JIPipe: visual batch processing for ImageJ

Aspergillus fumigatus-derived gliotoxin impacts innate immune cell activation through modulating lipid mediator production in macrophages

Gliotoxin (GT), a secondary metabolite and virulence factor of the fungal pathogen Aspergillus fumigatus, suppresses innate immunity and supports the suppression of host immune responses. Recently, we revealed that GT blocks the formation of the chemotactic lipid mediator leukotriene (LT)B4 in activated human neutrophils and monocytes, and in rodents in vivo, by directly inhibiting LTA4 hydrolase. Here, we elucidated the impact of GT on LTB4 biosynthesis and the entire lipid mediator networks in human M1- and M2-like monocyte-derived macrophages (MDMs) and in human tissue-resident alveolar macrophages. In activated M1-MDMs with high capacities to generate LTs, the formation of LTB4 was effectively suppressed by GT, connected to attenuated macrophage phagocytic activity as well as human neutrophil movement and migration. In resting macrophages, especially in M1-MDMs, GT elicited strong formation of prostaglandins, while bacterial exotoxins from Staphylococcus aureus evoked a broad spectrum of lipid mediator biosynthesis in both MDM phenotypes. We conclude that GT impairs functions of activated innate immune cells through selective suppression of LTB4 biosynthesis, while GT may also prime the immune system by provoking prostaglandin formation in macrophages.

ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ

ModularImageAnalysis (MIA) is an ImageJ plugin providing a code-free graphical environment in which complex automated analysis workflows can be constructed and distributed. The broad range of included modules cover all stages of a typical analysis workflow, from image loading through image processing, object detection, extraction of measurements, measurement-based filtering, visualisation and data exporting. MIA provides out-of-the-box compatibility with many advanced image processing plugins for ImageJ including Bio-Formats, DeepImageJ, MorphoLibJ and TrackMate, allowing these tools and their outputs to be directly incorporated into analysis workflows. By default, modules support spatially calibrated 5D images, meaning measurements can be acquired in both pixel and calibrated units. A hierarchical object relationship model allows for both parent-child (one-to-many) and partner (many-to-many) relationships to be established. These relationships underpin MIA's ability to track objects through time, represent complex spatial relationships (e.g. topological skeletons) and measure object distributions (e.g. count puncta per cell). MIA features dual graphical interfaces: the 'editing view' offers access to the full list of modules and parameters in the workflow, while the simplified 'processing view' can be configured to display only a focused subset of controls. All workflows are batch-enabled by default, with image files within a specified folder being processed automatically and exported to a single spreadsheet. Beyond the included modules, functionality can be extended both internally, through integration with the ImageJ scripting interface, and externally, by developing third-party Java modules that extend the core MIA framework. Here we describe the design and functionality of MIA in the context of a series of real-world example analyses.

Molecular probes for tracking lipid droplet membrane dynamics

Lipid droplets (LDs) feature a unique monolayer lipid membrane that has not been extensively studied due to the lack of suitable molecular probes that are able to distinguish this membrane from the LD lipid core. In this work, we present a three-pronged molecular probe design strategy that combines lipophilicity-based organelle targeting with microenvironment-dependent activation and design an LD membrane labeling pro-probe called LDM. Upon activation by the HClO/ClO- microenvironment that surrounds LDs, LDM pro-probe releases LDM-OH probe that binds to LD membrane proteins thus enabling visualization of the ring-like LD membrane. By utilizing LDM, we identify the dynamic mechanism of LD membrane contacts and their protein accumulation parameters. Taken together, LDM represents the first molecular probe for imaging LD membranes in live cells to the best of our knowledge, and represents an attractive tool for further investigations into the specific regulatory mechanisms with LD-related metabolism diseases and drug screening.

Arkitekt: streaming analysis and real-time workflows for microscopy

Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making 'smart microscopy' a reality.

