantify HTM images., A combination of holo-tomographic microscopy and computer vision allows the label-free observation of novel shape and dry mass dynamics of mammalian organelles such as lipid droplets and the investigation of complex organellar motion using pattern matching and homography analysis.

Screening cells for their differentiation potential requires a combination of tissue culture models and imaging methods that allow for long-term tracking of the location and function of cells. Embryonic kidney re-aggregation in vitro assays have been established which allow for the monitoring of organotypic cell behaviour in re-aggregated and chimeric renal organoids. However, evaluation of cell integration is hampered by the high photonic load of standard fluorescence microscopy which poses challenges for imaging three-dimensional systems in real-time over a time course. Therefore, we employed light sheet microscopy, a technique that vastly reduces photobleaching and phototoxic effects. We have also developed a new method for culturing the re-aggregates which involves immersed culture, generating organoids which more closely reflect development in vivo. To facilitate imaging from various angles, we embedded the organoids in a freely rotatable hydrogel cylinder. Endpoint fixing and staining were performed to provide additional biomolecular information. We succeeded in imaging labelled cells within re-aggregated kidney organoids over 15 hours and tracking their fate while simultaneously monitoring the development of organotypic morphological structures. Our results show that Wt1-expressing embryonic kidney cells obtained from transgenic mice could integrate into re-aggregated chimeric kidney organoids and contribute to developing nephrons. Furthermore, the nascent proximal tubules that formed in the re-aggregated tissues using the new culture method displayed secretory function, as evidenced by their ability to secrete an organic anion mimic into the tubular lumen.

Living tissues undergo deformation during morphogenesis. In this process, cells generate mechanical forces that drive the coordinated cell motion and shape changes. Recent advances in experimental and theoretical techniques have enabled in situ measurement of the mechanical forces, but the characterization of mechanical properties that determine how these forces quantitatively affect tissue deformation remains challenging, and this represents a major obstacle for the complete understanding of morphogenesis. Here, we proposed a non-invasive reverse-engineering approach for the estimation of the mechanical properties, by combining tissue mechanics modeling and statistical machine learning. Our strategy is to model the tissue as a continuum mechanical system and to use passive observations of spontaneous tissue deformation and force fields to statistically estimate the model parameters. This method was applied to the analysis of the collective migration of Madin-Darby canine kidney cells, and the tissue flow and force were simultaneously observed by the phase contrast imaging and traction force microscopy. We found that our monolayer elastic model, whose elastic moduli were reverse-engineered, enabled a long-term forecast of the traction force fields when given the tissue flow fields, indicating that the elasticity contributes to the evolution of the tissue stress. Furthermore, we investigated the tissues in which myosin was inhibited by blebbistatin treatment, and observed a several-fold reduction in the elastic moduli. The obtained results validate our framework, which paves the way to the estimation of mechanical properties of living tissues during morphogenesis., Author summary In order to shape the body of a multicellular organism, cells generate mechanical forces and undergo deformation. Although these forces are being increasingly determined, quantitative characterization of the relation between the deformation and forces at the tissue level remains challenging. To estimate these properties, we developed a reverse-engineering method by combining tissue mechanics modeling and statistical machine learning, and then tested this method on a common model system, the expansion of cultured cell monolayer. This statistically sound framework uses the passive observations of spontaneous deformation and force dynamics in tissues, and enables us to elucidate unperturbed mechanical processes underlying morphogenesis.

Circulating tumor cells (CTCs) are nowadays one of the most promising tumor biomarkers. It is well correlated with overall survival and progression-free survival in breast cancer, as well as in many other types of human cancer. In addition, enumeration and analysis of CTCs could be important for monitoring the response to different therapeutic agents, thus guiding the treatment of cancer patients and offering the promise of a more personalized approach. In this article, we present a new method that could be used for the automatic detection of CTC in blood, based on the microscopic appearance of unstained cells. The proposed method is based on the evaluation of image characteristics and boosting techniques. A dataset of 263 dark field microscopy images was constructed and used for our tests, containing blood spiked with three different types of tumor cells. An overall sensitivity of 92.87% and a specificity of 99.98% were obtained for the detection of CTC, performances which proved to be comparable to those obtained by human experts.

