Morphology-based molecular classification of spinal cord ependymomas using deep neural networks

Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE  = 84, nSP-EPN  = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.

Ion-exchange purification and structural characterization of five sulfated fucoidans from brown algae

Fucoidans are a diverse class of sulfated polysaccharides integral to the cell wall of brown algae, and due to their various bioactivities, they are potential drugs. Standardized work with fucoidans is required for structure-function studies, but remains challenging since available fucoidan preparations are often contaminated with other algal compounds. Additionally, fucoidans are structurally diverse depending on species and season, urging the need for standardized purification protocols. Here, we use ion-exchange chromatography to purify different fucoidans and found a high structural diversity between fucoidans. Ion-exchange chromatography efficiently removes the polysaccharides alginate and laminarin and other contaminants such as proteins and phlorotannins across a broad range of fucoidans from major brown algal orders including Ectocarpales, Laminariales and Fucales. By monomer composition, linkage analysis and NMR characterization, we identified galacturonic acid, glucuronic acid and O-acetylation as new structural features of certain fucoidans and provided a novel structure of fucoidan from Durvillaea potatorum with α-1,3-linked fucose backbone and β-1,6 and β-1,3 galactose branches. This study emphasizes the use of standardized ion-exchange chromatography to obtain defined fucoidans for subsequent molecular studies.