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Juvenile Neuronal Ceroid Lipofuscinosis (or Batten disease) is an autosomal recessive, rare neurodegenerative disorder that affects mainly children above the age of 5 years and is most commonly caused by mutations in the highly conservedCLN3gene. Here, we generatedcln3morphants and stable mutant lines in zebrafish. Although neither morphant nor mutantcln3larvae showed any obvious developmental or morphological defects, behavioral phenotyping of the mutant larvae revealed higher basal activity, hyposensitivity to abrupt light changes and hypersensitivity to pro-convulsive drugs. Importantly, in-depth metabolomics and lipidomics analyses revealed significant accumulation of several glycerophosphodiesters (GPDs) and a global decrease of bis(monoacylglycero)phosphate (BMP) species, two classes of molecules previously proposed as potential biomarkers forCLN3disease based on independent studies in other organisms. We could also demonstrate GPD accumulation in human-induced pluripotent stem cell-derived cerebral organoids carrying a pathogenic variant forCLN3. Our models revealed that GPDs accumulate at very early stages of life in the absence of functional CLN3 and highlight glycerophosphoinositol and BMP as promising biomarker candidates for pre-symptomaticCLN3disease.

Objectives The present study was performed to determine if minocycline HCl could influence the behavior of deproteinized bovine bone mineral (DBBM) and bioactive glass (BG) particles when used as filler material for new bone generation in a guided bone augmentation model. Materials and Methods Two occlusive titanium caps were placed on the rat calvaria. One filled with BG particles, the second with DBBM particles, both previously mixed with blood (control). In minocycline HCl loaded groups (experimental), grafts were additionally placed into a minocycline solution. Samples were harvested after 4, 8, and 16 weeks. Half of the samples were embedded in methylmethacrylate for undecalcified histology and the other half was fixed, decalcified, and embedded in paraffin for classical histologic analysis. Results The control groups highlighted osteoconductive and osteoinductive responses associated to BG particles, as well as an osteoconductive reaction, in DBBM sections. The addition of minocycline HCl to BG particles had no measurable influence on the result. In minocycline HCl loaded DBBM sections; however, areas of spontaneous ossification could be observed after 8 and 16 weeks. Conclusion Our observations suggest that minocycline HCl may add some osteoinductive properties to DBBM within the limitations of this study design. Further investigations are needed to refine the present results.

Background: Akey process of neurodegeneration in Parkinson’s disease (PD) is the transneuronal spreading of a-synuclein. Alpha-synuclein is a presynaptic protein that is implicated in the pathogenesis of PD and other synucleinopathies, where it forms, upon intracellular aggregation, pathological inclusions.Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarilytransneuronal spreading of a-synuclein particles,inclusion formation, or other mechanisms, such as inflammation,that cause neurodegeneration in PDis unclear.Methods: We used spreading/aggregation ofa-synuclein induced by intracerebral injection of a-synucleinpreformed fibrils into the mouse brain to address this question. We performed quantitative histological analysis for a-synuclein inclusions, neurodegeneration, and microgliosis in different brain regions, and a gene expression profiling of the ventral midbrain, at two different timepoints after disease induction.Results: We observed significant neurodegeneration and microgliosis in brain regions not only with,but alsowithouta-synuclein inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. In longitudinal gene expression profiling experiments, we observedearly and unique alterationslinkedto microglial mediated inflammation that preceded neurodegeneration, indicating an active role of microglia in inducing neurodegeneration. Our observations indicate that a-synuclein inclusion formation is not the major driverin the early phases of PD-like neurodegeneration, but that diffusible, oligomeric a-synuclein species,which induce unusual microglial reactivity, play a key role in this process.Conclusion: Our findings uncover new features of a-synuclein induced pathologies, in particular microgliosis, and point to the necessity of a broader view of the process of “prion-like spreading” of that protein.

BACKGROUND Raman Spectra have been shown to be sufficiently characteristic to their samples of origin that they can be used in a wide range of applications including distinction of intracranial tumors. While not replacing pathological analysis, the advantage of non-destructive sample analysis and extremely fast feedback make this technique an interesting tool for surgical use. METHODS We sampled intractanial tumors from more than 300 patients at the Centre Hospitalier Luxembourg over a period of three years and compared the spectra of different tumor entities, different tumor subregions and healthy surrounding tissue. We created machine-learning based classifiers that include tissue identification as well as diagnostics. RESULTS To this end, we solved several classes in the intracranial tumor classification, and developed classifiers to distinguish primary central nervous system lymphoma from glioblastoma, which is an important differential diagnosis, as well as meningioma from the surrounding healthy dura mater for identification of tumor tissue. Within glioblastoma, we resolve necrotic, vital tumor tissue and peritumoral infiltration zone.We are currently developing a multi-class classifier incorporating all tissue types measured. CONCLUSIONS Raman Spectroscopy has the potential to aid the surgeon in the surgery theater by providing a quick assessment of the tissue analyzed with regards to both tumor identity and tumor margin identification. Once a reliable classifier based on sufficient patient samples is developed, this may even be integrated into a surgical microscope or a neuronavigation system.

