The complement system drives local inflammatory tissue priming by metabolic reprogramming of synovial fibroblasts.

Arthritis typically involves recurrence and progressive worsening at specific predilection sites, but the checkpoints between remission and persistence remain unknown. Here, we defined the molecular and cellular mechanisms of this inflammation-mediated tissue priming. Re-exposure to inflammatory stimuli caused aggravated arthritis in rodent models. Tissue priming developed locally and independently of adaptive immunity. Repeatedly stimulated primed synovial fibroblasts (SFs) exhibited enhanced metabolic activity inducing functional changes with intensified migration, invasiveness and osteoclastogenesis. Meanwhile, human SF from patients with established arthritis displayed a similar primed phenotype. Transcriptomic and epigenomic analyses as well as genetic and pharmacological targeting demonstrated that inflammatory tissue priming relies on intracellular complement C3- and C3a receptor-activation and downstream mammalian target of rapamycin- and hypoxia-inducible factor 1α-mediated metabolic SF invigoration that prevents activation-induced senescence, enhances NLRP3 inflammasome activity, and in consequence sensitizes tissue for inflammation. Our study suggests possibilities for therapeutic intervention abrogating tissue priming without immunosuppression.

Calibration by differentiation - Self-supervised calibration for X-ray microscopy using a differentiable cone-beam reconstruction operator.

High-resolution X-ray microscopy (XRM) is gaining interest for biological investigations of extremely small-scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometres in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open-source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data-driven way using the gradient-based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self-supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artefacts and decreases the difference in grey values between outer and inner bone by 68-94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat-panel computed tomography systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step towards the goal of reducing the resolution limit of in vivo bone imaging to the single micrometre domain.

Aggregated neutrophil extracellular traps resolve inflammation by proteolysis of cytokines and chemokines and protection from antiproteases.

Papillon-Lefèvre syndrome (PLS) is characterized by nonfunctional neutrophil serine proteases (NSPs) and fulminant periodontal inflammation of unknown cause. Here we investigated neutrophil extracellular trap (NET)-associated aggregation and cytokine/chemokine-release/degradation by normal and NSP-deficient human and mouse granulocytes. Stimulated with solid or soluble NET inducers, normal neutrophils formed aggregates and both released and degraded cytokines/chemokines. With increasing cell density, proteolytic degradation outweighed release. Maximum output of cytokines/chemokines occurred mostly at densities between 2 × 107 and 4 × 107 neutrophils/cm3. Assessment of neutrophil density in vivo showed that these concentrations are surpassed during inflammation. Association with aggregated NETs conferred protection of neutrophil elastase against α1-antitrypsin. In contrast, eosinophils did not influence cytokine/chemokine concentrations. The proteolytic degradation of inflammatory mediators seen in NETs was abrogated in Papillon-Lefèvre syndrome (PLS) neutrophils. In summary, neutrophil-driven proteolysis of inflammatory mediators works as a built-in safeguard for inflammation. The absence of this negative feedback mechanism might be responsible for the nonresolving periodontitis seen in PLS.-Hahn, J., Schauer, C., Czegley, C., Kling, L., Petru, L., Schmid, B., Weidner, D., Reinwald, C., Biermann, M. H. C., Blunder, S., Ernst, J., Lesner, A., Bäuerle, T., Palmisano, R., Christiansen, S., Herrmann, M., Bozec, A., Gruber, R., Schett, G., Hoffmann, M. H. Aggregated neutrophil extracellular traps resolve inflammation by proteolysis of cytokines and chemokines and protection from antiproteases.

Characterizing Intraindividual Podocyte Morphology In Vitro with Different Innovative Microscopic and Spectroscopic Techniques.

