Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy.

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue.

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.

Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion.

Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples' classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.

CLN3 deficiency leads to neurological and metabolic perturbations during early development.

Juvenile neuronal ceroid lipofuscinosis (or Batten disease) is an autosomal recessive, rare neurodegenerative disorder that affects mainly children above the age of 5 yr and is most commonly caused by mutations in the highly conserved CLN3 gene. Here, we generated cln3 morphants and stable mutant lines in zebrafish. Although neither morphant nor mutant cln3 larvae showed any obvious developmental or morphological defects, behavioral phenotyping of the mutant larvae revealed 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 cholesteryl esters, and a global decrease in bis(monoacylglycero)phosphate species, two of which (GPDs and bis(monoacylglycero)phosphates) were previously proposed as potential biomarkers for CLN3 disease 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 for CLN3 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-symptomatic CLN3 disease.

Systematic assessment of tumor necrosis at baseline in cervical cancer - An independent factor associated with poor outcome.

Cervical cancer (CC) is a leading challenge in oncology worldwide, with high prevalence and mortality rates in young adults, most prominent in low to middle-income countries with marginal screening facilities. From the prospectively collected BioRAIDS (NCT02428842) cohort of primary squamous CC conducted in 7 European countries, a central pathology review was carried out on 294 patients' tumors. The focus was on identification of tumor-stromal characteristics such as CD8+, CD45+, CD68+ staining cells, PD-L1 expression, tumor infiltrating lymphocytes (TILs) together with the degree of tumor necrosis. Both (FIGO-2018) stage (I-II/III-IV) as well as tumor necrosis were highly significantly associated with Progression-free Survival (PFS); with tumor necrosis scoring as most potent independent factor in a multivariable analysis (p < 0.001). Tumor necrosis can be assessed in the very first diagnostic biopsyand our data suggest that this rapid, simple and cost-effective biomarker, should be routinely assessed prior to treatment decisions.

Correlative light and electron microscopy to explore the lytic immunological synapse between natural killer cells and cancer cells.

Cytotoxic lymphocytes, such as natural killer (NK) cells and cytotoxic T cells, can recognize and kill tumor cells by establishing a highly specialized cell-cell contact called the immunological synapse. The formation and lytic activity of the immunological synapse are accompanied by local changes in the organization, dynamics and molecular composition of the cell membrane, as well as the polarization of various cellular components, such as the cytoskeleton, vesicles and organelles. Characterization and understanding of the molecular and cellular processes underlying immunological synapse formation and activity requires the combination of complementary types of information provided by different imaging modalities, the correlation of which can be difficult. Correlative light and electron microscopy (CLEM) allows for the accurate correlation of functional information provided by fluorescent light microscopy with ultrastructural features provided by high-resolution electron microscopy. In this chapter, we present a detailed protocol describing each step to generate cell-cell conjugates between NK cells and cancer cells, and to analyze these conjugates by CLEM using separate confocal laser-scanning and transmission electron microscopes.

TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes.

The blood-brain barrier (BBB) is a selectively permeable boundary that separates the circulating blood from the extracellular fluid of the brain and is an essential component for brain homeostasis. In glioblastoma (GBM), the BBB of peritumoral vessels is often disrupted. Pericytes, being important to maintaining BBB integrity, can be functionally modified by GBM cells which induce proliferation and cell motility via the TGF-ß-mediated induction of central epithelial to mesenchymal transition (EMT) factors. We demonstrate that pericytes strengthen the integrity of the BBB in primary endothelial cell/pericyte co-cultures as an in vitro BBB model, using TEER measurement of the barrier integrity. In contrast, this effect was abrogated by TGF-ß or conditioned medium from TGF-ß secreting GBM cells, leading to the disruption of a so far intact and tight BBB. TGF-ß notably changed the metabolic behavior of pericytes, by shutting down the TCA cycle, driving energy generation from oxidative phosphorylation towards glycolysis, and by modulating pathways that are necessary for the biosynthesis of molecules used for proliferation and cell division. Combined metabolomic and transcriptomic analyses further underscored that the observed functional and metabolic changes of TGF-ß-treated pericytes are closely connected with their role as important supporting cells during angiogenic processes.

