Novel semi-automated fluorescence microscope imaging algorithm for monitoring IgG aggregates in serum.
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ABSTRACT:
SUBMITTER: Sreenivasan S
PROVIDER: S-EPMC8166854 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeTo detect and characterize the aggregation of therapeutic monoclonal antibodies in undiluted biological fluids.MethodsFluorescently labeled subvisible IgG aggregates formed by applying either heat stress or by pH-shift were investigated immediately after addition to human serum, and after 24 h. Unstressed and stressed IgG formulations were analyzed by fluorescence single particle tracking, confocal laser scanning microscopy and flow cytometry.ResultsUnstressed formulations remained free from subvisible aggregates in serum, whereas heat-stressed and pH-shift stressed formulations showed dissimilar aggregation behaviors. The aggregation profile of the heat-stressed formulation diluted in serum remained practically the same as the one diluted in buffer, even after the 24 h incubation period. The pH-shift stressed formulation had strikingly smaller and more numerous subvisible aggregates immediately after dilution in serum compared to buffer. These aggregates became noticeably larger in both diluents after 24 h, but in serum they appeared to be formed by other types of constituents than the labeled protein itself.ConclusionThese results show that subvisible therapeutic protein aggregates may undergo changes in number, type and size distribution upon contact with human serum. This emphasizes the importance of analytical strategies for monitoring aggregation in undiluted biological fluids.
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Project description:Most current superresolution (SR) microscope techniques surpass the diffraction limit at the expense of temporal resolution, compromising their applications to live-cell imaging. Here we describe a new SR fluorescence microscope based on confocal microscope optics, which we name the spinning disk superresolution microscope (SDSRM). Theoretically, the SDSRM is equivalent to a structured illumination microscope (SIM) and achieves a spatial resolution of 120 nm, double that of the diffraction limit of wide-field fluorescence microscopy. However, the SDSRM is 10 times faster than a conventional SIM because SR signals are recovered by optical demodulation through the stripe pattern of the disk. Therefore a single SR image requires only a single averaged image through the rotating disk. On the basis of this theory, we modified a commercial spinning disk confocal microscope. The improved resolution around 120 nm was confirmed with biological samples. The rapid dynamics of micro-tubules, mitochondria, lysosomes, and endosomes were observed with temporal resolutions of 30-100 frames/s. Because our method requires only small optical modifications, it will enable an easy upgrade from an existing spinning disk confocal to a SR microscope for live-cell imaging.
| S-EPMC4436784 | biostudies-literature
Project description:Laser scanning confocal microscopy has become an invaluable tool in biomedical research but regular quality testing is vital to maintain the system's performance for diagnostic and research purposes. Although many methods have been devised over the years to characterise specific aspects of a confocal microscope like measuring the optical point spread function or the field illumination, only very few analysis tools are available. Our aim was to develop a comprehensive quality assurance framework ranging from image acquisition to automated analysis and documentation. We created standardised test data to assess the performance of the lasers, the objective lenses and other key components required for optimum confocal operation. The ConfocalCheck software presented here analyses the data fully automatically. It creates numerous visual outputs indicating potential issues requiring further investigation. By storing results in a web browser compatible file format the software greatly simplifies record keeping allowing the operator to quickly compare old and new data and to spot developing trends. We demonstrate that the systematic monitoring of confocal performance is essential in a core facility environment and how the quantitative measurements obtained can be used for the detailed characterisation of system components as well as for comparisons across multiple instruments.
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Project description:Significance5-Aminolevulinic acid (5-ALA)-based fluorescence guidance in conventional neurosurgical microscopes is limited to strongly fluorescent tumor tissue. Therefore, more sensitive, intrasurgical 5-ALA fluorescence visualization is needed.AimMacroscopic fluorescence lifetime imaging (FLIM) was performed ex vivo on 5-ALA-labeled human glioma tissue through a surgical microscope to evaluate its feasibility and to compare it to fluorescence intensity imaging.ApproachFrequency-domain FLIM was integrated into a surgical microscope, which enabled parallel wide-field white-light and fluorescence imaging. We first characterized our system and performed imaging of two samples of suspected low-grade glioma, which were compared to histopathology.ResultsOur imaging system enabled macroscopic FLIM of a 6.5 × 6.5 mm2 field of view at spatial resolutions <20 μm. A frame of 512 × 512 pixels with a lifetime accuracy <1 ns was obtained in 65 s. Compared to conventional fluorescence imaging, FLIM considerably highlighted areas with weak 5-ALA fluorescence, which was in good agreement with histopathology.ConclusionsIntegration of macroscopic FLIM into a surgical microscope is feasible and a promising method for improved tumor delineation.
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Project description: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 ?m3 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.