Podocytes are critical components of the glomerular filtration barrier, sitting on the outside of the glomerular basement membrane. Primary and secondary foot processes are characteristic for podocytes, but cell processes that develop in culture were not studied much in the past. Moreover, protocols for diverse visualization methods mostly can only be used for one technique, due to differences in fixation, drying and handling. However, we detected by single-cell RNA sequencing (scRNAseq) analysis that cells reveal high variability in genes involved in cell type-specific morphology, even within one cell culture dish, highlighting the need for a compatible protocol that allows measuring the same cell with different methods. Here, we developed a new serial and correlative approach by using a combination of a wide variety of microscopic and spectroscopic techniques in the same cell for a better understanding of podocyte morphology. In detail, the protocol allowed for the sequential analysis of identical cells with light microscopy (LM), Raman spectroscopy, scanning electron microscopy (SEM) and atomic force microscopy (AFM). Skipping the fixation and drying process, the protocol was also compatible with scanning ion-conductance microscopy (SICM), allowing the determination of podocyte surface topography of nanometer-range in living cells. With the help of nanoGPS Oxyo®, tracking concordant regions of interest of untreated podocytes and podocytes stressed with TGF-ß were analyzed with LM, SEM, Raman spectroscopy, AFM and SICM, and revealed significant morphological alterations, including retraction of podocyte process, changes in cell surface morphology and loss of cell-cell contacts, as well as variations in lipid and protein content in TGF-ß treated cells. The combination of these consecutive techniques on the same cells provides a comprehensive understanding of podocyte morphology. Additionally, the results can also be used to train automated intelligence networks to predict various outcomes related to podocyte injury in the future.

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation.

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples.

Correlative Microscopy and Spectroscopy Workflow for Microplastics.

Microplastics (MPs) have been reported in various environmental compartments and their number is continuously increasing because of degradation into smaller fragments down to nanoplastics. Humans are exposed to these small-sized MPs through food and air with potential health consequences that still need to be determined. This requires, in the first place, efficient and detailed visualization, relocalization, and characterization of the same MPs with complementary analytical methods. Here, we show the first application of a correlative microscopy and spectroscopy workflow to MPs that meets these demands. For this purpose, standard MP particles on aluminum-coated polycarbonate membrane filters were investigated by an optical zoom microscope and a hyphenated scanning electron microscopy (SEM)-Raman system. By merging the obtained data in one software, it is possible to navigate on the entire filters' surface and correlate at identical locations MP morphology at the spatial resolutions of electron (1.6 nm at 1 kV for the used SEM, ∼100 nm minimum MP size in this study) and optical (∼1-10 µm) microscopies with chemical identification by micro-Raman spectroscopy. Moreover, we observed that low-voltage SEM works without a conductive coating of MPs, causes no detectable charging and structural changes, and provides high-resolution surface imaging of single and clustered MP particles, thus enabling subsequent Raman measurements. We believe that further work on the accurate identification and quantification of micro- and nanoplastics in real samples can potentially profit from this workflow.

Next-generation imaging of the skeletal system and its blood supply.

Bone is organized in a hierarchical 3D architecture. Traditionally, analysis of the skeletal system was based on bone mass assessment by radiographic methods or on the examination of bone structure by 2D histological sections. Advanced imaging technologies and big data analysis now enable the unprecedented examination of bone and provide new insights into its 3D macrostructure and microstructure. These technologies comprise ex vivo and in vivo methods including high-resolution computed tomography (CT), synchrotron-based imaging, X-ray microscopy, ultra-high-field magnetic resonance imaging (MRI), light-sheet fluorescence microscopy, confocal and intravital two-photon imaging. In concert, these techniques have been used to detect and quantify a novel vascular system of trans-cortical vessels in bone. Furthermore, structures such as the lacunar network, which harbours and connects osteocytes, become accessible for 3D imaging and quantification using these methods. Next-generation imaging of the skeletal system and its blood supply are anticipated to contribute to an entirely new understanding of bone tissue composition and function, from macroscale to nanoscale, in health and disease. These insights could provide the basis for early detection and precision-type intervention of bone disorders in the future.