Extracellular Vesicle Secretion by Leukemia Cells In Vivo Promotes CLL Progression by Hampering Antitumor T-cell Responses.

Small extracellular vesicle (sEV, or exosome) communication among cells in the tumor microenvironment has been modeled mainly in cell culture, whereas their relevance in cancer pathogenesis and progression in vivo is less characterized. Here we investigated cancer-microenvironment interactions in vivo using mouse models of chronic lymphocytic leukemia (CLL). sEVs isolated directly from CLL tissue were enriched in specific miRNA and immune-checkpoint ligands. Distinct molecular components of tumor-derived sEVs altered CD8+ T-cell transcriptome, proteome, and metabolome, leading to decreased functions and cell exhaustion ex vivo and in vivo. Using antagomiRs and blocking antibodies, we defined specific cargo-mediated alterations on CD8+ T cells. Abrogating sEV biogenesis by Rab27a/b knockout dramatically delayed CLL pathogenesis. This phenotype was rescued by exogenous leukemic sEV or CD8+ T-cell depletion. Finally, high expression of sEV-related genes correlated with poor outcomes in CLL patients, suggesting sEV profiling as a prognostic tool. In conclusion, sEVs shape the immune microenvironment during CLL progression. SIGNIFICANCE: sEVs produced in the leukemia microenvironment impair CD8+ T-cell mediated antitumor immune response and are indispensable for leukemia progression in vivo in murine preclinical models. In addition, high expression of sEV-related genes correlated with poor survival and unfavorable clinical parameters in CLL patients. See related commentary by Zhong and Guo, p. 5. This article is highlighted in the In This Issue feature, p. 1.

Effects of Low Intensity Pulsed Ultrasound Stimulation on the Temporal Dynamics of Irradiated Bone Tissue Healing: A Histomorphometric Study in Rabbits.

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.

Neurodegeneration and neuroinflammation are linked, but independent of alpha-synuclein inclusions, in a seeding/spreading mouse model of Parkinson's disease.

A key pathological process in Parkinson's disease (PD) is the transneuronal spreading of α-synuclein. Alpha-synuclein (α-syn) is a presynaptic protein that, in PD, forms pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of α-syn particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used a model of spreading of α-syn induced by striatal injection of α-syn preformed fibrils into the mouse striatum to address this question. We performed quantitative analysis for α-syn inclusions, neurodegeneration, and microgliosis in different brain regions, and generated gene expression profiles of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without α-syn inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. Using longitudinal gene expression profiling, we observed early gene expression changes, linked to neuroinflammation, that preceded neurodegeneration, indicating an active role of microglia in this process. Altered gene pathways overlapped with those typical of PD. Our observations indicate that α-syn inclusion formation is not the major driver in the early phases of PD-like neurodegeneration, but that microglia, activated by diffusible, oligomeric α-syn, may play a key role in this process. Our findings uncover new features of α-syn induced pathologies, in particular microgliosis, and point to the necessity for a broader view of the process of α-syn spreading.

From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives.

In recent years, Raman spectroscopy has been more and more frequently applied to address research questions in neuroscience. As a non-destructive technique based on inelastic scattering of photons, it can be used for a wide spectrum of applications including neurooncological tumor diagnostics or analysis of misfolded protein aggregates involved in neurodegenerative diseases. Progress in the technical development of this method allows for an increasingly detailed analysis of biological samples and may therefore open new fields of applications. The goal of our review is to provide an introduction into Raman scattering, its practical usage and also commonly associated pitfalls. Furthermore, intraoperative assessment of tumor recurrence using Raman based histology images as well as the search for non-invasive ways of diagnosis in neurodegenerative diseases are discussed. Some of the applications mentioned here may serve as a basis and possibly set the course for a future use of the technique in clinical practice. Covering a broad range of content, this overview can serve not only as a quick and accessible reference tool but also provide more in-depth information on a specific subtopic of interest.

Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma.

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" that could be used to differentiate tissue heterogeneity or diagnostic entities. RS has 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 morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up an SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. RESULTS: Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on the spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal-appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal-appearing brain tissue can be detected. CONCLUSION: These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.

Differentiation of primary CNS lymphoma and glioblastoma using Raman spectroscopy and machine learning algorithms.

Objective and Methods: Timely discrimination between primary CNS lymphoma (PCNSL) and glioblastoma is crucial for diagnosis and therapy, but also determines the intraoperative surgical course. Advanced radiological methods allow for their distinction to a certain extent but ultimately, biopsies are still necessary for final diagnosis. As an upcoming method that enables tissue analysis by tracking changes in the vibrational state of molecules via inelastic scattered photons, we used Raman Spectroscopy (RS) as a label free method to examine specimens of both tumor entities intraoperatively, as well as postoperatively in formalin fixed paraffin embedded (FFPE) samples. Results: We applied and compared statistical performance of linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest and XGBoost), and found that Random Forest classification distinguished the two tumor entities with a balanced accuracy of 82.4% in intraoperative tissue condition and with 94% using measurements of distinct tumor areas on FFPE tissue. Taking a deeper insight into the spectral properties of the tumor entities, we describe different tumor-specific Raman shifts of interest for classification. Conclusions: Due to our findings, we propose RS as an additional tool for fast and non-destructive tumor tissue discrimination, which may help to choose the proper treatment option. RS may further serve as a useful additional tool for neuropathological diagnostics with little requirements for tissue integrity.

Enhanced plasticity of mature granule cells reduces survival of newborn neurons in the adult mouse hippocampus.

Dentate granule cells are born throughout life in the mammalian hippocampus. The integration of newborn neurons into the dentate circuit is activity-dependent, and structural data characterizing synapse formation suggested that the survival of adult-born granule cells is regulated by competition for synaptic partners. Here we tested this hypothesis by using a mouse model with genetically enhanced plasticity of mature granule cells through temporally controlled expression of a nuclear inhibitor of protein phosphatase 1 (NIPP1*). Using thymidine analogues and retrovirus-mediated cell labeling, we show that synaptic integration and subsequent survival of newborn neurons is decreased in NIPP1*-expressing mice, suggesting that newborn neurons compete with preexisting granule cells for stable integration. The data presented here provides experimental evidence for a long-standing hypothesis and suggest cellular competition as a key mechanism regulating the integration and survival of newborn granule cells in the adult mammalian hippocampus.

Adult-born granule cells mature through two functionally distinct states.

Adult-born granule cells (ABGCs) are involved in certain forms of hippocampus-dependent learning and memory. It has been proposed that young but functionally integrated ABGCs (4-weeks-old) specifically contribute to pattern separation functions of the dentate gyrus due to their heightened excitability, whereas old ABGCs (>8 weeks old) lose these capabilities. Measuring multiple cellular and integrative characteristics of 3- 10-week-old individual ABGCs, we show that ABGCs consist of two functionally distinguishable populations showing highly distinct input integration properties (one group being highly sensitive to narrow input intensity ranges while the other group linearly reports input strength) that are largely independent of the cellular age and maturation stage, suggesting that 'classmate' cells (born during the same period) can contribute to the network with fundamentally different functions. Thus, ABGCs provide two temporally overlapping but functionally distinct neuronal cell populations, adding a novel level of complexity to our understanding of how life-long neurogenesis contributes to adult brain function.

Paradoxical increase in survival of newborn neurons in the dentate gyrus of mice with constitutive depletion of serotonin.

