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.

Cell adhesion affects the properties of interstitial fluid flow: A study using multiscale poroelastic composite modeling.

In this study, we conduct a multiscale, multiphysics modeling of the brain gray matter as a poroelastic composite. We develop a customized representative volume element based on cytoarchitectural features that encompass important microscopic components of the tissue, namely the extracellular space, the capillaries, the pericapillary space, the interstitial fluid, cell-cell and cell-capillary junctions, and neuronal and glial cell bodies. Using asymptotic homogenization and direct numerical simulation, the effective properties at the tissue level are identified based on microscopic properties. To analyze the influence of various microscopic elements on the effective/macroscopic properties and tissue response, we perform sensitivity analyses on cell junction (cluster) stiffness, cell junction diameter (dimensions), and pericapillary space width. The results of this study suggest that changes in cell adhesion can greatly affect both mechanical and hydraulic (interstitial fluid flow and porosity) features of brain tissue, consistent with the effects of neurodegenerative diseases.

Blocking TGF-ß- and Epithelial-to-Mesenchymal Transition (EMT)-mediated activation of vessel-associated mural cells in glioblastoma impacts tumor angiogenesis.

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults. GBM displays excessive and unfunctional vascularization which may, among others, be a reason for its devastating prognosis. Pericytes have been identified as the major component of the irregular vessel structure in GBM. In vitro data suggest an epithelial-to-mesenchymal transition (EMT)-like activation of glioma-associated pericytes, stimulated by GBM-secreted TGF-ß, to be involved in the formation of a chaotic and dysfunctional tumor vasculature. This study investigated whether TGF-ß impacts the function of vessel associated mural cells (VAMCs) in vivo via the induction of the EMT transcription factor SLUG and whether this is associated with the development of GBM-associated vascular abnormalities. Upon preventing the TGF-ß-/SLUG-mediated EMT induction in VAMCs, the number of PDGFRß and αSMA positive cells was significantly reduced, regardless of whether TGF-ß secretion by GBM cells was blocked or whether SLUG was specifically knocked out in VAMCs. The reduced amount of PDGFRß+ or αSMA+ cells observed under those conditions correlated with a lower vessel density and fewer vascular abnormalities. Our data provide evidence that the SLUG-mediated modulation of VAMC activity is induced by GBM-secreted TGF-߬ and that activated VAMCs are key contributors in neo-angiogenic processes. We suggest that a pathologically altered activation of GA-Peris in the tumor microenvironment is responsible for the unstructured tumor vasculature. There is emerging evidence that vessel normalization alleviates tumor hypoxia, reduces tumor-associated edema and improves drug delivery. Therefore, avoiding the generation of an unstructured and non-functional tumor vasculature during tumor recurrence might be a promising treatment approach for GBM and identifies pericytes as a potential novel therapeutic target.

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.

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.

Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study.

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.

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.

Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts.

BACKGROUND: A major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials. METHODS: Here, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry, and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors. RESULTS: We show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components. CONCLUSIONS: Our data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment.

A Th17 cell-intrinsic glutathione/mitochondrial-IL-22 axis protects against intestinal inflammation.

The intestinal tract generates significant reactive oxygen species (ROS), but the role of T cell antioxidant mechanisms in maintaining intestinal homeostasis is poorly understood. We used T cell-specific ablation of the catalytic subunit of glutamate cysteine ligase (Gclc), which impaired glutathione (GSH) production, crucially reducing IL-22 production by Th17 cells in the lamina propria, which is critical for gut protection. Under steady-state conditions, Gclc deficiency did not alter cytokine secretion; however, C. rodentium infection induced increased ROS and disrupted mitochondrial function and TFAM-driven mitochondrial gene expression, resulting in decreased cellular ATP. These changes impaired the PI3K/AKT/mTOR pathway, reducing phosphorylation of 4E-BP1 and consequently limiting IL-22 translation. The resultant low IL-22 levels led to poor bacterial clearance, severe intestinal damage, and high mortality. Our findings highlight a previously unrecognized, essential role of Th17 cell-intrinsic GSH in promoting mitochondrial function and cellular signaling for IL-22 protein synthesis, which is critical for intestinal integrity and defense against gastrointestinal infections.