Epithelial ovarian cancer (EOC) is the seventh most common cancer among women worldwide. The 5-year survival rate for women with EOC is only 30%-50%, which is largely due to the typically late diagnosis of this condition. EOC is difficult to detect in its early stage because of its asymptomatic nature. Recently, near-infrared fluorescent (NIRF) imaging has been developed as a potential tool for detecting EOC at the molecular level. In this study, a NIRF-sensitive probe was designed to detect matrix metalloproteinase (MMP) activity in ovarian cancer cells. A cyanine fluorochrome was conjugated to the amino terminus of a peptide substrate with enzymatic specificity for MMP-3. To analyze the novel MMP-3 probe, an in vivo EOC model was established by subcutaneously implanting SKOV3 cells, a serous-type EOC cell line, in mice. This novel MMP-3-sensitive probe specifically reacted with only the active MMP-3 enzyme, resulting in a significantly enhanced NIRF emission intensity. Histological analysis demonstrated that MMP-3 expression and activity were enhanced in the stromal cells surrounding the ovarian cancer cells. These studies establish a molecular imaging reporter for diagnosing early-stage EOC. Additional studies are required to confirm the early-stage activity of MMP-3 in EOC and its diagnostic and prognostic significance.

Miniaturized imaging devices have pushed the boundaries of point-of-care imaging, but existing mobile-phone-based imaging systems do not exploit the full potential of smart phones. This work demonstrates the use of simple imaging configurations to deliver superior image quality and the ability to handle a wide range of biological samples. Results presented in this work are from analysis of fluorescent beads under fluorescence imaging, as well as helminth eggs and freshwater mussel larvae under white light imaging. To demonstrate versatility of the systems, real time analysis and post-processing results of the sample count and sample size are presented in both still images and videos of flowing samples.

To understand the neural mechanisms underlying the therapeutic effects of crossing nerve transfer for brachial plexus injuries in human patients, we investigated the cortical responses after crossing nerve transfer in mice using conventional and tomographic optical imaging. The distal cut ends of the left median and ulnar nerves were connected to the central cut ends of the right median and ulnar nerves with a sciatic nerve graft at 8 weeks of age. Eight weeks after the operation, the responses in the primary somatosensory cortex (S1) elicited by vibratory stimulation applied to the left forepaw were visualized based on activity-dependent flavoprotein fluorescence changes. In untreated mice, the cortical responses to left forepaw stimulation were mainly observed in the right S1. In mice with nerve crossing transfer, cortical responses to left forepaw stimulation were observed in the left S1 together with clear cortical responses in the right S1. We expected that the right S1 responses in the untreated mice were produced by thalamic inputs to layer IV, whereas those in the operated mice were mediated by callosal inputs from the left S1 to layer II/III of the right S1. To confirm this hypothesis, we performed tomographic imaging of flavoprotein fluorescence responses by macroconfocal microscopy. Flavoprotein fluorescence responses in layer IV were dominant compared to those in layer II/III in untreated mice. In contrast, responses in layer II/III were dominant compared to those in layer IV in operated mice. The peak latency of the cortical responses in the operated mice was longer than that in the untreated mice. These results confirmed our expectation that drastic reorganization in the cortical circuits was induced after crossing nerve transfer in mice.

Hard x-ray lenses are useful elements in x-ray microscopy and in creating focused illumination for analytical applications such as x-ray fluorescence imaging. Recently, polymer compound refractive lenses for focused illumination in the soft x-ray regime (< 10 keV) have been created with nano-printing. However, there are no such lenses yet for hard x-rays, particularly of short focal lengths for benchtop microscopy. We report the first instance of a nano-printed lens for hard x-ray microscopy, and evaluate its imaging performance. The lens consists of a spherically focusing compound refractive lens designed for 22 keV photon energy, with a tightly packed structure to provide a short total length of 1.8 mm and a focal length of 21.5 mm. The resulting lens technology was found to enable benchtop microscopy at 74x magnification and 1.1 μm de-magnified image pixel size at the object plane. It was used to image and evaluate the focal spots of tungsten-anode micro-focus x-ray sources. The overall system resolution with broadband illumination from a tungsten-anode x-ray tube at 30 kV and 10 mm focal distance was measured to be 2.30±0.22 μm.

To date, few optical imaging systems are available in clinical practice to perform noninvasive measurements transcutaneously. Instead, functional imaging is performed using ionizing radiation or intense magnetic fields in most cases. The applicability of fluorescence imaging (e.g., for the detection of fluorescently labeled objects, such as tumors) is limited due to the restricted tissue penetration of light and the required long exposure time. Thus, the development of highly sensitive and easily manageable instruments is necessary to broaden the utility of optical imaging. To advance these developments, an improved fluorescence imaging system was designed in this study that operates on the principle of noncontact laser-induced fluorescence and enables the detection of fluorescence from deeper tissue layers as well as real-time imaging. The high performance of the developed optical laser scanner results from the combination of specific point illumination, an intensified charge-coupled device (ICCD) detector with a novel light trap, and a filtering strategy. The suitability of the laser scanner was demonstrated in two representative applications and an in vivo evaluation. In addition, a comparison with a planar imaging system was performed. The results show that the exposure time with the developed laser scanner can be reduced to a few milliseconds during measurements with a penetration depth of up to 32 mm. Due to these short exposure times, real-time fluorescence imaging can be easily achieved. The ability to measure fluorescence from deep tissue layers enables clinically relevant applications, such as the detection of fluorescently labeled malignant tumors.

