Three orthogonal techniques were used to provide new insights into thermally induced aggregation of the therapeutic protein Somatropin at pH 5.8 and 7.0. The techniques were Dynamic Light Scattering (DLS), Asymmetric Flow-Field Flow-Fractionation (AF4), and the TEM-based analysis system MiniTEM™. In addition, Differential Scanning Calorimetry (DSC) was used to study the thermal unfolding and stability. DSC and DLS were used to explain the initial aggregation process and aggregation rate at the two pH values. The results suggest that less electrostatic stabilization seems to be the main reason for the faster initial aggregation at pH 5.8, i.e., closer to the isoelectric point of Somatropin. AF4 and MiniTEM were used to investigate the aggregation pathway further. Combining the results allowed us to demonstrate Somatropin’s thermal aggregation pathway at pH 7.0. The growth of the aggregates appears to follow two steps. Smaller elongated aggregates are formed in the first step, possibly initiated by partly unfolded species. In the second step, occurring during longer heating, the smaller aggregates assemble into larger aggregates with more complex structures.

AbstractAttention Branch Networks (ABNs) have been shown to simultaneously provide visual explanation and improve the performance of deep convolutional neural networks (CNNs). In this work, we introduce Multi-Scale Attention Branch Networks (MSABN), which enhance the resolution of the generated attention maps, and improve the performance. We evaluate MSABN on benchmark image recognition and fine-grained recognition datasets where we observe MSABN outperforms ABN and baseline models. We also introduce a new data augmentation strategy utilizing the attention maps to incorporate human knowledge in the form of bounding box annotations of the objects of interest. We show that even with a limited number of edited samples, a significant performance gain can be achieved with this strategy. KeywordsVisual explanationFine-grained recognitionAttention mapHuman-in-the-loop

Transmission electron microscopy (TEM) is a gold standard analytical method for nanoparticle characterization and is playing a valuable role in virus-like particle (VLP) characterization extending to other biological entities such as viral vectors. A dedicated TEM facility is a challenge to both small and medium-sized enterprises (SMEs) and companies operating in low-and-middle income countries (LMICs) due to high start-up and running costs. A low-voltage TEM solution with assisted image acquisition and analysis such as the MiniTEM system, coupled with Vironova Imaging and Analysis Software (VIAS) could provide an affordable and practical alternative. The MiniTEM system has a small footprint and software that enables semi-automated data collection and image analysis workflows using built-in deep learning methods (convolutional neural networks) for automation in analysis, increasing speed of information processing and enabling scaling to larger datasets. In this perspective we outline the potential and challenges in the use of TEM as mainstream analytical tool in manufacturing settings. We highlight the rationale and preliminary findings from our proof-of-concept study aiming to develop a method to assess critical quality attributes (CQAs) of VLPs and facilitate adoption of TEM in manufacturing settings. In our study we explored all the steps, from sample preparation to data collection and analysis using synthetic VLPs as model systems. The applicability of the method in product development was verified at pilot-scale during the technology transfer of dengue VLPs development from a university setting to an LMIC- based vaccine manufacturing company, demonstrating the applicability of this analytical technique to VLP vaccine characterization.

In spite of continuous development of gene therapy vectors with thousands of drug candidates in clinical drug trials there are only a small number approved on the market today stressing the need to have characterization methods to assist in the validation of the drug development process. The level of packaging of the vector capsids appears to play a critical role in immunogenicity, hence an objective quantitative method assessing the content of particles containing a genome is an essential quality measurement. As transmission electron microscopy (TEM) allows direct visualization of the particles present in a specimen, it naturally seems as the most intuitive method of choice for characterizing recombinant adeno-associated virus (rAAV) particle packaging. Negative stain TEM (nsTEM) is an established characterization method for analysing the packaging of viral vectors. It has however shown limitations in terms of reliability. To overcome this drawback, we propose an analytical method based on CryoTEM that unambiguously and robustly determines the percentage of filled particles in an rAAV sample. In addition, we show that at a fixed number of vector particles the portion of filled particles correlates well with the potency of the drug. The method has been validated according to the ICH Q2 (R1) guidelines and the components investigated during the validation are presented in this study. The reliability of nsTEM as a method for the assessment of filled particles is also investigated along with a discussion about the origin of the observed variability of this method.

