Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.

Objective: Patients with ovarian cancer (OC) may develop anti‐Yo‐associated paraneoplastic cerebellar degeneration (PCD)—a cerebellar ataxia associated with tumor‐induced autoimmunity against CDR2 and CDR2L proteins. Dysregulation of circulating exosomal microRNAs (miRNAs) occur in OC. Here, we investigated whether PCD is associated with changes in the exosomal miRNA profiles of OC patients.Methods: Serum exosomes were isolated from patients with OC (n = 15), patients with OC and anti‐Yo‐associated PCD (n = 14) and healthy controls (HC, n = 15). Small RNA sequencing was used to identify differentially expressed miRNAs. Receiver operating characteristic curves were used to evaluate biomarker sensitivity and specificity, and miRNA target prediction analysis was employed to elucidate gene targets.Results: OC patients with PCD exhibited a distinct exosomal miRNA expression profile. We detected 103 differentially expressed exosomal miRNAs in PCD patients compared to OC patients without PCD and 139 differentially expressed exosomal miRNAs compared to controls. Particularly miR‐486‐5p, miR‐4732‐5p, miR‐98‐5p and miR‐21‐5p exhibited notable sensitivity and specificity for discriminating PCD patients from both OC patients without PCD and healthy controls. miRNA target prediction showed that several of the differentially expressed miRNAs in PCD patients targeted the CDR2 and CDR2L genes.Interpretation: Our results demonstrate that OC patients with anti‐Yo‐associated PCD exhibit a distinct exosomal miRNA profile compared to OC patients without PCD. Several of the differentially expressed exosomal miRNAs in PCD patients showed diagnostic potential and may hold relevance for understanding the pathogenesis of PCD.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system with heterogenous clinical course, lacking non-invasive biomarkers for phenotype differentiation. This study aimed to explore circulating extracellular vesicle (EV)-derived microRNA (miRNA) signatures and related molecular profiles capable of distinguishing stable relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS). Plasma samples were collected from stable RRMS ( n = 30), SPMS ( n = 30), and healthy controls (HC) ( n = 30), followed by total EVs isolation and characterization using transmission electron microscopy, dynamic light scattering, and flow cytometry. RNA was extracted from EVs, and miRNA profiles were analyzed via RNA sequencing and RT-qPCR. Cytokines and neuronal/astroglial injury biomarkers were quantified using the BioPlex system and ELISA. Functional enrichment and network analyses of miRNA targets were performed, alongside logistic regression modeling to explore potential distinguishing features. Four EV-derived miRNAs (miR-760, miR-98-5p, miR-301a-3p, miR-223-3p) showed significant differences ( p < 0.05) between stable RRMS and SPMS. An integrative model combining miRNAs with fibroblast growth factor (FGF) basic protein enabled accurate phenotypic differentiation (AUC = 0.942). miR-760 showed the strongest distinctive capacity for stable RRMS. Additionally, miR-98-5p was markedly up-regulated in both stable RRMS and SPMS compared to HC. Network analysis of miRNA targets suggested distinct immunoregulatory patterns across MS phenotypes. Plasma EV-derived miRNAs—particularly miR-760, and miR-98-5p—showed potential as molecular indicators associated with disease phenotype in MS. Integrating EV-miRNA profiling with protein markers support efforts toward more precise stratification of MS patients. Further studies in independent cohorts and functional validation are warranted before clinical translation.

The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the nervous system and a main cause of neurological disability in young adults. Most disease-modifying therapies are administrated as long-term maintenance therapies and may, thereby, increase the risk of infections and other immune-mediated side effects. In the last years, several cerebrospinal fluid and soluble blood biomarkers have been suggested as potential key tools for diagnosis, prognosis, and treatment monitoring of MS. Recently, the specific ability of brain-derived blood extracellular vesicles (EVs) that cross the blood-brain barrier into the bloodstream, reflecting the current immune status of the central nervous system, has kindled interest as potential biomarkers. In this review, we discuss the current trends of clinical brain-derived blood biomarkers, with a special focus on the emerging role of brain-derived blood EVs in MS.