Adherent cells such as endothelial cells sense applied mechanical stretch to adapt to changes in their surrounding mechanical environment. Despite numerous studies, signaling pathways underlying the cellular mechanosensing and adaptation remain to be fully elucidated partly because of the lack of tools that allow for a comprehensive screening approach. Conventionally, multi-well cell culture plates of standard configurations are used for comprehensive analyses in cell biology study to identify key molecules in a high-throughput manner. Given that situation, here we design a 96-well cell culture plate made of elastic silicone and mechanically stretchable using a motorized device. Computational analysis suggested that highly uniform stretch can be applied to each of the wells other than the peripheral wells. Elastic image registration-based experimental evaluation on stretch distributions within individual wells revealed the presence of larger variations among wells compared to those in the computational analysis, but a stretch level of 10%-that has been employed in conventional studies on cellular response to stretch-was almost achieved with our setup. We exposed vascular smooth muscle cells to cyclic stretch using the device to demonstrate morphological repolarization of the cells, i.e. typical cellular response to cyclic stretch. Because the deformable multi-well plate validated here is compatible with other high-throughput screening-oriented technologies, we expect this novel system to be utilized for future comprehensive analyses of stretch-related signaling pathways.

Sodium ions (Na+) are key regulators of molecular events in many cellular processes, yet the dynamics of this ion remain poorly defined. Developing approaches to identify and characterise Na+ microenvironments will enable more detailed elucidation of the mechanisms of signal transduction. Here we report the application of Spectral Phasor analysis to the Na+ fluorophore, CoroNa Green, to identify and spatially map spectral emissions that report Na+ microenvironments. We use differentiating stem cells where Na+ fluxes were reported as an antecedent. Myoblast stem cells were induced to differentiate by serum starvation and then fixed at intervals between 0 and 40-minutes of differentiation prior to addition of CoroNa Green. The fluorescent intensity was insufficient to identify discrete Na+ microenvironments. However, using Spectral Phasor analysis we identified spectral shifts in CoroNa Green fluorescence which is related to the Na+ microenvironment. Further, spectral-heterogeneity appears to be contingent on the distance of Na+ from the nucleus in the early stages of differentiation. Spectral Phasor analysis of CoroNa Green in fixed stem cells demonstrates for the first time that CoroNa Green has unique spectral emissions depending on the nature of the Na+ environment in differentiating stem cells. Applying Spectral Phasor analysis to CoroNa Green in live stem cells is likely to further elucidate the role of Na+ microenvironments in the differentiation process.

The integrins are a superfamily of transmembrane proteins composed of α and β subunit dimers involved in cell-cell and cell-extracellular matrix interactions. The largest integrin subgroup is integrin β1, which contributes to several malignant phenotypes. Recently, we have developed a novel imaging technology named scanning electron-assisted dielectric-impedance microscopy (SE-ADM), which visualizes untreated living mammalian cells in aqueous conditions with high contrast. Using the SE-ADM system, we observed 60-nm gold colloids with antibodies directly binding to the focal adhesion core containing integrin β1 on mammalian cancer cells without staining and fixation. The adhesion core contains three or four high-density regions of integrin β1 and connects to the actin filament. An adhesion core with high-density integrin β1 is suggested to contain 10-20 integrin dimers. Our SE-ADM system can also visualize various other membrane proteins in living cells in medium without staining and fixation.