AbstractObjectiveLarge-scale microscopy-based experiments often result in images with rich but sparse information content. An experienced microscopist can visually identify regions of interest (ROIs), but this becomes a cumbersome task with large datasets. Here we present SimSearch, a framework for quick and easy user-guided training of a deep neural model aimed at fast detection of ROIs in large-scale microscopy experiments.MethodsThe user manually selects a small number of patches representing different classes of ROIs. This is followed by feature extraction using a pre-trained deep-learning model, and interactive patch selection pruning, resulting in a smaller set of clean (user approved) and larger set of noisy (unapproved) training patches of ROIs and background. The pre-trained deep-learning model is thereafter first trained on the large set of noisy patches, followed by refined training using the clean patches.ResultsThe framework is evaluated on fluorescence microscopy images from a large-scale drug screening experiment, brightfield images of immunohistochemistry-stained patient tissue samples, and malaria-infected human blood smears, as well as transmission electron microscopy images of cell sections. Compared to state-of-the-art and manual/visual assessment, the results show similar performance with maximal flexibility and minimal a priori information and user interaction.ConclusionsSimSearch quickly adapts to different data sets, which demonstrates the potential to speed up many microscopy-based experiments based on a small amount of user interaction.SignificanceSimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.

The pyruvate dehydrogenase complex (PDC) is a multienzyme protein complex central to aerobic respiration, connecting glycolysis to mitochondrial oxidation of pyruvate. Its three main components (E1, E2, E3) exhibit a fascinating organization with multiple levels of symmetry. Despite its central metabolic role, important questions regarding its composition and regulation remain. Similar to the E3-binding protein (E3BP) of mammalian PDC, there is a “Protein X” (PX) that selectively recruits E3 to the fungal PDC, but its divergent sequence suggests a distinct structural mechanism. To explain the variation in stoichiometry reported for PX in fungal PDC, we have used cryo-electron microscopy to reconstruct PDC densities from the filamentous fungus Neurospora crassa, and show how PX is present interior to the PDC core instead of substituting E2 subunits as in mammals. We show how steric occlusion limits PX binding, resulting in predominantly tetrahedral symmetry, which explains the observations of a variety of stoichiometries in Saccharomyces cerevisiae. The PX-binding site is conserved in - and specific to - fungi, and complements possible C-terminal binding motifs in PX that are absent in mammalian E3BP. Consideration of multiple symmetries thus reveals a differential structural basis for E3BP-like function in fungal PDC, and suggests the PX oligomer stability and size are potential mechanisms that might adjust PDC composition in response to external cues.

Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.

Nano-drugs based on nanoparticles (NP) or on nano-assemblies as carriers of the active pharmaceutical ingredient (API) are often expected to perform better compared to conventional dosage forms. Maximum realization of this potential though requires optimization of multiple physico-chemical, including structural and morphological, parameters. Meaningful distributions of these parameters derived from sufficient populations of individual NPs rather than ensemble distributions are desirable for this task, provided that relevant high-resolution data is available. In this study we demonstrate powerful capabilities of the up-to-date cryogenic transmission electron-microscopy (cryo-TEM) as well as correlations with other techniques abundant in the nano-research milieu. We explored Doxil®-like (an anticancer drug and the first FDA-approved nano-drug) (75–100 nm) PEGylated liposomes encapsulating single doxorubicin-sulfate nano-rod-crystals (PLD). These crystals induce liposome sphere-to-ellipsoid deformation. Doxil® was characterized by a multitude of physicochemical methods. We demonstrate, that accompanied by advanced image-analysis means, cryo-TEM can successfully enable the determination of multiple structural parameters of such complex liposomal nano-drugs with an added value of statistically-sound distributions. The latter could not be achieved by most other physicochemical approaches. It seems that cryo-TEM is capable of quantitative description of individual liposome morphological features, including meaningful distributions of all structural elements, with averages that correlate with other physical methods. Here it is demonstrated that such quantitative cryo-TEM analysis is a powerful tool in determining what is the optimal drug to lipid ratio in PLD, which is found to be the drug to lipid ratio existing in Doxil®.

The pyruvate dehydrogenase complex (PDC) is a multienzyme complex central to aerobic respiration, connecting glycolysis to mitochondrial oxidation of pyruvate. Similar to the E3-binding protein (E3BP) of mammalian PDC, PX selectively recruits E3 to the fungal PDC, but its divergent sequence suggests a distinct structural mechanism. Here, we report reconstructions of PDC from the filamentous fungus Neurospora crassa by cryo-electron microscopy, where we find protein X (PX) interior to the PDC core as opposed to substituting E2 core subunits as in mammals. Steric occlusion limits PX binding, resulting in predominantly tetrahedral symmetry, explaining previous observations in Saccharomyces cerevisiae. The PX-binding site is conserved in (and specific to) fungi, and complements possible C-terminal binding motifs in PX that are absent in mammalian E3BP. Consideration of multiple symmetries thus reveals a differential structural basis for E3BP-like function in fungal PDC.