Biology subjects

Wadie, B.

Publications and source records attributed to Wadie, B..

5 recordsLinked to original sources

bioRxiv2024

HT SpaceM: A High-Throughput and Reproducible Method for Small-Molecule Single-Cell Metabolomics

Single-cell metabolomics promises to resolve metabolic cellular heterogeneity, yet current methods struggle with detecting small molecules, throughput, and reproducibility. Addressing these gaps, we developed HT SpaceM, a high-throughput single-cell metabolomics method with novel cell preparation, custom glass slides, small-molecule MALDI imaging mass spectrometry protocol, and batch processing. We propose a unified framework covering essential data analysis steps including quality control, characterization, differential analysis, structural validation and functional analysis. Interrogating human HeLa and mouse NIH3T3 cells, we detected 73 diverse small-molecule metabolites validated by bulk LC-MS/MS, achieving high reproducibility across wells and slides. Interrogating nine NCI-60 cancer cells and HeLa, we identified cell-type markers in small subpopulations. Functional analysis revealed overrepresented metabolic pathways, co-abundant metabolites, and metabolic hubs. We demonstrate the ability of SCM to analyze over 120,000 cells from over 112 samples, and provide guidance to interpret single-cell metabolic heterogeneity, revealing metabolic insights beyond population averages.

molecular biology↗

bioRxiv2024

Enrichment analysis for spatial and single-cell metabolomics accounting for molecular ambiguity

SummaryImaging mass spectrometry (imaging MS) has advanced spatial and single-cell metabolomics, but the reliance on MS1 data complicates the accurate identification of molecular structures, not being able to resolve isomeric and isobar molecules. This prevents application of conventional methods for overrepresentation analysis (ORA) and metabolite set enrichment analysis (MSEA). To address this, we introduce S2IsoMEr R package and a web app for METASPACE, which uses bootstrapping to propagate isomeric/isobaric ambiguities into the enrichment analysis. We demonstrate S2IsoMEr for single-cell metabolomics and the METASPACE web app for spatial metabolomics. Availability and ImplementationMETASPACE web app can be used on existing and new datasets submitted to METASPACE (https://metaspace2020.eu). The source code for the S2IsoMEr R package is available on GitHub (https://github.com/alexandrovteam/S2IsoMEr).

bioinformatics↗

bioRxiv2023

METASPACE-ML: Metabolite annotation for imaging mass spectrometry using machine learning

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1,710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

bioinformatics↗

bioRxiv2021

Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs

Linear motifs have an integral role in dynamic cell functions including cell signalling, the cell cycle and others. However, due to their small size, low complexity, degenerate nature, and frequent mutations, identifying novel functional motifs is a challenging task. Viral proteins rely extensively on the molecular mimicry of cellular linear motifs for modifying cell signalling and other processes in ways that favour viral infection. This study aims to discover human linear motifs convergently evolved also in disordered regions of viral proteins, under the hypothesis that these will result in enrichment in functional motif instances. We systematically apply computational motif prediction, combined with implementation of several functional and structural filters to the most recent publicly available human-viral and human-human protein interaction network. By limiting the search space to the sequences of viral proteins, we observed an increase in the sensitivity of motif prediction, as well as improved enrichment in known instances compared to the same analysis using only human protein interactions. We identified > 8,400 motif instances at various confidence levels, 105 of which were supported by all functional and structural filters applied. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalogue of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the mechanisms of viral interference with this.

systems biology↗

bioRxiv2020

Chromosomal arrangement of synthetic lethal gene pairs: repulsion or attraction?

Synthetic lethal interactions are of paramount importance both in biology and in medicine, and hence increasing efforts have been devoted to their systematic identification. Our previous computational analysis revealed that in prokaryotic species, synthetic lethal genes tend to be further away in chromosomes than random (i.e. repulsion), which was shown to provide bacterial genomes with greater robustness to large-scale DNA deletions. To test the generalizability of this observation in eukaryotic genomes, we leveraged the wealth of experimentally determined synthetic lethal genetic interactions of yeast that are curated in the BioGRID (Biological General Repository for Interaction Datasets) database. We observed an opposite trend that is the genomic proximity of synthetic lethal gene pairs both on the 2D and 3D chromosomal space of the yeast genome (i.e. 2D and 3D attraction). To gain mechanistic insights into the origin of the attraction of synthetic lethal gene pairs in S. cerevisiae, we characterized four classes of genes, in which synthetic lethal interactions are enriched and partly explain the observed patterns of genomic attraction: i) gene pairs operating on the same pathways, 2) co-expressed genes, 3) gene pairs whose protein products physically interact and 4) the paralogs. However, our analysis revealed that the contribution of these four types of genes is not sufficient to fully explain the observed 2D and 3D attraction of synthetic lethal gene pairs and hence its evolutionary origin still remains as an open question. Significance statementUnravelling the organizing principles underlying gene arrangements is one of the fundamental questions of research in evolutionary biology. One understudied aspect of this organization is the relative chromosomal arrangement of synthetic lethal gene pairs. In this study, by analyzing a wealth of synthetic lethality data in yeast, we provide evidence that synthetic lethal gene pairs tend to be attracted to each other both on 2D and 3D chromosomal space of the yeast genome. This observation is in sharp contrast with the repulsion of synthetic lethal (metabolic) gene pairs that we observed previously in bacterial genomes. Characterizing the evolutionary forces underlying this genomic pattern in yeast can open the door towards an evolutionary theory of genome organization in eukaryotes.

evolutionary biology↗