Biology subjects

Tholen, M. M. E.

Publications and source records attributed to Tholen, M. M. E..

3 recordsLinked to original sources

bioRxiv2026

Single-molecule imaging and tracking on clinical liquid biopsies reveals cancer biomarkers nanoscale organization and heterogeneity

Single-molecule imaging and tracking have revealed fundamental biological mechanisms at the molecular scale, yet their application to clinical research remains limited by technical complexity and sample preparation incompatible with patient-derived specimens. As a result, we lack information with molecular-scale resolution of clinically relevant biomarkers. Here, we develop a workflow enabling Points Accumulation in Nanoscale Topography (PAINT) combined with single-particle tracking (SPT) on clinical liquid biopsies, allowing analysis of biomarker nanoscale organization at the single-molecule level in cancer patients. Our approach features a sample preparation tailored to liquid biopsies and requires no fixation, covalent labelling, or genetic modification, making single-molecule imaging compatible with hospital clinical workflows. We demonstrate the methods diversity by imaging liquid biopsies from blood, bone marrow aspirates, and pleural effusions across different cancer types. PAINT-SPT captures both the expression and mobility of clinically relevant membrane receptor biomarkers, revealing pronounced inter- and intra-patient heterogeneity at the molecular and cellular levels. We discover that individual patients exhibit distinct molecular mobility fingerprints that reflect biomarker interaction states and correlate with clinical diagnostic readouts. Furthermore, these fingerprints distinguish healthy from cancer cells, enabling the development of a classifier that accurately identifies cancer cells based on their single-molecule behaviour. Together, our results establish a route to investigate patient-derived clinical samples at the single-molecule level and open new opportunities to understand cancer biology and biomarker function beyond ensemble-averaged measurements.

biophysics↗

bioRxiv2025

"Visualize, describe, compare" - nanoinformatics approaches for material-omics

Bioinformatics and cheminformatics are established disciplines, but nanoinformatics, the development of computational tools for understanding and designing nanomaterials, is still in its infancy. In light of the new data-driven approaches for nanomaterials discovery, there is a growing need for in silico tools tailored to analyze nanomaterials datasets. This is particularly crucial for soft materials, where a crystalline structure cannot be obtained and therefore the characterization datasets are less structured, and there are no standard methods for data mining. Here we present a computational package capable of visualizing, describing, and comparing nanoparticle datasets obtained with super-resolution microscopy at the single-particle and single-molecule level. Our method allows us to: i) visualize multiparametric nanoparticle datasets to grasp material properties and heterogeneity; ii) have a quantitative evaluation of a material through a series of molecular descriptors, and iii) compare different materials quantitatively and globally, going beyond comparison of a single property. We applied this method to a library of targeted nanoparticles revealing particle heterogeneity, similarities, and correlations between the synthesis and the physicochemical properties of the different nanomaterials. Finally, we show the potential of this approach to reveal batch-to-batch variations in time and between users hidden in standard analysis.

bioinformatics↗

bioRxiv2024

nanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images

Super-resolution microscopy and Single-Molecule Localization Microscopy (SMLM) are a powerful tool to characterize synthetic nanomaterials used for many applications such as drug delivery. In the last decade, imaging techniques like STORM, PALM, and PAINT have been used to study nanoparticle size, structure, and composition. While imaging has progressed significantly, often image analysis did not follow accordingly and many studies are limited to qualitative and semi-quantitative analysis. Therefore, it is imperative to have a robust and accurate method to analyze SMLM images of nanoparticles and extract quantitative features from them. Here we introduce nanoFeatures, a cross-platform Matlab-based app for the automatic and quantitative analysis of super-resolution images. nanoFeatures makes use of clustering algorithms to identify nanoparticles from the raw data (localization list) and extract quantitative information about size, shape, and molecular abundance at the single-particle and single-molecule levels. Moreover, it applies a series of quality controls, increasing data quality and avoiding artifacts. nanoFeatures, thanks to its intuitive interface is also accessible to non-experts and will facilitate analysis of super-resolution microscopy for materials scientists and nanotechnologies. This easy accessibility to expansive feature characterization at the single particle level will bring us one step closer to understanding the relationship between nanostructure features and their efficiency. https://github.com/n4nlab/nanoFeatures

bioinformatics↗