Biology subjects

Stopka, S.

Publications and source records attributed to Stopka, S..

4 recordsLinked to original sources

bioRxiv2026

Nanoparticle encapsulation enhances spatial distribution of Panobinostat to treat metastatic medulloblastoma via the intrathecal route

Medulloblastoma (MB) is an aggressive central nervous system (CNS) malignancy that primarily affects children and frequently exhibits metastasis to the leptomeninges of the brain and spinal cord. We developed a {beta}-Cyclodextrin-poly({beta}-Amino Ester) nanoparticle system to deliver the histone deactylase inhibitor (HDACi) Panobinostat to MB by the intrathecal route. Various imaging methods were utilized to study nanoparticle and payload fate following infusion into the cerebrospinal fluid (CSF) of mice via cisterna magna or lumbar access points. Nanoparticles dramatically improved penetration of hydrophobic small molecules into distal regions of the spinal cord. Panobinostat-loaded nanoparticles were effective at treating patient-derived MB, activating pharmacodynamic targets, slowing growth of the primary tumor, decreasing incidence of metastasis at the time of death, and ultimately prolonging survival. These studies provide insight into the mechanisms mediating transport of colloids and therapeutic molecules in the subarachnoid space and highlight new approaches for treating metastatic disease in the CNS.

bioengineering↗

bioRxiv2025

Predicting High-Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging (MSI) have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time- of-flight matrix-assisted laser desorption/ionization (MALDI-ToF), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging techniques. By leveraging FT-ICRs unparalleled spectral resolution and ToF-SIMSs exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

neuroscience↗

bioRxiv2024

Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

The spatial-omic analysis of biomolecules such as nucleic acids, lipids, metabolites, and proteins is advancing the study of biological systems and processes in a physio-pathological context. Here, we describe an innovative matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) method to detect metals within biological tissues using instrumentation that is widely available in research and clinical laboratories. We characterize the spatial distribution of metals in diverse settings including mouse embryogenesis, genetic disorders leading to abnormal metal accumulation, and preclinical testing for improved platinum-based chemotherapy delivery through focused ultrasound across the blood-brain barrier. Spatial metal profiling will advance research studies and the clinical analysis of metal-related diseases, enabling more precise use of metal-based therapies and advances in diverse scientific fields beyond biomedicine. One-Sentence SummarySpatial metallomic profiling maps native metals or those coordinated to xenobiotics, antibodies, and biomolecules in tissues.

cancer biology↗

bioRxiv2021

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MotivationMass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high dimensionality, and spectral non-linearity. Preprocessing, including peak picking, has been used to reduce raw data complexity, however peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. ResultsWe propose a deep learning model, massNet, that provides the desired qualities of scalability, non-linearity, and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The models performance was assessed using cross-validation, and the results demonstrate higher accuracy and a 174-fold gain in speed compared to the established classical machine learning method, support vector machine. Availability and ImplementationThe code is publicly available on GitHub.

bioinformatics↗