Biology subjects

Van Minsel, P.

Publications and source records attributed to Van Minsel, P..

4 recordsLinked to original sources

bioRxiv2026

LipoGrid: A High-Throughput Multi-omics Perturbation Screen Dissects the Genetic Architecture of Lipid Metabolism

Lipids constitute one of the largest and most diverse classes of cellular molecules, sustaining membrane architecture, energy storage, and signaling. Consequently, their dysregulation underlies a broad spectrum of human disease. However, the genetic mechanisms governing lipid homeostasis have remained largely inaccessible, owing to the lack of approaches capable of systematically linking defined genetic perturbations to large-scale changes in cellular lipidome composition. Here we introduce LipoGrid, a spatial mass spectrometry platform that resolves the genetic architecture of lipid metabolism at single-cell resolution. LipoGrid arrays CRISPR/Cas9-perturbed cells on a micropatterned grid and sequentially captures lipidomic and gRNA identity from the same cells, complemented by single-cell RNA sequencing of matched cell populations subjected to the same perturbations. Using this approach, we quantified the relative abundance of 158 distinct lipid species across 143 target genes in a rigorously controlled experimental framework. We find that most gene knockouts produced measurable alterations in lipid composition, often affecting specific lipid classes and molecular subspecies. The screen accurately recapitulated established gene-lipid relationships, including enzyme-substrate specificities, lipid pathway regulators, and disease-associated loss-of-function phenotypes, thereby demonstrating the sensitivity and accuracy of LipoGrid. By jointly profiling transcriptomic and lipidomic responses, we further uncover compensatory feedback mechanisms that buffer the impact of genetic perturbations on the cellular lipidome. Collectively, these findings establish LipoGrid as a scalable multimodal platform for systematically mapping gene-lipid interactions and reveal the regulatory networks linking gene perturbation, transcriptional adaptation, and lipidome remodeling. HighlightsO_LIMicropatterned single-cell growth enables spatial lipidomic perturbation screens C_LIO_LILipoGrid maps 143 gene knockouts to 158 lipid species and transcriptomic states C_LIO_LIPerturbed lipidomes reveal compensatory feedback and lipid-class-specific uptake C_LIO_LIRecovers enzyme substrate specificities and disease-linked lipid signatures C_LI

systems biology↗

bioRxiv2026

SubCellSpace: Automated characterization of subcellular mRNA localization patterns in spatial transcriptomics

The localized translation of transcripts is a universal phenomenon across biological domains. Many examples of subcellular RNA localization and their functional importance have been described. However, these examples remain anecdotal, and a more systematic genome and cell-type-wide analysis is needed. Current spatial transcriptomic techniques can characterize hundreds to thousands of transcript species at subcellular resolutions, enabling the large-scale investigation of subcellular mRNA localization. Here we describe SubCellSpace, a computational framework to learn general representations of mRNA localization patterns. By embedding observed single-cell subcellular localization patterns (SLPs) to an interpretable latent space, SubCellSpace can detect and statistically infer the presence of SLPs, uncover colocalizing gene-pairs and characterize cellular heterogeneity for pattern-presentation. We benchmark SubCellSpace in both synthetic and real data, showing it can correctly detect previously described apical/basal polarized genes in the enterocytes of mouse small-intestine, as well as encode the enterocytes orientation. Additionally, we provide a tailored spatial transcriptomics validation dataset for benchmarking SLP identification based on transcripts previously described to be enriched near subcellular structures in HEK293T cells. We propose a practical and computationally-efficient classification workflow that automatically detects localized transcript species and quantifies their degree of patterning, while controlling false positive rates. Finally, we showcase SubCellSpace in both supervised and unsupervised settings, to either classify pre-determined SLPs or to explore spatial patterning without specifying pattern types a priori. Automated AI models such as SubCellSpace and their integration in spatial transcriptomics analysis workflows will help characterize previously undiscovered subcellular RNA localization phenomena, providing novel insights into post-transcriptional regulation mechanisms.

bioinformatics↗

bioRxiv2026

Protein aggregation inhibitors induce divergent transcriptional responses in a cellular model of a-synuclein seeded aggregation

Parkinsons disease (PD), dementia with Lewy Bodies (DLB) and multiple system atrophy (MSA) are progressive neurodegenerative disorders marked by the pathological aggregation of alpha-synuclein ([a]Syn). Despite significant research efforts, effective therapeutic interventions remain elusive due to limited understanding of the cellular effects of [a]Syn aggregation and propagation. This study presents the development of a scalable cellular seeding assay for screening small molecules targeting cellular [a]Syn seeded aggregation. By leveraging a fluorescent reporter of [a]Syn and phenotypic screening, the assay enables high-throughput evaluation of potential inhibitors in a cellular environment mimicking disease pathology. We evaluated three different Syn aggregation inhibitors tested in clinical trials for PD: Minzasolmin, Emrusolmin and EGCG and profiled gene expression using multiplexed single cell RNA sequencing in order to examine their distinct effects on cellular pathways associated with [a]Syn overexpression or seeded aggregation. Two cellular activities were prominently affected: lipid metabolism and rRNA processing. Notably, while EGCG effects were confined to cells with aggregated Syn, Minzasolmin and Emrusolmin also produced transcriptional changes in cells without aggregated Syn. Each of the compounds tested induced a partial reversal of transcriptional effects resulting from Syn seeded aggregation. We identified 391 genes that were no longer significantly differentially expressed upon addition of compound, relative to cells with seeded aggregation. This platform bridges phenotypic screening and molecular pathway analysis, providing insights into druggable pathways for synucleinopathies. The molecular signatures identified here can assist in testing and benchmarking future drug discovery leads.

neuroscience↗

bioRxiv2022

GiRAFR improves gRNA detection and annotation in single cell CRISPR screens

Novel single cell RNA-seq analysis combined with CRISPR screens enables the high-throughput characterization of transcriptional changes caused by genetic perturbations. Dedicated software to annotate CRISPR guide RNA (gRNA) libraries and associate them with single cell transcriptomes are however lacking. Here, we generated a CRISPR droplet sequencing dataset. We demonstrate that the current default tool fails to detect mutant gRNAs. We therefore developed GiRAFR, a pysam-based software tool to characterize intact and mutant gRNAs. We show that mutant gRNAs are dysfunctional, and failure to detect and annotate them leads to an inflated estimate of the number of untransformed cells as well as an underestimated multiplet frequency. These findings are mirrored in publicly available datasets, where we find that up to 34 % of cells are transduced with a mutant gRNA. Applying GiRAFR hence stands to improve the annotation and quality of single cell CRISPR screens.

bioinformatics↗