Engineering Adhesion of the Probiotic Strain Escherichia coli Nissle to the Fungal Pathogen Candida albicans

Engineering live biotherapeutic products against fungal pathogens such as Candida albicans has been suggested as a means to tackle the increasing threat of fungal infections and the development of resistance to classical antifungal treatments. One important challenge in the design of live therapeutics is to control their localization inside the human body. The specific binding capability to target organisms or tissues would greatly increase their effectiveness by increasing the local concentration of effector molecules at the site of infection. In this study, we utilized surface display of carbohydrate binding domains to enable the probiotic E. coli Nissle 1917 to adhere specifically to the pathogenic yeast Candida albicans. Binding was quantified using a newly developed method based on the automated analysis of microscopic images. In addition to a rationally selected chitin binding domain, a synthetic peptide of identical length but distinct sequence also conferred binding. Efficient binding was specific to fungal hyphae, the invasive form of C. albicans, while the yeast form, as well as abiotic cellulose and PET particles, was only weakly recognized.

AspSnFR: A genetically encoded biosensor for real-time monitoring of aspartate in live cells

Aspartate is crucial for nucleotide synthesis, ammonia detoxification, and maintaining redox balance via the malate-aspartate-shuttle (MAS). To disentangle these multiple roles of aspartate metabolism, tools are required that measure aspartate concentrations in real time and in live cells. We introduce AspSnFR, a genetically encoded green fluorescent biosensor for intracellular aspartate, engineered through displaying and screening biosensor libraries on mammalian cells. In live cells, AspSnFR is able to precisely and quantitatively measure cytosolic aspartate concentrations and dissect its production from glutamine. Combining high-content imaging of AspSnFR with pharmacological perturbations exposes differences in metabolic vulnerabilities of aspartate levels based on nutrient availability. Further, AspSnFR facilitates tracking of aspartate export from mitochondria through SLC25A12, the MAS' key transporter. We show that SLC25A12 is a rapidly responding and direct route to couple Ca2+ signaling with mitochondrial aspartate export. This establishes SLC25A12 as a crucial link between cellular signaling, mitochondrial respiration, and metabolism.

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning

Quantitative imaging in life sciences has evolved into a powerful approach combining advanced microscopy acquisition and automated analysis of image data. The focus of the present study is on the imaging-based evaluation of the posterior cricoarytenoid muscle (PCA) influenced by long-term functional electrical stimulation (FES), which may assist the inspiration of patients with bilateral vocal fold paresis. To this end, muscle cross-sections of the PCA of sheep were examined by quantitative image analysis. Previous investigations of the muscle fibers and the collagen amount have not revealed signs of atrophy and fibrosis due to FES by a laryngeal pacemaker. It was therefore hypothesized that regardless of the stimulation parameters the fat in the muscle cross-sections would not be significantly altered. We here extending our previous investigations using quantitative imaging of intramuscular fat in cross-sections. In order to perform this analysis both reliably and faster than a qualitative evaluation and time-consuming manual annotation, the selection of the automated method was of crucial importance. To this end, our recently established deep neural network IMFSegNet, which provides more accurate results compared to standard machine learning approaches, was applied to more than 300 H&E stained muscle cross-sections from 22 sheep. It was found that there were no significant differences in the amount of intramuscular fat between the PCA with and without long-term FES, nor were any significant differences found between the low and high duty cycle stimulated groups. This study on a human-like animal model not only confirms the hypothesis that FES with the selected parameters has no negative impact on the PCA, but also demonstrates that objective and automated deep learning-based quantitative imaging is a powerful tool for such a challenging analysis.

Halogen-bonded charge-transfer co-crystal scintillators for high-resolution X-ray imaging

The development of high-quality organic scintillators encounters challenges primarily associated with the weak X-ray absorption ability resulting from the presence of low atomic number elements. An effective strategy involves the incorporation of halogen-containing molecules into the system through co-crystal engineering. Herein, we synthesized a highly fluorescent dye, 2,5-di(4-pyridyl)thiazolo[5,4-d]thiazole (Py2TTz), with a fluorescence quantum yield of 12.09%. Subsequently, Py2TTz was co-crystallized with 1,4-diiodotetrafluorobenzene (I2F4B) and 1,3,5-trifluoro-2,4,6-triiodobenzene (I3F3B) obtaining Py2TTz-I2F4 and Py2TTz-I3F3. Among them, Py2TTz-I2F4 exhibited exceptional scintillation properties, including an ultrafast decay time (1.426 ns), a significant radiation luminescence intensity (146% higher than Bi3Ge4O12), and a low detection limit (70.49 nGy s-1), equivalent to 1/78th of the detection limit for medical applications (5.5 μGy s-1). This outstanding scintillation performance can be attributed to the formation of halogen-bonding between I2F4B and Py2TTz. Theoretical calculations and single-crystal structures demonstrate the formation of halogen-bond-induced rather than π-π-induced charge-transfer cocrystals, which not only enhances the X-ray absorption ability and material conductivity under X-ray exposure, but also constrains molecular vibration and rotation, and thereby reducing non-radiative transition rate and sharply increasing its fluorescence quantum yields. Based on this, the flexible X-ray film prepared based on Py2TTz-I2F4 achieved an ultrahigh spatial resolution of 26.8 lp per mm, underscoring the superiority of this strategy in developing high-performance organic scintillators.