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Project description:The morphological features of ?-synuclein (AS) amyloid aggregation in vitro and in cells were elucidated at the nanoscale by far-field subdiffraction fluorescence localization microscopy. Labeling AS with rhodamine spiroamide probes allowed us to image AS fibrillar structures by fluorescence stochastic nanoscopy with an enhanced resolution at least 10-fold higher than that achieved with conventional, diffraction-limited techniques. The implementation of dual-color detection, combined with atomic force microscopy, revealed the propagation of individual fibrils in vitro. In cells, labeled protein appeared as amyloid aggregates of spheroidal morphology and subdiffraction sizes compatible with in vitro supramolecular intermediates perceived independently by atomic force microscopy and cryo-electron tomography. We estimated the number of monomeric protein units present in these minute structures. This approach is ideally suited for the investigation of the molecular mechanisms of amyloid formation both in vitro and in the cellular milieu.
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Project description:Background and objectiveEver since its discovery, calcium imaging has proven its worth in discovering new insights into the mechanisms of cellular communication. Yet, the analysis of the data generated by calcium imaging experiments demands a large amount of time from researchers. Tools enabling automated and semi-automated analysis are available, but often they allow automating only a part of the data analysis process. Therefore, we developed CALIMA (https://aethelraed.nl/calima), a free and open-source standalone software tool that provides an opportunity to quickly detect cells, to obtain the calcium spikes, and to determine the underlying network structure of neuronal cell cultures.MethodsOwing to the difference of Gaussians algorithm applied for the cell detection, CALIMA is able to detect regions of interest (ROIs) quickly. The z-scoring algorithm provides a means to set the requirements for spike detection, and the neuronal connections can be reconstructed by analyzing the cross-correlation between the cellular activity. We evaluated CALIMA's reliability, speed, and functionality with a special focus on neuronal cell detection and network reconstruction. The evaluation was performed by using real-life data such as a known example dataset (cultured primary rat cortical neurons, University of Pennsylvania) and by analyzing video graphic footage of in vitro brain cell samples (SH-SY5Y neuroblastoma cultures, one sample with synchronous neuron firing). The obtained results were compared to the corresponding outcomes observed on same datasets for other similar software solutions. Moreover, we compared the results of segmentation and peak detection analysis, the ones obtained using CALIMA and those acquired manually.ResultsCALIMA was able to detect the cells in the cultures within seconds. The average sensitivity was 82% across the datasets checked, comparing favorably with the alternative software solutions. Using the correct parameters, CALIMA's Ca-spikes detection sensitivity reached 96%. Lastly, neuronal networks were reconstructed by combining the data on the ROI's activity and the cell's positions, finding the most likely inter-cell connections.ConclusionsWe found that CALIMA proved to be a robust and fast tool to analyze the data of experiments for the digital reconstruction of the neuronal cellular network while being able to process the analysis steps with minimal user input required and in a time efficient manner.
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Project description:Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites. Semi-automatic object-based image analysis (OBIA) was used to classify digital photographs using a set of rules based on digital red, green, blue (RGB) and hue-saturation-intensity (HSI) value thresholds, assigning vegetation to categories of healthy, stressed or moribund moss and lichens. Comparison with traditional visual estimates showed that estimates of percent cover using semi-automated OBIA classification fell within the range of variation determined by visual methods. Overall moss health, as assessed using the mean percentages of healthy, stressed and moribund mosses within quadrats, changed over the 11 years at both sites. A marked increase in stress and decline in health was observed across both sites in 2008, followed by recovery to baseline levels of health by 2014 at one site, but with significantly more stressed or moribund moss remaining within the two communities at the other site. Our results confirm that vegetation cover can be reliably estimated using semi-automated OBIA, providing similar accuracy to visual estimation by experts. The resulting vegetation cover estimates provide a sensitive measure to assess change in vegetation health over time and have informed a conceptual framework for the changing condition of Antarctic mosses. In demonstrating that this method can be used to monitor ground cover vegetation at small scales, we suggest it may also be suitable for other extreme environments where repeat monitoring via images is required.
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Project description:We here present an improved version of AutoGrow (version 3.0), an evolutionary algorithm that works in conjunction with existing open-source software to automatically optimize candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow 3.0, unlike its predecessors, attempts to introduce some chemical intuition into the automated optimization process. AutoGrow 3.0 uses the rules of click chemistry to guide optimization, greatly enhancing synthesizability. Additionally, the program discards any growing ligand whose physical and chemical properties are not druglike. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 3.0 will help supplement the chemist's efforts. To demonstrate the utility of the program, we use AutoGrow 3.0 to generate predicted inhibitors of three important drug targets: Trypanosoma brucei RNA editing ligase 1, peroxisome proliferator-activated receptor ?, and dihydrofolate reductase. In all cases, AutoGrow generates druglike molecules with high predicted binding affinities. AutoGrow 3.0 is available free of charge (http://autogrow.ucsd.edu) under the terms of the GNU General Public License and has been tested on Linux and Mac OS X.
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Project description:Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR). Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009-2010 ("baseline images") and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading. Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving. Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.
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