Raman Spectroscopy is able to provide a fast identification method for healthy and pathological tissues without the need for sample preparation. This makes it highly interesting for intraoperative use in identifying tumor types and borders. However, the capability of a machine-learning classifier to recognize known tissues based on Raman spectra relies on a solid ground truth that is usually provided by measuring small tissue samples first and analyzing the sample later by histopathology. This approach is limited in very closely interspersed tissues that cannot be identified macroscopically, such as glioblastoma where vital tumor tissue, necrosis and peritumoral tissue with characteristic changes to healthy brain tissue are tightly arranged. These areas have been shown to be distinct in their spectral properties, which makes tumor border identification aided by Raman Spectroscopy challenging. As infiltrative tumors leaving no tumor-cell-free brain tissue, borders need to be defined on a clinical scale, where extensive resection must be weighed against retaining functional brain tissue. For this, spectral analysis beyond tumor identification is necessary. In a computational approach we use methods of unsupervised learning and K-means Clustering to investigate heterogeneity within glioblastomas in the native state in order to reduce the complexity of this tumor type. Thereby, it was possible to extract spectra of necrotic tumor areas and to calculate a prediction probability for necrotic tissue within glioblastomas. Several clusters with spectral similarity to each other could be identified, which might represent the different areas of a glioblastoma. Two of these clusters were identified as probable grey and white matter, respectively, by comparison with autopsy tissue. In addition, this assignment independently confirmed the prediction of the classifier as non-necrotic tissue. Deciphering the spectral heterogeneity of glioblastoma in this way may be a great advantage for the surgeon to identify tumor borders and assist in resection control.

BACKGROUND Raman spectroscopy (RS) has shown its applicability in neurooncological diagnostics ranging from intraoperative tumor identification to peri- and postoperative tissue analyses. In the present study, we applied RS to track changes in the molecular vibrational status of a broad spectrum of formalin fixed paraffin-embedded (FFPE) intracranial neoplasms (primary brain tumors, meningiomas, brain metastases) and evaluated its potential as an additional method in the neuropathology toolbox, considering specific challenges when employing RS on FFPE tissue. Material and METHODS We examined 82 cases of intracranial neoplasms (679 individual measurements) by RS and applied a machine learning pipeline for recognition of spectral properties. The discrimination potential of the machine learning algorithms was evaluated using standard performance metrics such as AUROC and AUPR values, macro and weighted average of accuracy, precision, recall, and f1 scores. To address occurring misclassifications and further evaluate our models we searched for important Raman bands usable for tumor identification. RESULTS Using our trained machine learning model, we differentiated between different types of gliomas and determined the primary origin in case of a brain metastasis. We further spectroscopically diagnosed tumor types solely based on biopsy fragments of necrosis, something not possible by means of light microscopy. During the validation process we confirmed a high complexity within the spectroscopic data, possibly resulting not only from biological tissue which has undergone a rough chemical procedure but also from residual components of the fixation/paraffination process. CONCLUSIONS Our study demonstrates possibilities and limits of RS as a potential diagnostic tool in neuropathology, considering accompanying difficulties in the vibrational spectroscopic examination of FFPE tissue.

The present study evaluated the influence of Low-Intensity Pulsed Ultrasound (LIPUS) on the regeneration processes of non-critical-size bone defects in irradiated and non-irradiated rabbit tibias. Bone defects were surgically created on both tibiae of six rabbits. The control group had no additional treatment. In one intervention group, one tibia was irradiated with 15 Gy in a single dose. A second group was treated with LIPUS, and a third with a combination of both treatments. The control samples showed 83.10% ± 17.79% of bone repair after 9 weeks, while the irradiated bone had regenerated significantly less during the same period (66.42% ± 29.36%). The LIPUS treatment on irradiated bones performed a 79.21% ± 21.07% bone fill and could not significantly improve the response compared to the non-treated irradiated specimens. However, LIPUS treatment on non-irradiated bone showed bone formations beyond the size defect (115.91% ± 33.69%), which was a highly significant increase when compared to the control group or any irradiated group. The application of ultrasound to healthy bone produced highly significant and enhanced bone formations with 36.70% more regenerated bone when compared to the same application on irradiated bone. LIPUS vibration stimuli may be considered as a promising complementary treatment approach in non-irradiated bone regeneration procedures to shorten the treatment and enhance bone healing. In irradiated bones, the effect of ultrasound application is less clear, and further studies are needed to refine the dynamics of the present results.

