Biology subjects

De Clerck, L.

Publications and source records attributed to De Clerck, L..

4 recordsLinked to original sources

bioRxiv2025

Photothermal transport for guiding nanoparticles through the vitreous humor

Visual impairments affect over 2.2 billion people worldwide, yet delivering drugs to the eyes posterior segment, including the retina, remains a major challenge. Intravitreal injection, the standard administration route to the posterior segment of the eye, often results in suboptimal drug diffusion through the vitreous, preventing drugs from reaching the retina. While various strategies have been explored to enhance the mobility of drug molecules and nanomedicines (drugs encapsulated in nanoparticles) in the vitreous, no method has demonstrated guided transport of drugs and particles through the vitreous. In this study, we explore photothermal transport of nanoparticles in the vitreous using a pulsed-laser and indocyanine green added to the vitreous, both being clinically approved modalities. We found that photothermal transport allows to guide nanoparticles from one place in the vitreous towards the laser illuminated area in the vitreous, at a distance of the injection spot of the nanoparticles. Using multiple-particle tracking and numerical simulations, we found that both thermal convection and thermophoresis contribute to photothermal transport of nanoparticles in the vitreous. We identified parameters for optimizing this effect, including dye concentration, particle size, distance from the laser focus, and laser fluence. Our findings establish a novel and clinically relevant paradigm for light-guided drug delivery in the eye. This study represents, to our knowledge, the first demonstration of guided light-controlled particle transport in the vitreous using ocular dyes and pulsed-lasers which are routinely used in ophthalmology.

biophysics↗

bioRxiv2021

Histone sample preparation for bottom-up mass spectrometry: a roadmap to informed decisions

Histone-based chromatin organization enabled eukaryotic genome complexity. This epigenetic control mechanism allowed for the differentiation of stable gene-expression and thus the very existence of multicellular organisms. This existential role in biology makes histones one of the most complexly modified molecules in the biotic world, which makes these key regulators notoriously hard to analyze. We here provide a roadmap to enable fast and informed selection of a bottom-up mass spectrometry sample preparation protocol that matches a specific research question. We therefore propose a two-step assessment procedure: (i) visualization of the coverage that is attained for a given workflow and (ii) direct alignment-between-runs to assess potential pitfalls at the ion level. To illustrate the applicability, we compare four different sample preparation protocols, while adding a new enzyme to the toolbox, i.e., RgpB (GingisREX(R), Genovis), an endoproteinase that selectively and efficiently cleaves at the c-terminal end of arginine residues. Raw data is available via ProteomeXchange with identifier PXD024423.

molecular biology↗

bioRxiv2019

Analyzing data independent acquisition mass spectrometry with noiseless ion-networks

Data-independent acquisition (DIA) mass spectrometry (MS) has introduced deter-ministic, periodic and simultaneous acquisition of all fragment ions. Despite the chimeric side-effects associated with this unprecedented data integrity, DIA data analysis approaches still use conventional spectra and extracted ion chromatograms (XICs) that represent individual precursors and fragments. Here, we introduce ion-networks, an alternative data format wherein nodes correspond to reproducible fragment ions from multiple runs and edges correspond to consistent co-elution. Each ion-network represents a complete experiment and computationally eliminates chimericy based on reproducibility without sacrificing data integrity.

bioinformatics↗

bioRxiv2019

The future of peptide-centric Data-Independent Acquisition is predicted

Data-Independent Acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. We here show that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison. Significance of the StudyData-independent acquisition (DIA) is quickly developing into the most comprehensive strategy to analyse a sample on a mass spectrometer. Correspondingly, a wave of data analysis strategies has followed suit, improving the yield from DIA experiments with each iteration. As a result, a worldwide wave of investments in DIA is already taking place in anticipation of clinical applications. Yet, there is considerable confusion about the most useful and efficient way to handle DIA data, given the plethora of possible approaches with little regard for compatibility and complementarity. In our manuscript, we outline the currently available peptide-centric DIA data analysis strategies in a unified graphic called the DIAmond DIAgram. This leads us to an innovative and easily adoptable approach based on predicted spectral information. Most importantly, our contribution removes what is arguably the biggest bottleneck in the field: the current need for Data Dependent Acquisition (DDA) prior to DIA analysis. Fractionation, stochastic data acquisition, processing and identification all introduce bias in the library. By generating libraries through data independent, i.e. deterministic acquisition, stochastic sampling in the DIA workflow is now fully omitted. This is a crucial step towards increased standardization. Additionally, our results demonstrate that a proteome-wide predicted spectral library can surrogate an exhaustive DDA Pan-Human library that was built based on 331 prior DDA runs.

bioinformatics↗