Objectives Myocardial infarction (MI) causes significant loss of cardiomyocytes, myocardial tissue damage, and impairment of myocardial function. The inability of cardiomyocytes to proliferate prevents the heart from self-regeneration. The treatment for advanced heart failure following an MI is heart transplantation despite the limited availability of the organs. Thus, stem-cell-based cardiac therapies could ultimately prevent heart failure by repairing injured myocardium that reverses cardiomyocyte loss. However, stem-cell-based therapies lack understanding of the mechanisms behind a successful therapy, including difficulty tracking stem cells to provide information on cell migration, proliferation and differentiation. In this study, we have investigated the interaction between different types of stem and inflammatory cells and cell-targeted imaging molecules, 18F-FDG and 6-NBDG, to identify uptake patterns and pharmacokinetics in vitro. Methods Macrophages (both M1 and M2), human induced pluripotent stem cells (hiPSCs), and human amniotic mesenchymal stem cells (hAMSCs) were incubated with either 18F-FDG or 6-NBDG. Excess radiotracer and fluorescence were removed and a 100 μm-thin CdWO4 scintillator plate was placed on top of the cells for radioluminescence microscopy imaging of 18F-FDG uptake, while no scintillator was needed for fluorescence imaging of 6-NBDG uptake. Light produced following beta decay was imaged with a highly sensitive inverted microscope (LV200, Olympus) and an Electron Multiplying Charge-Couple Device (EM-CCD) camera. Custom-written software was developed in MATLAB for image processing. Results The average cellular activity of 18F-FDG in a single cell of hAMSCs (0.670±0.028 fCi/μm2, P = 0.001) was 20% and 36% higher compared to uptake in hiPSCs (0.540±0.026 fCi/μm2, P = 0.003) and macrophages (0.430±0.023 fCi/μm2, P = 0.002), respectively. hAMSCs exhibited the slowest influx (0.210 min-1) but the fastest efflux (0.327 min-1) rate compared to the other tested cell lines for 18F-FDG. This cell line also has the highest phosphorylation but exhibited the lowest rate of de-phosphorylation. The uptake pattern for 6-NBDG was very different in these three cell lines. The average cellular activity of 6-NBDG in a single cell of macrophages (0.570±0.230 fM/μm2, P = 0.004) was 38% and 14% higher compared to hiPSCs (0.350±0.160 fM/μm2, P = 0.001) and hAMSCs (0.490±0.028 fM/μm2, P = 0.006), respectively. The influx (0.276 min-1), efflux (0.612 min-1), phosphorylation (0.269 min-1), and de-phosphorylation (0.049 min-1) rates were also highest for macrophages compared to the other two tested cell lines. Conclusion hAMSCs were found to be 2-3× more sensitive to 18F-FDG molecule compared to hiPSCs/macrophages. However, macrophages exhibited the most sensitivity towards 6-NBDG. Based on this result, hAMSCs targeted with 18F-FDG could be more suitable for understanding the mechanisms behind successful therapy for treating MI patients by gathering information on cell migration, proliferation and differentiation.

Calcium imaging is commonly used to measure the neural activity of large groups of neurons in mice. Genetically encoded calcium indicators (GECIs) can be delivered for this purpose using non-invasive genetic methods. Compared to viral gene transfer, transgenic targeting of GECIs provides stable long-term expression and obviates the need for invasive viral injections. Transgenic mice expressing the green GECI GCaMP6 are already widely used. Here we present the generation and characterizarion of transgenic mice expressing the sensitive red GECI jRGECO1a, driven by theThy1promoter. Four transgenic lines with different expression patterns showed sufficiently high expression for cellularin vivoimaging. We used two-photon microscopy to characterize visual responses of individual neurons in the visual cortexin vivo. The signal-to-noise ratio in transgenic mice was comparable to, or better than, for mice transduced with adeno-associated virus. We also show thatThy1-jRGECO1a transgenic mice are useful for transcranial population imaging and functional mapping using widefield fluorescecnce microscopy. We also demonstrate imaging of visual responses in retinal ganglion cells.Thy1-jRGECO1a transgenic mice are therefore a useful addition to the toolbox for imaging activity in intact neural networks.

Although bone is one of the most studied living materials, many questions about the manner in which bones form remain unresolved, including fine details of the skeletal structure during development. In this study, we monitored skeleton development of zebrafish larvae, using calcein fluorescence, high-resolution micro-CT 3D images and FIB-SEM in the block surface serial imaging mode. We compared calcein staining of the skeletons of the wild type and nacre mutants, which are transparent zebrafish, with micro-CT for the first 30 days post fertilization embryos, and identified significant differences. We quantified the bone volumes and mineral contents of bones, including otoliths, during development, and showed that such developmental differences, including otolith development, could be helpful in identifying phenotypes. In addition, high-resolution imaging revealed the presence of mineralized aggregates in the notochord, before the formation of the first bone in the axial skeleton. These structures might play a role in the storage of the mineral. Our results highlight the potential of these high-resolution 3D approaches to characterize the zebrafish skeleton, which in turn could prove invaluable information for better understanding the development and the characterization of skeletal phenotypes.