BACKGROUND Hot-spot mutations in the Isocitrate dehydrogenase 1 (IDH1) cause a new catalytic function resulting in the production of 2-HG, a hallmark in the development of low-grade glioma. The tumorigenic mechanism of this mutation as well as the cell of origin are not known and there is a lack of suitable disease models. Thus, we aim to create a model mimicking glioma development by introducing the IDH1 R132H into human induced pluripotent stem cells (hiPSC) and investigate the influence on stem cell properties and cell differentiation in neuronal progenitor cells. MATERIAL AND METHODS We use CRISPR/Cas9 based genome editing to induce the IDH1 R132H mutation into healthy-control-derived hiPSCs. Successful introduction of the mutation was confirmed on DNA, RNA and protein level. The hiPSCs are then differentiated into cerebral organoids and characterized using transcriptome sequencing and methylation arrays. RESULTS We successfully introduced the IDH1 R132H mutation into hiPSCs and confirmed expression of the mutated protein by Western Blot. Metabolite measurement using liquid chromatography tandem mass spectrometry (LC-MS/MS) showed a forty times increased concentration of 2-HG in IDH-mutated compared to the wildtype hiPSCs, proving that the mutated enzyme is functional. To investigate effects of IDH1 R132H on cell differentiation, we generated cerebral organoids from our iPSC-models. The IDH1 R132H mutation did not inhibit cell differentiation or maturation of cerebral organoids but led to a downregulation of splicosome, proteasome and DNA repair enzymes as well as an upregulation of ECM components. CONCLUSION AND OUTLOOK hiPSCs with R132H mutation pose a promising model for investigations on early glioma development. We are currently step-wise including TP53 and ATRX loss of function mutations in our hiPSC models to recapitulating tumor development in vivo.

BACKGROUND Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has so far been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. METHODS To address this issue, we examined FFPE samples of a broad range of intracranial tumors (e.g. glioblastoma and primary CNS lymphoma) and also different areas of morphologically highly heterogeneous glioblastoma tumor tissue. The latter in order to classify not only the tumor entity but also histologically defined GBM areas according to their spectral properties. We applied linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine) on our spectroscopic data and compared statistical performance of resulting classifiers. RESULTS We found that Random Forest classification distinguished between glioblastoma and primary CNS lymphoma with a balanced accuracy of 94%, only using Raman measurements on FFPE tissue. Furthermore, our established support vector machine-based classifier identified distinct histological areas in glioblastoma such as tumor core and necroses with an overall accuracy of 70.5% and showed a clear separation between the areas of necrosis and peritumoral zone. CONCLUSIONS This relatively cheap and easy-to-apply tool may serve useful to complement histopathological and molecular diagnostics. It provides an unbiased approach to tumor diagnostics with very little requirements (e.g. histopathological feature completeness of the tumor entity) to the sample. As a conclusion, we propose RS as a potential future additional method in the (neuro)-pathological toolbox for tumor diagnostics.

Raman spectroscopy (RS) has gained increasing interest for the analysis of biological tissues within the recent years. It is a label-free, non-destructive method providing insights in biochemical properties of tumor cells. It is possible to compare RS signals with histological properties of identical tissue parts. Therefore, RS bears promising potentials in neurosurgical neurooncology. On one hand, it could potentially be used for both intraoperative tumor diagnostics and resection control. On the other hand, it could provide important knowledge on tumor biochemistry and used for a subclassification of tumors with a potential impact on personalized therapy approaches. Within our group, we analyzed over 3000 measurement points in different brain tumors ex vivo with a robotized RS system and correlated the spectral curves with histopathological results. We separated and subclassified the data by AI-based methods. Additionally, we compared the latter results with those of a handheld probe, which is potentially navigatable for in vivo, intraoperative applications. We could demonstrate, that it is possible to separate distinct tumor groups only based on RS signals, especially by using computer-based signal analysis. Furthermore, we could demonstrate the differences of the spectra of deep-frozen and formalin-fixed tissues versus non-fixed tissues. Based on our results, we will highlight the potentials of RS for intraoperative neurosurgical application in resection control for brain tumors, as well as we will focus on the potentials for brain tumor diagnostics based purely on this method or by using it as an adjunct. Those methods bear additional potentials in the field of personalized chemotherapy approaches.