Selective Uptake Into Inflamed Human Intestinal Tissue and Immune Cell Targeting by Wormlike Polymer Micelles

Inflammatory bowel disease (IBD) has become a globally prevalent chronic disease with no causal therapeutic options. Targeted drug delivery systems with selectivity for inflamed areas in the gastrointestinal tract promise to reduce severe drug-related side effects. By creating three distinct nanostructures (vesicles, spherical, and wormlike micelles) from the same amphiphilic block copolymer poly(butyl acrylate)-block-poly(ethylene oxide) (PBA-b-PEO), the effect of nanoparticle shape on human mucosal penetration is systematically identified. An Ussing chamber technique is established to perform the ex vivo experiments on human colonic biopsies, demonstrating that the shape of polymeric nanostructures represents a rarely addressed key to tissue selectivity required for efficient IBD treatment. Wormlike micelles specifically enter inflamed mucosa from patients with IBD, but no significant uptake is observed in healthy tissue. Spheres (≈25 nm) and vesicles (≈120 nm) enter either both normal and inflamed tissue types or do not penetrate any tissue. According to quantitative image analysis, the wormlike nanoparticles localize mainly within immune cells, facilitating specific targeting, which is crucial for further increasing the efficacy of IBD treatment. These findings therefore demonstrate the untapped potential of wormlike nanoparticles not only to selectively target the inflamed human mucosa, but also to target key pro-inflammatory cells.

The global RNA-RNA interactome of Klebsiella pneumoniae unveils a small RNA regulator of cell division

The ubiquitous RNA chaperone Hfq is involved in the regulation of key biological processes in many species across the bacterial kingdom. In the opportunistic human pathogen Klebsiella pneumoniae, deletion of the hfq gene affects the global transcriptome, virulence, and stress resistance; however, the ligands of the major RNA-binding protein in this species have remained elusive. In this study, we have combined transcriptomic, co-immunoprecipitation, and global RNA interactome analyses to compile an inventory of conserved and species-specific RNAs bound by Hfq and to monitor Hfq-mediated RNA-RNA interactions. In addition to dozens of RNA-RNA pairs, our study revealed an Hfq-dependent small regulatory RNA (sRNA), DinR, which is processed from the 3' terminal portion of dinI mRNA. Transcription of dinI is controlled by the master regulator of the SOS response, LexA. As DinR accumulates in K. pneumoniae in response to DNA damage, the sRNA represses translation of the ftsZ transcript by occupation of the ribosome binding site. Ectopic overexpression of DinR causes depletion of ftsZ mRNA and inhibition of cell division, while deletion of dinR antagonizes cell elongation in the presence of DNA damage. Collectively, our work highlights the important role of RNA-based gene regulation in K. pneumoniae and uncovers the central role of DinR in LexA-controlled division inhibition during the SOS response.

Evaluation of reproducible cryogel preparation based on automated image analysis using deep learning

