Biology subjects

van Dijk, R.

Publications and source records attributed to van Dijk, R..

3 recordsLinked to original sources

bioRxiv2025

AAV-Delivered Anti-PC-OxPL Antibody Fragments: A NovelTherapeutic Approach to Target ALS

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of motor neurons and premature death. The limited understanding of the mechanisms underlying selective motor neuron death has significantly hindered the development of disease-modifying treatments. Ferroptosis, a form of cell death dependent on iron accumulation, has been implicated in the selective degeneration of motor neurons in ALS. Oxidized phosphatidylcholines (PC-OxPL) have been identified as key effectors in the pathophysiological processes associated with this pathway. Preclinical observations revealed a distinct PC-OxPL profile in the cerebrospinal fluid (CSF) of sporadic ALS (sALS) patients and identified apolipoprotein E (APOE) particles as the primary carriers of PC-OxPL in the CSF. Furthermore, in ALS brain and spinal cord tissue sections, PC-OxPL was found to be predominantly associated with motor neurons. Exposure of iPSC-derived motor neurons to PC-OxPL led to transcriptomic changes in genes known to be linked to ALS, as well as the induction of significant TDP-43 pathology and motor neuron death. To counter this, we developed a single-chain antibody fragment (scFv) encoded by an AAV-delivered DNA construct that specifically targets PC-OxPL neoepitopes (PC-OxPL-VecTab(R)). PC-OxPL-VecTab(R) effectively neutralized PC-OxPL-induced neurotoxicity and TDP-43 aggregation in motor neurons, while preventing motor neuron death and deficits in a sALS CSF mouse model. When administered intrathecally to minipigs, PC-OxPL-VecTab(R) was distributed to both upper and lower motor neurons and expressed at levels predicted to be therapeutically effective. Our work identifies PC- OxPL as a critical pathological factor and a key inducer of TDP-43 pathology in ALS, providing the foundation for a novel therapeutic intervention modality for patients with sALS. Furthermore, it offers the exciting potential to be expanded to diseases characterized by PC-OxPL neurotoxicity.

neuroscience↗

bioRxiv2023

Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.

bioinformatics↗

bioRxiv2023

Evaluating batch correction methods for image-based cell profiling

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.

bioinformatics↗