Learning with Known Operators reduces Maximum Training Error Bounds.

We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.

Frontline Science: Aggregated neutrophil extracellular traps prevent inflammation on the neutrophil-rich ocular surface.

Eye rheum is a physiological discharge, which accumulates at the medial angle of the healthy eye soon after opening in the morning. Microscopic evaluation of eye rheum revealed the presence of viable neutrophils, bacteria, epithelial cells, and particles, aggregated by neutrophil extracellular traps. We observed that in the evening, during eye closure, high C5a recruited neutrophils to the tear film and activated them. In this hypoxic area rich in CO2 , neutrophils fight microbial aggressors by degranulation. Immediately after eye opening, the microenvironment of the ocular surface changes, the milieu gets normoxic, and loss of CO2 induces subtle alkalinization of tear film. These conditions favored the formation of neutrophil extracellular traps (NETs) that initially covers the ocular surface and tend to aggregate by eyelid blinking. These aggregated neutrophil extracellular traps (aggNETs) are known as eye rheum and contain several viable neutrophils, epithelial cells, dust particles, and crystals packed together by NETs. Similar to aggNETs induced by monosodium urate crystals, the eye rheum shows a robust proteolytic activity that degraded inflammatory mediators before clinically overt inflammation occur. Finally, the eye rheum passively floats with the tear flow to the medial angle of the eye for disposal. We conclude that the aggNETs-based eye rheum promotes cleaning of the ocular surface and ameliorates the inflammation on the neutrophil-rich ocular surfaces.

A network of trans-cortical capillaries as mainstay for blood circulation in long bones.

Closed circulatory systems (CCS) underlie the function of vertebrate organs, but in long bones their structure is unclear, although they constitute the exit route for bone marrow (BM) leukocytes. To understand neutrophil emigration from BM, we studied the vascular system of murine long bones. Here we show that hundreds of capillaries originate in BM, cross murine cortical bone perpendicularly along the shaft and connect to the periosteal circulation. Structures similar to these trans-cortical-vessels (TCVs) also exist in human limb bones. TCVs express arterial or venous markers and transport neutrophils. Furthermore, over 80% arterial and 59% venous blood passes through TCVs. Genetic and drug-mediated modulation of osteoclast count and activity leads to substantial changes in TCV numbers. In a murine model of chronic arthritic bone inflammation, new TCVs develop within weeks. Our data indicate that TCVs are a central component of the CCS in long bones and may represent an important route for immune cell export from the BM.

Hollow silica capsules for amphiphilic transport and sustained delivery of antibiotic and anticancer drugs.

Hollow mesoporous silica capsules (HMSC) are potential drug transport vehicles due to their biocompatibility, high loading capacity and sufficient stability in biological milieu. Herein, we report the synthesis of ellipsoid-shaped HMSC (aspect ratio ∼2) performed using hematite particles as solid templates that were coated with a conformal silica shell through cross-condensation reactions. For obtaining hollow silica capsules, the iron oxide core was removed by acidic leaching. Gas sorption studies on HMSC revealed mesoscopic pores (main pore width ∼38 Å) and a high surface area of 308.8 m2 g-1. Cell uptake of dye-labeled HMSC was confirmed by incubating them with human cervical cancer (HeLa) cells and analyzing the internalization through confocal microscopy. The amphiphilic nature of HMSC for drug delivery applications was tested by loading antibiotic (ciprofloxacin) and anticancer (curcumin) compounds as model drugs for hydrophilic and hydrophobic therapeutics, respectively. The versatility of HMSC in transporting hydrophilic as well as hydrophobic drugs and a pH dependent drug release over several days under physiological conditions was demonstrated in both cases by UV-vis spectroscopy. Ciprofloxacin-loaded HMSC were additionally evaluated towards Gram negative (E. coli) bacteria and demonstrated their efficacy even at low concentrations (10 µg ml-1) in inhibiting complete bacterial growth over 18 hours.