Cryogels represent a class of porous sponge-like materials possessing unique properties including high-fidelity reproduction of tissue structure and maximized permeability. Their architecture is mainly based on an interconnected network of macropores that provides sufficient stability while allowing the movement of substances through the material. In most cryogel applications, the pore size is very important, especially when the material is used as a 3D scaffold for tissue culture, applied as a filter, or utilized as a membrane. In this study, poly(dimethylacrylamide-co-2-hydroxyethyl methacrylate) cryogels have been prepared by two preparation methods to investigate the reproducibility of homogeneous pore structures and pore sizes. Automated image analysis algorithms were developed to rapidly evaluate cryogel pore sizes based on scanning electron microscopy (SEM) images. The quantification approach contained a unique combination of classical and deep learning-based algorithms. To validate the accuracy of the two models, we compared the results obtained from automated SEM image analysis with those from manual pore size determinations and mercury intrusion porosimetry (MIP) measurements. Effect sizes were calculated to compare the results from manual and automated pore size measurements for the cryogel reproducibility series. 81% of the values obtained revealed only trivial differences, which strongly suggests that automated image analysis can reliably substitute the manual evaluation of cryogel pore sizes. The use of an adapted reactor setup yielded cryogels with heterogeneous morphologies in the absence of recognizable pore structures. With the conventional cryogel preparation using plastic syringes, the obtained cryogels represented highly reproducible morphologies and pore sizes in the range between 17 and 22 μm. Calculated effect sizes within the cryogel replicate series revealed only trivial differences between the obtained pore sizes in 83.5% or 99.4% of the data (classical approach and deep learning-based approach, respectively).

The role of pneumococcal extracellular vesicles on the pathophysiology of the kidney disease hemolytic uremic syndrome

Streptococcus pneumoniae-induced hemolytic uremic syndrome (Sp-HUS) is a kidney disease characterized by microangiopathic hemolytic anemia, thrombocytopenia, and acute kidney injury. This disease is frequently underdiagnosed and its pathophysiology is poorly understood. In this work, we compared clinical strains, isolated from infant Sp-HUS patients, with a reference pathogenic strain D39, for host cytotoxicity and further explored the role of Sp-derived extracellular vesicles (EVs) in the pathogenesis of an HUS infection. In comparison with the wild-type strain, pneumococcal HUS strains caused significant lysis of human erythrocytes and increased the release of hydrogen peroxide. Isolated Sp-HUS EVs were characterized by performing dynamic light-scattering microscopy and proteomic analysis. Sp-HUS strain released EVs at a constant concentration during growth, yet the size of the EVs varied and several subpopulations emerged at later time points. The cargo of the Sp-HUS EVs included several virulence factors at high abundance, i.e., the ribosomal subunit assembly factor BipA, the pneumococcal surface protein A, the lytic enzyme LytC, several sugar utilization, and fatty acid synthesis proteins. Sp-HUS EVs strongly downregulated the expression of the endothelial surface marker platelet endothelial cell adhesion molecule-1 and were internalized by human endothelial cells. Sp-HUS EVs elicited the release of pro-inflammatory cytokines (interleukin [IL]-1β, IL-6) and chemokines (CCL2, CCL3, CXCL1) by human monocytes. These findings shed new light on the overall function of Sp-EVs, in the scope of infection-mediated HUS, and suggest new avenues of research for exploring the usefulness of Sp-EVs as therapeutic and diagnostic targets. IMPORTANCE Streptococcus pneumoniae-associated hemolytic uremic syndrome (Sp-HUS) is a serious and underdiagnosed deadly complication of invasive pneumococcal disease. Despite the introduction of the pneumococcal vaccine, cases of Sp-HUS continue to emerge, especially in children under the age of 2. While a lot has been studied regarding pneumococcal proteins and their role on Sp-HUS pathophysiology, little is known about the role of extracellular vesicles (EVs). In our work, we isolate and initially characterize EVs from a reference pathogenic strain (D39) and a strain isolated from a 2-year-old patient suffering from Sp-HUS. We demonstrate that despite lacking cytotoxicity toward human cells, Sp-HUS EVs are highly internalized by endothelial cells and can trigger cytokine and chemokine production in monocytes. In addition, this work specifically highlights the distinct morphological characteristics of Sp-HUS EVs and their unique cargo. Overall, this work sheds new light into potentially relevant players contained in EVs that might elucidate about pneumococcal EVs biogenesis or pose as interesting candidates for vaccine design.