Increased adult neurogenesis is a major neurobiological correlate of the beneficial effects of antidepressants. Indeed, selective serotonin (5-HT) re-uptake inhibitors, which increase 5-HT transmission, enhance adult neurogenesis in the dentate gyrus (DG) of the hippocampus. However, the consequences of 5-HT depletion are still unclear as studies using neurotoxins that target serotonergic neurons reached contradictory conclusions on the role of 5-HT on DG cell proliferation. Here, we analysed two genetic models of 5-HT depletion, the Pet1(-/-) and the VMAT2(f/f) ; SERT(cre/+) mice, which have, respectively, 80 and 95% reductions in hippocampal 5-HT. In both models, we found unchanged cell proliferation of the neural precursors in the DG subgranular zone, whereas a significant increase in the survival of newborn neurons was noted 1 and 4 weeks after BrdU injections. This pro-survival trait was phenocopied pharmacologically with 5-HT synthesis inhibitor PCPA treatment in adults, indicating that this effect was not developmental. Furthermore, a 1-week administration of the 5-HT1A receptor agonist 8-OH-DPAT in Pet1(-/-) and PCPA-treated mice normalised hippocampal cell survival. Overall, our results indicate that constitutive 5-HT depletion does not alter the proliferation of neural precursors in the DG but promotes the survival of newborn cells, an effect which involves activation of postsynaptic 5-HT1A receptors. The role of 5-HT in selective neuronal elimination points to a new facet in its multiple effects in controlling neural circuit maturation.

Imaging neurite development of adult-born granule cells.

Neural stem/progenitor cells (NSPCs) generate new neurons throughout life in the mammalian hippocampus. Newborn granule cells mature over several weeks to functionally integrate into the pre-existing neural circuitry. Even though an increasing number of genes that regulate neuronal polarization and neurite extension have been identified, the cellular mechanisms underlying the extension of neurites arising from newborn granule cells remain largely unknown. This is mainly because of the current lack of longitudinal observations of neurite growth within the endogenous niche. Here we used a novel slice culture system of the adult mouse hippocampal formation combined with in vivo retroviral labeling of newborn neurons and longitudinal confocal imaging to analyze the mode and velocity of neurite growth extending from immature granule cells. Using this approach we show that dendritic processes show a linear growth pattern with a speed of 2.19±0.2 µm per hour, revealing a much faster growth dynamic than expected by snapshot-based in vivo time series. Thus, we here identified the growth pattern of neurites extending from newborn neurons within their niche and describe a novel technology that will be useful to monitor neuritic growth in physiological and disease states that are associated with altered dendritic morphology, such as rodent models of epilepsy.

Zif268/egr1 gene controls the selection, maturation and functional integration of adult hippocampal newborn neurons by learning.

New neurons are continuously added to the dentate gyrus of the adult mammalian brain. During the critical period of a few weeks after birth when newborn neurons progressively mature, a restricted fraction is competitively selected to survive in an experience-dependent manner, a condition for their contribution to memory processes. The mechanisms that control critical stages of experience-dependent functional incorporation of adult newborn neurons remain largely unknown. Here, we identify a unique transcriptional regulator of the functional integration of newborn neurons, the inducible immediate early gene zif268/egr1. We show that newborn neurons in zif268-KO mice undergo accelerated death during the critical period of 2-3 wk around their birth and exhibit deficient neurochemical and morphological maturation, including reduced GluR1 expression, increased NKCC1/KCC2b chloride cotransporter ratio, altered dendritic development, and marked spine growth defect. Investigating responsiveness of newborn neurons to activity-dependent expression of zif268 in learning, we demonstrate that in the absence of zif268, training in a spatial learning task during this critical period fails to recruit newborn neurons and promote their survival, leading to impaired long-term memory. This study reveals a previously unknown mechanism for the control of the selection, functional maturation, and experience-dependent recruitment of dentate gyrus newborn neurons that depends on the inducible immediate early gene zif268, processes that are critical for their contribution to hippocampal-dependent long-term memory.