Disturbed flow can eliminate the alignment of endothelial cells in the direction of laminar flow, and significantly impacts on atherosclerosis in collateral arteries near the bifurcation and high curvature regions. While shear stress induced Rac polarity has been shown to play crucial roles in cell polarity and migration, little is known about the spatiotemporal map of Rac under disturbed flow, and the mechanism of flow-induced cell polarity still needs to be elucidated. In this paper, disturbed flow or laminar flow with 15 dyn/cm2 of average shear stress was applied on bovine aortic endothelial cells (BAECs) for 30 minutes. A genetically-encoded PAK-PBD-GFP reporter was transfected into BAECs to visualize the real-time activation of Rac in living cell under fluorescence microscope. The imaging of the fluorescence intensity was analyzed by Matlab and the normalized data was converted into 3D spatiotemporal map. Then the changes of data upon chemical interference were fitted with logistic curve to explore the rule and mechanism of Rac polarity under laminar or disturbed flow. A polarized Rac activation was observed at the downstream edge along the laminar flow, which was enhanced by benzol alcohol-enhanced membrane fluidity but inhibited by nocodazole-disrupted microtubules or cholesterol-inhibited membrane fluidity, while no obvious polarized Rac activation could be found upon disturbed flow application. It is concluded that disturbed flow inhibits the flow-induced Rac polarized activation, which is related to the interaction of cell membrane and cytoskeleton, especially the microtubules.

Extracellular adenosine triphosphate (ATP) is a key purinergic signal that mediates cell-to-cell communication both within and between organ systems. We address the need for a robust and minimally invasive approach to measuring extracellular ATP by re-engineering the ATeam ATP sensor to be expressed on the cell surface. Using this approach, we image real-time changes in extracellular ATP levels with a sensor that is fully genetically-encoded and does not require an exogenous substrate. In addition, the sensor is ratiometric to allow for reliable quantitation of extracellular ATP fluxes. Using live-cell microscopy, we characterize sensor performance when expressed on cultured Neuro2A cells, and we measure both stimulated release of ATP and its clearance by ectonucleotidases. Thus, this proof-of-principle demonstrates a first-generation sensor to report extracellular ATP dynamics that may be useful for studying purinergic signaling in living specimens.

In vivo tracking of transplanted mesenchymal stem cells (MSCs) migration and homing is vital for understanding the mechanisms of beneficial effects of MSCs transplantation in animal models of diseases and in clinical trials. Transplanted cells can be labeled with superparamagnetic iron oxide (SPIO) particles and visualized in vivo using a number of iron sensitive MRI techniques. However, the applicability of those techniques for SPIO-labeled MSCs tracking in live brain has not been sufficiently investigated. The goal of this study was to estimate the efficiency of various MRI techniques of SPIO-labeled cell tracing in the brain. To achieve that goal, the precision and specificity of T2WI, T2*WI and SWI (Susceptibility-Weighted Imaging) techniques of SPIO-labeled MSCs tracing in vitro and in live rat brain were for the first time compared in the same experiment. We have shown that SWI presents the most sensitive pulse sequence for SPIO-labeled MSCs MR visualization. After intracerebral administration due to limitations caused by local micro-hemorrhages the visualization threshold was 102 cells, while after intra-arterial transplantation SWI permitted detection of several cells or even single cells. There is just one publication claiming detection of individual SPIO-labeled MSCs in live brain, while the other state much lower sensitivity, describe detection of different cell types or high resolution tracing of MSCs in other tissues. This study confirms the possibility of single cell tracing in live brain and outlines the necessary conditions. SWI is a method convenient for the detection of single SPIO labeled MSCs and small groups of SPIO labeled MSCs in brain tissue and can be appropriate for monitoring migration and homing of transplanted cells in basic and translational neuroscience.

Fluorescence imaging is a powerful technique with diverse applications in intraoperative settings. Visualization of three dimensional (3D) structures and depth assessment of lesions, however, are oftentimes limited in planar fluorescence imaging systems. In this study, a novel Fluorescence Imaging Topography Scanning (FITS) system has been developed, which offers color reflectance imaging, fluorescence imaging and surface topography scanning capabilities. The system is compact and portable, and thus suitable for deployment in the operating room without disturbing the surgical flow. For system performance, parameters including near infrared fluorescence detection limit, contrast transfer functions and topography depth resolution were characterized. The developed system was tested in chicken tissues ex vivo with simulated tumors for intraoperative imaging. We subsequently conducted in vivo multimodal imaging of sentinel lymph nodes in mice using FITS and PET/CT. The PET/CT/optical multimodal images were co-registered and conveniently presented to users to guide surgeries. Our results show that the developed system can facilitate multimodal intraoperative imaging.