IMFSegNet: Cost-effective and objective quantification of intramuscular fat in histological sections by deep learning

The assessment of muscle condition is of great importance in various research areas. In particular, evaluating the degree of intramuscular fat (IMF) in tissue sections is a challenging task, which today is still mostly performed qualitatively or quantitatively by a highly subjective and error-prone manual analysis. We here realize the mission to make automated IMF analysis possible that (i) minimizes subjectivity, (ii) provides accurate and quantitative results quickly, and (iii) is cost-effective using standard hematoxylin and eosin (H&E) stained tissue sections. To address all these needs in a deep learning approach, we utilized the convolutional encoder-decoder network SegNet to train the specialized network IMFSegNet allowing to accurately quantify the spatial distribution of IMF in histological sections. Our fully automated analysis was validated on 17 H&E-stained muscle sections from individual sheep and compared to various state-of-the-art approaches. Not only does IMFSegNet outperform all other approaches, but this neural network also provides fully automated and highly accurate results utilizing the most cost-effective procedures of sample preparation and imaging. Furthermore, we shed light on the opacity of black-box approaches such as neural networks by applying an explainable artificial intelligence technique to clarify that the success of IMFSegNet actually lies in identifying the hard-to-detect IMF structures. Embedded in our open-source visual programming language JIPipe that does not require programming skills, it can be expected that IMFSegNet advances muscle condition assessment in basic research across multiple areas as well as in research fields focusing on translational clinical applications.

Polymer-based particles against pathogenic fungi: A non-uptake delivery of compounds

The therapy of life-threatening fungal infections is limited and needs urgent improvement. This is in part due to toxic side effects of clinically used antifungal compounds or their limited delivery to fungal structures. Until today, it is a matter of debate how drugs or drug-delivery systems can efficiently reach the intracellular lumen of fungal cells and how this can be improved. Here, we addressed both questions by applying two different polymeric particles for delivery of compounds. Their formulation was based on two biocompatible polymers, i.e., poly(lactic-co-glycolic acid)_50:50 and poly(methyl methacrylate-stat-methacrylic acid)_90:10 yielding particles with hydrodynamic diameters ranging from 100 to 300 nm. The polymers were covalently labeled with the fluorescent dye DY-550 to monitor the interaction between particles and fungi by confocal laser scanning microscopy. Furthermore, the fluorescent dye coumarin-6 and the antifungal drug itraconazole were successfully encapsulated in particles to study the fate of both the cargo and the particle when interacting with the clinically most important human-pathogenic fungi Aspergillus fumigatus, A. terreus, Candida albicans, and Cryptococcus neoformans. While the polymers were exclusively located on the fungal surface, the encapsulated cargo was efficiently transported into fungal hyphae, indicated by increased intracellular fluorescence signals due to coumarin-6. In accordance with this finding, compared to the pristine drug a reduced minimal inhibitory concentration for itraconazole was determined, when it was encapsulated. Together, the herein used polymeric particles were not internalized by pathogenic fungi but were able to efficiently deliver hydrophobic cargos into fungal cells.

Tuning the corona-core ratio of polyplex micelles for selective oligonucleotide delivery to hepatocytes or hepatic immune cells

Targeted delivery of oligonucleotides or small molecular drugs to hepatocytes, the liver's parenchymal cells, is challenging without targeting moiety due to the highly efficient mononuclear phagocyte system (MPS) of the liver. The MPS comprises Kupffer cells and specialized sinusoidal endothelial cells, efficiently clearing nanocarriers regardless of their size and surface properties. Physiologically, this non-parenchymal shield protects hepatocytes; however, these local barriers must be overcome for drug delivery. Nanocarrier structural properties strongly influence tissue penetration, in vivo pharmacokinetics, and biodistribution profile. Here we demonstrate the in vivo biodistribution of polyplex micelles formed by polyion complexation of short interfering (si)RNA with modified poly(ethylene glycol)-block-poly(allyl glycidyl ether) (PEG-b-PAGE) diblock copolymer that carries amino moieties in the side chain. The ratio between PEG corona and siRNA complexed PAGE core of polyplex micelles was chemically varied by altering the degree of polymerization of PAGE. Applying Raman-spectroscopy and dynamic in silico modeling on the polyplex micelles, we determined the corona-core ratio (CCR) and visualized the possible micellar structure with varying CCR. The results for this model system reveal that polyplex micelles with higher CCR, i.e., better PEG coverage, exclusively accumulate and thus allow passive cell-type-specific targeting towards hepatocytes, overcoming the macrophage-rich reticuloendothelial barrier of the liver.